DAG Testing Skill

Use af commands to test, debug, and fix DAGs in iterative cycles.

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.

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Quick Validation with Astro CLI

If the user has the Astro CLI available, these commands provide fast feedback without needing a running Airflow instance:

# Parse DAGs to catch import errors, syntax issues, and DAG-level problems
astro dev parse

# Run pytest against DAGs (runs tests in tests/ directory)
astro dev pytest

Use these for quick validation during development. For full end-to-end testing against a live Airflow instance, continue to the trigger-and-wait workflow below.

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FIRST ACTION: Just Trigger the DAG

When the user asks to test a DAG, your FIRST AND ONLY action should be:

af runs trigger-wait <dag_id>

DO NOT:

• Call af dags list first
• Call af dags get first
• Call af dags errors first
• Use grep or ls or any other bash command
• Do any "pre-flight checks"

Just trigger the DAG. If it fails, THEN debug.

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Testing Workflow Overview

┌─────────────────────────────────────┐
│ 1. TRIGGER AND WAIT                 │
│    Run DAG, wait for completion     │
└─────────────────────────────────────┘
                 ↓
        ┌───────┴───────┐
        ↓               ↓
   ┌─────────┐    ┌──────────┐
   │ SUCCESS │    │ FAILED   │
   │ Done!   │    │ Debug... │
   └─────────┘    └──────────┘
                       ↓
        ┌─────────────────────────────────────┐
        │ 2. DEBUG (only if failed)           │
        │    Get logs, identify root cause    │
        └─────────────────────────────────────┘
                       ↓
        ┌─────────────────────────────────────┐
        │ 3. FIX AND RETEST                   │
        │    Apply fix, restart from step 1   │
        └─────────────────────────────────────┘

Philosophy: Try first, debug on failure. Don't waste time on pre-flight checks — just run the DAG and diagnose if something goes wrong.

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Phase 1: Trigger and Wait

Use af runs trigger-wait to test the DAG:

Primary Method: Trigger and Wait

af runs trigger-wait <dag_id> --timeout 300

Example:

af runs trigger-wait my_dag --timeout 300

Why this is the preferred method:

• Single command handles trigger + monitoring
• Returns immediately when DAG completes (success or failure)
• Includes failed task details if run fails
• No manual polling required

Response Interpretation

Success:

{
  "dag_run": {
    "dag_id": "my_dag",
    "dag_run_id": "manual__2025-01-14T...",
    "state": "success",
    "start_date": "...",
    "end_date": "..."
  },
  "timed_out": false,
  "elapsed_seconds": 45.2
}

Failure:

{
  "dag_run": {
    "state": "failed"
  },
  "timed_out": false,
  "elapsed_seconds": 30.1,
  "failed_tasks": [
    {
      "task_id": "extract_data",
      "state": "failed",
      "try_number": 2
    }
  ]
}

Timeout:

{
  "dag_id": "my_dag",
  "dag_run_id": "manual__...",
  "state": "running",
  "timed_out": true,
  "elapsed_seconds": 300.0,
  "message": "Timed out after 300 seconds. DAG run is still running."
}

Alternative: Trigger and Monitor Separately

Use this only when you need more control:

# Step 1: Trigger
af runs trigger my_dag
# Returns: {"dag_run_id": "manual__...", "state": "queued"}

# Step 2: Check status
af runs get my_dag manual__2025-01-14T...
# Returns current state

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Handling Results

If Success

The DAG ran successfully. Summarize for the user:

• Total elapsed time
• Number of tasks completed
• Any notable outputs (if visible in logs)

You're done!

If Timed Out

The DAG is still running. Options:

1. Check current status: af runs get <dag_id> <dag_run_id>
2. Ask user if they want to continue waiting
3. Increase timeout and try again

If Failed

Move to Phase 2 (Debug) to identify the root cause.

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Phase 2: Debug Failures (Only If Needed)

When a DAG run fails, use these commands to diagnose:

Get Comprehensive Diagnosis

af runs diagnose <dag_id> <dag_run_id>

Returns in one call:

• Run metadata (state, timing)
• All task instances with states
• Summary of failed tasks
• State counts (success, failed, skipped, etc.)

Get Task Logs

af tasks logs <dag_id> <dag_run_id> <task_id>

Example:

af tasks logs my_dag manual__2025-01-14T... extract_data

For specific retry attempt:

af tasks logs my_dag manual__2025-01-14T... extract_data --try 2

Look for:

• Exception messages and stack traces
• Connection errors (database, API, S3)
• Permission errors
• Timeout errors
• Missing dependencies

Check Upstream Tasks

If a task shows upstream_failed, the root cause is in an upstream task. Use af runs diagnose to find which task actually failed.

Check Import Errors (If DAG Didn't Run)

If the trigger failed because the DAG doesn't exist:

af dags errors

This reveals syntax errors or missing dependencies that prevented the DAG from loading.

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Phase 3: Fix and Retest

Once you identify the issue:

Common Fixes

Issue	Fix
Missing import	Add to DAG file
Missing package	Add to requirements.txt
Connection error	Check af config connections, verify credentials
Variable missing	Check af config variables, create if needed
Timeout	Increase task timeout or optimize query
Permission error	Check credentials in connection

After Fixing

1. Save the file
2. Retest: af runs trigger-wait <dag_id>

Repeat the test → debug → fix loop until the DAG succeeds.

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CLI Quick Reference

Phase	Command	Purpose
Test	af runs trigger-wait <dag_id>	Primary test method — start here
Test	af runs trigger <dag_id>	Start run (alternative)
Test	af runs get <dag_id> <run_id>	Check run status
Debug	af runs diagnose <dag_id> <run_id>	Comprehensive failure diagnosis
Debug	af tasks logs <dag_id> <run_id> <task_id>	Get task output/errors
Debug	af dags errors	Check for parse errors (if DAG won't load)
Debug	af dags get <dag_id>	Verify DAG config
Debug	af dags explore <dag_id>	Full DAG inspection
Config	af config connections	List connections
Config	af config variables	List variables

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Testing Scenarios

Scenario 1: Test a DAG (Happy Path)

af runs trigger-wait my_dag
# Success! Done.

Scenario 2: Test a DAG (With Failure)

# 1. Run and wait
af runs trigger-wait my_dag
# Failed...

# 2. Find failed tasks
af runs diagnose my_dag manual__2025-01-14T...

# 3. Get error details
af tasks logs my_dag manual__2025-01-14T... extract_data

# 4. [Fix the issue in DAG code]

# 5. Retest
af runs trigger-wait my_dag

Scenario 3: DAG Doesn't Exist / Won't Load

# 1. Trigger fails - DAG not found
af runs trigger-wait my_dag
# Error: DAG not found

# 2. Find parse error
af dags errors

# 3. [Fix the issue in DAG code]

# 4. Retest
af runs trigger-wait my_dag

Scenario 4: Debug a Failed Scheduled Run

# 1. Get failure summary
af runs diagnose my_dag scheduled__2025-01-14T...

# 2. Get error from failed task
af tasks logs my_dag scheduled__2025-01-14T... failed_task_id

# 3. [Fix the issue]

# 4. Retest
af runs trigger-wait my_dag

Scenario 5: Test with Custom Configuration

af runs trigger-wait my_dag --conf '{"env": "staging", "batch_size": 100}' --timeout 600

Scenario 6: Long-Running DAG

# Wait up to 1 hour
af runs trigger-wait my_dag --timeout 3600

# If timed out, check current state
af runs get my_dag manual__2025-01-14T...

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Debugging Tips

Common Error Patterns

Connection Refused / Timeout:

• Check af config connections for correct host/port
• Verify network connectivity to external system
• Check if connection credentials are correct

ModuleNotFoundError:

• Package missing from requirements.txt
• After adding, may need environment restart

PermissionError:

• Check IAM roles, database grants, API keys
• Verify connection has correct credentials

Task Timeout:

• Query or operation taking too long
• Consider adding timeout parameter to task
• Optimize underlying query/operation

Reading Task Logs

Task logs typically show:

1. Task start timestamp
2. Any print/log statements from task code
3. Return value (for @task decorated functions)
4. Exception + full stack trace (if failed)
5. Task end timestamp and duration

Focus on the exception at the bottom of failed task logs.

On Astro

Astro deployments support environment promotion, which helps structure your testing workflow:

• Dev deployment: Test DAGs freely with astro deploy --dags for fast iteration
• Staging deployment: Run integration tests against production-like data
• Production deployment: Deploy only after validation in lower environments
• Use separate Astro deployments for each environment and promote code through them

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Related Skills

• authoring-dags: For creating new DAGs (includes validation before testing)
• debugging-dags: For general Airflow troubleshooting
• deploying-airflow: For deploying DAGs to production after testing