Understanding DAG Scheduling Failures in Apache Airflow

Airflow relies on a scheduler to trigger DAGs and distribute tasks across workers. If the scheduler or database is not properly configured, DAGs may not start as expected, leading to incomplete workflows and data pipeline failures.

Common Causes of DAG Execution Failures

  • Scheduler backlog: Too many queued tasks overload the scheduler.
  • Misconfigured Celery workers: Tasks are not properly assigned to available workers.
  • Database performance issues: The metadata database cannot keep up with task scheduling.
  • Time zone inconsistencies: Scheduled tasks do not align with expected execution times.

Diagnosing Airflow DAG Execution Issues

Checking Scheduler Logs

Inspect Airflow scheduler logs for errors:

tail -f $AIRFLOW_HOME/logs/scheduler/latest.log

Verifying Worker Status

Ensure Celery workers are running and available:

airflow celery worker status

Monitoring Database Performance

Check for slow queries affecting DAG execution:

SELECT * FROM pg_stat_activity WHERE state = 'active';

Fixing DAG Scheduling and Execution Failures

Clearing Stuck DAG Runs

Reset failed DAG runs to allow re-execution:

airflow dags reprocess --dag-id=DAG_NAME

Restarting Airflow Scheduler

Restart the scheduler to clear task backlogs:

airflow scheduler restart

Optimizing Celery Worker Configuration

Ensure Celery workers are correctly assigned:

celery -A airflow worker --loglevel=info

Improving Database Performance

Enable connection pooling for metadata database:

[core]
sql_alchemy_pool_size = 20
sql_alchemy_max_overflow = 5

Preventing Future DAG Execution Issues

  • Monitor scheduler logs for performance bottlenecks.
  • Scale Celery workers dynamically based on workload.
  • Regularly optimize the metadata database to prevent slow queries.

Conclusion

Apache Airflow DAG execution failures can disrupt data pipelines due to scheduler overload, worker misconfigurations, or database performance issues. By clearing stuck DAGs, optimizing workers, and tuning the database, users can ensure reliable scheduling and execution.

FAQs

1. Why is my Airflow DAG stuck in a queued state?

Possible reasons include worker unavailability, scheduler overload, or database connection issues.

2. How can I check if my Airflow scheduler is working?

Inspect the scheduler logs using tail -f $AIRFLOW_HOME/logs/scheduler/latest.log.

3. What should I do if my Airflow tasks are not assigned to workers?

Ensure Celery workers are running and properly connected to the scheduler.

4. How do I improve Airflow database performance?

Enable connection pooling and regularly optimize metadata queries.

5. Can I manually trigger a stuck DAG in Airflow?

Yes, use airflow dags reprocess --dag-id=DAG_NAME to retry execution.