airflow-best-practices

name	airflow-best-practices
description	Apache Airflow best practices for DAG authoring, testing, and production deployment
scope	core
user-invocable	false

Apache Airflow Best Practices

DAG Authoring

Top-Level Code (CRITICAL)

• Avoid heavy computation at module level (executed on every DAG parse)
• Minimize imports at module level
• Use @task decorator (TaskFlow API) for Python tasks
• Keep DAG file under 1000 lines

Scheduling

• Use cron expressions or timetables
• Set catchup=False for most cases
• Use data-aware scheduling (datasets) for dependencies
• Configure SLA monitoring

Task Dependencies

• Use >> / << for clarity
• Group related tasks with TaskGroup
• Avoid deep nesting (max 3 levels)

Testing

Unit Tests

• Test DAG import without errors
• Detect cycles in dependencies
• Mock external connections
• Test task logic independently

Integration Tests

• Use Airflow test mode
• Validate end-to-end workflows
• Test with sample data

Production Deployment

Performance

• Lazy-load heavy libraries inside tasks
• Use connection pooling
• Minimize DAG parse time
• Enable parallelism

Reliability

• Set appropriate retries and retry_delay
• Use SLA callbacks for monitoring
• Implement proper error handling
• Log important events

References

• Airflow Best Practices