What are common anti-patterns for Airflow?

Apache Airflow is a powerful tool for orchestrating complex workflows, but there are several common anti-patterns that teams should avoid to ensure efficient and maintainable DAGs (Directed Acyclic Graphs). Here are some of the most prevalent anti-patterns:

1. Using XComs for Large Data Transfers

Using XComs to transfer large amounts of data between tasks can lead to performance issues. Instead, it is advisable to store large data in external systems (like cloud storage) and pass only references (e.g., file paths).

2. Hardcoding Values

Hardcoding configuration values directly in the DAG can make future changes cumbersome. Instead, utilize environment variables or configuration files to manage these settings.

3. Lack of Retry and Error Handling

Not implementing retries or proper error handling can lead to tasks failing silently. Always configure retries and use the on_failure_callback parameter to handle failures effectively.

4. Overloading the Scheduler

Running too many tasks concurrently can overload the Airflow scheduler, leading to delays and timeouts. Use the max_active_runs property to manage concurrency effectively.

5. Too Many Dependencies

Creating intricate dependencies can make DAGs complex and difficult to understand. Aim for simplicity by minimizing dependencies where possible.