Strategies for Scaling Airflow at Airbnb: Key Learnings

Hey everyone,

This thread is dedicated to discussing the practical steps and insights gained from optimizing Apache Airflow at scale, drawing from experiences at companies like Airbnb. We've encountered numerous challenges and developed robust solutions to handle massive DAGs, large numbers of tasks, and ensure high availability.

Some key areas we focused on:

• Executor Choice: Evaluating and tuning the CeleryExecutor for distributed task execution.
• Database Optimization: Strategies for keeping the Airflow metadata database performant under heavy load (e.g., indexing, partitioning, periodic cleanup).
• Task Instance Management: Efficiently handling task retries, concurrency limits, and idempotency.
• DAG Parsing Performance: Techniques to speed up DAG loading and avoid parsing bottlenecks.
• Monitoring and Alerting: Setting up comprehensive monitoring for Airflow components and DAG runs.

I'd love to hear about your experiences and any specific optimizations you've implemented. Let's share knowledge and build a more resilient Airflow ecosystem!

Looking forward to the discussion!