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Hey everyone, I'm looking for some best practices and practical tips on optimizing Airflow for a large-scale Airbnb data processing pipeline. We're currently experiencing performance bottlenecks, especially during peak loads, and I'm keen to explore strategies like: - **DAG design patterns:** How to structure complex DAGs effectively? Any known anti-patterns to avoid? - **Resource management:** What are the best configurations for worker types, parallelism, and queueing? - **Database optimization:** Any tips for the Airflow metadata database? - **Task execution strategies:** When to use different executors (Celery, Kubernetes)? - **Monitoring and alerting:** Essential metrics and alert configurations. - **Cost reduction:** Specific techniques for optimizing Airflow costs, especially in cloud environments. I'd appreciate any insights, war stories, or recommended resources you might have. Let's discuss how to make Airflow fly! Thanks in advance!

Great topic, John! We've been wrestling with similar challenges. For DAG design, I highly recommend keeping tasks atomic and avoiding excessive branching. Dynamic DAG generation can also be a lifesaver for managing many similar pipelines. Use `XComs` sparingly; it's better to store intermediate results in a persistent storage like S3 or GCS. Regarding resource management, we found that carefully tuning `parallelism` and `dag_concurrency` at the Airflow config level, and then using Celery queues with appropriately sized workers, made a significant difference. Kubernetes executor is powerful but has a higher operational overhead. For the metadata DB, ensure it's on a robust instance and consider periodic vacuuming.

Building on Alice's points: **DAG Design:** The "composer" pattern for large, interconnected DAGs is excellent. Break down logical units into separate DAGs and use `TriggerDagRunOperator` or `ExternalTaskSensor` to manage dependencies. Avoid `SubDagOperator` if possible due to its performance quirks. **Resource Management:** If you're on Kubernetes, explore autoscaling options for your pods. Also, consider task-specific resource requests and limits in your Kubernetes manifests. **Monitoring:** Datadog or Prometheus/Grafana are essential. Key metrics include task retry counts, task durations, scheduler heartbeat, and resource utilization per worker. Alerts on long-running tasks and frequent retries are crucial.