How do you scale Airflow deployments?

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1. Scale Infrastructure Components

Worker Autoscaling: In Composer 2, workers run in Kubernetes pods. You can configure the number of worker pods to auto-scale based on demand.

Scheduler Resources: Increase the CPU and memory for the Airflow scheduler if DAG parsing or scheduling is slow.

Web Server Scaling: If many users access the Airflow UI, scale the web server instance size.

✅ 2. Optimize DAG Design

Limit DAG Size: Break large DAGs into smaller, modular DAGs to reduce parsing time.

Efficient Operators: Avoid heavy logic in PythonOperators. Use external systems for processing.

Parallelism: Adjust dag_concurrency, max_active_tasks, and max_active_runs_per_dag to increase parallel execution.

✅ 3. Use Airflow Configs

Tune parameters like:

worker_concurrency

parallelism

dag_dir_list_interval

min_file_process_interval

Proper resource allocation and DAG optimization ensure that Airflow can scale to handle high workloads efficiently.

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