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Airflow Performance Tuning: Best Practices and Common Pitfalls

Posted by airflow_enthusiast on October 27, 2023 Views: 12.5k Replies: 158

Hey everyone,

I've been working with Airflow for a while now, and while it's incredibly powerful, I've hit a few performance bottlenecks. I wanted to start a discussion on best practices for tuning Airflow's performance, both for the scheduler and the workers.

Specifically, I'm looking for insights on:

Scheduler Optimization: How to handle a large number of DAGs, minimize scheduler heartbeat delays, and manage task scheduling concurrency.
Worker Configuration: Best practices for executor choice (Celery, Kubernetes, Local), worker resource allocation, and scaling strategies.
Database Performance: Tips for optimizing the Airflow metadata database, connection pooling, and avoiding common query issues.
DAG Design: How to write efficient DAGs, avoid long-running tasks, and leverage Airflow's features for better performance.
Monitoring and Profiling: Tools and techniques for identifying performance bottlenecks in your Airflow setup.

I've already explored some standard recommendations like adjusting `parallelism`, `dag_concurrency`, and `max_active_runs_per_dag`, but I'm curious about more advanced strategies and real-world experiences.

Any advice, case studies, or common pitfalls to watch out for would be greatly appreciated!

Thanks in advance!

# Example of a potentially inefficient task (for illustration)
def long_running_task():
    import time
    print("Starting long task...")
    time.sleep(600) # Sleeping for 10 minutes
    print("Task finished.")

performance tuning airflow scheduler workers optimization

Replies (158)

John Doe October 27, 2023 at 10:15 AM

Great topic! One thing I found incredibly useful is to analyze the scheduler logs closely. Look for messages related to "task state updates" and "heartbeats". Slowdowns there often point to scheduler contention or database issues.

Also, consider increasing the `max_threads` in your `airflow.cfg` if you're running the LocalExecutor and have multiple cores available. For Celery, ensure your broker (e.g., Redis or RabbitMQ) is also performing well.

# In airflow.cfg
[core]
parallelism = 32
dag_concurrency = 16
max_threads = 8  # Example for LocalExecutor

Sarah Kim October 27, 2023 at 11:05 AM

For DAG design, I highly recommend breaking down complex tasks into smaller, modular sub-DAGs or even separate DAGs if they represent distinct logical units. This improves readability and allows for better parallelization.

Avoid fetching large datasets directly within tasks unless absolutely necessary. Use incremental loads and staging tables. Also, be mindful of the number of sensors in your DAGs, as they can consume significant scheduler resources.

Michael Pham October 27, 2023 at 1:30 PM

Database performance is crucial. We moved our metadata DB to a dedicated, properly provisioned RDS instance and saw a dramatic improvement. Regular vacuuming and analyzing of the database tables also helped a lot. For connections, ensure you're not opening/closing DB connections within your tasks; use connection pooling where possible.

If you have many pools, consider consolidating them or ensuring their `slots_count` is appropriately set.

Airflow Performance Tuning: Best Practices and Common Pitfalls

Replies (158)

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