Airflow Performance Optimization

Hey everyone,

I'm looking to squeeze every bit of performance out of our Airflow setup. We're seeing some significant delays in task execution, especially during peak hours. I've already implemented some basic DAG structuring and used the executor recommended for our scale, but I feel there's more to explore.

Specifically, I'm interested in:

• Database query optimization related to Airflow metadata.
• Strategies for parallelizing tasks effectively beyond simple dependencies.
• Tips for reducing the overhead of task scheduling and monitoring.
• Any best practices for configuring the Celery or Kubernetes executor for maximum throughput.

What are your go-to methods for optimizing Airflow performance?

Great topic! For database performance, ensure your metadata database is properly indexed. Running airflow db shell and checking query execution plans can be very insightful. Also, consider increasing the sql_alchemy_pool_size and related connection settings in your airflow.cfg if you're hitting connection limits.

For parallelization, have you explored:

• Using TaskGroups effectively to visually group and manage parallelism?
• Employing trigger_rules like ALL_DONE or ONE_FAILED to manage downstream task execution based on upstream outcomes.
• Considering external triggers or dynamic task generation if your DAG structure is highly variable.

We found that aggressively pruning old records from the metadata DB (e.g., old DAG runs and task logs) also significantly improved query speeds.

Regarding executor configuration (Celery/Kubernetes), make sure your worker concurrency settings are tuned correctly. Over-provisioning can lead to resource contention, while under-provisioning creates bottlenecks. For Kubernetes, we found that using dedicated node pools for Airflow workers, along with autoscaling, was crucial. Also, monitor your scheduler heartbeats and consider increasing the parallelism and dag_concurrency settings in airflow.cfg, but with caution to avoid overwhelming your system.

Here's a snippet of our Kubernetes executor configuration:

[core]
executor = KubernetesExecutor
dags_folder_sync_interval = 5 # Faster sync for dynamic DAGs
parallelism = 100
dag_concurrency = 16

[kubernetes_executor]
worker_container_repository = my-airflow-worker
worker_container_tag = latest
namespace = airflow
max_workers = 20
# ... other Kubernetes specific settings
                        

Don't forget about efficient serialization! Using libraries like pickle for XComs can be slow; consider alternatives if your XComs are large.

Thanks for the detailed responses, @JSmith and @CloudNative! The `TaskGroup` and `trigger_rule` suggestions are definitely areas I need to dive deeper into. And @CloudNative, that Kubernetes snippet is very helpful; I'll be reviewing our `parallelism` and `dag_concurrency` settings.

One more thing: has anyone had success with offloading specific heavy computations (e.g., Spark jobs) to dedicated services and simply triggering them from Airflow? We're thinking of integrating more with Spark, but want to ensure Airflow remains the orchestrator and not the compute engine for everything.