Mastering Parallelism, Max Active Runs, and DAG Concurrency in Apache Airflow

Apache Airflow is an open-source tool widely used for orchestrating complex workflows. When it comes to managing the execution of multiple tasks and DAGs (Directed Acyclic Graphs), understanding three key parameters – parallelism, max_active_runs_per_dag, and dag_concurrency – is crucial. Let’s delve into what each of these settings means and how they impact the execution of workflows in Airflow.

Parallelism (parallelism)

Parallelism in Airflow refers to the maximum number of task instances that can run simultaneously across the whole Airflow environment. This setting is crucial for controlling overall system performance and resource utilization.

Example

If parallelism is set to 10, only ten tasks (regardless of the number of DAGs they belong to) can run concurrently across the whole Airflow environment. If the eleventh task is triggered while ten tasks are already running, it will remain in a queued state until one of the running tasks completes.

Max Active Runs Per DAG (max_active_runs_per_dag)

This setting specifies the maximum number of active DAG runs for a particular DAG. It prevents a single DAG from monopolizing system resources and helps in managing the overall load.

Example

Consider a DAG designed to run hourly data processing jobs with max_active_runs_per_dag set to 3. Even if there’s a backlog or delay, Airflow will not allow more than three instances of this particular DAG to run concurrently. If a fourth run is triggered while three runs are active, it will be queued until one of the active runs completes.

DAG Concurrency (dag_concurrency)

DAG concurrency refers to the maximum number of tasks that can run simultaneously within a single DAG. It's a way to limit resource usage and manage performance at the DAG level.

Example

If you have a DAG with several tasks and dag_concurrency is set to 5, only five tasks within this DAG can execute at the same time. If more tasks are ready to run, they will be queued until one of the currently running tasks completes.

Practical Application

Consider an Airflow environment configured with the following settings:

• parallelism: 10

• max_active_runs_per_dag: 3

• dag_concurrency: 5

Now, let’s assume you have a DAG (DAG_A) with 10 tasks and another DAG (DAG_B) with 10 tasks. Here’s how these settings work together:

• If DAG_A triggers 10 tasks, only 5 will run concurrently due to dag_concurrency. The rest will be queued.

• If DAG_A is running 3 active runs with 5 tasks each, it reaches its max_active_runs_per_dag limit. Any new trigger for DAG_A will be queued.

• At the same time, only up to 10 tasks can run across DAG_A and DAG_B due to the parallelism limit. If DAG_A is running 5 tasks, only 5 tasks from DAG_B can run concurrently.

These parameters are essential tools for managing and optimizing the performance of Airflow environments, especially when dealing with a large number of tasks and complex workflows. By fine-tuning these settings, you can ensure efficient resource utilization and prevent system overload, maintaining a smooth and reliable workflow execution.