Executor Guide

This guide provides an in-depth look at the various executors available in Apache Airflow, their functionalities, and how to choose the right one for your needs.

What is an Executor?

In Airflow, an executor is responsible for executing the tasks of your DAGs. When a task needs to be run, the scheduler delegates it to the executor. Different executors offer different capabilities, such as running tasks locally, in parallel on a cluster, or on dedicated infrastructure.

Available Executors

Local Executors

These executors run tasks on the same machine where the Airflow worker (or scheduler, in some configurations) is running.

SequentialExecutor

This is the simplest executor. It runs tasks one after another, in the order they are defined within a DAG. It's useful for debugging and testing DAGs as it's easy to set up and understand, but it does not support parallelism.

LocalExecutor

The LocalExecutor allows tasks to be run in parallel on the same machine using multiple processes. It's a good choice for development or small-scale production deployments where a single machine can handle the workload. You can configure the number of parallel tasks.

# In airflow.cfg
[core]
executor = LocalExecutor
parallelism = 32
dag_concurrency = 16

Distributed Executors

These executors distribute task execution across multiple machines or services, enabling true parallelism and scalability.

CeleryExecutor

The CeleryExecutor leverages the Celery distributed task queue. It allows you to run tasks on a cluster of worker machines. This requires a message broker (like RabbitMQ or Redis) and a Celery worker setup.

Advantages:

• Highly scalable
• Decouples task execution from the scheduler
• Can handle a large number of tasks concurrently

Setup requires:

• A message broker (e.g., RabbitMQ, Redis)
• Celery workers running on separate machines

# In airflow.cfg
[core]
executor = CeleryExecutor

[celery]
broker_url = redis://localhost:6379/0
result_backend = db+sqlite:////path/to/airflow/airflow.db

KubernetesExecutor

The KubernetesExecutor launches each task in its own Kubernetes pod. This provides excellent isolation and scalability, as Kubernetes manages resource allocation and scaling of pods. Each task runs in a clean environment, ensuring no task interferes with another.

Advantages:

• Strong isolation for each task
• Leverages Kubernetes' powerful scaling and orchestration capabilities
• Ideal for environments already using Kubernetes

Setup requires:

• A running Kubernetes cluster
• Appropriate RBAC permissions for Airflow

# In airflow.cfg
[core]
executor = KubernetesExecutor

[kubernetes]
# Configuration for connecting to your Kubernetes cluster
# e.g., kubernetes_conn_id = kubernetes_default
# namespace = airflow
# image = apache/airflow:latest-python3.9

DaskExecutor

The DaskExecutor allows Airflow tasks to be run on a Dask cluster. Dask is a flexible parallel computing library for Python, making it suitable for scaling Python workloads.

Advantages:

• Leverages Dask's parallel computing capabilities
• Good for Python-heavy workloads

Setup requires:

• A running Dask cluster

Choosing the Right Executor

The choice of executor depends heavily on your environment, scalability requirements, and operational complexity tolerance.

Configuration

The executor is configured in the airflow.cfg file under the [core] section. You set the executor parameter to the desired executor class name. Additional configuration parameters are specific to each executor and can be found in their respective sections (e.g., [celery], [kubernetes]).

Monitoring Executors

Monitoring your executor is crucial for understanding performance and identifying issues. Airflow's UI provides insights into task status, worker health (for Celery), and pod status (for Kubernetes). You should also monitor the underlying infrastructure (message broker, Kubernetes cluster, Dask cluster) that supports your chosen executor.