Deployment Guide

This guide provides essential information for deploying Apache Airflow in a production environment. It covers key considerations, configurations, and best practices.

Introduction

Deploying Apache Airflow involves setting up and configuring its core components: the Webserver, the Scheduler, and the Executor. The choice of Executor, database backend, and infrastructure significantly impacts the scalability, reliability, and performance of your Airflow instance. This document aims to guide you through these critical aspects.

Production Considerations

Before deploying to production, carefully consider the following:

  • Scalability: How will your Airflow setup handle an increasing number of DAGs, tasks, and users?
  • Reliability: What measures will you take to ensure Airflow is always available and tasks are executed reliably? This includes redundancy and fault tolerance.
  • Performance: How can you optimize Airflow's performance to meet your workflow execution SLAs?
  • Security: How will you secure access to Airflow, its metadata database, and the resources it interacts with?
  • Monitoring: How will you track the health of Airflow components, task execution, and resource utilization?
  • Maintainability: How easy will it be to update, upgrade, and manage your Airflow deployment?

Webserver Configuration

The Airflow Webserver provides the user interface for monitoring and managing DAGs. Key configuration options include:

  • [webserver] web_server_port: The port the webserver listens on (default: 8080).
  • [webserver] default_timezone: The default timezone for the UI.
  • [webserver] authenticate: Enables or disables authentication.
  • [webserver] auth_provider: Specifies the authentication backend (e.g., airflow.providers.fab.auth_manager.fab_auth_manager.FabAuthManager for default RBAC).
  • [webserver] base_url: The publicly accessible URL for the Airflow UI.

For production, it's highly recommended to run the webserver behind a reverse proxy (like Nginx or Apache HTTP Server) for SSL termination, load balancing, and better security.

Scheduler Configuration

The Airflow Scheduler is responsible for monitoring DAGs, triggering task instances, and sending them to the executor.

  • [scheduler] dag_dir_list_interval: How often the scheduler scans the DAGs folder (in seconds).
  • [scheduler] max_threads: The maximum number of threads the scheduler can use.
  • [scheduler] min_file_process_interval: The minimum interval between parsing DAG files (in seconds).
  • [scheduler] scheduler_heartbeat_sec: How often the scheduler sends heartbeats.
  • [scheduler] parallelism: The maximum number of task instances that can be running at any given time across all active DAGs.
  • [scheduler] dag_concurrency: The maximum number of task instances allowed to run concurrently within a single DAG.

For high availability, consider running multiple scheduler instances. Airflow's HA scheduler feature ensures that if one scheduler fails, another can take over.

Executor Options

The Executor defines how tasks are run. Common choices for production include:

  • CeleryExecutor: Distributes tasks to a pool of workers using Celery. Requires a message broker (e.g., RabbitMQ, Redis). Good for scaling task execution independently.
  • KubernetesExecutor: Launches each task as a Kubernetes pod. Ideal for environments already using Kubernetes, offering excellent isolation and scalability.
  • LocalExecutor: Runs tasks in parallel on the same machine as the scheduler. Suitable for smaller deployments or development, but not for production scaling.
  • CeleryKubernetesExecutor: A hybrid executor for Celery environments that leverages Kubernetes for task execution.

The choice depends on your infrastructure and scaling requirements.

Database Backends

Airflow uses a metadata database to store information about DAGs, tasks, runs, connections, etc.

  • PostgreSQL: A robust and widely used relational database. Recommended for production.
  • MySQL: Another viable option for the metadata database.
  • SQLite: Only suitable for development and testing purposes; not recommended for production due to its limitations in concurrency and performance.

Ensure your chosen database is properly configured for high availability and performance.

High Availability (HA)

To ensure Airflow is highly available, consider the following:

  • HA Scheduler: Run multiple scheduler instances. Configure the scheduler to use a high-availability mode.
  • Redundant Webservers: Deploy multiple webserver instances behind a load balancer.
  • Resilient Database: Use a managed database service or set up a replicated database cluster.
  • HA Executor: If using Celery, ensure your Celery workers and message broker are configured for redundancy. For KubernetesExecutor, rely on Kubernetes's built-in HA capabilities.
  • Shared File System: Ensure your DAGs are accessible from all scheduler and worker nodes (e.g., using NFS, S3, or a distributed file system).

Security

Securing your Airflow deployment is paramount:

  • Authentication & Authorization: Enable authentication and configure RBAC (Role-Based Access Control) to manage user permissions.
  • SSL/TLS: Use HTTPS for your webserver and secure communication between Airflow components.
  • Secrets Management: Use a dedicated secrets backend (e.g., HashiCorp Vault, AWS Secrets Manager, GCP Secret Manager) for storing sensitive information like database passwords and API keys.
  • Network Security: Restrict network access to Airflow components and the metadata database.
  • Principle of Least Privilege: Ensure Airflow and its components run with the minimum necessary permissions.

Refer to the Security Documentation for detailed guidance.

Monitoring

Effective monitoring is crucial for maintaining a healthy Airflow instance.

  • Airflow UI: Provides a visual overview of DAGs, task statuses, and recent runs.
  • Logs: Configure centralized logging to collect logs from all Airflow components. Tools like Elasticsearch, Fluentd, and Kibana (EFK) or Loki, Promtail, and Grafana (LPG) can be used.
  • Metrics: Expose Airflow metrics (e.g., using StatsD or Prometheus) to track component health, queue lengths, execution times, etc. Integrate with monitoring systems like Prometheus and Grafana.
  • Alerting: Set up alerts for critical events, such as failed tasks, scheduler downtime, or resource exhaustion.

Consider using dedicated monitoring tools and dashboards to gain deep insights into your Airflow deployment's performance and health.