Best Practices Guide

This document outlines recommended practices for using and managing Apache Airflow effectively.

Core Principles

Adhering to these principles will lead to more robust, maintainable, and scalable Airflow deployments.

• Idempotency: Design your tasks to be idempotent. Running a task multiple times should yield the same result as running it once.
• Modularity: Break down complex workflows into smaller, reusable tasks.
• Testability: Write tests for your DAGs and custom operators.
• Version Control: Store your DAGs and custom code in a version control system (e.g., Git).
• Configuration Management: Externalize configuration settings and avoid hardcoding sensitive information.

DAG Design

Task Dependencies

Clearly define dependencies between tasks to ensure correct execution order. Use operators' dependency management features (e.g., >>, <<, set_upstream, set_downstream).

from airflow import DAG
from airflow.operators.bash import BashOperator
from datetime import datetime

with DAG(
    dag_id='dependency_example',
    start_date=datetime(2023, 1, 1),
    schedule_interval=None,
    catchup=False,
) as dag:
    task_a = BashOperator(task_id='task_a', bash_command='echo "Task A"')
    task_b = BashOperator(task_id='task_b', bash_command='echo "Task B"')
    task_c = BashOperator(task_id='task_c', bash_command='echo "Task C"')

    task_a >> task_b >> task_c
            

Task Granularity

Avoid overly large or overly small tasks. Tasks should represent logical units of work. A good balance is key for readability, debugging, and retries.

Error Handling

Implement robust error handling within your tasks. Utilize Airflow's retry mechanisms and consider using callbacks for notifications on failure.

Operator Usage

Built-in vs. Custom Operators

Leverage Airflow's rich set of built-in operators whenever possible. If a specific functionality is not covered, develop custom operators following best practices.

Parameterization

Use Jinja templating and Airflow Variables/Connections to parameterize your operator configurations, making DAGs more flexible and dynamic.

Configuration and Deployment

Executor Choice

Select the appropriate executor (e.g., LocalExecutor, CeleryExecutor, KubernetesExecutor) based on your workload and scaling needs.

Resource Management

Configure appropriate resources (CPU, memory) for your Airflow workers and scheduler to ensure optimal performance.

Security

• Use secure connections for databases and external services.
• Manage secrets using Airflow Connections or external secret management tools.
• Configure RBAC (Role-Based Access Control) to limit user permissions.

Monitoring and Maintenance

Logging

Ensure proper logging is configured for your tasks and Airflow components. Centralized logging solutions are highly recommended.

Alerting

Set up alerts for task failures, SLA misses, and other critical events.

Regular Updates

Keep your Airflow installation and dependencies updated to benefit from new features and security patches.