airflow-dag-patterns

SKILL.md

Apache Airflow DAG Patterns

Production-ready patterns for Apache Airflow including DAG design, operators, sensors, testing, and deployment strategies.

When to Use This Skill

Creating data pipeline orchestration with Airflow

Designing DAG structures and dependencies

Implementing custom operators and sensors

Testing Airflow DAGs locally

Setting up Airflow in production

Debugging failed DAG runs

Core Concepts

1. DAG Design Principles

Principle
Description

Idempotent
Running twice produces same result

Atomic
Tasks succeed or fail completely

Incremental
Process only new/changed data

Observable
Logs, metrics, alerts at every step

2. Task Dependencies

# Linear
task1 >> task2 >> task3

# Fan-out
task1 >> [task2, task3, task4]

# Fan-in
[task1, task2, task3] >> task4

# Complex
task1 >> task2 >> task4
task1 >> task3 >> task4

Quick Start

# dags/example_dag.py
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.operators.empty import EmptyOperator

default_args = {
    'owner': 'data-team',
    'depends_on_past': False,
    'email_on_failure': True,
    'email_on_retry': False,
    'retries': 3,
    'retry_delay': timedelta(minutes=5),
    'retry_exponential_backoff': True,
    'max_retry_delay': timedelta(hours=1),
}

with DAG(
    dag_id='example_etl',
    default_args=default_args,
    description='Example ETL pipeline',
    schedule='0 6 * * *',  # Daily at 6 AM
    start_date=datetime(2024, 1, 1),
    catchup=False,
    tags=['etl', 'example'],
    max_active_runs=1,
) as dag:

    start = EmptyOperator(task_id='start')

    def extract_data(**context):
        execution_date = context['ds']
        # Extract logic here
        return {'records': 1000}

    extract = PythonOperator(
        task_id='extract',
        python_callable=extract_data,
    )

    end = EmptyOperator(task_id='end')

    start >> extract >> end

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Best Practices

Do's

Use TaskFlow API - Cleaner code, automatic XCom

Set timeouts - Prevent zombie tasks

Use mode='reschedule' - For sensors, free up workers

Test DAGs - Unit tests and integration tests

Idempotent tasks - Safe to retry

Don'ts

Don't use depends_on_past=True - Creates bottlenecks

Don't hardcode dates - Use {{ ds }} macros

Don't use global state - Tasks should be stateless

Don't skip catchup blindly - Understand implications

Don't put heavy logic in DAG file - Import from modules