airflow-dag-patterns

★ 37,559

by wshobson · part of wshobson/agents

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

🧩 One of 7 skills in the wshobson/agents package — works on its own, and pairs well with its siblings.

This is the playbook your agent receives when the skill activates — you don't need to read it to use the skill, but it's here to audit before installing.

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

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

Copy & paste — that's it

npx skills add https://github.com/wshobson/agents --skill airflow-dag-patterns

Run this in your project — your agent picks the skill up automatically.

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