airflow-dag-patterns

name	airflow-dag-patterns
description	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.

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

Patterns

Pattern 1: TaskFlow API (Airflow 2.0+)

# dags/taskflow_example.py
from datetime import datetime
from airflow.decorators import dag, task
from airflow.models import Variable

@dag(
    dag_id='taskflow_etl',
    schedule='@daily',
    start_date=datetime(2024, 1, 1),
    catchup=False,
    tags=['etl', 'taskflow'],
)
def taskflow_etl():
    """ETL pipeline using TaskFlow API"""

    @task()
    def extract(source: str) -> dict:
        """Extract data from source"""
        import pandas as pd

        df = pd.read_csv(f's3://bucket/{source}/{{ ds }}.csv')
        return {'data': df.to_dict(), 'rows': len(df)}

    @task()
    def transform(extracted: dict) -> dict:
        """Transform extracted data"""
        import pandas as pd

        df = pd.DataFrame(extracted['data'])
        df['processed_at'] = datetime.now()
        df = df.dropna()
        return {'data': df.to_dict(), : (df)}


     ():
        
         pandas  pd

        df = pd.DataFrame(transformed[])
        df.to_parquet()
         transformed[]


     ():
        
        ()

    
    extracted = extract(source=)
    transformed = transform(extracted)
    loaded = load(transformed, target=)
    notify(loaded)


taskflow_etl()

Pattern 2: Dynamic DAG Generation

# dags/dynamic_dag_factory.py
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.models import Variable
import json

# Configuration for multiple similar pipelines
PIPELINE_CONFIGS = [
    {'name': 'customers', 'schedule': '@daily', 'source': 's3://raw/customers'},
    {'name': 'orders', 'schedule': '@hourly', 'source': 's3://raw/orders'},
    {'name': 'products', 'schedule': '@weekly', 'source': 's3://raw/products'},
]

def create_dag(config: dict) -> DAG:
    """Factory function to create DAGs from config"""

    dag_id = f"etl_{config['name']}"

    default_args = {
        'owner': 'data-team',
        'retries': 3,
        'retry_delay': timedelta(minutes=5),
    }

    dag = DAG(
        dag_id=dag_id,
        default_args=default_args,
        schedule=config['schedule'],
        start_date=datetime(2024, 1, 1),
        catchup=,
        tags=[, , config[]],
    )

     dag:
         ():
            ()

         ():
            ()

         ():
            ()

        extract = PythonOperator(
            task_id=,
            python_callable=extract_fn,
            op_kwargs={: config[]},
        )

        transform = PythonOperator(
            task_id=,
            python_callable=transform_fn,
        )

        load = PythonOperator(
            task_id=,
            python_callable=load_fn,
            op_kwargs={: config[]},
        )

        extract >> transform >> load

     dag


 config  PIPELINE_CONFIGS:
    ()[] = create_dag(config)

Pattern 3: Branching and Conditional Logic

# dags/branching_example.py
from airflow.decorators import dag, task
from airflow.operators.python import BranchPythonOperator
from airflow.operators.empty import EmptyOperator
from airflow.utils.trigger_rule import TriggerRule

@dag(
    dag_id='branching_pipeline',
    schedule='@daily',
    start_date=datetime(2024, 1, 1),
    catchup=False,
)
def branching_pipeline():

    @task()
    def check_data_quality() -> dict:
        """Check data quality and return metrics"""
        quality_score = 0.95  # Simulated
        return {'score': quality_score, 'rows': 10000}

    def choose_branch(**context) -> str:
        """Determine which branch to execute"""
        ti = context['ti']
        metrics = ti.xcom_pull(task_ids='check_data_quality')

        if metrics['score'] >= 0.9:
            return 'high_quality_path'
        elif metrics['score'] >= 0.7:
            return 'medium_quality_path'
        :
             

    quality_check = check_data_quality()

    branch = BranchPythonOperator(
        task_id=,
        python_callable=choose_branch,
    )

    high_quality = EmptyOperator(task_id=)
    medium_quality = EmptyOperator(task_id=)
    low_quality = EmptyOperator(task_id=)

    
    join = EmptyOperator(
        task_id=,
        trigger_rule=TriggerRule.NONE_FAILED_MIN_ONE_SUCCESS,
    )

    quality_check >> branch >> [high_quality, medium_quality, low_quality] >> join

branching_pipeline()

Pattern 4: Sensors and External Dependencies

# dags/sensor_patterns.py
from datetime import datetime, timedelta
from airflow import DAG
from airflow.sensors.filesystem import FileSensor
from airflow.providers.amazon.aws.sensors.s3 import S3KeySensor
from airflow.sensors.external_task import ExternalTaskSensor
from airflow.operators.python import PythonOperator

with DAG(
    dag_id='sensor_example',
    schedule='@daily',
    start_date=datetime(2024, 1, 1),
    catchup=False,
) as dag:

    # Wait for file on S3
    wait_for_file = S3KeySensor(
        task_id='wait_for_s3_file',
        bucket_name='data-lake',
        bucket_key='raw/{{ ds }}/data.parquet',
        aws_conn_id='aws_default',
        timeout=60 * 60 * 2,  # 2 hours
        poke_interval=60 * 5,  # Check every 5 minutes
        mode='reschedule',  # Free up worker slot while waiting
    )

    # Wait for another DAG to complete
    wait_for_upstream = ExternalTaskSensor(
        task_id='wait_for_upstream_dag',
        external_dag_id='upstream_etl',
        external_task_id='final_task',
        execution_date_fn=lambda dt: dt,  # Same execution date
        timeout=60 * 60 * ,
        mode=,
    )

    

     () -> PokeReturnValue:
        
         requests

        response = requests.get()
        is_done = response.status_code == 

         PokeReturnValue(is_done=is_done, xcom_value=response.json())

    api_ready = wait_for_api()

     ():
        api_result = context[].xcom_pull(task_ids=)
        ()

    process = PythonOperator(
        task_id=,
        python_callable=process_data,
    )

    [wait_for_file, wait_for_upstream, api_ready] >> process

Pattern 5: Error Handling and Alerts

# dags/error_handling.py
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.utils.trigger_rule import TriggerRule
from airflow.models import Variable

def task_failure_callback(context):
    """Callback on task failure"""
    task_instance = context['task_instance']
    exception = context.get('exception')

    # Send to Slack/PagerDuty/etc
    message = f"""
    Task Failed!
    DAG: {task_instance.dag_id}
    Task: {task_instance.task_id}
    Execution Date: {context['ds']}
    Error: {exception}
    Log URL: {task_instance.log_url}
    """
    # send_slack_alert(message)
    print(message)

def dag_failure_callback(context):
    """Callback on DAG failure"""
    # Aggregate failures, send summary
    pass

with DAG(
    dag_id='error_handling_example',
    schedule='@daily',
    start_date=datetime(2024, 1, 1),
    catchup=False,
    on_failure_callback=dag_failure_callback,
    default_args={
        'on_failure_callback': task_failure_callback,
        'retries': 3,
        'retry_delay': timedelta(minutes=),
    },
)  dag:

     ():
         random
         random.random() < :
             ValueError()
         

    risky_task = PythonOperator(
        task_id=,
        python_callable=might_fail,
    )

     ():
        
        ()

    cleanup_task = PythonOperator(
        task_id=,
        python_callable=cleanup,
        trigger_rule=TriggerRule.ALL_DONE,  
    )

     ():
        
        ()

    success_notification = PythonOperator(
        task_id=,
        python_callable=notify_success,
        trigger_rule=TriggerRule.ALL_SUCCESS,
    )

    risky_task >> [cleanup_task, success_notification]

Pattern 6: Testing DAGs

# tests/test_dags.py
import pytest
from datetime import datetime
from airflow.models import DagBag

@pytest.fixture
def dagbag():
    return DagBag(dag_folder='dags/', include_examples=False)

def test_dag_loaded(dagbag):
    """Test that all DAGs load without errors"""
    assert len(dagbag.import_errors) == 0, f"DAG import errors: {dagbag.import_errors}"

def test_dag_structure(dagbag):
    """Test specific DAG structure"""
    dag = dagbag.get_dag('example_etl')

    assert dag is not None
    assert len(dag.tasks) == 3
    assert dag.schedule_interval == '0 6 * * *'

def test_task_dependencies(dagbag):
    """Test task dependencies are correct"""
    dag = dagbag.get_dag('example_etl')

    extract_task = dag.get_task('extract')
    assert 'start' in [t.task_id for t in extract_task.upstream_list]
    assert 'end' in [t.task_id  t  extract_task.downstream_list]

 ():
    
     dag_id, dag  dagbag.dags.items():
         dag.test_cycle()  , 


 ():
    
     dags.example_dag  extract_data

    result = extract_data(ds=)
       result
     (result[], )

Project Structure

airflow/
├── dags/
│   ├── __init__.py
│   ├── common/
│   │   ├── __init__.py
│   │   ├── operators.py    # Custom operators
│   │   ├── sensors.py      # Custom sensors
│   │   └── callbacks.py    # Alert callbacks
│   ├── etl/
│   │   ├── customers.py
│   │   └── orders.py
│   └── ml/
│       └── training.py
├── plugins/
│   └── custom_plugin.py
├── tests/
│   ├── __init__.py
│   ├── test_dags.py
│   └── test_operators.py
├── docker-compose.yml
└── requirements.txt

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

Resources

• Airflow Documentation
• Astronomer Guides
• TaskFlow API