Managing DAGs

This section covers essential aspects of managing your Directed Acyclic Graphs (DAGs) within Apache Airflow. Effective DAG management is crucial for maintaining a healthy and efficient workflow orchestration system.

DAG Loading and Parsing

Airflow scans a designated DAGs folder periodically to discover and load DAGs. The frequency of this scan can be configured.

DAG File Structure

DAG files are typically Python scripts placed in a specific directory configured in your airflow.cfg file under the [core] dags_folder setting.

Each Python file in the DAGs folder should define at least one DAG object. It's a common practice to organize DAGs into subdirectories based on their functionality or team.

DAG Versioning

While Airflow itself doesn't have built-in DAG versioning like source control, it's highly recommended to integrate version control systems (like Git) into your DAG management workflow.

Commit your DAG files to a Git repository.
Use branches for development and testing.
Deploy DAGs by checking out specific commits or branches.

DAG Discovery and Refresh

Airflow's scheduler automatically discovers and parses DAG files. If you add, remove, or modify a DAG file, Airflow will eventually pick up the changes. The dag_dir_list_interval setting in airflow.cfg controls how often the DAGs folder is scanned.

DAG File Best Practices

Keep DAGs Simple and Focused

Each DAG should represent a single, well-defined workflow. Avoid creating monolithic DAGs that handle too many disparate tasks.

Use Imports Wisely

Minimize the number of external dependencies imported within your DAG files. Heavy imports can slow down DAG parsing and increase scheduler memory usage.

Avoid Dynamic DAG Generation in the Top Level

While dynamic DAG generation is powerful, avoid doing it directly at the top level of your DAG file, especially if it involves complex or time-consuming operations. Consider generating DAGs within a function called conditionally.

Common DAG Management Scenarios

Disabling a DAG

To temporarily disable a DAG without deleting its file, you can add a property to the DAG object:


from airflow import DAG
from airflow.operators.empty import EmptyOperator
from datetime import datetime

with DAG(
    dag_id='my_disabled_dag',
    start_date=datetime(2023, 1, 1),
    schedule_interval=None,
    catchup=False,
    tags=['example'],
    is_paused_upon_creation=True, # This will pause the DAG
) as dag:
    start = EmptyOperator(task_id='start')

Alternatively, you can set the schedule_interval to None and catchup to False, or use the Airflow UI to pause the DAG.

Unpausing a DAG

To unpause a DAG, use the Airflow UI to toggle the pause button or set is_paused_upon_creation=False in your DAG definition (though changes via UI are generally preferred for existing DAGs).

DAG Directory Organization

A well-organized DAGs folder makes it easier to manage numerous DAGs. Consider a structure like this:


dags/
├── __init__.py
├── my_etl_dag/
│   ├── __init__.py
│   └── etl_pipeline.py
├── marketing_dags/
│   ├── __init__.py
│   └── campaign_reporting.py
├── data_science_dags/
│   ├── __init__.py
│   └── model_training.py
└── utils/
    ├── __init__.py
    └── helpers.py

DAG File Performance Considerations

Slow-parsing DAG files can impact the scheduler's ability to manage tasks efficiently. Here are some tips:

Optimize Imports: Import only what you need.
Avoid Heavy Computation: Move complex calculations or data loading outside the DAG parsing phase.
Minimize Dependencies: Reduce reliance on external Python libraries that are not essential for DAG definition.
Use Caching: If certain data structures or configurations are needed, consider caching them to avoid recomputing them on every parse.

Tip: Regularly check the scheduler logs for any DAG parsing errors or warnings. These can indicate issues with your DAG files.

Related Concepts

Executors: Understand how tasks are executed.
Variables: Managing configuration values.
Connections: Storing credentials for external services.

Administration Guide