airflow-config 0.1.1

Airflow utilities for configuration of many DAGs and DAG environments

airflow-config

Apache Airflow utilities for for configuration of many DAGs and DAG environments

Overview

This library allows for YAML-driven configuration of Airflow, including DAGs, Operators, and declaratively defined DAGs (à la dag-factory). It is built with Pydantic, Hydra, and OmegaConf.

Consider the following basic DAG:

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

with DAG(
    dag_id="test-dag",
    default_args={
        "depends_on_past": False,
        "email": ["[e-mail]"],
        "email_on_failure": False,
        "email_on_retry": False,
        "retries": 0,
    },
    description="test that dag is working properly",
    schedule=timedelta(minutes=1),
    start_date=datetime(2024, 1, 1),
    catchup=False,
    tags=["utility", "test"],
):
    BashOperator(
        task_id="test-task",
        bash_command="echo 'test'",
    )

We can already see many options that we might want to drive centrally via config, perhaps based on some notion of environment (e.g. dev, prod, etc).

• "email": ["[e-mail]"]
• "email_on_failure": False
• "email_on_retry": False
• "retries": 0
• schedule=timedelta(minutes=1)
• tags=["utility", "test"]

If we want to change these in our DAG, we need to modify code. Now imagine we have hundreds of DAGs, this can quickly get out of hand, especially since Airflow DAGs are Python code, and we might easily inject a syntax error or a trailing comma or other common problem.

Now consider the alternative, config-driven approach:

config/config.yaml

# @package _global_
_target_: airflow_config.Configuration
default_args:
  _target_: airflow_config.DefaultArgs
  owner: test
  email: [[e-mail]]
  email_on_failure: false
  email_on_retry: false
  retries: 0
  depends_on_past: false
all_dags:
  _target_: airflow_config.DagArgs
  schedule: "01:00"
  start_date: "2024-01-01"
  catchup: false
  tags: ["utility", "test"]

from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow_config import DAG, load_config

config = load_config()

with DAG(dag_id="test-dag", description="test that dag is working properly", config=config):
    BashOperator(
        task_id="test-task",
        bash_command="echo 'test'",
    )

This has a number of benefits:

• Make changes without code changes, with static type validation
• Make changes across any number of DAGs without having to copy-paste
• Organize collections of DAGs into groups, e.g. via enviroment like dev, prod, etc

Configuration

More documentation coming soon!

License

This software is licensed under the Apache 2.0 license. See the LICENSE file for details.