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": ["my.email@myemail.com"],
        "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": ["my.email@myemail.com"]
• "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/dev.yaml

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

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

config = load_config(config_name="dev")

with DAG(
    dag_id="test-dag",
    description="test that dag is working properly",
    schedule=timedelta(minutes=1),
    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

Features

• Configure DAGs from a central config file or...
• from multiple env-specific config files (e.g. dev, uat, prod)
• Specialize DAGs by dag_id from a single file (e.g. set each DAG's schedule from a single shared file)
• Generate entire DAGs declaratively, like astronomer/dag-factory
• Configure other extensions like airflow-priority, airflow-supervisor

Configuration

class Configuration(BaseModel):
    # default task args
    # https://airflow.apache.org/docs/apache-airflow/stable/_api/airflow/models/baseoperator/index.html#airflow.models.baseoperator.BaseOperator
    default_task_args: TaskArgs

    # default dag args
    # https://airflow.apache.org/docs/apache-airflow/stable/_api/airflow/models/dag/index.html#airflow.models.dag.DAG
    default_dag_args: DagArgs

    # string (dag id) to Dag mapping
    dags: Optional[Dict[str, Dag]]

    # string (dag id) to Task mapping
    tasks: Optional[Dict[str, Task]]

    # used for extensions to inject arbitrary configuration.
    # See e.g.: https://github.com/airflow-laminar/airflow-supervisor?tab=readme-ov-file#example-dag-airflow-config
    extensions: Optional[Dict[str, BaseModel]]

Examples - Load defaults from config

# config/test.yaml
# @package _global_
_target_: airflow_config.Configuration
default_args:
  _target_: airflow_config.DefaultTaskArgs
  owner: test

from airflow_config import load_config, DAG, create_dag

conf = load_config("config", "test")
d = create_dag("config", "test")
# or d = DAG(dag_id="test-dag", config=conf)
assert conf.default_args.owner == "test"

Examples - Load more defaults from config

# config/test.yaml
# @package _global_
_target_: airflow_config.Configuration
default_args:
  _target_: airflow_config.DefaultTaskArgs
  owner: test
  email: [myemail@myemail.com]
  email_on_failure: false
  email_on_retry: false
  retries: 0
  depends_on_past: false
default_dag_args:
  _target: airflow_config.DagArgs
  schedule: "01:10"
  start_date: "2024-01-01"
  catchup: false
  tags: ["utility", "test"]

from airflow_config import load_config, DAG, create_dag

conf = load_config("config", "test")
d = create_dag("config", "test")
# or d = DAG(dag_id="test-dag", config=conf)
assert conf.default_args.owner == "test"
assert conf.default_args.email == ["myemail@myemail.com"]
assert conf.default_args.email_on_failure is False
assert conf.default_args.email_on_retry is False
assert conf.default_args.retries == 0
assert conf.default_args.depends_on_past is False
assert conf.default_dag_args.start_date == datetime(2024, 1, 1)
assert conf.default_dag_args.catchup is False
assert conf.default_dag_args.tags == ["utility", "test"]

Examples - Specialize individual DAGs

# config/test.yaml
# @package _global_
_target_: airflow_config.Configuration
default_args:
  _target_: airflow_config.TaskArgs
  owner: test
  email: [myemail@myemail.com]
  email_on_failure: false
  email_on_retry: false
  retries: 0
  depends_on_past: false

default_dag_args:
  _target: airflow_config.DagArgs
  schedule: "01:00"
  start_date: "2024-01-01"
  catchup: false
  tags: ["utility", "test"]

dags:
  example_dag:
    default_args:
      owner: "custom_owner"
    description: "this is an example dag"
    schedule: "0 3 * * *"

  example_dag2:
    default_args:
      owner: "custom_owner2"
    schedule: "0 4 * * *"

from airflow_config import load_config, DAG, create_dag

conf = load_config("config", "test")
d = create_dag("config", "test")
# or d = DAG(dag_id="test-dag", config=conf)
assert d.default_args["owner"] == "test"
assert d.default_args["email"] == ["myemail@myemail.com"]
assert d.default_args["email_on_failure"] is False
assert d.default_args["email_on_retry"] is False
assert d.default_args["retries"] == 0
assert d.default_args["depends_on_past"] is False
assert d.schedule_interval == timedelta(seconds=3600)
assert isinstance(d.timetable, DeltaDataIntervalTimetable)
assert isinstance(d.timetable._delta, timedelta)
assert d.start_date.year == 2024
assert d.start_date.month == 1
assert d.start_date.day == 1
assert d.catchup is False
assert d.tags == ["utility", "test"]

# specialized by dag_id from shared config file
d = DAG(dag_id="example_dag", config=conf)
assert d.default_args["owner"] == "custom_owner"
assert d.default_args["email"] == ["myemail@myemail.com"]
assert d.schedule_interval == "0 3 * * *"

# specialized by dag_id from shared config file
d = DAG(dag_id="example_dag2", config=conf)
assert d.default_args["owner"] == "custom_owner2"
assert d.default_args["email"] == ["myemail@myemail.com"]
assert d.schedule_interval == "0 4 * * *"

Examples - DAG Factory

Integrations

Configuration can be arbitrarily extended by the key extensions. Support is built in for airflow-priority, but can be extended to any aribitrary pydantic model as seen in the README of airflow-supervisor.

License

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