Airflow Programming Model

Internal

• Airflow

requirements.txt

apache-airflow == 2.3.3

Declaring a DAG

With @dag Decorator

The @dag decorator turns a Python function into a DAG generator function. This is only available in Airflow 2 and newer. Also see:

Airflow Concepts | Declaring a DAG

TO PARSE:

• https://airflow.apache.org/docs/apache-airflow/stable/concepts/dags.html#the-dag-decorator
• https://airflow.apache.org/docs/apache-airflow/2.0.0/concepts.html#dag-decorator.

With DAG() Constructor

Also see:

Airflow Concepts | Declaring a DAG

import time
from datetime import datetime, timedelta
from airflow import DAG
from airflow.operators.empty import EmptyOperator
from airflow.operators.python import PythonOperator

default_args = {
    'owner': 'somebody',
    "depends_on_past": False,
    "email": ["somebody@example.com"],
    "email_on_failure": True,
    "email_on_retry": False
}

dag = DAG('some_dag',
          max_active_runs=1,
          catchup=True,
          start_date=datetime(2022, 7, 13, 0),
          schedule_interval="* * * * *",
          dagrun_timeout=timedelta(minutes=2),
          default_args=default_args
          )


def some_function():
    print("something")


with dag:
    start = EmptyOperator(task_id='start', dag=dag)
    end = EmptyOperator(task_id='end', dag=dag)
    some_function = PythonOperator(
        task_id='some_function',
        python_callable=some_function,
        dag=dag
    )

    start >> some_function >> end

Module Management

TO PROCESS: https://airflow.apache.org/docs/apache-airflow/2.3.2/modules_management.html?highlight=import