Apache Airflow (Python) DAG File
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An Apache Airflow (Python) DAG File is a workflow DAG file that specifies an Airflow DAG (for Apache Airflow).
- See: Automated Workflow.
References
2023
- chat
- Q: what is the structure of an Airflow DAG file?
- A: An Apache Airflow DAG (Directed Acyclic Graph) is a Python script that defines a series of tasks and their dependencies. Here is a basic structure of an Airflow DAG file:
from airflow import DAG from airflow.operators.python_operator import PythonOperator # default_args are the default arguments for the DAG default_args = { "owner": "me", "start_date": "2022-01-01", "depends_on_past": False, "retries": 1, "retry_delay": timedelta(minutes=5), } # Create the DAG dag = DAG( "my_dag_id", default_args=default_args, schedule_interval=timedelta(hours=1), ) # Define a task using the PythonOperator task1 = PythonOperator( task_id="task1", python_callable=some_function, dag=dag, ) # Define another task task2 = PythonOperator( task_id="task2", python_callable=some_other_function, dag=dag, ) # Set task dependencies task2.set_upstream(task1)
- In this example, the DAG has two tasks: task1 and task2. The task2 depends on the task1, meaning that task1 will be executed before task2. The DAG has a schedule_interval of 1 hour, which means it will run every hour. The default_args dictionary contains default arguments for the DAG, such as the owner, the start date, and the retry behavior.
This is a very basic example of an Airflow DAG file. You can define more complex DAGs with multiple tasks, branching and looping logic, and dynamic task generation. You can also use a wide range of operators provided by Airflow or create your own custom operators.
- In this example, the DAG has two tasks: task1 and task2. The task2 depends on the task1, meaning that task1 will be executed before task2. The DAG has a schedule_interval of 1 hour, which means it will run every hour. The default_args dictionary contains default arguments for the DAG, such as the owner, the start date, and the retry behavior.
2023
- chat
- Q: write an Airflow DAG file with branching and looping logic
- A: Here is an example of an Apache Airflow DAG file that includes branching and looping logic:
from airflow import DAG from airflow.operators.python_operator import PythonOperator from airflow.operators.dummy_operator import DummyOperator # default_args are the default arguments for the DAG default_args = { "owner": "me", "start_date": "2022-01-01", "depends_on_past": False, "retries": 1, "retry_delay": timedelta(minutes=5), } # Create the DAG dag = DAG( "my_dag_id", default_args=default_args, schedule_interval=timedelta(hours=1), ) # Define a task that branches based on a condition def branching_task(**kwargs): if some_condition(kwargs["execution_date"]): return "task_a" else: return "task_b" branching_op = PythonOperator( task_id="branching_task", python_callable=branching_task, provide_context=True, dag=dag, ) # Define the tasks that will be run depending on the result of the branching task task_a = DummyOperator(task_id="task_a", dag=dag) task_b = DummyOperator(task_id="task_b", dag=dag) # Set task dependencies branching_op >> task_a branching_op >> task_b # Define a task that will run in a loop def loop_task(**kwargs): for i in range(5): task_id = f"task_{i}" task = DummyOperator(task_id=task_id, dag=dag) kwargs["ti"].xcom_push(key=task_id, value=i) yield task loop_op = PythonOperator( task_id="loop_task", python_callable=loop_task, provide_context=True, dag=dag, ) # Set task dependencies loop_op >> task_b
- In this example, the DAG has a task called branching_task that branches based on the result of the some_condition function. Depending on the result of this function, the DAG will run either task_a or task_b. The loop_task runs in a loop and generates a series of tasks, task_0, task_1, etc., that are each run one after the other. The loop task pushes the current loop iteration index to the XCom (short for cross-communication) store, which allows other tasks to retrieve the value and use it in their execution.