Can a loop be made in an Airflow DAG: task 3 returns to execute task 1 when there is insufficient data?

In Airflow DAG, it is not possible to implement a traditional loop with a return to the previous task directly, as a DAG is a directed acyclic graph, and cycles are prohibited.

However, similar behavior can be achieved using:

• The use of TriggerRule and conditional operators within tasks to rerun tasks when necessary.
• Organizing reruns of the DAG with sensors or external triggers.
• Using BranchPythonOperator to choose the execution path based on conditions.

If you need to repeat execution with data checks, it is better to implement retry logic within the task itself or use retry mechanisms with delays.

Example with BranchPythonOperator to select the next task:

from airflow import DAG
from airflow.operators.python import PythonOperator, BranchPythonOperator
from airflow.utils.dates import days_ago

def check_data(**kwargs):
    data_available = False  # data check logic
    if data_available:
        return 'task_2'
    else:
        return 'task_1'

def task_1_func():
    print('Processing data or waiting')

def task_2_func():
    print('Further processing')

dag = DAG('example_loop', start_date=days_ago(1), schedule_interval=None)

branch = BranchPythonOperator(
    task_id='check_data',
    python_callable=check_data,
    dag=dag
)

task_1 = PythonOperator(task_id='task_1', python_callable=task_1_func, dag=dag)

task_2 = PythonOperator(task_id='task_2', python_callable=task_2_func, dag=dag)

branch >> [task_1, task_2]

Thus, you can control branching but not create cycles in a DAG.