Debugging Airflow dags¶

Testing dags with dag.test()¶

To debug dags in an IDE, you can set up the dag.test command in your dag file and run through your DAG in a single serialized python process.

This approach can be used with any supported database (including a local SQLite database) and will fail fast as all tasks run in a single process.

To set up dag.test, add these two lines to the bottom of your dag file:

if __name__ == "__main__":
    dag.test()

and that’s it! You can add optional arguments to fine tune the testing but otherwise you can run or debug dags as needed. Here are some examples of arguments:

execution_date if you want to test argument-specific DAG runs
use_executor if you want to test the DAG using an executor. By default dag.test runs the DAG without an executor, it just runs all the tasks locally. By providing this argument, the DAG is executed using the executor configured in the Airflow environment.

Conditionally skipping tasks¶

If you don’t wish to execute some subset of tasks in your local environment (e.g. dependency check sensors or cleanup steps), you can automatically mark them successful supplying a pattern matching their task_id in the mark_success_pattern argument.

In the following example, testing the dag won’t wait for either of the upstream dags to complete. Instead, testing data is manually ingested. The cleanup step is also skipped, making the intermediate csv is available for inspection.

with DAG("example_dag", default_args=default_args) as dag:
    sensor = ExternalTaskSensor(task_id="wait_for_ingestion_dag", external_dag_id="ingest_raw_data")
    sensor2 = ExternalTaskSensor(task_id="wait_for_dim_dag", external_dag_id="ingest_dim")
    collect_stats = PythonOperator(task_id="extract_stats_csv", python_callable=extract_stats_csv)
    # ... run other tasks
    cleanup = PythonOperator(task_id="cleanup", python_callable=Path.unlink, op_args=[collect_stats.output])

    [sensor, sensor2] >> collect_stats >> cleanup

if __name__ == "__main__":
    ingest_testing_data()
    run = dag.test(mark_success_pattern="wait_for_.*|cleanup")
    print(f"Intermediate csv: {run.get_task_instance('collect_stats').xcom_pull(task_id='collect_stats')}")

Debugging Airflow dags on the command line¶

With the same two line addition as mentioned in the above section, you can now easily debug a DAG using pdb as well. Run python -m pdb <path to dag file>.py for an interactive debugging experience on the command line.

root@ef2c84ad4856:/opt/airflow# python -m pdb airflow/example_dags/example_bash_operator.py
> /opt/airflow/airflow/example_dags/example_bash_operator.py(18)<module>()
-> """Example DAG demonstrating the usage of the BashOperator."""
(Pdb) b 45
Breakpoint 1 at /opt/airflow/airflow/example_dags/example_bash_operator.py:45
(Pdb) c
> /opt/airflow/airflow/example_dags/example_bash_operator.py(45)<module>()
-> bash_command='echo 1',
(Pdb) run_this_last
<Task(EmptyOperator): run_this_last>

IDE setup steps:

Add main block at the end of your DAG file to make it runnable.

if __name__ == "__main__":
    dag.test()

Run / debug the DAG file.