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')}")

Comparison with DebugExecutor¶

The dag.test command has the following benefits over the DebugExecutor class, which is now deprecated:

It does not require running an executor at all. Tasks are run one at a time with no executor or scheduler logs.
It is significantly faster than running code with a DebugExecutor as it does not need to go through a scheduler loop.
It does not perform a backfill.

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>

Debug Executor (deprecated)¶

The DebugExecutor is meant as a debug tool and can be used from IDE. It is a single process executor that queues TaskInstance and executes them by running _run_raw_task method.

Due to its nature the executor can be used with SQLite database. When used with sensors the executor will change sensor mode to reschedule to avoid blocking the execution of DAG.

Additionally DebugExecutor can be used in a fail-fast mode that will make all other running or scheduled tasks fail immediately. To enable this option set AIRFLOW__DEBUG__FAIL_FAST=True or adjust fail_fast option in your airflow.cfg. For more information on setting the configuration, see Setting Configuration Options.

IDE setup steps:

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

It will run a backfill job:

if __name__ == "__main__":
    from airflow.utils.state import State

    dag.clear()
    dag.run()

Setup AIRFLOW__CORE__EXECUTOR=DebugExecutor in run configuration of your IDE. In this step you should also setup all environment variables required by your DAG.
Run / debug the DAG file.