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 argument such as
execution_date if you want to test argument-specific DAG runs, but otherwise
you can run or debug DAGs as needed.
Comparison with DebugExecutor¶
dag.test command has the following benefits over the
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.
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)¶
DebugExecutor is meant as
a debug tool and can be used from IDE. It is a single process executor that
TaskInstance and executes them by running
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.
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
For more information on setting the configuration, see Setting Configuration Options.
IDE setup steps:
mainblock 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()
AIRFLOW__CORE__EXECUTOR=DebugExecutorin run configuration of your IDE. In this step you should also setup all environment variables required by your DAG.
Run / debug the DAG file.