docs/apache-airflow/core-concepts/executor/debug.rst

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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:

.. code-block:: python

  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
*****************************

The ``dag.test`` command has the following benefits over the :class:`~airflow.executors.debug_executor.DebugExecutor`
class, which is now deprecated:

1. It does not require running an executor at all. Tasks are run one at a time with no executor or scheduler logs.
2. It is significantly faster than running code with a DebugExecutor as it does not need to go through a scheduler loop.
3. 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.

.. code-block:: bash

  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>

.. _executor:DebugExecutor:

Debug Executor (deprecated)
===========================

The :class:`~airflow.executors.debug_executor.DebugExecutor` is meant as
a debug tool and can be used from IDE. It is a single process executor that
queues :class:`~airflow.models.taskinstance.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 :doc:`../../howto/set-config`.

**IDE setup steps:**

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

It will run a backfill job:

.. code-block:: python

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

      dag.clear()
      dag.run()


2. 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.

3. Run / debug the DAG file.