:py:mod:`airflow.executors.celery_executor`
===========================================

.. py:module:: airflow.executors.celery_executor

.. autoapi-nested-parse::

   CeleryExecutor

   .. seealso::
       For more information on how the CeleryExecutor works, take a look at the guide:
       :ref:`executor:CeleryExecutor`


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   airflow.executors.celery_executor.ExceptionWithTraceback
   airflow.executors.celery_executor.CeleryExecutor
   airflow.executors.celery_executor.BulkStateFetcher


Functions
~~~~~~~~~

.. autoapisummary::

   airflow.executors.celery_executor.execute_command
   airflow.executors.celery_executor.send_task_to_executor
   airflow.executors.celery_executor.on_celery_import_modules
   airflow.executors.celery_executor.fetch_celery_task_state


Attributes
~~~~~~~~~~

.. autoapisummary::

   airflow.executors.celery_executor.log
   airflow.executors.celery_executor.CELERY_FETCH_ERR_MSG_HEADER
   airflow.executors.celery_executor.CELERY_SEND_ERR_MSG_HEADER
   airflow.executors.celery_executor.OPERATION_TIMEOUT
   airflow.executors.celery_executor.celery_configuration
   airflow.executors.celery_executor.app
   airflow.executors.celery_executor.TaskInstanceInCelery


.. py:data:: log
   

.. py:data:: CELERY_FETCH_ERR_MSG_HEADER
   :annotation: = Error fetching Celery task state

   
.. py:data:: CELERY_SEND_ERR_MSG_HEADER
   :annotation: = Error sending Celery task

   
.. py:data:: OPERATION_TIMEOUT
   

   To start the celery worker, run the command:
   airflow celery worker


.. py:data:: celery_configuration
   

.. py:data:: app
   

.. py:function:: execute_command(command_to_exec)

   Executes command.


.. py:class:: ExceptionWithTraceback(exception, exception_traceback)

   Wrapper class used to propagate exceptions to parent processes from subprocesses.

   :param exception: The exception to wrap
   :param exception_traceback: The stacktrace to wrap


.. py:data:: TaskInstanceInCelery
   

.. py:function:: send_task_to_executor(task_tuple)

   Sends task to executor.


.. py:function:: on_celery_import_modules(*args, **kwargs)

   Preload some "expensive" airflow modules so that every task process doesn't have to import it again and
   again.

   Loading these for each task adds 0.3-0.5s *per task* before the task can run. For long running tasks this
   doesn't matter, but for short tasks this starts to be a noticeable impact.


.. py:class:: CeleryExecutor

   Bases: :py:obj:`airflow.executors.base_executor.BaseExecutor`

   CeleryExecutor is recommended for production use of Airflow. It allows
   distributing the execution of task instances to multiple worker nodes.

   Celery is a simple, flexible and reliable distributed system to process
   vast amounts of messages, while providing operations with the tools
   required to maintain such a system.

   .. py:attribute:: supports_ad_hoc_ti_run
      :annotation: :bool = True

      
   .. py:method:: start(self)

      Executors may need to get things started.


   .. py:method:: sync(self)

      Sync will get called periodically by the heartbeat method.
      Executors should override this to perform gather statuses.


   .. py:method:: debug_dump(self)

      Called in response to SIGUSR2 by the scheduler


   .. py:method:: update_all_task_states(self)

      Updates states of the tasks.


   .. py:method:: change_state(self, key, state, info=None)

      Changes state of the task.

      :param info: Executor information for the task instance
      :param key: Unique key for the task instance
      :param state: State to set for the task.


   .. py:method:: update_task_state(self, key, state, info)

      Updates state of a single task.


   .. py:method:: end(self, synchronous = False)

      This method is called when the caller is done submitting job and
      wants to wait synchronously for the job submitted previously to be
      all done.


   .. py:method:: terminate(self)

      This method is called when the daemon receives a SIGTERM


   .. py:method:: try_adopt_task_instances(self, tis)

      Try to adopt running task instances that have been abandoned by a SchedulerJob dying.

      Anything that is not adopted will be cleared by the scheduler (and then become eligible for
      re-scheduling)

      :return: any TaskInstances that were unable to be adopted
      :rtype: list[airflow.models.TaskInstance]


.. py:function:: fetch_celery_task_state(async_result)

   Fetch and return the state of the given celery task. The scope of this function is
   global so that it can be called by subprocesses in the pool.

   :param async_result: a tuple of the Celery task key and the async Celery object used
       to fetch the task's state
   :return: a tuple of the Celery task key and the Celery state and the celery info
       of the task
   :rtype: tuple[str, str, str]


.. py:class:: BulkStateFetcher(sync_parallelism=None)

   Bases: :py:obj:`airflow.utils.log.logging_mixin.LoggingMixin`

   Gets status for many Celery tasks using the best method available

   If BaseKeyValueStoreBackend is used as result backend, the mget method is used.
   If DatabaseBackend is used as result backend, the SELECT ...WHERE task_id IN (...) query is used
   Otherwise, multiprocessing.Pool will be used. Each task status will be downloaded individually.

   .. py:method:: get_many(self, async_results)

      Gets status for many Celery tasks using the best method available.