docs-archive/apache-airflow/2.2.5/_sources/_api/airflow/executors/celery_kubernetes_executor/index.rst.txt

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

.. py:module:: airflow.executors.celery_kubernetes_executor


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

Classes
~~~~~~~

.. autoapisummary::

   airflow.executors.celery_kubernetes_executor.CeleryKubernetesExecutor




.. py:class:: CeleryKubernetesExecutor(celery_executor, kubernetes_executor)

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

   CeleryKubernetesExecutor consists of CeleryExecutor and KubernetesExecutor.
   It chooses an executor to use based on the queue defined on the task.
   When the queue is the value of ``kubernetes_queue`` in section ``[celery_kubernetes_executor]``
   of the configuration (default value: `kubernetes`), KubernetesExecutor is selected to run the task,
   otherwise, CeleryExecutor is used.

   .. py:attribute:: KUBERNETES_QUEUE
      

      

   .. py:method:: queued_tasks(self) -> Dict[airflow.models.taskinstance.TaskInstanceKey, airflow.executors.base_executor.QueuedTaskInstanceType]
      :property:

      Return queued tasks from celery and kubernetes executor


   .. py:method:: running(self) -> Set[airflow.models.taskinstance.TaskInstanceKey]
      :property:

      Return running tasks from celery and kubernetes executor


   .. py:method:: job_id(self)
      :property:

      This is a class attribute in BaseExecutor but since this is not really an executor, but a wrapper
      of executors we implement as property so we can have custom setter.


   .. py:method:: start(self) -> None

      Start celery and kubernetes executor


   .. py:method:: slots_available(self)
      :property:

      Number of new tasks this executor instance can accept


   .. py:method:: queue_command(self, task_instance: airflow.models.taskinstance.TaskInstance, command: airflow.executors.base_executor.CommandType, priority: int = 1, queue: Optional[str] = None)

      Queues command via celery or kubernetes executor


   .. py:method:: queue_task_instance(self, task_instance: airflow.models.taskinstance.TaskInstance, mark_success: bool = False, pickle_id: Optional[str] = None, ignore_all_deps: bool = False, ignore_depends_on_past: bool = False, ignore_task_deps: bool = False, ignore_ti_state: bool = False, pool: Optional[str] = None, cfg_path: Optional[str] = None) -> None

      Queues task instance via celery or kubernetes executor


   .. py:method:: has_task(self, task_instance: airflow.models.taskinstance.TaskInstance) -> bool

      Checks if a task is either queued or running in either celery or kubernetes executor.

      :param task_instance: TaskInstance
      :return: True if the task is known to this executor


   .. py:method:: heartbeat(self) -> None

      Heartbeat sent to trigger new jobs in celery and kubernetes executor


   .. py:method:: get_event_buffer(self, dag_ids=None) -> Dict[airflow.models.taskinstance.TaskInstanceKey, airflow.executors.base_executor.EventBufferValueType]

      Returns and flush the event buffer from celery and kubernetes executor

      :param dag_ids: to dag_ids to return events for, if None returns all
      :return: a dict of events


   .. py:method:: try_adopt_task_instances(self, tis: List[airflow.models.taskinstance.TaskInstance]) -> List[airflow.models.taskinstance.TaskInstance]

      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:method:: end(self) -> None

      End celery and kubernetes executor


   .. py:method:: terminate(self) -> None

      Terminate celery and kubernetes executor


   .. py:method:: debug_dump(self)

      Called in response to SIGUSR2 by the scheduler