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

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

.. py:module:: airflow.executors.base_executor

.. autoapi-nested-parse::

   Base executor - this is the base class for all the implemented executors.



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

Classes
~~~~~~~

.. autoapisummary::

   airflow.executors.base_executor.BaseExecutor




Attributes
~~~~~~~~~~

.. autoapisummary::

   airflow.executors.base_executor.PARALLELISM
   airflow.executors.base_executor.NOT_STARTED_MESSAGE
   airflow.executors.base_executor.QUEUEING_ATTEMPTS
   airflow.executors.base_executor.CommandType
   airflow.executors.base_executor.QueuedTaskInstanceType
   airflow.executors.base_executor.EventBufferValueType


.. py:data:: PARALLELISM
   :annotation: :int

   

.. py:data:: NOT_STARTED_MESSAGE
   :annotation: = The executor should be started first!

   

.. py:data:: QUEUEING_ATTEMPTS
   :annotation: = 5

   

.. py:data:: CommandType
   

   

.. py:data:: QueuedTaskInstanceType
   

   

.. py:data:: EventBufferValueType
   

   

.. py:class:: BaseExecutor(parallelism: int = PARALLELISM)

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

   Class to derive in order to interface with executor-type systems
   like Celery, Kubernetes, Local, Sequential and the likes.

   :param parallelism: how many jobs should run at one time. Set to
       ``0`` for infinity

   .. py:attribute:: job_id
      :annotation: :Optional[str]

      

   .. py:method:: __repr__(self)

      Return repr(self).


   .. py:method:: start(self)

      Executors may need to get things started.


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

      Queues command to task


   .. 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.


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

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

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


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

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


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

      Heartbeat sent to trigger new jobs.


   .. py:method:: order_queued_tasks_by_priority(self) -> List[Tuple[airflow.models.taskinstance.TaskInstanceKey, QueuedTaskInstanceType]]

      Orders the queued tasks by priority.

      :return: List of tuples from the queued_tasks according to the priority.


   .. py:method:: trigger_tasks(self, open_slots: int) -> None

      Triggers tasks

      :param open_slots: Number of open slots


   .. py:method:: change_state(self, key: airflow.models.taskinstance.TaskInstanceKey, state: str, info=None) -> 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:: fail(self, key: airflow.models.taskinstance.TaskInstanceKey, info=None) -> None

      Set fail state for the event.

      :param info: Executor information for the task instance
      :param key: Unique key for the task instance


   .. py:method:: success(self, key: airflow.models.taskinstance.TaskInstanceKey, info=None) -> None

      Set success state for the event.

      :param info: Executor information for the task instance
      :param key: Unique key for the task instance


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

      Returns and flush the event buffer. In case dag_ids is specified
      it will only return and flush events for the given dag_ids. Otherwise
      it returns and flushes all events.

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


   .. py:method:: execute_async(self, key: airflow.models.taskinstance.TaskInstanceKey, command: CommandType, queue: Optional[str] = None, executor_config: Optional[Any] = None) -> None
      :abstractmethod:

      This method will execute the command asynchronously.

      :param key: Unique key for the task instance
      :param command: Command to run
      :param queue: name of the queue
      :param executor_config: Configuration passed to the executor.


   .. py:method:: end(self) -> None
      :abstractmethod:

      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)
      :abstractmethod:

      This method is called when the daemon receives a SIGTERM


   .. 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:: slots_available(self)
      :property:

      Number of new tasks this executor instance can accept


   .. py:method:: validate_command(command: List[str]) -> None
      :staticmethod:

      Check if the command to execute is airflow command


   .. py:method:: debug_dump(self)

      Called in response to SIGUSR2 by the scheduler