| :py:mod:`airflow.executors.celery_executor` |
| =========================================== |
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| .. py:module:: airflow.executors.celery_executor |
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| .. autoapi-nested-parse:: |
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| CeleryExecutor |
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| .. seealso:: |
| For more information on how the CeleryExecutor works, take a look at the guide: |
| :ref:`executor:CeleryExecutor` |
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| Module Contents |
| --------------- |
| |
| Classes |
| ~~~~~~~ |
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| .. autoapisummary:: |
| |
| airflow.executors.celery_executor.ExceptionWithTraceback |
| airflow.executors.celery_executor.CeleryExecutor |
| airflow.executors.celery_executor.BulkStateFetcher |
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| Functions |
| ~~~~~~~~~ |
| |
| .. autoapisummary:: |
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| 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 |
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| Attributes |
| ~~~~~~~~~~ |
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| .. autoapisummary:: |
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| 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 |
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| .. py:data:: log |
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| .. py:data:: CELERY_FETCH_ERR_MSG_HEADER |
| :annotation: = Error fetching Celery task state |
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| .. py:data:: CELERY_SEND_ERR_MSG_HEADER |
| :annotation: = Error sending Celery task |
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| .. py:data:: OPERATION_TIMEOUT |
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| To start the celery worker, run the command: |
| airflow celery worker |
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| .. py:data:: celery_configuration |
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| .. py:data:: app |
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| .. py:function:: execute_command(command_to_exec: airflow.executors.base_executor.CommandType) -> None |
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| Executes command. |
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| .. py:class:: ExceptionWithTraceback(exception: Exception, exception_traceback: str) |
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| Wrapper class used to propagate exceptions to parent processes from subprocesses. |
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| :param exception: The exception to wrap |
| :type exception: Exception |
| :param exception_traceback: The stacktrace to wrap |
| :type exception_traceback: str |
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| .. py:data:: TaskInstanceInCelery |
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| .. py:function:: send_task_to_executor(task_tuple: TaskInstanceInCelery) -> Tuple[airflow.models.taskinstance.TaskInstanceKey, airflow.executors.base_executor.CommandType, Union[celery.result.AsyncResult, ExceptionWithTraceback]] |
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| Sends task to executor. |
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| .. py:function:: on_celery_import_modules(*args, **kwargs) |
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| Preload some "expensive" airflow modules so that every task process doesn't have to import it again and |
| again. |
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| 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. |
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| .. py:class:: CeleryExecutor |
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| Bases: :py:obj:`airflow.executors.base_executor.BaseExecutor` |
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| 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. |
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| .. py:method:: start(self) -> None |
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| Executors may need to get things started. |
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| .. py:method:: trigger_tasks(self, open_slots: int) -> None |
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| Overwrite trigger_tasks function from BaseExecutor |
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| :param open_slots: Number of open slots |
| :return: |
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| .. py:method:: sync(self) -> None |
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| Sync will get called periodically by the heartbeat method. |
| Executors should override this to perform gather statuses. |
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| .. py:method:: debug_dump(self) -> None |
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| Called in response to SIGUSR2 by the scheduler |
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| .. py:method:: update_all_task_states(self) -> None |
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| Updates states of the tasks. |
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| .. py:method:: change_state(self, key: airflow.models.taskinstance.TaskInstanceKey, state: str, info=None) -> None |
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| Changes state of the task. |
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| :param info: Executor information for the task instance |
| :param key: Unique key for the task instance |
| :param state: State to set for the task. |
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| .. py:method:: update_task_state(self, key: airflow.models.taskinstance.TaskInstanceKey, state: str, info: Any) -> None |
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| Updates state of a single task. |
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| .. py:method:: end(self, synchronous: bool = False) -> None |
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| 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. |
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| .. py:method:: execute_async(self, key: airflow.models.taskinstance.TaskInstanceKey, command: airflow.executors.base_executor.CommandType, queue: Optional[str] = None, executor_config: Optional[Any] = None) |
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| Do not allow async execution for Celery executor. |
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| .. py:method:: terminate(self) |
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| This method is called when the daemon receives a SIGTERM |
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| .. py:method:: try_adopt_task_instances(self, tis: List[airflow.models.taskinstance.TaskInstance]) -> List[airflow.models.taskinstance.TaskInstance] |
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| Try to adopt running task instances that have been abandoned by a SchedulerJob dying. |
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| Anything that is not adopted will be cleared by the scheduler (and then become eligible for |
| re-scheduling) |
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| :return: any TaskInstances that were unable to be adopted |
| :rtype: list[airflow.models.TaskInstance] |
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| .. py:function:: fetch_celery_task_state(async_result: celery.result.AsyncResult) -> Tuple[str, Union[str, ExceptionWithTraceback], Any] |
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| 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. |
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| :param async_result: a tuple of the Celery task key and the async Celery object used |
| to fetch the task's state |
| :type async_result: tuple(str, celery.result.AsyncResult) |
| :return: a tuple of the Celery task key and the Celery state and the celery info |
| of the task |
| :rtype: tuple[str, str, str] |
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| .. py:class:: BulkStateFetcher(sync_parallelism=None) |
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| Bases: :py:obj:`airflow.utils.log.logging_mixin.LoggingMixin` |
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| Gets status for many Celery tasks using the best method available |
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| 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. |
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| .. py:method:: get_many(self, async_results) -> Mapping[str, airflow.executors.base_executor.EventBufferValueType] |
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| Gets status for many Celery tasks using the best method available. |
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