airflow.executors.local_kubernetes_executor¶
Module Contents¶
Classes¶
| LocalKubernetesExecutor consists of LocalExecutor and KubernetesExecutor. | 
- class airflow.executors.local_kubernetes_executor.LocalKubernetesExecutor(local_executor, kubernetes_executor)[source]¶
- Bases: - airflow.utils.log.logging_mixin.LoggingMixin- LocalKubernetesExecutor consists of LocalExecutor and KubernetesExecutor. It chooses the executor to use based on the queue defined on the task. When the task’s queue is the value of - kubernetes_queuein section- [local_kubernetes_executor]of the configuration (default value: kubernetes), KubernetesExecutor is selected to run the task, otherwise, LocalExecutor is used.- property job_id(self)[source]¶
- 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. 
 - queue_command(self, task_instance, command, priority=1, queue=None)[source]¶
- Queues command via local or kubernetes executor 
 - queue_task_instance(self, task_instance, mark_success=False, pickle_id=None, ignore_all_deps=False, ignore_depends_on_past=False, ignore_task_deps=False, ignore_ti_state=False, pool=None, cfg_path=None)[source]¶
- Queues task instance via local or kubernetes executor 
 - has_task(self, task_instance)[source]¶
- Checks if a task is either queued or running in either local or kubernetes executor. - Parameters
- task_instance (airflow.models.taskinstance.TaskInstance) – TaskInstance 
- Returns
- True if the task is known to this executor 
- Return type
 
 - get_event_buffer(self, dag_ids=None)[source]¶
- Returns and flush the event buffer from local and kubernetes executor - Parameters
- dag_ids (Optional[List[str]]) – dag_ids to return events for, if None returns all 
- Returns
- a dict of events 
- Return type
- Dict[airflow.models.taskinstance.TaskInstanceKey, airflow.executors.base_executor.EventBufferValueType] 
 
 - try_adopt_task_instances(self, tis)[source]¶
- 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) - Returns
- any TaskInstances that were unable to be adopted 
- Return type
- list[airflow.models.TaskInstance] 
 
 
