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from __future__ import annotations

import time
import warnings
from functools import cached_property
from typing import TYPE_CHECKING, Any, Sequence

from airflow.configuration import conf
from airflow.exceptions import AirflowException
from airflow.hooks.base import BaseHook
from airflow.models import BaseOperator, BaseOperatorLink, XCom
from import (
from import AzureDataFactoryTrigger
from airflow.utils.log.logging_mixin import LoggingMixin

    from airflow.models.taskinstancekey import TaskInstanceKey
    from airflow.utils.context import Context

[docs]class AzureDataFactoryRunPipelineOperator(BaseOperator): """ Execute a data factory pipeline. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:AzureDataFactoryRunPipelineOperator` :param azure_data_factory_conn_id: The connection identifier for connecting to Azure Data Factory. :param pipeline_name: The name of the pipeline to execute. :param wait_for_termination: Flag to wait on a pipeline run's termination. By default, this feature is enabled but could be disabled to perform an asynchronous wait for a long-running pipeline execution using the ``AzureDataFactoryPipelineRunSensor``. :param resource_group_name: The resource group name. If a value is not passed in to the operator, the ``AzureDataFactoryHook`` will attempt to use the resource group name provided in the corresponding connection. :param factory_name: The data factory name. If a value is not passed in to the operator, the ``AzureDataFactoryHook`` will attempt to use the factory name provided in the corresponding connection. :param reference_pipeline_run_id: The pipeline run identifier. If this run ID is specified the parameters of the specified run will be used to create a new run. :param is_recovery: Recovery mode flag. If recovery mode is set to `True`, the specified referenced pipeline run and the new run will be grouped under the same ``groupId``. :param start_activity_name: In recovery mode, the rerun will start from this activity. If not specified, all activities will run. :param start_from_failure: In recovery mode, if set to true, the rerun will start from failed activities. The property will be used only if ``start_activity_name`` is not specified. :param parameters: Parameters of the pipeline run. These parameters are referenced in a pipeline via ``@pipeline().parameters.parameterName`` and will be used only if the ``reference_pipeline_run_id`` is not specified. :param timeout: Time in seconds to wait for a pipeline to reach a terminal status for non-asynchronous waits. Used only if ``wait_for_termination`` is True. :param check_interval: Time in seconds to check on a pipeline run's status for non-asynchronous waits. Used only if ``wait_for_termination`` is True. :param deferrable: Run operator in deferrable mode. """
[docs] template_fields: Sequence[str] = ( "azure_data_factory_conn_id", "resource_group_name", "factory_name", "pipeline_name", "reference_pipeline_run_id", "parameters", )
[docs] template_fields_renderers = {"parameters": "json"}
[docs] ui_color = "#0678d4"
def __init__( self, *, pipeline_name: str, azure_data_factory_conn_id: str = AzureDataFactoryHook.default_conn_name, resource_group_name: str, factory_name: str, wait_for_termination: bool = True, reference_pipeline_run_id: str | None = None, is_recovery: bool | None = None, start_activity_name: str | None = None, start_from_failure: bool | None = None, parameters: dict[str, Any] | None = None, timeout: int = 60 * 60 * 24 * 7, check_interval: int = 60, deferrable: bool = conf.getboolean("operators", "default_deferrable", fallback=False), **kwargs, ) -> None: super().__init__(**kwargs) self.azure_data_factory_conn_id = azure_data_factory_conn_id self.pipeline_name = pipeline_name self.wait_for_termination = wait_for_termination self.resource_group_name = resource_group_name self.factory_name = factory_name self.reference_pipeline_run_id = reference_pipeline_run_id self.is_recovery = is_recovery self.start_activity_name = start_activity_name self.start_from_failure = start_from_failure self.parameters = parameters self.timeout = timeout self.check_interval = check_interval self.deferrable = deferrable @cached_property
[docs] def hook(self) -> AzureDataFactoryHook: """Create and return an AzureDataFactoryHook (cached).""" return AzureDataFactoryHook(azure_data_factory_conn_id=self.azure_data_factory_conn_id)
[docs] def execute(self, context: Context) -> None:"Executing the %s pipeline.", self.pipeline_name) response = self.hook.run_pipeline( self.pipeline_name, self.resource_group_name, self.factory_name, reference_pipeline_run_id=self.reference_pipeline_run_id, is_recovery=self.is_recovery, start_activity_name=self.start_activity_name, start_from_failure=self.start_from_failure, parameters=self.parameters, ) self.run_id = vars(response)["run_id"] # Push the ``run_id`` value to XCom regardless of what happens during execution. This allows for # retrieval the executed pipeline's ``run_id`` for downstream tasks especially if performing an # asynchronous wait. context["ti"].xcom_push(key="run_id", value=self.run_id) if self.wait_for_termination: if self.deferrable is False:"Waiting for pipeline run %s to terminate.", self.run_id) if self.hook.wait_for_pipeline_run_status( self.run_id, AzureDataFactoryPipelineRunStatus.SUCCEEDED, self.resource_group_name, self.factory_name, check_interval=self.check_interval, timeout=self.timeout, ):"Pipeline run %s has completed successfully.", self.run_id) else: raise AzureDataFactoryPipelineRunException( f"Pipeline run {self.run_id} has failed or has been cancelled." ) else: end_time = time.time() + self.timeout pipeline_run_status = self.hook.get_pipeline_run_status( self.run_id, self.resource_group_name, self.factory_name ) if pipeline_run_status not in AzureDataFactoryPipelineRunStatus.TERMINAL_STATUSES: self.defer( timeout=self.execution_timeout, trigger=AzureDataFactoryTrigger( azure_data_factory_conn_id=self.azure_data_factory_conn_id, run_id=self.run_id, wait_for_termination=self.wait_for_termination, resource_group_name=self.resource_group_name, factory_name=self.factory_name, check_interval=self.check_interval, end_time=end_time, ), method_name="execute_complete", ) elif pipeline_run_status == AzureDataFactoryPipelineRunStatus.SUCCEEDED:"Pipeline run %s has completed successfully.", self.run_id) elif pipeline_run_status in AzureDataFactoryPipelineRunStatus.FAILURE_STATES: raise AzureDataFactoryPipelineRunException( f"Pipeline run {self.run_id} has failed or has been cancelled." ) else: if self.deferrable is True: warnings.warn( "Argument `wait_for_termination` is False and `deferrable` is True , hence " "`deferrable` parameter doesn't have any effect", UserWarning, stacklevel=2, )
[docs] def execute_complete(self, context: Context, event: dict[str, str]) -> None: """ Return immediately - callback for when the trigger fires. Relies on trigger to throw an exception, otherwise it assumes execution was successful. """ if event: if event["status"] == "error": raise AirflowException(event["message"])["message"])
[docs] def on_kill(self) -> None: if self.run_id: self.hook.cancel_pipeline_run( run_id=self.run_id, resource_group_name=self.resource_group_name, factory_name=self.factory_name, ) # Check to ensure the pipeline run was cancelled as expected. if self.hook.wait_for_pipeline_run_status( run_id=self.run_id, expected_statuses=AzureDataFactoryPipelineRunStatus.CANCELLED, check_interval=self.check_interval, timeout=self.timeout, resource_group_name=self.resource_group_name, factory_name=self.factory_name, ):"Pipeline run %s has been cancelled successfully.", self.run_id) else: raise AzureDataFactoryPipelineRunException(f"Pipeline run {self.run_id} was not cancelled.")

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