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from __future__ import annotations

from datetime import timedelta
from functools import cached_property
from typing import TYPE_CHECKING, Sequence

from airflow.configuration import conf
from airflow.exceptions import AirflowException, AirflowSkipException
from import (
from import ADFPipelineRunStatusSensorTrigger
from airflow.sensors.base import BaseSensorOperator

    from airflow.utils.context import Context

[docs]class AzureDataFactoryPipelineRunStatusSensor(BaseSensorOperator): """ Checks the status of a pipeline run. :param azure_data_factory_conn_id: The connection identifier for connecting to Azure Data Factory. :param run_id: The pipeline run identifier. :param resource_group_name: The resource group name. :param factory_name: The data factory name. :param deferrable: Run sensor in the deferrable mode. """
[docs] template_fields: Sequence[str] = ( "azure_data_factory_conn_id", "resource_group_name", "factory_name", "run_id", )
[docs] ui_color = "#50e6ff"
def __init__( self, *, run_id: str, azure_data_factory_conn_id: str = AzureDataFactoryHook.default_conn_name, resource_group_name: str, factory_name: str, 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.run_id = run_id self.resource_group_name = resource_group_name self.factory_name = factory_name self.deferrable = deferrable @cached_property
[docs] def hook(self): """Create and return an AzureDataFactoryHook (cached).""" return AzureDataFactoryHook(azure_data_factory_conn_id=self.azure_data_factory_conn_id)
[docs] def poke(self, context: Context) -> bool: pipeline_run_status = self.hook.get_pipeline_run_status( run_id=self.run_id, resource_group_name=self.resource_group_name, factory_name=self.factory_name, ) if pipeline_run_status == AzureDataFactoryPipelineRunStatus.FAILED: # TODO: remove this if check when min_airflow_version is set to higher than 2.7.1 message = f"Pipeline run {self.run_id} has failed." if self.soft_fail: raise AirflowSkipException(message) raise AzureDataFactoryPipelineRunException(message) if pipeline_run_status == AzureDataFactoryPipelineRunStatus.CANCELLED: # TODO: remove this if check when min_airflow_version is set to higher than 2.7.1 message = f"Pipeline run {self.run_id} has been cancelled." if self.soft_fail: raise AirflowSkipException(message) raise AzureDataFactoryPipelineRunException(message) return pipeline_run_status == AzureDataFactoryPipelineRunStatus.SUCCEEDED
[docs] def execute(self, context: Context) -> None: """ Poll for state of the job run. In deferrable mode, the polling is deferred to the triggerer. Otherwise the sensor waits synchronously. """ if not self.deferrable: super().execute(context=context) else: if not self.poke(context=context): self.defer( timeout=timedelta(seconds=self.timeout), trigger=ADFPipelineRunStatusSensorTrigger( run_id=self.run_id, azure_data_factory_conn_id=self.azure_data_factory_conn_id, resource_group_name=self.resource_group_name, factory_name=self.factory_name, poke_interval=self.poke_interval, ), method_name="execute_complete", )
[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": # TODO: remove this if check when min_airflow_version is set to higher than 2.7.1 if self.soft_fail: raise AirflowSkipException(event["message"]) raise AirflowException(event["message"])["message"]) return None

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