Source code for airflow.providers.microsoft.azure.sensors.data_factory
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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
from airflow.providers.microsoft.azure.hooks.data_factory import (
AzureDataFactoryHook,
AzureDataFactoryPipelineRunException,
AzureDataFactoryPipelineRunStatus,
)
from airflow.providers.microsoft.azure.triggers.data_factory import ADFPipelineRunStatusSensorTrigger
from airflow.sensors.base import BaseSensorOperator
if TYPE_CHECKING:
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",
)
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:
message = f"Pipeline run {self.run_id} has failed."
raise AzureDataFactoryPipelineRunException(message)
if pipeline_run_status == AzureDataFactoryPipelineRunStatus.CANCELLED:
message = f"Pipeline run {self.run_id} has been cancelled."
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":
raise AirflowException(event["message"])
self.log.info(event["message"])
return None