Source code for airflow.providers.microsoft.azure.sensors.data_factory

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from __future__ import annotations

import warnings
from datetime import timedelta
from typing import TYPE_CHECKING, Any, Sequence

from airflow.exceptions import AirflowException, AirflowProviderDeprecationWarning
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",
    )

[docs]    ui_color = "#50e6ff"

    def __init__(
        self,
        *,
        run_id: str,
        azure_data_factory_conn_id: str = AzureDataFactoryHook.default_conn_name,
        resource_group_name: str | None = None,
        factory_name: str | None = None,
        deferrable: bool = 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

[docs]    def poke(self, context: Context) -> bool:
        self.hook = AzureDataFactoryHook(azure_data_factory_conn_id=self.azure_data_factory_conn_id)
        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:
            raise AzureDataFactoryPipelineRunException(f"Pipeline run {self.run_id} has failed.")

        if pipeline_run_status == AzureDataFactoryPipelineRunStatus.CANCELLED:
            raise AzureDataFactoryPipelineRunException(f"Pipeline run {self.run_id} has been cancelled.")

        return pipeline_run_status == AzureDataFactoryPipelineRunStatus.SUCCEEDED

[docs]    def execute(self, context: Context) -> None:
        """Defers trigger class to poll for state of the job run until
        it reaches a failure state or success state
        """
        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:
        """
        Callback for when the trigger fires - returns immediately.
        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


[docs]class AzureDataFactoryPipelineRunStatusAsyncSensor(AzureDataFactoryPipelineRunStatusSensor):
    """
    Checks the status of a pipeline run asynchronously.

    :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 poke_interval: polling period in seconds to check for the status
    :param deferrable: Run sensor in the deferrable mode.
    """

    def __init__(self, **kwargs: Any) -> None:
        warnings.warn(
            "Class `AzureDataFactoryPipelineRunStatusAsyncSensor` is deprecated and "
            "will be removed in a future release. "
            "Please use `AzureDataFactoryPipelineRunStatusSensor` and "
            "set `deferrable` attribute to `True` instead",
            AirflowProviderDeprecationWarning,
            stacklevel=2,
        )
        super().__init__(**kwargs, deferrable=True)