docs-archive/apache-airflow-providers-microsoft-azure/4.2.0/_sources/_api/airflow/providers/microsoft/azure/operators/data_factory/index.rst.txt

:py:mod:`airflow.providers.microsoft.azure.operators.data_factory`
==================================================================

.. py:module:: airflow.providers.microsoft.azure.operators.data_factory


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.microsoft.azure.operators.data_factory.AzureDataFactoryPipelineRunLink
   airflow.providers.microsoft.azure.operators.data_factory.AzureDataFactoryRunPipelineOperator




.. py:class:: AzureDataFactoryPipelineRunLink

   Bases: :py:obj:`airflow.models.BaseOperatorLink`

   Constructs a link to monitor a pipeline run in Azure Data Factory.

   .. py:attribute:: name
      :annotation: = Monitor Pipeline Run

      

   .. py:method:: get_link(operator, dttm=None, *, ti_key = None)

      Link to external system.

      Note: The old signature of this function was ``(self, operator, dttm: datetime)``. That is still
      supported at runtime but is deprecated.

      :param operator: The Airflow operator object this link is associated to.
      :param ti_key: TaskInstance ID to return link for.
      :return: link to external system



.. py:class:: AzureDataFactoryRunPipelineOperator(*, pipeline_name, azure_data_factory_conn_id = AzureDataFactoryHook.default_conn_name, wait_for_termination = True, resource_group_name = None, factory_name = None, reference_pipeline_run_id = None, is_recovery = None, start_activity_name = None, start_from_failure = None, parameters = None, timeout = 60 * 60 * 24 * 7, check_interval = 60, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Executes 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 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.

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['azure_data_factory_conn_id', 'resource_group_name', 'factory_name', 'pipeline_name',...

      

   .. py:attribute:: template_fields_renderers
      

      

   .. py:attribute:: ui_color
      :annotation: = #0678d4

      

   .. py:attribute:: operator_extra_links
      

      

   .. py:method:: execute(context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


   .. py:method:: on_kill()

      Override this method to cleanup subprocesses when a task instance
      gets killed. Any use of the threading, subprocess or multiprocessing
      module within an operator needs to be cleaned up or it will leave
      ghost processes behind.