:py:mod:`airflow.providers.google.cloud.sensors.dataproc`
=========================================================

.. py:module:: airflow.providers.google.cloud.sensors.dataproc

.. autoapi-nested-parse::

   This module contains a Dataproc Job sensor.


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

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.sensors.dataproc.DataprocJobSensor


.. py:class:: DataprocJobSensor(*, dataproc_job_id, region, project_id = None, gcp_conn_id = 'google_cloud_default', wait_timeout = None, **kwargs)

   Bases: :py:obj:`airflow.sensors.base.BaseSensorOperator`

   Check for the state of a previously submitted Dataproc job.

   :param dataproc_job_id: The Dataproc job ID to poll. (templated)
   :param region: Required. The Cloud Dataproc region in which to handle the request. (templated)
   :param project_id: The ID of the google cloud project in which
       to create the cluster. (templated)
   :param gcp_conn_id: The connection ID to use connecting to Google Cloud Platform.
   :param wait_timeout: How many seconds wait for job to be ready.

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['project_id', 'region', 'dataproc_job_id']

      
   .. py:attribute:: ui_color
      :annotation: = #f0eee4

      
   .. py:method:: execute(self, 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:: poke(self, context)

      Function that the sensors defined while deriving this class should
      override.