:py:mod:`airflow.providers.google.cloud.operators.dataflow`
===========================================================

.. py:module:: airflow.providers.google.cloud.operators.dataflow

.. autoapi-nested-parse::

   This module contains Google Dataflow operators.


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

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.google.cloud.operators.dataflow.CheckJobRunning
   airflow.providers.google.cloud.operators.dataflow.DataflowConfiguration
   airflow.providers.google.cloud.operators.dataflow.DataflowCreateJavaJobOperator
   airflow.providers.google.cloud.operators.dataflow.DataflowTemplatedJobStartOperator
   airflow.providers.google.cloud.operators.dataflow.DataflowStartFlexTemplateOperator
   airflow.providers.google.cloud.operators.dataflow.DataflowStartSqlJobOperator
   airflow.providers.google.cloud.operators.dataflow.DataflowCreatePythonJobOperator


.. py:class:: CheckJobRunning

   Bases: :py:obj:`enum.Enum`

   Helper enum for choosing what to do if job is already running
   IgnoreJob - do not check if running
   FinishIfRunning - finish current dag run with no action
   WaitForRun - wait for job to finish and then continue with new job

   .. py:attribute:: IgnoreJob
      :annotation: = 1

      
   .. py:attribute:: FinishIfRunning
      :annotation: = 2

      
   .. py:attribute:: WaitForRun
      :annotation: = 3

      
.. py:class:: DataflowConfiguration(*, job_name = '{{task.task_id}}', append_job_name = True, project_id = None, location = DEFAULT_DATAFLOW_LOCATION, gcp_conn_id = 'google_cloud_default', delegate_to = None, poll_sleep = 10, impersonation_chain = None, drain_pipeline = False, cancel_timeout = 5 * 60, wait_until_finished = None, multiple_jobs = None, check_if_running = CheckJobRunning.WaitForRun, service_account = None)

   Dataflow configuration that can be passed to
   :py:class:`~airflow.providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator` and
   :py:class:`~airflow.providers.apache.beam.operators.beam.BeamRunPythonPipelineOperator`.

   :param job_name: The 'jobName' to use when executing the Dataflow job
       (templated). This ends up being set in the pipeline options, so any entry
       with key ``'jobName'`` or  ``'job_name'``in ``options`` will be overwritten.
   :param append_job_name: True if unique suffix has to be appended to job name.
   :param project_id: Optional, the Google Cloud project ID in which to start a job.
       If set to None or missing, the default project_id from the Google Cloud connection is used.
   :param location: Job location.
   :param gcp_conn_id: The connection ID to use connecting to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param poll_sleep: The time in seconds to sleep between polling Google
       Cloud Platform for the dataflow job status while the job is in the
       JOB_STATE_RUNNING state.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).
   :param drain_pipeline: Optional, set to True if want to stop streaming job by draining it
       instead of canceling during killing task instance. See:
       https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
   :param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
       successfully cancelled when task is being killed. (optional) default to 300s
   :param wait_until_finished: (Optional)
       If True, wait for the end of pipeline execution before exiting.
       If False, only submits job.
       If None, default behavior.

       The default behavior depends on the type of pipeline:

       * for the streaming pipeline, wait for jobs to start,
       * for the batch pipeline, wait for the jobs to complete.

       .. warning::

           You cannot call ``PipelineResult.wait_until_finish`` method in your pipeline code for the operator
           to work properly. i. e. you must use asynchronous execution. Otherwise, your pipeline will
           always wait until finished. For more information, look at:
           `Asynchronous execution
           <https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_10>`__

       The process of starting the Dataflow job in Airflow consists of two steps:

       * running a subprocess and reading the stderr/stderr log for the job id.
       * loop waiting for the end of the job ID from the previous step.
         This loop checks the status of the job.

       Step two is started just after step one has finished, so if you have wait_until_finished in your
       pipeline code, step two will not start until the process stops. When this process stops,
       steps two will run, but it will only execute one iteration as the job will be in a terminal state.

       If you in your pipeline do not call the wait_for_pipeline method but pass wait_until_finish=True
       to the operator, the second loop will wait for the job's terminal state.

       If you in your pipeline do not call the wait_for_pipeline method, and pass wait_until_finish=False
       to the operator, the second loop will check once is job not in terminal state and exit the loop.
   :param multiple_jobs: If pipeline creates multiple jobs then monitor all jobs. Supported only by
       :py:class:`~airflow.providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator`
   :param check_if_running: Before running job, validate that a previous run is not in process.
       IgnoreJob = do not check if running.
       FinishIfRunning = if job is running finish with nothing.
       WaitForRun = wait until job finished and the run job.
       Supported only by:
       :py:class:`~airflow.providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator`
   :param service_account: Run the job as a specific service account, instead of the default GCE robot.

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['job_name', 'location']

      
.. py:class:: DataflowCreateJavaJobOperator(*, jar, job_name = '{{task.task_id}}', dataflow_default_options = None, options = None, project_id = None, location = DEFAULT_DATAFLOW_LOCATION, gcp_conn_id = 'google_cloud_default', delegate_to = None, poll_sleep = 10, job_class = None, check_if_running = CheckJobRunning.WaitForRun, multiple_jobs = False, cancel_timeout = 10 * 60, wait_until_finished = None, **kwargs)

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

   Start a Java Cloud Dataflow batch job. The parameters of the operation
   will be passed to the job.

   This class is deprecated.
   Please use `providers.apache.beam.operators.beam.BeamRunJavaPipelineOperator`.

   **Example**: ::

       default_args = {
           "owner": "airflow",
           "depends_on_past": False,
           "start_date": (2016, 8, 1),
           "email": ["alex@vanboxel.be"],
           "email_on_failure": False,
           "email_on_retry": False,
           "retries": 1,
           "retry_delay": timedelta(minutes=30),
           "dataflow_default_options": {
               "project": "my-gcp-project",
               "zone": "us-central1-f",
               "stagingLocation": "gs://bucket/tmp/dataflow/staging/",
           },
       }

       dag = DAG("test-dag", default_args=default_args)

       task = DataflowCreateJavaJobOperator(
           gcp_conn_id="gcp_default",
           task_id="normalize-cal",
           jar="{{var.value.gcp_dataflow_base}}pipeline-ingress-cal-normalize-1.0.jar",
           options={
               "autoscalingAlgorithm": "BASIC",
               "maxNumWorkers": "50",
               "start": "{{ds}}",
               "partitionType": "DAY",
           },
           dag=dag,
       )


   .. seealso::
       For more detail on job submission have a look at the reference:
       https://cloud.google.com/dataflow/pipelines/specifying-exec-params

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:DataflowCreateJavaJobOperator`

   :param jar: The reference to a self executing Dataflow jar (templated).
   :param job_name: The 'jobName' to use when executing the Dataflow job
       (templated). This ends up being set in the pipeline options, so any entry
       with key ``'jobName'`` in ``options`` will be overwritten.
   :param dataflow_default_options: Map of default job options.
   :param options: Map of job specific options.The key must be a dictionary.
       The value can contain different types:

       * If the value is None, the single option - ``--key`` (without value) will be added.
       * If the value is False, this option will be skipped
       * If the value is True, the single option - ``--key`` (without value) will be added.
       * If the value is list, the many options will be added for each key.
         If the value is ``['A', 'B']`` and the key is ``key`` then the ``--key=A --key=B`` options
         will be left
       * Other value types will be replaced with the Python textual representation.

       When defining labels (``labels`` option), you can also provide a dictionary.
   :param project_id: Optional, the Google Cloud project ID in which to start a job.
       If set to None or missing, the default project_id from the Google Cloud connection is used.
   :param location: Job location.
   :param gcp_conn_id: The connection ID to use connecting to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param poll_sleep: The time in seconds to sleep between polling Google
       Cloud Platform for the dataflow job status while the job is in the
       JOB_STATE_RUNNING state.
   :param job_class: The name of the dataflow job class to be executed, it
       is often not the main class configured in the dataflow jar file.

   :param multiple_jobs: If pipeline creates multiple jobs then monitor all jobs
   :param check_if_running: before running job, validate that a previous run is not in process
       if job is running finish with nothing, WaitForRun= wait until job finished and the run job)
       ``jar``, ``options``, and ``job_name`` are templated so you can use variables in them.
   :param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
       successfully cancelled when task is being killed.
   :param wait_until_finished: (Optional)
       If True, wait for the end of pipeline execution before exiting.
       If False, only submits job.
       If None, default behavior.

       The default behavior depends on the type of pipeline:

       * for the streaming pipeline, wait for jobs to start,
       * for the batch pipeline, wait for the jobs to complete.

       .. warning::

           You cannot call ``PipelineResult.wait_until_finish`` method in your pipeline code for the operator
           to work properly. i. e. you must use asynchronous execution. Otherwise, your pipeline will
           always wait until finished. For more information, look at:
           `Asynchronous execution
           <https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_10>`__

       The process of starting the Dataflow job in Airflow consists of two steps:

       * running a subprocess and reading the stderr/stderr log for the job id.
       * loop waiting for the end of the job ID from the previous step.
         This loop checks the status of the job.

       Step two is started just after step one has finished, so if you have wait_until_finished in your
       pipeline code, step two will not start until the process stops. When this process stops,
       steps two will run, but it will only execute one iteration as the job will be in a terminal state.

       If you in your pipeline do not call the wait_for_pipeline method but pass wait_until_finish=True
       to the operator, the second loop will wait for the job's terminal state.

       If you in your pipeline do not call the wait_for_pipeline method, and pass wait_until_finish=False
       to the operator, the second loop will check once is job not in terminal state and exit the loop.

   Note that both
   ``dataflow_default_options`` and ``options`` will be merged to specify pipeline
   execution parameter, and ``dataflow_default_options`` is expected to save
   high-level options, for instances, project and zone information, which
   apply to all dataflow operators in the DAG.

   It's a good practice to define dataflow_* parameters in the default_args of the dag
   like the project, zone and staging location.

   .. code-block:: python

      default_args = {
          "dataflow_default_options": {
              "zone": "europe-west1-d",
              "stagingLocation": "gs://my-staging-bucket/staging/",
          }
      }

   You need to pass the path to your dataflow as a file reference with the ``jar``
   parameter, the jar needs to be a self executing jar (see documentation here:
   https://beam.apache.org/documentation/runners/dataflow/#self-executing-jar).
   Use ``options`` to pass on options to your job.

   .. code-block:: python

      t1 = DataflowCreateJavaJobOperator(
          task_id="dataflow_example",
          jar="{{var.value.gcp_dataflow_base}}pipeline/build/libs/pipeline-example-1.0.jar",
          options={
              "autoscalingAlgorithm": "BASIC",
              "maxNumWorkers": "50",
              "start": "{{ds}}",
              "partitionType": "DAY",
              "labels": {"foo": "bar"},
          },
          gcp_conn_id="airflow-conn-id",
          dag=my_dag,
      )


   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['options', 'jar', 'job_name']

      
   .. py:attribute:: ui_color
      :annotation: = #0273d4

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

      Execute the Apache Beam Pipeline.


   .. py:method:: on_kill(self)

      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.


.. py:class:: DataflowTemplatedJobStartOperator(*, template, job_name = '{{task.task_id}}', options = None, dataflow_default_options = None, parameters = None, project_id = None, location = DEFAULT_DATAFLOW_LOCATION, gcp_conn_id = 'google_cloud_default', delegate_to = None, poll_sleep = 10, impersonation_chain = None, environment = None, cancel_timeout = 10 * 60, wait_until_finished = None, **kwargs)

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

   Start a Templated Cloud Dataflow job. The parameters of the operation
   will be passed to the job.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:DataflowTemplatedJobStartOperator`

   :param template: The reference to the Dataflow template.
   :param job_name: The 'jobName' to use when executing the Dataflow template
       (templated).
   :param options: Map of job runtime environment options.
       It will update environment argument if passed.

       .. seealso::
           For more information on possible configurations, look at the API documentation
           `https://cloud.google.com/dataflow/pipelines/specifying-exec-params
           <https://cloud.google.com/dataflow/docs/reference/rest/v1b3/RuntimeEnvironment>`__

   :param dataflow_default_options: Map of default job environment options.
   :param parameters: Map of job specific parameters for the template.
   :param project_id: Optional, the Google Cloud project ID in which to start a job.
       If set to None or missing, the default project_id from the Google Cloud connection is used.
   :param location: Job location.
   :param gcp_conn_id: The connection ID to use connecting to Google Cloud.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param poll_sleep: The time in seconds to sleep between polling Google
       Cloud Platform for the dataflow job status while the job is in the
       JOB_STATE_RUNNING state.
   :param impersonation_chain: Optional service account to impersonate using short-term
       credentials, or chained list of accounts required to get the access_token
       of the last account in the list, which will be impersonated in the request.
       If set as a string, the account must grant the originating account
       the Service Account Token Creator IAM role.
       If set as a sequence, the identities from the list must grant
       Service Account Token Creator IAM role to the directly preceding identity, with first
       account from the list granting this role to the originating account (templated).
   :param environment: Optional, Map of job runtime environment options.

       .. seealso::
           For more information on possible configurations, look at the API documentation
           `https://cloud.google.com/dataflow/pipelines/specifying-exec-params
           <https://cloud.google.com/dataflow/docs/reference/rest/v1b3/RuntimeEnvironment>`__
   :param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
       successfully cancelled when task is being killed.
   :param wait_until_finished: (Optional)
       If True, wait for the end of pipeline execution before exiting.
       If False, only submits job.
       If None, default behavior.

       The default behavior depends on the type of pipeline:

       * for the streaming pipeline, wait for jobs to start,
       * for the batch pipeline, wait for the jobs to complete.

       .. warning::

           You cannot call ``PipelineResult.wait_until_finish`` method in your pipeline code for the operator
           to work properly. i. e. you must use asynchronous execution. Otherwise, your pipeline will
           always wait until finished. For more information, look at:
           `Asynchronous execution
           <https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_10>`__

       The process of starting the Dataflow job in Airflow consists of two steps:

       * running a subprocess and reading the stderr/stderr log for the job id.
       * loop waiting for the end of the job ID from the previous step.
         This loop checks the status of the job.

       Step two is started just after step one has finished, so if you have wait_until_finished in your
       pipeline code, step two will not start until the process stops. When this process stops,
       steps two will run, but it will only execute one iteration as the job will be in a terminal state.

       If you in your pipeline do not call the wait_for_pipeline method but pass wait_until_finish=True
       to the operator, the second loop will wait for the job's terminal state.

       If you in your pipeline do not call the wait_for_pipeline method, and pass wait_until_finish=False
       to the operator, the second loop will check once is job not in terminal state and exit the loop.

   It's a good practice to define dataflow_* parameters in the default_args of the dag
   like the project, zone and staging location.

   .. seealso::
       https://cloud.google.com/dataflow/docs/reference/rest/v1b3/LaunchTemplateParameters
       https://cloud.google.com/dataflow/docs/reference/rest/v1b3/RuntimeEnvironment

   .. code-block:: python

      default_args = {
          "dataflow_default_options": {
              "zone": "europe-west1-d",
              "tempLocation": "gs://my-staging-bucket/staging/",
          }
      }

   You need to pass the path to your dataflow template as a file reference with the
   ``template`` parameter. Use ``parameters`` to pass on parameters to your job.
   Use ``environment`` to pass on runtime environment variables to your job.

   .. code-block:: python

      t1 = DataflowTemplatedJobStartOperator(
          task_id="dataflow_example",
          template="{{var.value.gcp_dataflow_base}}",
          parameters={
              "inputFile": "gs://bucket/input/my_input.txt",
              "outputFile": "gs://bucket/output/my_output.txt",
          },
          gcp_conn_id="airflow-conn-id",
          dag=my_dag,
      )

   ``template``, ``dataflow_default_options``, ``parameters``, and ``job_name`` are
   templated so you can use variables in them.

   Note that ``dataflow_default_options`` is expected to save high-level options
   for project information, which apply to all dataflow operators in the DAG.

       .. seealso::
           https://cloud.google.com/dataflow/docs/reference/rest/v1b3
           /LaunchTemplateParameters
           https://cloud.google.com/dataflow/docs/reference/rest/v1b3/RuntimeEnvironment
           For more detail on job template execution have a look at the reference:
           https://cloud.google.com/dataflow/docs/templates/executing-templates

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['template', 'job_name', 'options', 'parameters', 'project_id', 'location', 'gcp_conn_id',...

      
   .. py:attribute:: ui_color
      :annotation: = #0273d4

      
   .. py:attribute:: operator_extra_links
      

   .. 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:: on_kill(self)

      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.


.. py:class:: DataflowStartFlexTemplateOperator(body, location, project_id = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, drain_pipeline = False, cancel_timeout = 10 * 60, wait_until_finished = None, *args, **kwargs)

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

   Starts flex templates with the Dataflow pipeline.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:DataflowStartFlexTemplateOperator`

   :param body: The request body. See:
       https://cloud.google.com/dataflow/docs/reference/rest/v1b3/projects.locations.flexTemplates/launch#request-body
   :param location: The location of the Dataflow job (for example europe-west1)
   :param project_id: The ID of the GCP project that owns the job.
       If set to ``None`` or missing, the default project_id from the GCP connection is used.
   :param gcp_conn_id: The connection ID to use connecting to Google Cloud
       Platform.
   :param delegate_to: The account to impersonate, if any.
       For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param drain_pipeline: Optional, set to True if want to stop streaming job by draining it
       instead of canceling during killing task instance. See:
       https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
   :param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
       successfully cancelled when task is being killed.
   :param wait_until_finished: (Optional)
       If True, wait for the end of pipeline execution before exiting.
       If False, only submits job.
       If None, default behavior.

       The default behavior depends on the type of pipeline:

       * for the streaming pipeline, wait for jobs to start,
       * for the batch pipeline, wait for the jobs to complete.

       .. warning::

           You cannot call ``PipelineResult.wait_until_finish`` method in your pipeline code for the operator
           to work properly. i. e. you must use asynchronous execution. Otherwise, your pipeline will
           always wait until finished. For more information, look at:
           `Asynchronous execution
           <https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_10>`__

       The process of starting the Dataflow job in Airflow consists of two steps:

       * running a subprocess and reading the stderr/stderr log for the job id.
       * loop waiting for the end of the job ID from the previous step.
         This loop checks the status of the job.

       Step two is started just after step one has finished, so if you have wait_until_finished in your
       pipeline code, step two will not start until the process stops. When this process stops,
       steps two will run, but it will only execute one iteration as the job will be in a terminal state.

       If you in your pipeline do not call the wait_for_pipeline method but pass wait_until_finish=True
       to the operator, the second loop will wait for the job's terminal state.

       If you in your pipeline do not call the wait_for_pipeline method, and pass wait_until_finish=False
       to the operator, the second loop will check once is job not in terminal state and exit the loop.

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['body', 'location', 'project_id', 'gcp_conn_id']

      
   .. py:attribute:: operator_extra_links
      

   .. 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:: on_kill(self)

      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.


.. py:class:: DataflowStartSqlJobOperator(job_name, query, options, location = DEFAULT_DATAFLOW_LOCATION, project_id = None, gcp_conn_id = 'google_cloud_default', delegate_to = None, drain_pipeline = False, *args, **kwargs)

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

   Starts Dataflow SQL query.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:DataflowStartSqlJobOperator`

   .. warning::
       This operator requires ``gcloud`` command (Google Cloud SDK) must be installed on the Airflow worker
       <https://cloud.google.com/sdk/docs/install>`__

   :param job_name: The unique name to assign to the Cloud Dataflow job.
   :param query: The SQL query to execute.
   :param options: Job parameters to be executed. It can be a dictionary with the following keys.

       For more information, look at:
       `https://cloud.google.com/sdk/gcloud/reference/beta/dataflow/sql/query
       <gcloud beta dataflow sql query>`__
       command reference

   :param location: The location of the Dataflow job (for example europe-west1)
   :param project_id: The ID of the GCP project that owns the job.
       If set to ``None`` or missing, the default project_id from the GCP connection is used.
   :param gcp_conn_id: The connection ID to use connecting to Google Cloud
       Platform.
   :param delegate_to: The account to impersonate, if any.
       For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param drain_pipeline: Optional, set to True if want to stop streaming job by draining it
       instead of canceling during killing task instance. See:
       https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['job_name', 'query', 'options', 'location', 'project_id', 'gcp_conn_id']

      
   .. py:attribute:: template_fields_renderers
      

   .. 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:: on_kill(self)

      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.


.. py:class:: DataflowCreatePythonJobOperator(*, py_file, job_name = '{{task.task_id}}', dataflow_default_options = None, options = None, py_interpreter = 'python3', py_options = None, py_requirements = None, py_system_site_packages = False, project_id = None, location = DEFAULT_DATAFLOW_LOCATION, gcp_conn_id = 'google_cloud_default', delegate_to = None, poll_sleep = 10, drain_pipeline = False, cancel_timeout = 10 * 60, wait_until_finished = None, **kwargs)

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

   Launching Cloud Dataflow jobs written in python. Note that both
   dataflow_default_options and options will be merged to specify pipeline
   execution parameter, and dataflow_default_options is expected to save
   high-level options, for instances, project and zone information, which
   apply to all dataflow operators in the DAG.

   This class is deprecated.
   Please use `providers.apache.beam.operators.beam.BeamRunPythonPipelineOperator`.

   .. seealso::
       For more detail on job submission have a look at the reference:
       https://cloud.google.com/dataflow/pipelines/specifying-exec-params

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:DataflowCreatePythonJobOperator`

   :param py_file: Reference to the python dataflow pipeline file.py, e.g.,
       /some/local/file/path/to/your/python/pipeline/file. (templated)
   :param job_name: The 'job_name' to use when executing the Dataflow job
       (templated). This ends up being set in the pipeline options, so any entry
       with key ``'jobName'`` or ``'job_name'`` in ``options`` will be overwritten.
   :param py_options: Additional python options, e.g., ["-m", "-v"].
   :param dataflow_default_options: Map of default job options.
   :param options: Map of job specific options.The key must be a dictionary.
       The value can contain different types:

       * If the value is None, the single option - ``--key`` (without value) will be added.
       * If the value is False, this option will be skipped
       * If the value is True, the single option - ``--key`` (without value) will be added.
       * If the value is list, the many options will be added for each key.
         If the value is ``['A', 'B']`` and the key is ``key`` then the ``--key=A --key=B`` options
         will be left
       * Other value types will be replaced with the Python textual representation.

       When defining labels (``labels`` option), you can also provide a dictionary.
   :param py_interpreter: Python version of the beam pipeline.
       If None, this defaults to the python3.
       To track python versions supported by beam and related
       issues check: https://issues.apache.org/jira/browse/BEAM-1251
   :param py_requirements: Additional python package(s) to install.
       If a value is passed to this parameter, a new virtual environment has been created with
       additional packages installed.

       You could also install the apache_beam package if it is not installed on your system or you want
       to use a different version.
   :param py_system_site_packages: Whether to include system_site_packages in your virtualenv.
       See virtualenv documentation for more information.

       This option is only relevant if the ``py_requirements`` parameter is not None.
   :param gcp_conn_id: The connection ID to use connecting to Google Cloud.
   :param project_id: Optional, the Google Cloud project ID in which to start a job.
       If set to None or missing, the default project_id from the Google Cloud connection is used.
   :param location: Job location.
   :param delegate_to: The account to impersonate using domain-wide delegation of authority,
       if any. For this to work, the service account making the request must have
       domain-wide delegation enabled.
   :param poll_sleep: The time in seconds to sleep between polling Google
       Cloud Platform for the dataflow job status while the job is in the
       JOB_STATE_RUNNING state.
   :param drain_pipeline: Optional, set to True if want to stop streaming job by draining it
       instead of canceling during killing task instance. See:
       https://cloud.google.com/dataflow/docs/guides/stopping-a-pipeline
   :param cancel_timeout: How long (in seconds) operator should wait for the pipeline to be
       successfully cancelled when task is being killed.
   :param wait_until_finished: (Optional)
       If True, wait for the end of pipeline execution before exiting.
       If False, only submits job.
       If None, default behavior.

       The default behavior depends on the type of pipeline:

       * for the streaming pipeline, wait for jobs to start,
       * for the batch pipeline, wait for the jobs to complete.

       .. warning::

           You cannot call ``PipelineResult.wait_until_finish`` method in your pipeline code for the operator
           to work properly. i. e. you must use asynchronous execution. Otherwise, your pipeline will
           always wait until finished. For more information, look at:
           `Asynchronous execution
           <https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#python_10>`__

       The process of starting the Dataflow job in Airflow consists of two steps:

       * running a subprocess and reading the stderr/stderr log for the job id.
       * loop waiting for the end of the job ID from the previous step.
         This loop checks the status of the job.

       Step two is started just after step one has finished, so if you have wait_until_finished in your
       pipeline code, step two will not start until the process stops. When this process stops,
       steps two will run, but it will only execute one iteration as the job will be in a terminal state.

       If you in your pipeline do not call the wait_for_pipeline method but pass wait_until_finish=True
       to the operator, the second loop will wait for the job's terminal state.

       If you in your pipeline do not call the wait_for_pipeline method, and pass wait_until_finish=False
       to the operator, the second loop will check once is job not in terminal state and exit the loop.

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['options', 'dataflow_default_options', 'job_name', 'py_file']

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

      Execute the python dataflow job.


   .. py:method:: on_kill(self)

      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.