:py:mod:`airflow.providers.apache.beam.hooks.beam`
==================================================

.. py:module:: airflow.providers.apache.beam.hooks.beam

.. autoapi-nested-parse::

   This module contains a Apache Beam Hook.


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

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.apache.beam.hooks.beam.BeamRunnerType
   airflow.providers.apache.beam.hooks.beam.BeamCommandRunner
   airflow.providers.apache.beam.hooks.beam.BeamHook


Functions
~~~~~~~~~

.. autoapisummary::

   airflow.providers.apache.beam.hooks.beam.beam_options_to_args


.. py:class:: BeamRunnerType

   Helper class for listing runner types.
   For more information about runners see:
   https://beam.apache.org/documentation/

   .. py:attribute:: DataflowRunner
      :annotation: = DataflowRunner

      
   .. py:attribute:: DirectRunner
      :annotation: = DirectRunner

      
   .. py:attribute:: SparkRunner
      :annotation: = SparkRunner

      
   .. py:attribute:: FlinkRunner
      :annotation: = FlinkRunner

      
   .. py:attribute:: SamzaRunner
      :annotation: = SamzaRunner

      
   .. py:attribute:: NemoRunner
      :annotation: = NemoRunner

      
   .. py:attribute:: JetRunner
      :annotation: = JetRunner

      
   .. py:attribute:: Twister2Runner
      :annotation: = Twister2Runner

      
.. py:function:: beam_options_to_args(options)

   Returns a formatted pipeline options from a dictionary of arguments

   The logic of this method should be compatible with Apache Beam:
   https://github.com/apache/beam/blob/b56740f0e8cd80c2873412847d0b336837429fb9/sdks/python/
   apache_beam/options/pipeline_options.py#L230-L251

   :param options: Dictionary with options
   :return: List of arguments
   :rtype: List[str]


.. py:class:: BeamCommandRunner(cmd, process_line_callback = None, working_directory = None)

   Bases: :py:obj:`airflow.utils.log.logging_mixin.LoggingMixin`

   Class responsible for running pipeline command in subprocess

   :param cmd: Parts of the command to be run in subprocess
   :param process_line_callback: Optional callback which can be used to process
       stdout and stderr to detect job id
   :param working_directory: Working directory

   .. py:method:: wait_for_done(self)

      Waits for Apache Beam pipeline to complete.


.. py:class:: BeamHook(runner)

   Bases: :py:obj:`airflow.hooks.base.BaseHook`

   Hook for Apache Beam.

   All the methods in the hook where project_id is used must be called with
   keyword arguments rather than positional.

   :param runner: Runner type

   .. py:method:: start_python_pipeline(self, variables, py_file, py_options, py_interpreter = 'python3', py_requirements = None, py_system_site_packages = False, process_line_callback = None)

      Starts Apache Beam python pipeline.

      :param variables: Variables passed to the pipeline.
      :param py_file: Path to the python file to execute.
      :param py_options: Additional options.
      :param py_interpreter: Python version of the Apache 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 process_line_callback: (optional) Callback that can be used to process each line of
          the stdout and stderr file descriptors.


   .. py:method:: start_java_pipeline(self, variables, jar, job_class = None, process_line_callback = None)

      Starts Apache Beam Java pipeline.

      :param variables: Variables passed to the job.
      :param jar: Name of the jar for the pipeline
      :param job_class: Name of the java class for the pipeline.
      :param process_line_callback: (optional) Callback that can be used to process each line of
          the stdout and stderr file descriptors.


   .. py:method:: start_go_pipeline(self, variables, go_file, process_line_callback = None, should_init_module = False)

      Starts Apache Beam Go pipeline.

      :param variables: Variables passed to the job.
      :param go_file: Path to the Go file with your beam pipeline.
      :param go_file:
      :param process_line_callback: (optional) Callback that can be used to process each line of
          the stdout and stderr file descriptors.
      :param should_init_module: If False (default), will just execute a `go run` command. If True, will
          init a module and dependencies with a ``go mod init`` and ``go mod tidy``, useful when pulling
          source with GCSHook.
      :return: