docs-archive/apache-airflow-providers-apache-spark/3.0.0/_sources/_api/airflow/providers/apache/spark/hooks/spark_submit/index.rst.txt

:py:mod:`airflow.providers.apache.spark.hooks.spark_submit`
===========================================================

.. py:module:: airflow.providers.apache.spark.hooks.spark_submit


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

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.apache.spark.hooks.spark_submit.SparkSubmitHook




.. py:class:: SparkSubmitHook(conf = None, conn_id = 'spark_default', files = None, py_files = None, archives = None, driver_class_path = None, jars = None, java_class = None, packages = None, exclude_packages = None, repositories = None, total_executor_cores = None, executor_cores = None, executor_memory = None, driver_memory = None, keytab = None, principal = None, proxy_user = None, name = 'default-name', num_executors = None, status_poll_interval = 1, application_args = None, env_vars = None, verbose = False, spark_binary = None)

   Bases: :py:obj:`airflow.hooks.base.BaseHook`, :py:obj:`airflow.utils.log.logging_mixin.LoggingMixin`

   This hook is a wrapper around the spark-submit binary to kick off a spark-submit job.
   It requires that the "spark-submit" binary is in the PATH or the spark_home to be
   supplied.

   :param conf: Arbitrary Spark configuration properties
   :param spark_conn_id: The :ref:`spark connection id <howto/connection:spark>` as configured
       in Airflow administration. When an invalid connection_id is supplied, it will default
       to yarn.
   :param files: Upload additional files to the executor running the job, separated by a
       comma. Files will be placed in the working directory of each executor.
       For example, serialized objects.
   :param py_files: Additional python files used by the job, can be .zip, .egg or .py.
   :param archives: Archives that spark should unzip (and possibly tag with #ALIAS) into
       the application working directory.
   :param driver_class_path: Additional, driver-specific, classpath settings.
   :param jars: Submit additional jars to upload and place them in executor classpath.
   :param java_class: the main class of the Java application
   :param packages: Comma-separated list of maven coordinates of jars to include on the
       driver and executor classpaths
   :param exclude_packages: Comma-separated list of maven coordinates of jars to exclude
       while resolving the dependencies provided in 'packages'
   :param repositories: Comma-separated list of additional remote repositories to search
       for the maven coordinates given with 'packages'
   :param total_executor_cores: (Standalone & Mesos only) Total cores for all executors
       (Default: all the available cores on the worker)
   :param executor_cores: (Standalone, YARN and Kubernetes only) Number of cores per
       executor (Default: 2)
   :param executor_memory: Memory per executor (e.g. 1000M, 2G) (Default: 1G)
   :param driver_memory: Memory allocated to the driver (e.g. 1000M, 2G) (Default: 1G)
   :param keytab: Full path to the file that contains the keytab
   :param principal: The name of the kerberos principal used for keytab
   :param proxy_user: User to impersonate when submitting the application
   :param name: Name of the job (default airflow-spark)
   :param num_executors: Number of executors to launch
   :param status_poll_interval: Seconds to wait between polls of driver status in cluster
       mode (Default: 1)
   :param application_args: Arguments for the application being submitted
   :param env_vars: Environment variables for spark-submit. It
       supports yarn and k8s mode too.
   :param verbose: Whether to pass the verbose flag to spark-submit process for debugging
   :param spark_binary: The command to use for spark submit.
                        Some distros may use spark2-submit.

   .. py:attribute:: conn_name_attr
      :annotation: = conn_id

      

   .. py:attribute:: default_conn_name
      :annotation: = spark_default

      

   .. py:attribute:: conn_type
      :annotation: = spark

      

   .. py:attribute:: hook_name
      :annotation: = Spark

      

   .. py:method:: get_ui_field_behaviour()
      :staticmethod:

      Returns custom field behaviour


   .. py:method:: get_conn(self)

      Returns connection for the hook.


   .. py:method:: submit(self, application = '', **kwargs)

      Remote Popen to execute the spark-submit job

      :param application: Submitted application, jar or py file
      :param kwargs: extra arguments to Popen (see subprocess.Popen)


   .. py:method:: on_kill(self)

      Kill Spark submit command