airflow.providers.apache.spark.operators.spark_submit

Module Contents

Classes

SparkSubmitOperator

Wrap the spark-submit binary to kick off a spark-submit job; requires "spark-submit" binary in the PATH.
class airflow.providers.apache.spark.operators.spark_submit.SparkSubmitOperator(*, application='', 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='arrow-spark', num_executors=None, status_poll_interval=1, application_args=None, env_vars=None, verbose=False, spark_binary=None, properties_file=None, queue=None, deploy_mode=None, use_krb5ccache=False, **kwargs)[source]

Bases: airflow.models.BaseOperator

Wrap the spark-submit binary to kick off a spark-submit job; requires “spark-submit” binary in the PATH.

See also

For more information on how to use this operator, take a look at the guide: SparkSubmitOperator

Parameters

  • application (str) – The application that submitted as a job, either jar or py file. (templated)

  • conf (dict[str, Any] | None) – Arbitrary Spark configuration properties (templated)

  • conn_id (str) – The spark connection id as configured in Airflow administration. When an invalid connection_id is supplied, it will default to yarn.

  • files (str | None) – 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. (templated)

  • py_files (str | None) – Additional python files used by the job, can be .zip, .egg or .py. (templated)

  • jars (str | None) – Submit additional jars to upload and place them in executor classpath. (templated)

  • driver_class_path (str | None) – Additional, driver-specific, classpath settings. (templated)

  • java_class (str | None) – the main class of the Java application

  • packages (str | None) – Comma-separated list of maven coordinates of jars to include on the driver and executor classpaths. (templated)

  • exclude_packages (str | None) – Comma-separated list of maven coordinates of jars to exclude while resolving the dependencies provided in ‘packages’ (templated)

  • repositories (str | None) – Comma-separated list of additional remote repositories to search for the maven coordinates given with ‘packages’

  • total_executor_cores (int | None) – (Standalone & Mesos only) Total cores for all executors (Default: all the available cores on the worker)

  • executor_cores (int | None) – (Standalone & YARN only) Number of cores per executor (Default: 2)

  • executor_memory (str | None) – Memory per executor (e.g. 1000M, 2G) (Default: 1G)

  • driver_memory (str | None) – Memory allocated to the driver (e.g. 1000M, 2G) (Default: 1G)

  • keytab (str | None) – Full path to the file that contains the keytab (templated)

  • principal (str | None) – The name of the kerberos principal used for keytab (templated)

  • proxy_user (str | None) – User to impersonate when submitting the application (templated)

  • name (str) – Name of the job (default airflow-spark). (templated)

  • num_executors (int | None) – Number of executors to launch

  • status_poll_interval (int) – Seconds to wait between polls of driver status in cluster mode (Default: 1)

  • application_args (list[Any] | None) – Arguments for the application being submitted (templated)

  • env_vars (dict[str, Any] | None) – Environment variables for spark-submit. It supports yarn and k8s mode too. (templated)

  • verbose (bool) – Whether to pass the verbose flag to spark-submit process for debugging

  • spark_binary (str | None) – The command to use for spark submit. Some distros may use spark2-submit or spark3-submit. (will overwrite any spark_binary defined in the connection’s extra JSON)

  • properties_file (str | None) – Path to a file from which to load extra properties. If not specified, this will look for conf/spark-defaults.conf.

  • queue (str | None) – The name of the YARN queue to which the application is submitted. (will overwrite any yarn queue defined in the connection’s extra JSON)

  • deploy_mode (str | None) – Whether to deploy your driver on the worker nodes (cluster) or locally as a client. (will overwrite any deployment mode defined in the connection’s extra JSON)

  • use_krb5ccache (bool) – if True, configure spark to use ticket cache instead of relying on keytab for Kerberos login

template_fields: Sequence[str] = ('application', 'conf', 'files', 'py_files', 'jars', 'driver_class_path', 'packages',...[source]
ui_color[source]
execute(context)[source]

Call the SparkSubmitHook to run the provided spark job.

on_kill()[source]

Override this method to clean up 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.

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