Source code for airflow.providers.apache.spark.operators.spark_submit

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from typing import TYPE_CHECKING, Any, Dict, List, Optional, Sequence

from airflow.models import BaseOperator
from airflow.providers.apache.spark.hooks.spark_submit import SparkSubmitHook
from airflow.settings import WEB_COLORS

    from airflow.utils.context import Context

[docs]class SparkSubmitOperator(BaseOperator): """ 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 is set in the extra on the connection. .. seealso:: For more information on how to use this operator, take a look at the guide: :ref:`howto/operator:SparkSubmitOperator` :param application: The application that submitted as a job, either jar or py file. (templated) :param conf: Arbitrary Spark configuration properties (templated) :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. (templated) :param py_files: Additional python files used by the job, can be .zip, .egg or .py. (templated) :param jars: Submit additional jars to upload and place them in executor classpath. (templated) :param driver_class_path: Additional, driver-specific, classpath settings. (templated) :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. (templated) :param exclude_packages: Comma-separated list of maven coordinates of jars to exclude while resolving the dependencies provided in 'packages' (templated) :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 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 (templated) :param principal: The name of the kerberos principal used for keytab (templated) :param proxy_user: User to impersonate when submitting the application (templated) :param name: Name of the job (default airflow-spark). (templated) :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 (templated) :param env_vars: Environment variables for spark-submit. It supports yarn and k8s mode too. (templated) :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. """
[docs] template_fields: Sequence[str] = ( '_application', '_conf', '_files', '_py_files', '_jars', '_driver_class_path', '_packages', '_exclude_packages', '_keytab', '_principal', '_proxy_user', '_name', '_application_args', '_env_vars',
[docs] ui_color = WEB_COLORS['LIGHTORANGE']
def __init__( self, *, application: str = '', conf: Optional[Dict[str, Any]] = None, conn_id: str = 'spark_default', files: Optional[str] = None, py_files: Optional[str] = None, archives: Optional[str] = None, driver_class_path: Optional[str] = None, jars: Optional[str] = None, java_class: Optional[str] = None, packages: Optional[str] = None, exclude_packages: Optional[str] = None, repositories: Optional[str] = None, total_executor_cores: Optional[int] = None, executor_cores: Optional[int] = None, executor_memory: Optional[str] = None, driver_memory: Optional[str] = None, keytab: Optional[str] = None, principal: Optional[str] = None, proxy_user: Optional[str] = None, name: str = 'arrow-spark', num_executors: Optional[int] = None, status_poll_interval: int = 1, application_args: Optional[List[Any]] = None, env_vars: Optional[Dict[str, Any]] = None, verbose: bool = False, spark_binary: Optional[str] = None, **kwargs: Any, ) -> None: super().__init__(**kwargs) self._application = application self._conf = conf self._files = files self._py_files = py_files self._archives = archives self._driver_class_path = driver_class_path self._jars = jars self._java_class = java_class self._packages = packages self._exclude_packages = exclude_packages self._repositories = repositories self._total_executor_cores = total_executor_cores self._executor_cores = executor_cores self._executor_memory = executor_memory self._driver_memory = driver_memory self._keytab = keytab self._principal = principal self._proxy_user = proxy_user self._name = name self._num_executors = num_executors self._status_poll_interval = status_poll_interval self._application_args = application_args self._env_vars = env_vars self._verbose = verbose self._spark_binary = spark_binary self._hook: Optional[SparkSubmitHook] = None self._conn_id = conn_id
[docs] def execute(self, context: "Context") -> None: """Call the SparkSubmitHook to run the provided spark job""" if self._hook is None: self._hook = self._get_hook() self._hook.submit(self._application)
[docs] def on_kill(self) -> None: if self._hook is None: self._hook = self._get_hook() self._hook.on_kill()
def _get_hook(self) -> SparkSubmitHook: return SparkSubmitHook( conf=self._conf, conn_id=self._conn_id, files=self._files, py_files=self._py_files, archives=self._archives, driver_class_path=self._driver_class_path, jars=self._jars, java_class=self._java_class, packages=self._packages, exclude_packages=self._exclude_packages, repositories=self._repositories, total_executor_cores=self._total_executor_cores, executor_cores=self._executor_cores, executor_memory=self._executor_memory, driver_memory=self._driver_memory, keytab=self._keytab, principal=self._principal, proxy_user=self._proxy_user, name=self._name, num_executors=self._num_executors, status_poll_interval=self._status_poll_interval, application_args=self._application_args, env_vars=self._env_vars, verbose=self._verbose, spark_binary=self._spark_binary,

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