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

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from __future__ import annotations

from typing import TYPE_CHECKING, Any, Sequence

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

if TYPE_CHECKING:
    from airflow.utils.context import Context


[docs]class SparkSubmitOperator(BaseOperator): """ Wrap the spark-submit binary to kick off a spark-submit job; requires "spark-submit" binary in the PATH. .. 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 conn_id: The :ref:`spark connection id <howto/connection:spark-submit>` 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 or spark3-submit. (will overwrite any spark_binary defined in the connection's extra JSON) :param properties_file: Path to a file from which to load extra properties. If not specified, this will look for conf/spark-defaults.conf. :param yarn_queue: The name of the YARN queue to which the application is submitted. (will overwrite any yarn queue defined in the connection's extra JSON) :param deploy_mode: 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) :param use_krb5ccache: if True, configure spark to use ticket cache instead of relying on keytab for Kerberos login """
[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", "properties_file", )
[docs] ui_color = WEB_COLORS["LIGHTORANGE"]
def __init__( self, *, application: str = "", conf: dict[str, Any] | None = None, conn_id: str = "spark_default", files: str | None = None, py_files: str | None = None, archives: str | None = None, driver_class_path: str | None = None, jars: str | None = None, java_class: str | None = None, packages: str | None = None, exclude_packages: str | None = None, repositories: str | None = None, total_executor_cores: int | None = None, executor_cores: int | None = None, executor_memory: str | None = None, driver_memory: str | None = None, keytab: str | None = None, principal: str | None = None, proxy_user: str | None = None, name: str = "arrow-spark", num_executors: int | None = None, status_poll_interval: int = 1, application_args: list[Any] | None = None, env_vars: dict[str, Any] | None = None, verbose: bool = False, spark_binary: str | None = None, properties_file: str | None = None, yarn_queue: str | None = None, deploy_mode: str | None = None, use_krb5ccache: bool = False, **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.properties_file = properties_file self._yarn_queue = yarn_queue self._deploy_mode = deploy_mode self._hook: SparkSubmitHook | None = None self._conn_id = conn_id self._use_krb5ccache = use_krb5ccache
[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, properties_file=self.properties_file, yarn_queue=self._yarn_queue, deploy_mode=self._deploy_mode, use_krb5ccache=self._use_krb5ccache, )

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