airflow spark_submit 源码

airflow spark_submit 代码

文件路径：/airflow/providers/apache/spark/operators/spark_submit.py

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# to you under the Apache License, Version 2.0 (the
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# specific language governing permissions and limitations
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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


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.
    """

    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',
    )
    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,
        **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: SparkSubmitHook | None = None
        self._conn_id = conn_id

    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)

    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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