Source code for airflow.contrib.operators.spark_submit_operator

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from airflow.contrib.hooks.spark_submit_hook import SparkSubmitHook
from airflow.models import BaseOperator
from airflow.settings import WEB_COLORS
from airflow.utils.decorators import apply_defaults

[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. :param application: The application that submitted as a job, either jar or py file. (templated) :type application: str :param conf: Arbitrary Spark configuration properties :type conf: dict :param conn_id: The connection id as configured in Airflow administration. When an invalid connection_id is supplied, it will default to yarn. :type conn_id: str :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. :type files: str :param py_files: Additional python files used by the job, can be .zip, .egg or .py. :type py_files: str :param jars: Submit additional jars to upload and place them in executor classpath. :param driver_classpath: Additional, driver-specific, classpath settings. :type driver_classpath: str :type jars: str :param java_class: the main class of the Java application :type java_class: str :param packages: Comma-separated list of maven coordinates of jars to include on the driver and executor classpaths. (templated) :type packages: str :param exclude_packages: Comma-separated list of maven coordinates of jars to exclude while resolving the dependencies provided in 'packages' :type exclude_packages: str :param repositories: Comma-separated list of additional remote repositories to search for the maven coordinates given with 'packages' :type repositories: str :param total_executor_cores: (Standalone & Mesos only) Total cores for all executors (Default: all the available cores on the worker) :type total_executor_cores: int :param executor_cores: (Standalone & YARN only) Number of cores per executor (Default: 2) :type executor_cores: int :param executor_memory: Memory per executor (e.g. 1000M, 2G) (Default: 1G) :type executor_memory: str :param driver_memory: Memory allocated to the driver (e.g. 1000M, 2G) (Default: 1G) :type driver_memory: str :param keytab: Full path to the file that contains the keytab :type keytab: str :param principal: The name of the kerberos principal used for keytab :type principal: str :param name: Name of the job (default airflow-spark). (templated) :type name: str :param num_executors: Number of executors to launch :type num_executors: int :param application_args: Arguments for the application being submitted :type application_args: list :param env_vars: Environment variables for spark-submit. It supports yarn and k8s mode too. :type env_vars: dict :param verbose: Whether to pass the verbose flag to spark-submit process for debugging :type verbose: bool """ template_fields = ('_name', '_application_args', '_packages') ui_color = WEB_COLORS['LIGHTORANGE'] @apply_defaults def __init__(self, application='', conf=None, conn_id='spark_default', files=None, py_files=None, driver_classpath=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, name='airflow-spark', num_executors=None, application_args=None, env_vars=None, verbose=False, *args, **kwargs): super(SparkSubmitOperator, self).__init__(*args, **kwargs) self._application = application self._conf = conf self._files = files self._py_files = py_files self._driver_classpath = driver_classpath 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._name = name self._num_executors = num_executors self._application_args = application_args self._env_vars = env_vars self._verbose = verbose self._hook = None self._conn_id = conn_id
[docs] def execute(self, context): """ Call the SparkSubmitHook to run the provided spark job """ self._hook = SparkSubmitHook( conf=self._conf, conn_id=self._conn_id, files=self._files, py_files=self._py_files, driver_classpath=self._driver_classpath, 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, name=self._name, num_executors=self._num_executors, application_args=self._application_args, env_vars=self._env_vars, verbose=self._verbose ) self._hook.submit(self._application)
def on_kill(self): self._hook.on_kill()