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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>` 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.
"""
[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: 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
[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,
)
