airflow.contrib.hooks.spark_submit_hook

Module Contents

class airflow.contrib.hooks.spark_submit_hook.SparkSubmitHook(conf=None, conn_id='spark_default', files=None, py_files=None, archives=None, driver_class_path=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='default-name', num_executors=None, application_args=None, env_vars=None, verbose=False, spark_binary='spark-submit')[source]

Bases:airflow.hooks.base_hook.BaseHook, airflow.utils.log.logging_mixin.LoggingMixin

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 to be supplied.

Parameters

  • conf (dict) – Arbitrary Spark configuration properties

  • conn_id (str) – The connection id as configured in Airflow administration. When an invalid connection_id is supplied, it will default to yarn.

  • files (str) – 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.

  • py_files (str) – Additional python files used by the job, can be .zip, .egg or .py.

  • driver_class_path (str) – Additional, driver-specific, classpath settings.

  • jars (str) – Submit additional jars to upload and place them in executor classpath.

  • java_class (str) – the main class of the Java application

  • packages (str) – Comma-separated list of maven coordinates of jars to include on the driver and executor classpaths

  • exclude_packages (str) – Comma-separated list of maven coordinates of jars to exclude while resolving the dependencies provided in ‘packages’

  • repositories (str) – Comma-separated list of additional remote repositories to search for the maven coordinates given with ‘packages’

  • total_executor_cores (int) – (Standalone & Mesos only) Total cores for all executors (Default: all the available cores on the worker)

  • executor_cores (int) – (Standalone, YARN and Kubernetes only) Number of cores per executor (Default: 2)

  • executor_memory (str) – Memory per executor (e.g. 1000M, 2G) (Default: 1G)

  • driver_memory (str) – Memory allocated to the driver (e.g. 1000M, 2G) (Default: 1G)

  • keytab (str) – Full path to the file that contains the keytab

  • principal (str) – The name of the kerberos principal used for keytab

  • name (str) – Name of the job (default airflow-spark)

  • num_executors (int) – Number of executors to launch

  • application_args (list) – Arguments for the application being submitted

  • env_vars (dict) – Environment variables for spark-submit. It supports yarn and k8s mode too.

  • verbose (bool) – Whether to pass the verbose flag to spark-submit process for debugging

  • spark_binary (str) – The command to use for spark submit. Some distros may use spark2-submit.

Param

archives: Archives that spark should unzip (and possibly tag with #ALIAS) into the application working directory.

_resolve_should_track_driver_status(self)[source]

Determines whether or not this hook should poll the spark driver status through subsequent spark-submit status requests after the initial spark-submit request :return: if the driver status should be tracked

_resolve_connection(self)[source]
get_conn(self)[source]
_get_spark_binary_path(self)[source]
_build_spark_submit_command(self, application)[source]

Construct the spark-submit command to execute. :param application: command to append to the spark-submit command :type application: str :return: full command to be executed

_build_track_driver_status_command(self)[source]

Construct the command to poll the driver status.

Returns

full command to be executed

submit(self, application='', **kwargs)[source]

Remote Popen to execute the spark-submit job

Parameters

  • application (str) – Submitted application, jar or py file

  • kwargs – extra arguments to Popen (see subprocess.Popen)

_process_spark_submit_log(self, itr)[source]

Processes the log files and extracts useful information out of it.

If the deploy-mode is ‘client’, log the output of the submit command as those are the output logs of the Spark worker directly.

Remark: If the driver needs to be tracked for its status, the log-level of the spark deploy needs to be at least INFO (log4j.logger.org.apache.spark.deploy=INFO)

Parameters

itr – An iterator which iterates over the input of the subprocess

_process_spark_status_log(self, itr)[source]

parses the logs of the spark driver status query process

Parameters

itr – An iterator which iterates over the input of the subprocess

_start_driver_status_tracking(self)[source]

Polls the driver based on self._driver_id to get the status. Finish successfully when the status is FINISHED. Finish failed when the status is ERROR/UNKNOWN/KILLED/FAILED.

Possible status:

SUBMITTED

Submitted but not yet scheduled on a worker

RUNNING

Has been allocated to a worker to run

FINISHED

Previously ran and exited cleanly

RELAUNCHING

Exited non-zero or due to worker failure, but has not yet started running again

UNKNOWN

The status of the driver is temporarily not known due to master failure recovery

KILLED

A user manually killed this driver

FAILED

The driver exited non-zero and was not supervised

ERROR

Unable to run or restart due to an unrecoverable error (e.g. missing jar file)

_build_spark_driver_kill_command(self)[source]

Construct the spark-submit command to kill a driver. :return: full command to kill a driver

on_kill(self)[source]