:mod:`airflow.contrib.hooks.spark_submit_hook` ============================================== .. py:module:: airflow.contrib.hooks.spark_submit_hook Module Contents --------------- .. py:class:: 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=None) Bases: :class:`airflow.hooks.base_hook.BaseHook`, :class:`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. :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: archives: Archives that spark should unzip (and possibly tag with #ALIAS) into the application working directory. :param driver_class_path: Additional, driver-specific, classpath settings. :type driver_class_path: str :param jars: Submit additional jars to upload and place them in executor classpath. :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 :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 and Kubernetes 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) :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 :param spark_binary: The command to use for spark submit. Some distros may use spark2-submit. :type spark_binary: str .. method:: _resolve_should_track_driver_status(self) 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 .. method:: _resolve_connection(self) .. method:: get_conn(self) .. method:: _get_spark_binary_path(self) .. method:: _build_spark_submit_command(self, application) 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 .. method:: _build_track_driver_status_command(self) Construct the command to poll the driver status. :return: full command to be executed .. method:: submit(self, application='', **kwargs) Remote Popen to execute the spark-submit job :param application: Submitted application, jar or py file :type application: str :param kwargs: extra arguments to Popen (see subprocess.Popen) .. method:: _process_spark_submit_log(self, itr) 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) :param itr: An iterator which iterates over the input of the subprocess .. method:: _process_spark_status_log(self, itr) parses the logs of the spark driver status query process :param itr: An iterator which iterates over the input of the subprocess .. method:: _start_driver_status_tracking(self) 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) .. method:: _build_spark_driver_kill_command(self) Construct the spark-submit command to kill a driver. :return: full command to kill a driver .. method:: on_kill(self)