airflow.providers.apache.spark.hooks.spark_submit¶
Module Contents¶
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class
airflow.providers.apache.spark.hooks.spark_submit.SparkSubmitHook(conf: Optional[Dict[str, Any]] = None, conn_id: str = 'spark_default', files: Optional[str] = None, py_files: Optional[str] = None, archives: Optional[str] = None, driver_class_path: Optional[str] = None, jars: Optional[str] = None, java_class: Optional[str] = None, packages: Optional[str] = None, exclude_packages: Optional[str] = None, repositories: Optional[str] = None, total_executor_cores: Optional[int] = None, executor_cores: Optional[int] = None, executor_memory: Optional[str] = None, driver_memory: Optional[str] = None, keytab: Optional[str] = None, principal: Optional[str] = None, proxy_user: Optional[str] = None, name: str = 'default-name', num_executors: Optional[int] = None, status_poll_interval: int = 1, application_args: Optional[List[Any]] = None, env_vars: Optional[Dict[str, Any]] = None, verbose: bool = False, spark_binary: Optional[str] = None)[source]¶ Bases:
airflow.hooks.base.BaseHook,airflow.utils.log.logging_mixin.LoggingMixinThis 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
proxy_user (str) – User to impersonate when submitting the application
name (str) – Name of the job (default airflow-spark)
num_executors (int) – Number of executors to launch
status_poll_interval (int) – Seconds to wait between polls of driver status in cluster mode (Default: 1)
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.
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_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
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_build_spark_submit_command(self, application: str)[source]¶ Construct the spark-submit command to execute.
- Parameters
application (str) – command to append to the spark-submit command
- Returns
full command to be executed
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_build_track_driver_status_command(self)[source]¶ Construct the command to poll the driver status.
- Returns
full command to be executed
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submit(self, application: str = '', **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)
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_process_spark_submit_log(self, itr: Iterator[Any])[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
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_process_spark_status_log(self, itr: Iterator[Any])[source]¶ Parses the logs of the spark driver status query process
- Parameters
itr – An iterator which iterates over the input of the subprocess
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_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)