airflow.providers.apache.spark.hooks.spark_submit¶
Module Contents¶
Classes¶
This hook is a wrapper around the spark-submit binary to kick off a spark-submit job. |
- class airflow.providers.apache.spark.hooks.spark_submit.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, proxy_user=None, name='default-name', num_executors=None, status_poll_interval=1, application_args=None, env_vars=None, verbose=False, spark_binary=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 (Optional[Dict[str, Any]]) -- Arbitrary Spark configuration properties
spark_conn_id -- The spark connection id as configured in Airflow administration. When an invalid connection_id is supplied, it will default to yarn.
files (Optional[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 (Optional[str]) -- Additional python files used by the job, can be .zip, .egg or .py.
archives (Optional[str]) -- Archives that spark should unzip (and possibly tag with #ALIAS) into the application working directory.
driver_class_path (Optional[str]) -- Additional, driver-specific, classpath settings.
jars (Optional[str]) -- Submit additional jars to upload and place them in executor classpath.
java_class (Optional[str]) -- the main class of the Java application
packages (Optional[str]) -- Comma-separated list of maven coordinates of jars to include on the driver and executor classpaths
exclude_packages (Optional[str]) -- Comma-separated list of maven coordinates of jars to exclude while resolving the dependencies provided in 'packages'
repositories (Optional[str]) -- Comma-separated list of additional remote repositories to search for the maven coordinates given with 'packages'
total_executor_cores (Optional[int]) -- (Standalone & Mesos only) Total cores for all executors (Default: all the available cores on the worker)
executor_cores (Optional[int]) -- (Standalone, YARN and Kubernetes only) Number of cores per executor (Default: 2)
executor_memory (Optional[str]) -- Memory per executor (e.g. 1000M, 2G) (Default: 1G)
driver_memory (Optional[str]) -- Memory allocated to the driver (e.g. 1000M, 2G) (Default: 1G)
keytab (Optional[str]) -- Full path to the file that contains the keytab
principal (Optional[str]) -- The name of the kerberos principal used for keytab
proxy_user (Optional[str]) -- User to impersonate when submitting the application
name (str) -- Name of the job (default airflow-spark)
num_executors (Optional[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 (Optional[List[Any]]) -- Arguments for the application being submitted
env_vars (Optional[Dict[str, Any]]) -- 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 (Optional[str]) -- The command to use for spark submit. Some distros may use spark2-submit.