airflow.providers.apache.spark.hooks.spark_submit¶
Module Contents¶
Classes¶
Wrap the spark-submit binary to kick off a spark-submit job; requires "spark-submit" binary in the PATH. |
Attributes¶
- 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, properties_file=None, yarn_queue=None, deploy_mode=None, *, use_krb5ccache=False)[source]¶
Bases:
airflow.hooks.base.BaseHook,airflow.utils.log.logging_mixin.LoggingMixinWrap the spark-submit binary to kick off a spark-submit job; requires “spark-submit” binary in the PATH.
- Parameters
conf (dict[str, Any] | None) – 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 (str | None) – 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 | None) – Additional python files used by the job, can be .zip, .egg or .py.
archives (str | None) – Archives that spark should unzip (and possibly tag with #ALIAS) into the application working directory.
driver_class_path (str | None) – Additional, driver-specific, classpath settings.
jars (str | None) – Submit additional jars to upload and place them in executor classpath.
java_class (str | None) – the main class of the Java application
packages (str | None) – Comma-separated list of maven coordinates of jars to include on the driver and executor classpaths
exclude_packages (str | None) – Comma-separated list of maven coordinates of jars to exclude while resolving the dependencies provided in ‘packages’
repositories (str | None) – Comma-separated list of additional remote repositories to search for the maven coordinates given with ‘packages’
total_executor_cores (int | None) – (Standalone & Mesos only) Total cores for all executors (Default: all the available cores on the worker)
executor_cores (int | None) – (Standalone, YARN and Kubernetes only) Number of cores per executor (Default: 2)
executor_memory (str | None) – Memory per executor (e.g. 1000M, 2G) (Default: 1G)
driver_memory (str | None) – Memory allocated to the driver (e.g. 1000M, 2G) (Default: 1G)
keytab (str | None) – Full path to the file that contains the keytab (will overwrite any keytab defined in the connection’s extra JSON)
principal (str | None) – The name of the kerberos principal used for keytab (will overwrite any principal defined in the connection’s extra JSON)
proxy_user (str | None) – User to impersonate when submitting the application
name (str) – Name of the job (default airflow-spark)
num_executors (int | None) – 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[Any] | None) – Arguments for the application being submitted
env_vars (dict[str, Any] | None) – 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 | None) – The command to use for spark submit. Some distros may use spark2-submit or spark3-submit. (will overwrite any spark_binary defined in the connection’s extra JSON)
properties_file (str | None) – Path to a file from which to load extra properties. If not specified, this will look for conf/spark-defaults.conf.
yarn_queue (str | None) – The name of the YARN queue to which the application is submitted. (will overwrite any yarn queue defined in the connection’s extra JSON)
deploy_mode (str | None) – Whether to deploy your driver on the worker nodes (cluster) or locally as an client. (will overwrite any deployment mode defined in the connection’s extra JSON)
use_krb5ccache (bool) – if True, configure spark to use ticket cache instead of relying on keytab for Kerberos login
- classmethod get_ui_field_behaviour()[source]¶
Return custom UI field behaviour for Spark connection.
- classmethod get_connection_form_widgets()[source]¶
Return connection widgets to add to Spark connection form.