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from __future__ import annotations
from typing import TYPE_CHECKING, Any
from airflow.providers.apache.spark.hooks.spark_jdbc import SparkJDBCHook
from airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperator
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class SparkJDBCOperator(SparkSubmitOperator):
"""
Extend the SparkSubmitOperator to perform data transfers to/from JDBC-based databases with Apache Spark.
As with the SparkSubmitOperator, it assumes that the "spark-submit" binary is available on the PATH.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:SparkJDBCOperator`
:param spark_app_name: Name of the job (default airflow-spark-jdbc)
:param spark_conn_id: The :ref:`spark connection id <howto/connection:spark>`
as configured in Airflow administration
:param spark_conf: Any additional Spark configuration properties
:param spark_py_files: Additional python files used (.zip, .egg, or .py)
:param spark_files: Additional files to upload to the container running the job
:param spark_jars: Additional jars to upload and add to the driver and
executor classpath
:param num_executors: number of executor to run. This should be set so as to manage
the number of connections made with the JDBC database
:param executor_cores: Number of cores per executor
:param executor_memory: Memory per executor (e.g. 1000M, 2G)
:param driver_memory: Memory allocated to the driver (e.g. 1000M, 2G)
:param verbose: Whether to pass the verbose flag to spark-submit for debugging
:param keytab: Full path to the file that contains the keytab
:param principal: The name of the kerberos principal used for keytab
:param cmd_type: Which way the data should flow. 2 possible values:
spark_to_jdbc: data written by spark from metastore to jdbc
jdbc_to_spark: data written by spark from jdbc to metastore
:param jdbc_table: The name of the JDBC table
:param jdbc_conn_id: Connection id used for connection to JDBC database
:param jdbc_driver: Name of the JDBC driver to use for the JDBC connection. This
driver (usually a jar) should be passed in the 'jars' parameter
:param metastore_table: The name of the metastore table,
:param jdbc_truncate: (spark_to_jdbc only) Whether or not Spark should truncate or
drop and recreate the JDBC table. This only takes effect if
'save_mode' is set to Overwrite. Also, if the schema is
different, Spark cannot truncate, and will drop and recreate
:param save_mode: The Spark save-mode to use (e.g. overwrite, append, etc.)
:param save_format: (jdbc_to_spark-only) The Spark save-format to use (e.g. parquet)
:param batch_size: (spark_to_jdbc only) The size of the batch to insert per round
trip to the JDBC database. Defaults to 1000
:param fetch_size: (jdbc_to_spark only) The size of the batch to fetch per round trip
from the JDBC database. Default depends on the JDBC driver
:param num_partitions: The maximum number of partitions that can be used by Spark
simultaneously, both for spark_to_jdbc and jdbc_to_spark
operations. This will also cap the number of JDBC connections
that can be opened
:param partition_column: (jdbc_to_spark-only) A numeric column to be used to
partition the metastore table by. If specified, you must
also specify:
num_partitions, lower_bound, upper_bound
:param lower_bound: (jdbc_to_spark-only) Lower bound of the range of the numeric
partition column to fetch. If specified, you must also specify:
num_partitions, partition_column, upper_bound
:param upper_bound: (jdbc_to_spark-only) Upper bound of the range of the numeric
partition column to fetch. If specified, you must also specify:
num_partitions, partition_column, lower_bound
:param create_table_column_types: (spark_to_jdbc-only) The database column data types
to use instead of the defaults, when creating the
table. Data type information should be specified in
the same format as CREATE TABLE columns syntax
(e.g: "name CHAR(64), comments VARCHAR(1024)").
The specified types should be valid spark sql data
types.
"""
def __init__(
self,
*,
spark_app_name: str = "airflow-spark-jdbc",
spark_conn_id: str = "spark-default",
spark_conf: dict[str, Any] | None = None,
spark_py_files: str | None = None,
spark_files: str | None = None,
spark_jars: str | None = None,
num_executors: int | None = None,
executor_cores: int | None = None,
executor_memory: str | None = None,
driver_memory: str | None = None,
verbose: bool = False,
principal: str | None = None,
keytab: str | None = None,
cmd_type: str = "spark_to_jdbc",
jdbc_table: str | None = None,
jdbc_conn_id: str = "jdbc-default",
jdbc_driver: str | None = None,
metastore_table: str | None = None,
jdbc_truncate: bool = False,
save_mode: str | None = None,
save_format: str | None = None,
batch_size: int | None = None,
fetch_size: int | None = None,
num_partitions: int | None = None,
partition_column: str | None = None,
lower_bound: str | None = None,
upper_bound: str | None = None,
create_table_column_types: str | None = None,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self._spark_app_name = spark_app_name
self._spark_conn_id = spark_conn_id
self._spark_conf = spark_conf
self._spark_py_files = spark_py_files
self._spark_files = spark_files
self._spark_jars = spark_jars
self._num_executors = num_executors
self._executor_cores = executor_cores
self._executor_memory = executor_memory
self._driver_memory = driver_memory
self._verbose = verbose
self._keytab = keytab
self._principal = principal
self._cmd_type = cmd_type
self._jdbc_table = jdbc_table
self._jdbc_conn_id = jdbc_conn_id
self._jdbc_driver = jdbc_driver
self._metastore_table = metastore_table
self._jdbc_truncate = jdbc_truncate
self._save_mode = save_mode
self._save_format = save_format
self._batch_size = batch_size
self._fetch_size = fetch_size
self._num_partitions = num_partitions
self._partition_column = partition_column
self._lower_bound = lower_bound
self._upper_bound = upper_bound
self._create_table_column_types = create_table_column_types
self._hook: SparkJDBCHook | None = None
[docs] def execute(self, context: Context) -> None:
"""Call the SparkSubmitHook to run the provided spark job."""
if self._hook is None:
self._hook = self._get_hook()
self._hook.submit_jdbc_job()
[docs] def on_kill(self) -> None:
if self._hook is None:
self._hook = self._get_hook()
self._hook.on_kill()
def _get_hook(self) -> SparkJDBCHook:
return SparkJDBCHook(
spark_app_name=self._spark_app_name,
spark_conn_id=self._spark_conn_id,
spark_conf=self._spark_conf,
spark_py_files=self._spark_py_files,
spark_files=self._spark_files,
spark_jars=self._spark_jars,
num_executors=self._num_executors,
executor_cores=self._executor_cores,
executor_memory=self._executor_memory,
driver_memory=self._driver_memory,
verbose=self._verbose,
keytab=self._keytab,
principal=self._principal,
cmd_type=self._cmd_type,
jdbc_table=self._jdbc_table,
jdbc_conn_id=self._jdbc_conn_id,
jdbc_driver=self._jdbc_driver,
metastore_table=self._metastore_table,
jdbc_truncate=self._jdbc_truncate,
save_mode=self._save_mode,
save_format=self._save_format,
batch_size=self._batch_size,
fetch_size=self._fetch_size,
num_partitions=self._num_partitions,
partition_column=self._partition_column,
lower_bound=self._lower_bound,
upper_bound=self._upper_bound,
create_table_column_types=self._create_table_column_types,
)
