Source code for airflow.providers.apache.spark.hooks.spark_jdbc

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import os
from typing import Any, Dict, Optional

from airflow.exceptions import AirflowException
from airflow.providers.apache.spark.hooks.spark_submit import SparkSubmitHook

[docs]class SparkJDBCHook(SparkSubmitHook): """ This hook extends the SparkSubmitHook specifically for performing data transfers to/from JDBC-based databases with Apache Spark. :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. """
[docs] conn_name_attr = 'spark_conn_id'
[docs] default_conn_name = 'spark_default'
[docs] conn_type = 'spark_jdbc'
[docs] hook_name = 'Spark JDBC'
def __init__( self, spark_app_name: str = 'airflow-spark-jdbc', spark_conn_id: str = default_conn_name, spark_conf: Optional[Dict[str, Any]] = None, spark_py_files: Optional[str] = None, spark_files: Optional[str] = None, spark_jars: Optional[str] = None, num_executors: Optional[int] = None, executor_cores: Optional[int] = None, executor_memory: Optional[str] = None, driver_memory: Optional[str] = None, verbose: bool = False, principal: Optional[str] = None, keytab: Optional[str] = None, cmd_type: str = 'spark_to_jdbc', jdbc_table: Optional[str] = None, jdbc_conn_id: str = 'jdbc-default', jdbc_driver: Optional[str] = None, metastore_table: Optional[str] = None, jdbc_truncate: bool = False, save_mode: Optional[str] = None, save_format: Optional[str] = None, batch_size: Optional[int] = None, fetch_size: Optional[int] = None, num_partitions: Optional[int] = None, partition_column: Optional[str] = None, lower_bound: Optional[str] = None, upper_bound: Optional[str] = None, create_table_column_types: Optional[str] = None, *args: Any, **kwargs: Any, ): super().__init__(*args, **kwargs) self._name = spark_app_name self._conn_id = spark_conn_id self._conf = spark_conf or {} self._py_files = spark_py_files self._files = spark_files self._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._jdbc_connection = self._resolve_jdbc_connection() def _resolve_jdbc_connection(self) -> Dict[str, Any]: conn_data = {'url': '', 'schema': '', 'conn_prefix': '', 'user': '', 'password': ''} try: conn = self.get_connection(self._jdbc_conn_id) if conn.port: conn_data['url'] = f"{}:{conn.port}" else: conn_data['url'] = conn_data['schema'] = conn.schema conn_data['user'] = conn.login conn_data['password'] = conn.password extra = conn.extra_dejson conn_data['conn_prefix'] = extra.get('conn_prefix', '') except AirflowException: self.log.debug( "Could not load jdbc connection string %s, defaulting to %s", self._jdbc_conn_id, "" ) return conn_data def _build_jdbc_application_arguments(self, jdbc_conn: Dict[str, Any]) -> Any: arguments = [] arguments += ["-cmdType", self._cmd_type] if self._jdbc_connection['url']: arguments += [ '-url', f"{jdbc_conn['conn_prefix']}{jdbc_conn['url']}/{jdbc_conn['schema']}", ] if self._jdbc_connection['user']: arguments += ['-user', self._jdbc_connection['user']] if self._jdbc_connection['password']: arguments += ['-password', self._jdbc_connection['password']] if self._metastore_table: arguments += ['-metastoreTable', self._metastore_table] if self._jdbc_table: arguments += ['-jdbcTable', self._jdbc_table] if self._jdbc_truncate: arguments += ['-jdbcTruncate', str(self._jdbc_truncate)] if self._jdbc_driver: arguments += ['-jdbcDriver', self._jdbc_driver] if self._batch_size: arguments += ['-batchsize', str(self._batch_size)] if self._fetch_size: arguments += ['-fetchsize', str(self._fetch_size)] if self._num_partitions: arguments += ['-numPartitions', str(self._num_partitions)] if self._partition_column and self._lower_bound and self._upper_bound and self._num_partitions: # these 3 parameters need to be used all together to take effect. arguments += [ '-partitionColumn', self._partition_column, '-lowerBound', self._lower_bound, '-upperBound', self._upper_bound, ] if self._save_mode: arguments += ['-saveMode', self._save_mode] if self._save_format: arguments += ['-saveFormat', self._save_format] if self._create_table_column_types: arguments += ['-createTableColumnTypes', self._create_table_column_types] return arguments
[docs] def submit_jdbc_job(self) -> None: """Submit Spark JDBC job""" self._application_args = self._build_jdbc_application_arguments(self._jdbc_connection) self.submit(application=os.path.dirname(os.path.abspath(__file__)) + "/")
[docs] def get_conn(self) -> Any: pass

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