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

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from __future__ import annotations

import argparse
from typing import Any

from pyspark.sql import SparkSession

[docs]SPARK_WRITE_TO_JDBC: str = "spark_to_jdbc"
[docs]SPARK_READ_FROM_JDBC: str = "jdbc_to_spark"


[docs]def set_common_options(
    spark_source: Any,
    url: str = "localhost:5432",
    jdbc_table: str = "default.default",
    user: str = "root",
    password: str = "root",
    driver: str = "driver",
) -> Any:
    """
    Get Spark source from JDBC connection.

    :param spark_source: Spark source, here is Spark reader or writer
    :param url: JDBC resource url
    :param jdbc_table: JDBC resource table name
    :param user: JDBC resource user name
    :param password: JDBC resource password
    :param driver: JDBC resource driver
    """
    spark_source = (
        spark_source.format("jdbc")
        .option("url", url)
        .option("dbtable", jdbc_table)
        .option("user", user)
        .option("password", password)
        .option("driver", driver)
    )
    return spark_source


[docs]def spark_write_to_jdbc(
    spark_session: SparkSession,
    url: str,
    user: str,
    password: str,
    metastore_table: str,
    jdbc_table: str,
    driver: Any,
    truncate: bool,
    save_mode: str,
    batch_size: int,
    num_partitions: int,
    create_table_column_types: str,
) -> None:
    """Transfer data from Spark to JDBC source."""
    writer = spark_session.table(metastore_table).write
    # first set common options
    writer = set_common_options(writer, url, jdbc_table, user, password, driver)

    # now set write-specific options
    if truncate:
        writer = writer.option("truncate", truncate)
    if batch_size:
        writer = writer.option("batchsize", batch_size)
    if num_partitions:
        writer = writer.option("numPartitions", num_partitions)
    if create_table_column_types:
        writer = writer.option("createTableColumnTypes", create_table_column_types)

    writer.save(mode=save_mode)


[docs]def spark_read_from_jdbc(
    spark_session: SparkSession,
    url: str,
    user: str,
    password: str,
    metastore_table: str,
    jdbc_table: str,
    driver: Any,
    save_mode: str,
    save_format: str,
    fetch_size: int,
    num_partitions: int,
    partition_column: str,
    lower_bound: str,
    upper_bound: str,
) -> None:
    """Transfer data from JDBC source to Spark."""
    # first set common options
    reader = set_common_options(spark_session.read, url, jdbc_table, user, password, driver)

    # now set specific read options
    if fetch_size:
        reader = reader.option("fetchsize", fetch_size)
    if num_partitions:
        reader = reader.option("numPartitions", num_partitions)
    if partition_column and lower_bound and upper_bound:
        reader = (
            reader.option("partitionColumn", partition_column)
            .option("lowerBound", lower_bound)
            .option("upperBound", upper_bound)
        )

    reader.load().write.saveAsTable(metastore_table, format=save_format, mode=save_mode)


def _parse_arguments(args: list[str] | None = None) -> Any:
    parser = argparse.ArgumentParser(description="Spark-JDBC")
    parser.add_argument("-cmdType", dest="cmd_type", action="store")
    parser.add_argument("-url", dest="url", action="store")
    parser.add_argument("-user", dest="user", action="store")
    parser.add_argument("-password", dest="password", action="store")
    parser.add_argument("-metastoreTable", dest="metastore_table", action="store")
    parser.add_argument("-jdbcTable", dest="jdbc_table", action="store")
    parser.add_argument("-jdbcDriver", dest="jdbc_driver", action="store")
    parser.add_argument("-jdbcTruncate", dest="truncate", action="store")
    parser.add_argument("-saveMode", dest="save_mode", action="store")
    parser.add_argument("-saveFormat", dest="save_format", action="store")
    parser.add_argument("-batchsize", dest="batch_size", action="store")
    parser.add_argument("-fetchsize", dest="fetch_size", action="store")
    parser.add_argument("-name", dest="name", action="store")
    parser.add_argument("-numPartitions", dest="num_partitions", action="store")
    parser.add_argument("-partitionColumn", dest="partition_column", action="store")
    parser.add_argument("-lowerBound", dest="lower_bound", action="store")
    parser.add_argument("-upperBound", dest="upper_bound", action="store")
    parser.add_argument("-createTableColumnTypes", dest="create_table_column_types", action="store")
    return parser.parse_args(args=args)


def _create_spark_session(arguments: Any) -> SparkSession:
    return SparkSession.builder.appName(arguments.name).enableHiveSupport().getOrCreate()


def _run_spark(arguments: Any) -> None:
    # Disable dynamic allocation by default to allow num_executors to take effect.
    spark = _create_spark_session(arguments)

    if arguments.cmd_type == SPARK_WRITE_TO_JDBC:
        spark_write_to_jdbc(
            spark,
            arguments.url,
            arguments.user,
            arguments.password,
            arguments.metastore_table,
            arguments.jdbc_table,
            arguments.jdbc_driver,
            arguments.truncate,
            arguments.save_mode,
            arguments.batch_size,
            arguments.num_partitions,
            arguments.create_table_column_types,
        )
    elif arguments.cmd_type == SPARK_READ_FROM_JDBC:
        spark_read_from_jdbc(
            spark,
            arguments.url,
            arguments.user,
            arguments.password,
            arguments.metastore_table,
            arguments.jdbc_table,
            arguments.jdbc_driver,
            arguments.save_mode,
            arguments.save_format,
            arguments.fetch_size,
            arguments.num_partitions,
            arguments.partition_column,
            arguments.lower_bound,
            arguments.upper_bound,
        )


if __name__ == "__main__":  # pragma: no cover
    _run_spark(arguments=_parse_arguments())