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jdbc_to_bigquery.py
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jdbc_to_bigquery.py
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# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict, Sequence, Optional, Any
from logging import Logger
import argparse
import pprint
from pyspark.sql import SparkSession, DataFrame, DataFrameWriter
from dataproc_templates import BaseTemplate
import dataproc_templates.util.template_constants as constants
import dataproc_templates.util.secret_manager as secret_manager
__all__ = ['JDBCToBigQueryTemplate']
class JDBCToBigQueryTemplate(BaseTemplate):
"""
Dataproc template implementing loads from JDBC into BigQuery
"""
@staticmethod
def parse_args(args: Optional[Sequence[str]] = None) -> Dict[str, Any]:
parser: argparse.ArgumentParser = argparse.ArgumentParser()
parser.add_argument(
f'--{constants.JDBC_BQ_OUTPUT_DATASET}',
dest=constants.JDBC_BQ_OUTPUT_DATASET,
required=True,
help='BigQuery dataset for the output table'
)
parser.add_argument(
f'--{constants.JDBC_BQ_OUTPUT_TABLE}',
dest=constants.JDBC_BQ_OUTPUT_TABLE,
required=True,
help='BigQuery output table name'
)
parser.add_argument(
f'--{constants.JDBC_BQ_LD_TEMP_BUCKET_NAME}',
dest=constants.JDBC_BQ_LD_TEMP_BUCKET_NAME,
required=True,
help='Spark BigQuery connector temporary bucket'
)
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument(
f'--{constants.JDBC_BQ_INPUT_URL}',
dest=constants.JDBC_BQ_INPUT_URL,
required=False,
default="",
help='JDBC input URL'
)
group.add_argument(
f'--{constants.JDBC_BQ_INPUT_URL_SECRET}',
dest=constants.JDBC_BQ_INPUT_URL_SECRET,
required=False,
default="",
help='JDBC input URL secret name'
)
parser.add_argument(
f'--{constants.JDBC_BQ_INPUT_DRIVER}',
dest=constants.JDBC_BQ_INPUT_DRIVER,
required=True,
help='JDBC input driver name'
)
parser.add_argument(
f'--{constants.JDBC_BQ_INPUT_TABLE}',
dest=constants.JDBC_BQ_INPUT_TABLE,
required=True,
help='JDBC input table name'
)
parser.add_argument(
f'--{constants.JDBC_BQ_INPUT_PARTITIONCOLUMN}',
dest=constants.JDBC_BQ_INPUT_PARTITIONCOLUMN,
required=False,
default="",
help='JDBC input table partition column name'
)
parser.add_argument(
f'--{constants.JDBC_BQ_INPUT_LOWERBOUND}',
dest=constants.JDBC_BQ_INPUT_LOWERBOUND,
required=False,
default="",
help='JDBC input table partition column lower bound which is used to decide the partition stride'
)
parser.add_argument(
f'--{constants.JDBC_BQ_INPUT_UPPERBOUND}',
dest=constants.JDBC_BQ_INPUT_UPPERBOUND,
required=False,
default="",
help='JDBC input table partition column upper bound which is used to decide the partition stride'
)
parser.add_argument(
f'--{constants.JDBC_BQ_NUMPARTITIONS}',
dest=constants.JDBC_BQ_NUMPARTITIONS,
required=False,
default="10",
help='The maximum number of partitions that can be used for parallelism in table reading and writing. Default set to 10'
)
parser.add_argument(
f'--{constants.JDBC_BQ_INPUT_FETCHSIZE}',
dest=constants.JDBC_BQ_INPUT_FETCHSIZE,
required=False,
default=0,
type=int,
help='Determines how many rows to fetch per round trip'
)
parser.add_argument(
f'--{constants.JDBC_BQ_SESSIONINITSTATEMENT}',
dest=constants.JDBC_BQ_SESSIONINITSTATEMENT,
required=False,
default="",
help='Custom SQL statement to execute in each reader database session'
)
parser.add_argument(
f'--{constants.JDBC_BQ_OUTPUT_MODE}',
dest=constants.JDBC_BQ_OUTPUT_MODE,
required=False,
default=constants.OUTPUT_MODE_APPEND,
help=(
'Output write mode '
'(one of: append,overwrite,ignore,errorifexists) '
'(Defaults to append)'
),
choices=[
constants.OUTPUT_MODE_OVERWRITE,
constants.OUTPUT_MODE_APPEND,
constants.OUTPUT_MODE_IGNORE,
constants.OUTPUT_MODE_ERRORIFEXISTS
]
)
known_args: argparse.Namespace
known_args, _ = parser.parse_known_args(args)
return vars(known_args)
def run(self, spark: SparkSession, args: Dict[str, Any]) -> None:
logger: Logger = self.get_logger(spark=spark)
# Arguments
big_query_dataset: str = args[constants.JDBC_BQ_OUTPUT_DATASET]
big_query_table: str = args[constants.JDBC_BQ_OUTPUT_TABLE]
bq_temp_bucket: str = args[constants.JDBC_BQ_LD_TEMP_BUCKET_NAME]
#check if secret is passed or the connection string in the agruments
if str(args[constants.JDBC_BQ_INPUT_URL])=="":
input_jdbc_url: str = secret_manager.access_secret_version(args[constants.JDBC_BQ_INPUT_URL_SECRET])
else:
input_jdbc_url: str = args[constants.JDBC_BQ_INPUT_URL]
input_jdbc_driver: str = args[constants.JDBC_BQ_INPUT_DRIVER]
input_jdbc_table: str = args[constants.JDBC_BQ_INPUT_TABLE]
input_jdbc_partitioncolumn: str = args[constants.JDBC_BQ_INPUT_PARTITIONCOLUMN]
input_jdbc_lowerbound: str = args[constants.JDBC_BQ_INPUT_LOWERBOUND]
input_jdbc_upperbound: str = args[constants.JDBC_BQ_INPUT_UPPERBOUND]
jdbc_numpartitions: str = args[constants.JDBC_BQ_NUMPARTITIONS]
input_jdbc_fetchsize: int = args[constants.JDBC_BQ_INPUT_FETCHSIZE]
input_jdbc_sessioninitstatement: str = args[constants.JDBC_BQ_SESSIONINITSTATEMENT]
output_mode: str = args[constants.JDBC_BQ_OUTPUT_MODE]
ignore_keys = {constants.JDBC_BQ_INPUT_URL}
filtered_args = {key:val for key,val in args.items() if key not in ignore_keys}
logger.info(
"Starting JDBC to BigQuery Spark job with parameters:\n"
f"{pprint.pformat(filtered_args)}"
)
# Read
input_data: DataFrame
partition_parameters = str(input_jdbc_partitioncolumn) + str(input_jdbc_lowerbound) + str(input_jdbc_upperbound)
if ((partition_parameters != "") & ((input_jdbc_partitioncolumn == "") | (input_jdbc_lowerbound == "") | (input_jdbc_upperbound == ""))):
logger.error("Set all the sql partitioning parameters together-jdbctogcs.input.partitioncolumn,jdbctogcs.input.lowerbound,jdbctogcs.input.upperbound. Refer to README.md for more instructions.")
exit (1)
properties = {constants.JDBC_URL: input_jdbc_url,
constants.JDBC_DRIVER: input_jdbc_driver,
constants.JDBC_TABLE: input_jdbc_table,
constants.JDBC_NUMPARTITIONS: jdbc_numpartitions,
constants.JDBC_FETCHSIZE: input_jdbc_fetchsize}
if input_jdbc_sessioninitstatement:
properties[constants.JDBC_SESSIONINITSTATEMENT] = input_jdbc_sessioninitstatement
if partition_parameters:
properties.update({constants.JDBC_PARTITIONCOLUMN: input_jdbc_partitioncolumn,
constants.JDBC_LOWERBOUND: input_jdbc_lowerbound,
constants.JDBC_UPPERBOUND: input_jdbc_upperbound})
input_data = spark.read \
.format(constants.FORMAT_JDBC) \
.options(**properties) \
.load()
# Write
input_data.write \
.format(constants.FORMAT_BIGQUERY) \
.option(constants.TABLE, big_query_dataset + "." + big_query_table) \
.option(constants.GCS_BQ_TEMP_BUCKET, bq_temp_bucket) \
.mode(output_mode) \
.save()