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mongo_to_bq.py
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mongo_to_bq.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, DataFrameWriter
from dataproc_templates import BaseTemplate
from dataproc_templates.util.argument_parsing import add_spark_options
from dataproc_templates.util.dataframe_writer_wrappers import persist_dataframe_to_cloud_storage
import dataproc_templates.util.template_constants as constants
__all__ = ['MongoToBigQueryTemplate']
class MongoToBigQueryTemplate(BaseTemplate):
"""
Dataproc template implementing exports from Mongo to BigQuery
"""
@staticmethod
def parse_args(args: Optional[Sequence[str]] = None) -> Dict[str, Any]:
parser: argparse.ArgumentParser = argparse.ArgumentParser()
parser.add_argument(
f'--{constants.MONGO_BQ_INPUT_URI}',
dest=constants.MONGO_BQ_INPUT_URI,
required=True,
help='Mongo Input Connection Uri'
)
parser.add_argument(
f'--{constants.MONGO_BQ_INPUT_DATABASE}',
dest=constants.MONGO_BQ_INPUT_DATABASE,
required=True,
help='Mongo Input Database Name'
)
parser.add_argument(
f'--{constants.MONGO_BQ_INPUT_COLLECTION}',
dest=constants.MONGO_BQ_INPUT_COLLECTION,
required=True,
help='Mongo Input Collection Name'
)
parser.add_argument(
f'--{constants.MONGO_BQ_OUTPUT_DATASET}',
dest=constants.MONGO_BQ_OUTPUT_DATASET,
required=True,
help='BigQuery Output Dataset Name'
)
parser.add_argument(
f'--{constants.MONGO_BQ_OUTPUT_TABLE}',
dest=constants.MONGO_BQ_OUTPUT_TABLE,
required=True,
help='BigQuery Output Table Name'
)
parser.add_argument(
f'--{constants.MONGO_BQ_OUTPUT_MODE}',
dest=constants.MONGO_BQ_OUTPUT_MODE,
required=False,
default=constants.OUTPUT_MODE_APPEND,
help=(
'BigQuery 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
]
)
parser.add_argument(
f'--{constants.MONGO_BQ_TEMP_BUCKET_NAME}',
dest=constants.MONGO_BQ_TEMP_BUCKET_NAME,
required=True,
help='GCS Temp Bucket Name'
)
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
input_uri: str = args[constants.MONGO_BQ_INPUT_URI]
input_database: str = args[constants.MONGO_BQ_INPUT_DATABASE]
input_collection: str = args[constants.MONGO_BQ_INPUT_COLLECTION]
output_mode: str = args[constants.MONGO_BQ_OUTPUT_MODE]
big_query_output_dataset: str = args[constants.MONGO_BQ_OUTPUT_DATASET]
big_query_output_table: str = args[constants.MONGO_BQ_OUTPUT_TABLE]
big_query_temp_bucket: str = args[constants.MONGO_BQ_TEMP_BUCKET_NAME]
ignore_keys = {constants.MONGO_BQ_INPUT_URI}
filtered_args = {key: val for key,
val in args.items() if key not in ignore_keys}
logger.info(
"Starting Mongo to Big Query Spark job with parameters:\n"
f"{pprint.pformat(filtered_args)}"
)
# Read
input_data = spark.read \
.format(constants.FORMAT_MONGO) \
.option(constants.MONGO_INPUT_URI, input_uri) \
.option(constants.MONGO_DATABASE, input_database) \
.option(constants.MONGO_COLLECTION, input_collection) \
.load()
# Write
input_data.write \
.format(constants.FORMAT_BIGQUERY) \
.option(constants.TABLE, big_query_output_dataset + "." + big_query_output_table) \
.option(constants.TEMP_GCS_BUCKET, big_query_temp_bucket) \
.mode(output_mode) \
.save()