python/dataproc_templates/hbase/hbase_to_gcs.py (76 lines of code) (raw):
# 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
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__ = ['HbaseToGCSTemplate']
class HbaseToGCSTemplate(BaseTemplate):
"""
Dataproc template implementing loads from Hbase into GCS
"""
@staticmethod
def parse_args(args: Optional[Sequence[str]] = None) -> Dict[str, Any]:
parser: argparse.ArgumentParser = argparse.ArgumentParser()
parser.add_argument(
f'--{constants.HBASE_GCS_OUTPUT_LOCATION}',
dest=constants.HBASE_GCS_OUTPUT_LOCATION,
required=True,
help='Cloud Storage location for output files'
)
parser.add_argument(
f'--{constants.HBASE_GCS_OUTPUT_FORMAT}',
dest=constants.HBASE_GCS_OUTPUT_FORMAT,
required=True,
help='Output file format (one of: avro,parquet,csv,json)',
choices=[
constants.FORMAT_AVRO,
constants.FORMAT_PRQT,
constants.FORMAT_CSV,
constants.FORMAT_JSON
]
)
parser.add_argument(
f'--{constants.HBASE_GCS_OUTPUT_MODE}',
dest=constants.HBASE_GCS_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
]
)
parser.add_argument(
f'--{constants.HBASE_GCS_CATALOG_JSON}',
dest=constants.HBASE_GCS_CATALOG_JSON,
required=True,
help='Hbase catalog JSON'
)
add_spark_options(parser, constants.get_csv_output_spark_options("hbase.gcs.output."))
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
output_location: str = args[constants.HBASE_GCS_OUTPUT_LOCATION]
output_format: str = args[constants.HBASE_GCS_OUTPUT_FORMAT]
output_mode: str = args[constants.HBASE_GCS_OUTPUT_MODE]
catalog: str = ''.join(args[constants.HBASE_GCS_CATALOG_JSON].split())
logger.info(
"Starting Hbase to Cloud Storage Spark job with parameters:\n"
f"{pprint.pformat(args)}"
)
# Read
input_data: DataFrame
input_data = spark.read.format(constants.FORMAT_HBASE) \
.options(catalog=catalog) \
.option("hbase.spark.use.hbasecontext", "false") \
.load()
# Write
writer: DataFrameWriter = input_data.write.mode(output_mode)
persist_dataframe_to_cloud_storage(writer, args, output_location, output_format, "hbase.gcs.output.")