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.")