python/dataproc_templates/gcs/text_to_bigquery.py

# Copyright 2023 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

from dataproc_templates import BaseTemplate
from dataproc_templates.util.argument_parsing import add_spark_options
from dataproc_templates.util.dataframe_reader_wrappers import ingest_dataframe_from_cloud_storage
import dataproc_templates.util.template_constants as constants


__all__ = ['TextToBigQueryTemplate']


class TextToBigQueryTemplate(BaseTemplate):
    """
    Dataproc template implementing Text loads from GCS into BigQuery
    """

    @staticmethod
    def parse_args(args: Optional[Sequence[str]] = None) -> Dict[str, Any]:
        parser: argparse.ArgumentParser = argparse.ArgumentParser()

        parser.add_argument(
            f'--{constants.TEXT_BQ_INPUT_LOCATION}',
            dest=constants.TEXT_BQ_INPUT_LOCATION,
            required=True,
            help='Cloud Storage location of the input text files'
        )
        add_spark_options(parser, constants.get_csv_input_spark_options("text.bigquery.input."))
        parser.add_argument(
            f'--{constants.TEXT_BQ_OUTPUT_DATASET}',
            dest=constants.TEXT_BQ_OUTPUT_DATASET,
            required=True,
            help='BigQuery dataset for the output table'
        )
        parser.add_argument(
            f'--{constants.TEXT_BQ_OUTPUT_TABLE}',
            dest=constants.TEXT_BQ_OUTPUT_TABLE,
            required=True,
            help='BigQuery output table name'
        )
        parser.add_argument(
            f'--{constants.TEXT_BQ_LD_TEMP_BUCKET_NAME}',
            dest=constants.TEXT_BQ_LD_TEMP_BUCKET_NAME,
            required=True,
            help='Spark BigQuery connector temporary bucket'
        )
        parser.add_argument(
            f'--{constants.TEXT_BQ_OUTPUT_MODE}',
            dest=constants.TEXT_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
            ]
        )
        parser.add_argument(
            f'--{constants.TEXT_INPUT_COMPRESSION}',
            dest=constants.TEXT_INPUT_COMPRESSION,
            required=True,
            help='Input file compression format (one of: bzip2,deflate,lz4,gzip,None)',
            default=None,
            choices=[
                constants.COMPRESSION_BZIP2,
                constants.COMPRESSION_GZIP,
                constants.COMPRESSION_DEFLATE,
                constants.COMPRESSION_LZ4,
                constants.COMPRESSION_NONE
            ]
        )
        parser.add_argument(
                    f'--{constants.TEXT_INPUT_DELIMITER}',
                    dest=constants.TEXT_INPUT_DELIMITER,
                    required=False,
                    help=(
                        'Input column delimiter '
                        '(example: ",", ";", "|", "/","\" ) '
                    )
                )


        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_location: str = args[constants.TEXT_BQ_INPUT_LOCATION]
        big_query_dataset: str = args[constants.TEXT_BQ_OUTPUT_DATASET]
        big_query_table: str = args[constants.TEXT_BQ_OUTPUT_TABLE]
        bq_temp_bucket: str = args[constants.TEXT_BQ_LD_TEMP_BUCKET_NAME]
        output_mode: str = args[constants.TEXT_BQ_OUTPUT_MODE]
        # These options are redundant but left in place until this template is removed
        # via issue https://github.com/GoogleCloudPlatform/dataproc-templates/issues/721
        # input_delimiter: str = args[constants.TEXT_INPUT_DELIMITER]
        # input_file_codec_format: str = args[constants.TEXT_INPUT_COMPRESSION]

        logger.info(
            "Starting GCS to Bigquery Spark job with parameters:\n"
            f"{pprint.pformat(args)}"
        )

        # Read
        input_data = ingest_dataframe_from_cloud_storage(
            spark,
            args,
            input_location,
            constants.FORMAT_CSV,
            "text.bigquery.input.",
        )

        # Write
        input_data.write \
            .format(constants.FORMAT_BIGQUERY) \
            .option(constants.TABLE, big_query_dataset + "." + big_query_table) \
            .option(constants.TEXT_BQ_TEMP_BUCKET, bq_temp_bucket) \
            .mode(output_mode) \
            .save()