wordcount.py

#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements.  See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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
#
#    http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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#

"""A word-counting workflow."""

# pytype: skip-file

# beam-playground:
#   name: WordCount
#   description: An example that counts words in Shakespeare's works.
#   multifile: false
#   pipeline_options: --output output.txt
#   context_line: 44
#   categories:
#     - Combiners
#     - Options
#     - Quickstart

import argparse
import logging
import re

import apache_beam as beam
from apache_beam.io import ReadFromText
from apache_beam.io import WriteToText
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import SetupOptions


class WordExtractingDoFn(beam.DoFn):
    """Parse each line of input text into words."""

    def process(self, element):
        """Returns an iterator over the words of this element.

        The element is a line of text.  If the line is blank, note that, too.

        Args:
          element: the element being processed

        Returns:
          The processed element.
        """
        return re.findall(r"[\w\']+", element, re.UNICODE)


def run(argv=None, save_main_session=True):
    """Main entry point; defines and runs the wordcount pipeline."""
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--input",
        dest="input",
        default="gs://dataflow-samples/shakespeare/kinglear.txt",
        help="Input file to process.",
    )
    parser.add_argument(
        "--output",
        dest="output",
        required=True,
        help="Output file to write results to.",
    )
    known_args = parser.parse_args(argv)

    # We use the save_main_session option because one or more DoFn's in this
    # workflow rely on global context (e.g., a module imported at module level).
    pipeline_options = PipelineOptions(dict(runner="SparkRunner", spark_version=3))
    pipeline_options.view_as(SetupOptions).save_main_session = save_main_session

    # The pipeline will be run on exiting the with block.
    with beam.Pipeline(options=pipeline_options) as p:

        # Read the text file[pattern] into a PCollection.
        lines = p | "Read" >> ReadFromText(known_args.input)

        counts = (
            lines
            | "Split" >> (beam.ParDo(WordExtractingDoFn()).with_output_types(str))
            | "PairWithOne" >> beam.Map(lambda x: (x, 1))
            | "GroupAndSum" >> beam.CombinePerKey(sum)
        )

        # Format the counts into a PCollection of strings.
        def format_result(word, count):
            return "%s: %d" % (word, count)

        output = counts | "Format" >> beam.MapTuple(format_result)

        # Write the output using a "Write" transform that has side effects.
        # pylint: disable=expression-not-assigned
        output | "Write" >> WriteToText(known_args.output)


if __name__ == "__main__":
    logging.getLogger().setLevel(logging.INFO)
    run()