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
#   complexity: MEDIUM
#   tags:
#     - options
#     - count
#     - combine
#     - strings

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):
    """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, pipeline_args = parser.parse_known_args(argv)

    pipeline_options = PipelineOptions(pipeline_args)

    # 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()