github.com/apache/beam/sdks/v2@v2.48.2/python/apache_beam/examples/wordcount.py (about)

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    17  
    18  """A word-counting workflow."""
    19  
    20  # pytype: skip-file
    21  
    22  # beam-playground:
    23  #   name: WordCount
    24  #   description: An example that counts words in Shakespeare's works.
    25  #   multifile: false
    26  #   pipeline_options: --output output.txt
    27  #   context_line: 44
    28  #   categories:
    29  #     - Combiners
    30  #     - Options
    31  #     - Quickstart
    32  #   complexity: MEDIUM
    33  #   tags:
    34  #     - options
    35  #     - count
    36  #     - combine
    37  #     - strings
    38  
    39  import argparse
    40  import logging
    41  import re
    42  
    43  import apache_beam as beam
    44  from apache_beam.io import ReadFromText
    45  from apache_beam.io import WriteToText
    46  from apache_beam.options.pipeline_options import PipelineOptions
    47  from apache_beam.options.pipeline_options import SetupOptions
    48  
    49  
    50  class WordExtractingDoFn(beam.DoFn):
    51    """Parse each line of input text into words."""
    52    def process(self, element):
    53      """Returns an iterator over the words of this element.
    54  
    55      The element is a line of text.  If the line is blank, note that, too.
    56  
    57      Args:
    58        element: the element being processed
    59  
    60      Returns:
    61        The processed element.
    62      """
    63      return re.findall(r'[\w\']+', element, re.UNICODE)
    64  
    65  
    66  def run(argv=None, save_main_session=True):
    67    """Main entry point; defines and runs the wordcount pipeline."""
    68    parser = argparse.ArgumentParser()
    69    parser.add_argument(
    70        '--input',
    71        dest='input',
    72        default='gs://dataflow-samples/shakespeare/kinglear.txt',
    73        help='Input file to process.')
    74    parser.add_argument(
    75        '--output',
    76        dest='output',
    77        required=True,
    78        help='Output file to write results to.')
    79    known_args, pipeline_args = parser.parse_known_args(argv)
    80  
    81    # We use the save_main_session option because one or more DoFn's in this
    82    # workflow rely on global context (e.g., a module imported at module level).
    83    pipeline_options = PipelineOptions(pipeline_args)
    84    pipeline_options.view_as(SetupOptions).save_main_session = save_main_session
    85  
    86    # The pipeline will be run on exiting the with block.
    87    with beam.Pipeline(options=pipeline_options) as p:
    88  
    89      # Read the text file[pattern] into a PCollection.
    90      lines = p | 'Read' >> ReadFromText(known_args.input)
    91  
    92      counts = (
    93          lines
    94          | 'Split' >> (beam.ParDo(WordExtractingDoFn()).with_output_types(str))
    95          | 'PairWithOne' >> beam.Map(lambda x: (x, 1))
    96          | 'GroupAndSum' >> beam.CombinePerKey(sum))
    97  
    98      # Format the counts into a PCollection of strings.
    99      def format_result(word, count):
   100        return '%s: %d' % (word, count)
   101  
   102      output = counts | 'Format' >> beam.MapTuple(format_result)
   103  
   104      # Write the output using a "Write" transform that has side effects.
   105      # pylint: disable=expression-not-assigned
   106      output | 'Write' >> WriteToText(known_args.output)
   107  
   108  
   109  if __name__ == '__main__':
   110    logging.getLogger().setLevel(logging.INFO)
   111    run()