sdks/python/apache_beam/examples/streaming_wordcount.py

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"""A streaming word-counting workflow.

Important: streaming pipeline support in Python Dataflow is in development
and is not yet available for use.
"""

from __future__ import absolute_import

import argparse
import logging


import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import StandardOptions
import apache_beam.transforms.window as window


def split_fn(lines):
  import re
  return re.findall(r'[A-Za-z\']+', lines)


def run(argv=None):
  """Build and run the pipeline."""
  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--input_topic', required=True,
      help=('Input PubSub topic of the form '
            '"projects/<PROJECT>/topics/<TOPIC>".'))
  parser.add_argument(
      '--output_topic', required=True,
      help=('Output PubSub topic of the form '
            '"projects/<PROJECT>/topic/<TOPIC>".'))
  known_args, pipeline_args = parser.parse_known_args(argv)
  options = PipelineOptions(pipeline_args)
  options.view_as(StandardOptions).streaming = True

  with beam.Pipeline(options=options) as p:

    # Read from PubSub into a PCollection.
    lines = p | beam.io.ReadStringsFromPubSub(known_args.input_topic)

    # Capitalize the characters in each line.
    transformed = (lines
                   # Use a pre-defined function that imports the re package.
                   | 'Split' >> (
                       beam.FlatMap(split_fn).with_output_types(unicode))
                   | 'PairWithOne' >> beam.Map(lambda x: (x, 1))
                   | beam.WindowInto(window.FixedWindows(15, 0))
                   | 'Group' >> beam.GroupByKey()
                   | 'Count' >> beam.Map(lambda (word, ones): (word, sum(ones)))
                   | 'Format' >> beam.Map(lambda tup: '%s: %d' % tup))

    # Write to PubSub.
    # pylint: disable=expression-not-assigned
    transformed | beam.io.WriteStringsToPubSub(known_args.output_topic)


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