Added Python example

Author: jorgeacf

dataflow/python/word-count-beam/wordcount.py

@@ -0,0 +1,75 @@
+"""A word-counting workflow."""
+
+# pytype: skip-file
+
+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, pipeline_args = parser.parse_known_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(pipeline_args)
+  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)
+  print("...............................")
+  run()