github.com/apache/beam/sdks/v2@v2.48.2/python/apache_beam/examples/wordcount_debugging.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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

"""An example that verifies the counts and includes best practices.

On top of the basic concepts in the wordcount example, this workflow introduces
logging to Cloud Logging, and using assertions in a Dataflow pipeline.

To execute this pipeline locally, specify a local output file or output prefix
on GCS::

  --output [YOUR_LOCAL_FILE | gs://YOUR_OUTPUT_PREFIX]

To execute this pipeline using the Google Cloud Dataflow service, specify
pipeline configuration::

  --project YOUR_PROJECT_ID
  --staging_location gs://YOUR_STAGING_DIRECTORY
  --temp_location gs://YOUR_TEMP_DIRECTORY
  --region GCE_REGION
  --job_name YOUR_JOB_NAME
  --runner DataflowRunner

and an output prefix on GCS::

  --output gs://YOUR_OUTPUT_PREFIX
"""

# pytype: skip-file

# beam-playground:
#   name: WordCountDebugging
#   description: An example that counts words in Shakespeare's works.
#     includes regex filter("Flourish|stomach").
#   multifile: false
#   pipeline_options: --output output.txt
#   context_line: 74
#   categories:
#     - Flatten
#     - Debugging
#     - Options
#     - Combiners
#     - Filtering
#     - Quickstart
#   complexity: ADVANCED
#   tags:
#     - count
#     - debug
#     - string

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.metrics import Metrics
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import SetupOptions
from apache_beam.testing.util import assert_that
from apache_beam.testing.util import equal_to


class FilterTextFn(beam.DoFn):
  """A DoFn that filters for a specific key based on a regular expression."""
  def __init__(self, pattern):
    # TODO(BEAM-6158): Revert the workaround once we can pickle super() on py3.
    # super().__init__()
    beam.DoFn.__init__(self)
    self.pattern = pattern
    # A custom metric can track values in your pipeline as it runs. Those
    # values will be available in the monitoring system of the runner used
    # to run the pipeline. These metrics below track the number of
    # matched and unmatched words.
    self.matched_words = Metrics.counter(self.__class__, 'matched_words')
    self.umatched_words = Metrics.counter(self.__class__, 'umatched_words')

  def process(self, element):
    word, _ = element
    if re.match(self.pattern, word):
      # Log at INFO level each element we match. When executing this pipeline
      # using the Dataflow service, these log lines will appear in the Cloud
      # Logging UI.
      logging.info('Matched %s', word)
      self.matched_words.inc()
      yield element
    else:
      # Log at the "DEBUG" level each element that is not matched. Different log
      # levels can be used to control the verbosity of logging providing an
      # effective mechanism to filter less important information.
      # Note currently only "INFO" and higher level logs are emitted to the
      # Cloud Logger. This log message will not be visible in the Cloud Logger.
      logging.debug('Did not match %s', word)
      self.umatched_words.inc()


class CountWords(beam.PTransform):
  """A transform to count the occurrences of each word.

  A PTransform that converts a PCollection containing lines of text into a
  PCollection of (word, count) tuples.
  """
  def expand(self, pcoll):
    def count_ones(word_ones):
      (word, ones) = word_ones
      return (word, sum(ones))

    return (
        pcoll
        | 'split' >> (
            beam.FlatMap(
                lambda x: re.findall(r'[A-Za-z\']+', x)).with_output_types(str))
        | 'pair_with_one' >> beam.Map(lambda x: (x, 1))
        | 'group' >> beam.GroupByKey()
        | 'count' >> beam.Map(count_ones))


def run(argv=None, save_main_session=True):
  """Runs the debugging 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
  with beam.Pipeline(options=pipeline_options) as p:

    # Read the text file[pattern] into a PCollection, count the occurrences of
    # each word and filter by a list of words.
    filtered_words = (
        p | 'read' >> ReadFromText(known_args.input)
        | CountWords()
        | 'FilterText' >> beam.ParDo(FilterTextFn('Flourish|stomach')))

    # assert_that is a convenient PTransform that checks a PCollection has an
    # expected value. Asserts are best used in unit tests with small data sets
    # but is demonstrated here as a teaching tool.
    #
    # Note assert_that does not provide any output and that successful
    # completion of the Pipeline implies that the expectations were  met. Learn
    # more at https://cloud.google.com/dataflow/pipelines/testing-your-pipeline
    # on how to best test your pipeline.
    assert_that(filtered_words, equal_to([('Flourish', 3), ('stomach', 1)]))

    # Format the counts into a PCollection of strings and write the output using
    # a "Write" transform that has side effects.
    # pylint: disable=unused-variable
    def format_result(word_count):
      (word, count) = word_count
      return '%s: %s' % (word, count)

    output = (
        filtered_words
        | 'format' >> beam.Map(format_result)
        | 'write' >> WriteToText(known_args.output))


if __name__ == '__main__':
  # Cloud Logging would contain only logging.INFO and higher level logs logged
  # by the root logger. All log statements emitted by the root logger will be
  # visible in the Cloud Logging UI. Learn more at
  # https://cloud.google.com/logging about the Cloud Logging UI.
  #
  # You can set the default logging level to a different level when running
  # locally.
  logging.getLogger().setLevel(logging.INFO)
  run()