| # |
| # 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. |
| # |
| |
| """A word-counting workflow.""" |
| |
| # pytype: skip-file |
| |
| from __future__ import absolute_import |
| |
| import argparse |
| import logging |
| import re |
| |
| from past.builtins import unicode |
| |
| 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.metrics.metric import MetricsFilter |
| 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 __init__(self): |
| # TODO(BEAM-6158): Revert the workaround once we can pickle super() on py3. |
| # super(WordExtractingDoFn, self).__init__() |
| beam.DoFn.__init__(self) |
| self.words_counter = Metrics.counter(self.__class__, 'words') |
| self.word_lengths_counter = Metrics.counter(self.__class__, 'word_lengths') |
| self.word_lengths_dist = Metrics.distribution( |
| self.__class__, 'word_len_dist') |
| self.empty_line_counter = Metrics.counter(self.__class__, 'empty_lines') |
| |
| 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. |
| """ |
| text_line = element.strip() |
| if not text_line: |
| self.empty_line_counter.inc(1) |
| words = re.findall(r'[\w\']+', text_line, re.UNICODE) |
| for w in words: |
| self.words_counter.inc() |
| self.word_lengths_counter.inc(len(w)) |
| self.word_lengths_dist.update(len(w)) |
| return words |
| |
| |
| 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 |
| p = beam.Pipeline(options=pipeline_options) |
| |
| # Read the text file[pattern] into a PCollection. |
| lines = p | 'read' >> ReadFromText(known_args.input) |
| |
| # Count the occurrences of each word. |
| def count_ones(word_ones): |
| (word, ones) = word_ones |
| return (word, sum(ones)) |
| |
| counts = ( |
| lines |
| | 'split' >> |
| (beam.ParDo(WordExtractingDoFn()).with_output_types(unicode)) |
| | 'pair_with_one' >> beam.Map(lambda x: (x, 1)) |
| | 'group' >> beam.GroupByKey() |
| | 'count' >> beam.Map(count_ones)) |
| |
| # Format the counts into a PCollection of strings. |
| def format_result(word_count): |
| (word, count) = word_count |
| return '%s: %d' % (word, count) |
| |
| output = counts | 'format' >> beam.Map(format_result) |
| |
| # Write the output using a "Write" transform that has side effects. |
| # pylint: disable=expression-not-assigned |
| output | 'write' >> WriteToText(known_args.output) |
| |
| result = p.run() |
| result.wait_until_finish() |
| |
| # Do not query metrics when creating a template which doesn't run |
| if (not hasattr(result, 'has_job') # direct runner |
| or result.has_job): # not just a template creation |
| empty_lines_filter = MetricsFilter().with_name('empty_lines') |
| query_result = result.metrics().query(empty_lines_filter) |
| if query_result['counters']: |
| empty_lines_counter = query_result['counters'][0] |
| logging.info('number of empty lines: %d', empty_lines_counter.result) |
| |
| word_lengths_filter = MetricsFilter().with_name('word_len_dist') |
| query_result = result.metrics().query(word_lengths_filter) |
| if query_result['distributions']: |
| word_lengths_dist = query_result['distributions'][0] |
| logging.info('average word length: %d', word_lengths_dist.result.mean) |
| |
| |
| if __name__ == '__main__': |
| logging.getLogger().setLevel(logging.INFO) |
| run() |