# # 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.""" 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()