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# |
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# Licensed to the Apache Software Foundation (ASF) under one or more |
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# contributor license agreements. See the NOTICE file distributed with |
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# this work for additional information regarding copyright ownership. |
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# The ASF licenses this file to You under the Apache License, Version 2.0 |
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# (the "License"); you may not use this file except in compliance with |
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# the License. You may obtain a copy of the License at |
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# |
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# http://www.apache.org/licenses/LICENSE-2.0 |
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# |
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# Unless required by applicable law or agreed to in writing, software |
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# distributed under the License is distributed on an "AS IS" BASIS, |
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
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# See the License for the specific language governing permissions and |
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# limitations under the License. |
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# |
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"""A word-counting workflow.""" |
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from __future__ import absolute_import |
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import argparse |
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import logging |
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import re |
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from past.builtins import unicode |
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import apache_beam as beam |
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from apache_beam.io import ReadFromText |
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from apache_beam.io import WriteToText |
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from apache_beam.metrics import Metrics |
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from apache_beam.metrics.metric import MetricsFilter |
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from apache_beam.options.pipeline_options import PipelineOptions |
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from apache_beam.options.pipeline_options import SetupOptions |
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class WordExtractingDoFn(beam.DoFn): |
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"""Parse each line of input text into words.""" |
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def __init__(self): |
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self.words_counter = Metrics.counter(self.__class__, 'words') |
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self.word_lengths_counter = Metrics.counter(self.__class__, 'word_lengths') |
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self.word_lengths_dist = Metrics.distribution( |
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self.__class__, 'word_len_dist') |
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self.empty_line_counter = Metrics.counter(self.__class__, 'empty_lines') |
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def process(self, element): |
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"""Returns an iterator over the words of this element. |
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The element is a line of text. If the line is blank, note that, too. |
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Args: |
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element: the element being processed |
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Returns: |
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The processed element. |
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""" |
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text_line = element.strip() |
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if not text_line: |
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self.empty_line_counter.inc(1) |
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words = re.findall(r'[\w\']+', text_line, re.UNICODE) |
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for w in words: |
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self.words_counter.inc() |
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self.word_lengths_counter.inc(len(w)) |
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self.word_lengths_dist.update(len(w)) |
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return words |
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def run(argv=None): |
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"""Main entry point; defines and runs the wordcount pipeline.""" |
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parser = argparse.ArgumentParser() |
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parser.add_argument('--input', |
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dest='input', |
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default='gs://dataflow-samples/shakespeare/kinglear.txt', |
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help='Input file to process.') |
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parser.add_argument('--output', |
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dest='output', |
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required=True, |
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help='Output file to write results to.') |
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known_args, pipeline_args = parser.parse_known_args(argv) |
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# We use the save_main_session option because one or more DoFn's in this |
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# workflow rely on global context (e.g., a module imported at module level). |
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pipeline_options = PipelineOptions(pipeline_args) |
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pipeline_options.view_as(SetupOptions).save_main_session = True |
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p = beam.Pipeline(options=pipeline_options) |
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# Read the text file[pattern] into a PCollection. |
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lines = p | 'read' >> ReadFromText(known_args.input) |
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# Count the occurrences of each word. |
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def count_ones(word_ones): |
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(word, ones) = word_ones |
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return (word, sum(ones)) |
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counts = (lines |
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| 'split' >> (beam.ParDo(WordExtractingDoFn()) |
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.with_output_types(unicode)) |
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| 'pair_with_one' >> beam.Map(lambda x: (x, 1)) |
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| 'group' >> beam.GroupByKey() |
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| 'count' >> beam.Map(count_ones)) |
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# Format the counts into a PCollection of strings. |
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def format_result(word_count): |
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(word, count) = word_count |
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return '%s: %d' % (word, count) |
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output = counts | 'format' >> beam.Map(format_result) |
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# Write the output using a "Write" transform that has side effects. |
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# pylint: disable=expression-not-assigned |
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output | 'write' >> WriteToText(known_args.output) |
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result = p.run() |
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result.wait_until_finish() |
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# Do not query metrics when creating a template which doesn't run |
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if (not hasattr(result, 'has_job') # direct runner |
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or result.has_job): # not just a template creation |
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empty_lines_filter = MetricsFilter().with_name('empty_lines') |
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query_result = result.metrics().query(empty_lines_filter) |
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if query_result['counters']: |
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empty_lines_counter = query_result['counters'][0] |
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logging.info('number of empty lines: %d', empty_lines_counter.result) |
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word_lengths_filter = MetricsFilter().with_name('word_len_dist') |
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query_result = result.metrics().query(word_lengths_filter) |
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if query_result['distributions']: |
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word_lengths_dist = query_result['distributions'][0] |
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logging.info('average word length: %d', word_lengths_dist.result.mean) |
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if __name__ == '__main__': |
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logging.getLogger().setLevel(logging.INFO) |
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run() |