| # |
| # 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 streaming word-counting workflow. |
| |
| Important: streaming pipeline support in Python Dataflow is in development |
| and is not yet available for use. |
| """ |
| |
| from __future__ import absolute_import |
| |
| import argparse |
| import logging |
| |
| from past.builtins import unicode |
| |
| import apache_beam as beam |
| import apache_beam.transforms.window as window |
| |
| TABLE_SCHEMA = ('word:STRING, count:INTEGER, ' |
| 'window_start:TIMESTAMP, window_end:TIMESTAMP') |
| |
| |
| def find_words(element): |
| import re |
| return re.findall(r'[A-Za-z\']+', element) |
| |
| |
| class FormatDoFn(beam.DoFn): |
| def process(self, element, window=beam.DoFn.WindowParam): |
| ts_format = '%Y-%m-%d %H:%M:%S.%f UTC' |
| window_start = window.start.to_utc_datetime().strftime(ts_format) |
| window_end = window.end.to_utc_datetime().strftime(ts_format) |
| return [{'word': element[0], |
| 'count': element[1], |
| 'window_start':window_start, |
| 'window_end':window_end}] |
| |
| |
| def run(argv=None): |
| """Build and run the pipeline.""" |
| |
| parser = argparse.ArgumentParser() |
| parser.add_argument( |
| '--input_topic', required=True, |
| help='Input PubSub topic of the form "/topics/<PROJECT>/<TOPIC>".') |
| parser.add_argument( |
| '--output_table', required=True, |
| help= |
| ('Output BigQuery table for results specified as: PROJECT:DATASET.TABLE ' |
| 'or DATASET.TABLE.')) |
| known_args, pipeline_args = parser.parse_known_args(argv) |
| |
| with beam.Pipeline(argv=pipeline_args) as p: |
| |
| # Read the text from PubSub messages. |
| lines = p | beam.io.ReadFromPubSub(known_args.input_topic) |
| |
| # Get the number of appearances of a word. |
| def count_ones(word_ones): |
| (word, ones) = word_ones |
| return (word, sum(ones)) |
| |
| transformed = (lines |
| | 'Split' >> (beam.FlatMap(find_words) |
| .with_output_types(unicode)) |
| | 'PairWithOne' >> beam.Map(lambda x: (x, 1)) |
| | beam.WindowInto(window.FixedWindows(2*60, 0)) |
| | 'Group' >> beam.GroupByKey() |
| | 'Count' >> beam.Map(count_ones) |
| | 'Format' >> beam.ParDo(FormatDoFn())) |
| |
| # Write to BigQuery. |
| # pylint: disable=expression-not-assigned |
| transformed | 'Write' >> beam.io.WriteToBigQuery( |
| known_args.output_table, |
| schema=TABLE_SCHEMA, |
| create_disposition=beam.io.BigQueryDisposition.CREATE_IF_NEEDED, |
| write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND) |
| |
| |
| if __name__ == '__main__': |
| logging.getLogger().setLevel(logging.INFO) |
| run() |