mergecontacts.py

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"""Merge phone, email, and mailing address information.

A Dataflow pipeline that merges phone, email, and address information associated
with the same names. Each input "database" is a tab-delimited text file pairing
names with one phone number/email address/mailing address; multiple entries
associated with the same name are allowed. Outputs are a tab-delimited text file
with the merged information and another file containing some simple statistics.
See mergecontacts_test.py for example inputs and outputs.

A demonstration of:
  CoGroupByKey
  Non-linear pipelines (i.e., pipelines with branches)
"""

from __future__ import absolute_import

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.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


def run(argv=None, assert_results=None):

  parser = argparse.ArgumentParser()
  parser.add_argument(
      '--input_email',
      required=True,
      help='Email database, with each line formatted as "name<TAB>email".')
  parser.add_argument(
      '--input_phone',
      required=True,
      help='Phonebook, with each line formatted as "name<TAB>phone number".')
  parser.add_argument(
      '--input_snailmail',
      required=True,
      help='Address database, with each line formatted as "name<TAB>address".')
  parser.add_argument('--output_tsv',
                      required=True,
                      help='Tab-delimited output file.')
  parser.add_argument('--output_stats',
                      required=True,
                      help='Output file for statistics about the input.')
  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 = True
  with beam.Pipeline(options=pipeline_options) as p:

    # Helper: read a tab-separated key-value mapping from a text file,
    # escape all quotes/backslashes, and convert it a PCollection of
    # (key, value) pairs.
    def read_kv_textfile(label, textfile):
      return (p
              | 'Read: %s' % label >> ReadFromText(textfile)
              | 'Backslash: %s' % label >> beam.Map(
                  lambda x: re.sub(r'\\', r'\\\\', x))
              | 'EscapeQuotes: %s' % label >> beam.Map(
                  lambda x: re.sub(r'"', r'\"', x))
              | 'Split: %s' % label >> beam.Map(
                  lambda x: re.split(r'\t+', x, 1)))

    # Read input databases.
    email = read_kv_textfile('email', known_args.input_email)
    phone = read_kv_textfile('phone', known_args.input_phone)
    snailmail = read_kv_textfile('snailmail', known_args.input_snailmail)

    # Group together all entries under the same name.
    grouped = (email, phone, snailmail) | 'group_by_name' >> beam.CoGroupByKey()

    # Prepare tab-delimited output; something like this:
    # "name"<TAB>"email_1,email_2"<TAB>"phone"<TAB>"first_snailmail_only"
    tsv_lines = grouped | beam.Map(
        lambda (name, (email, phone, snailmail)): '\t'.join(
            ['"%s"' % name,
             '"%s"' % ','.join(email),
             '"%s"' % ','.join(phone),
             '"%s"' % next(iter(snailmail), '')]))

    # Compute some stats about our database of people.
    luddites = grouped | beam.Filter(  # People without email.
        lambda (name, (email, phone, snailmail)): not next(iter(email), None))
    writers = grouped | beam.Filter(   # People without phones.
        lambda (name, (email, phone, snailmail)): not next(iter(phone), None))
    nomads = grouped | beam.Filter(    # People without addresses.
        lambda (name, (e, p, snailmail)): not next(iter(snailmail), None))

    num_luddites = luddites | 'Luddites' >> beam.combiners.Count.Globally()
    num_writers = writers | 'Writers' >> beam.combiners.Count.Globally()
    num_nomads = nomads | 'Nomads' >> beam.combiners.Count.Globally()

    # Write tab-delimited output.
    # pylint: disable=expression-not-assigned
    tsv_lines | 'WriteTsv' >> WriteToText(known_args.output_tsv)

    # TODO(silviuc): Move the assert_results logic to the unit test.
    if assert_results is not None:
      expected_luddites, expected_writers, expected_nomads = assert_results
      assert_that(num_luddites, equal_to([expected_luddites]),
                  label='assert:luddites')
      assert_that(num_writers, equal_to([expected_writers]),
                  label='assert:writers')
      assert_that(num_nomads, equal_to([expected_nomads]),
                  label='assert:nomads')


if __name__ == '__main__':
  logging.getLogger().setLevel(logging.INFO)
  run()