sdks/python/apache_beam/io/gcp/bigquery_io_read_pipeline.py

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# Copyright 2016 Google Inc. All Rights Reserved.
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"""A Dataflow job that counts the number of rows in a BQ table.

   Can be configured to simulate slow reading for a given number of rows.
"""

from __future__ import absolute_import

import argparse
import logging
import random
import time

import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.testing.test_pipeline import TestPipeline
from apache_beam.testing.util import assert_that
from apache_beam.testing.util import equal_to


class RowToStringWithSlowDown(beam.DoFn):

  def process(self, element, num_slow=0, *args, **kwargs):

    if num_slow == 0:
      yield ['row']
    else:
      rand = random.random() * 100
      if rand < num_slow:
        time.sleep(0.01)
        yield ['slow_row']
      else:
        yield ['row']


def run(argv=None):
  parser = argparse.ArgumentParser()
  parser.add_argument('--input_table', required=True,
                      help='Input table to process.')
  parser.add_argument('--num_records', required=True,
                      help='The expected number of records', type=int)
  parser.add_argument('--num_slow', default=0,
                      help=('Percentage of rows that will be slow. '
                            'Must be in the range [0, 100)'))
  known_args, pipeline_args = parser.parse_known_args(argv)

  p = TestPipeline(options=PipelineOptions(pipeline_args))

  # pylint: disable=expression-not-assigned
  count = (p | 'read' >> beam.io.Read(beam.io.BigQuerySource(
      known_args.input_table))
           | 'row to string' >> beam.ParDo(RowToStringWithSlowDown(),
                                           num_slow=known_args.num_slow)
           | 'count' >> beam.combiners.Count.Globally())

  assert_that(count, equal_to([known_args.num_records]))

  p.run()


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