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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
[docs]class RowToStringWithSlowDown(beam.DoFn):
[docs] 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']
[docs]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()