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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.
"""
# pytype: skip-file
from __future__ import absolute_import
import argparse
import logging
import random
import time
import apache_beam as beam
from apache_beam.io.gcp.bigquery import ReadFromBigQuery
from apache_beam.options.pipeline_options import GoogleCloudOptions
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)'))
parser.add_argument(
'--beam_bq_source',
default=False,
type=bool,
help=(
'Whether to use the new ReadFromBigQuery'
' transform, or the BigQuerySource.'))
known_args, pipeline_args = parser.parse_known_args(argv)
options = PipelineOptions(pipeline_args)
with TestPipeline(options=options) as p:
if known_args.beam_bq_source:
reader = ReadFromBigQuery(
table='%s:%s' %
(options.view_as(GoogleCloudOptions).project, known_args.input_table))
else:
reader = beam.io.Read(beam.io.BigQuerySource(known_args.input_table))
# pylint: disable=expression-not-assigned
count = (
p | 'read' >> reader
| '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]))
if __name__ == '__main__':
logging.getLogger().setLevel(logging.INFO)
run()