Source code for apache_beam.io.gcp.bigquery_io_read_pipeline

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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()