[BEAM-9228] _SDFBoundedSourceWrapper doesn't distribute data to multiple workers

• Type: Bug
• Status: Triage Needed
• Priority: P2
• Resolution: Fixed
• Affects Version/s: 2.16.0, 2.18.0, 2.19.0
• Fix Version/s: 2.20.0
• Component/s: sdk-py-core
• Assignee: Hannah Jiang
• Reporter: Hannah Jiang
• Created: 30/Jan/20 23:18
• Updated: 13/Apr/23 10:59
• Resolved: 26/Feb/20 08:16

Description

A user reported following issue.

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I have a set of tfrecord files, obtained by converting parquet files with Spark. Each file is roughly 1GB and I have 11 of those.

I would expect simple statistics gathering (ie counting number of items of all files) to scale linearly with respect to the number of cores on my system.

I am able to reproduce the issue with the minimal snippet below

import apache_beam as beam
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.runners.portability import fn_api_runner
from apache_beam.portability.api import beam_runner_api_pb2
from apache_beam.portability import python_urns
import sys

pipeline_options = PipelineOptions(['--direct_num_workers', '4'])

file_pattern = 'part-r-00*
runner=fn_api_runner.FnApiRunner(
          default_environment=beam_runner_api_pb2.Environment(
              urn=python_urns.SUBPROCESS_SDK,
              payload=b'%s -m apache_beam.runners.worker.sdk_worker_main'
                        % sys.executable.encode('ascii')))

p = beam.Pipeline(runner=runner, options=pipeline_options)

lines = (p | 'read' >> beam.io.tfrecordio.ReadFromTFRecord(file_pattern)
                 | beam.combiners.Count.Globally()
                 | beam.io.WriteToText('/tmp/output'))

p.run()

Only one combination of apache_beam revision / worker type seems to work (I refer to https://beam.apache.org/documentation/runners/direct/ for the worker types)

• beam 2.16; neither multithread nor multiprocess achieve high cpu usage on multiple cores
• beam 2.17: able to achieve high cpu usage on all 4 cores
• beam 2.18: not tested the mulithreaded mode but the multiprocess mode fails when trying to serialize the Environment instance most likely because of a change from 2.17 to 2.18.

I also tried briefly SparkRunner with version 2.16 but was no able to achieve any throughput.

What is the recommnended way to achieve what I am trying to ? How can I troubleshoot ?
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This is caused by this PR.

A workaround is tried, which is rolling back iobase.py not to use _SDFBoundedSourceWrapper. This confirmed that data is distributed to multiple workers, however, there are some regressions with SDF wrapper tests.

Issue Links

• links to: GitHub Pull Request #10847