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https://issues.apache.org/jira/browse/BEAM-9228?focusedWorklogId=386296&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-386296
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ASF GitHub Bot logged work on BEAM-9228:
----------------------------------------
Author: ASF GitHub Bot
Created on: 12/Feb/20 23:09
Start Date: 12/Feb/20 23:09
Worklog Time Spent: 10m
Work Description: Hannah-Jiang commented on pull request #10847:
[WIP][BEAM-9228] Support further partition for FnApi ListBuffer
URL: https://github.com/apache/beam/pull/10847
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Issue Time Tracking
-------------------
Worklog Id: (was: 386296)
Remaining Estimate: 0h
Time Spent: 10m
> _SDFBoundedSourceWrapper doesn't distribute data to multiple workers
> --------------------------------------------------------------------
>
> Key: BEAM-9228
> URL: https://issues.apache.org/jira/browse/BEAM-9228
> Project: Beam
> Issue Type: Bug
> Components: sdk-py-core
> Affects Versions: 2.16.0, 2.18.0
> Reporter: Hannah Jiang
> Assignee: Hannah Jiang
> Priority: Major
> Time Spent: 10m
> Remaining Estimate: 0h
>
> A user reported following issue.
> -------------------------------------------------
> 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
> {code:java}
> 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()
> {code}
> 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 ?
> ----------------------------------------------------------------------------------------------------------------------------------------------
> This is caused by [this
> PR|https://github.com/apache/beam/commit/02f8ad4eee3ec0ea8cbdc0f99c1dad29f00a9f60].
> A [workaround|https://github.com/apache/beam/pull/10729] 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.
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