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https://issues.apache.org/jira/browse/BEAM-8944?focusedWorklogId=360433&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-360433
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ASF GitHub Bot logged work on BEAM-8944:
----------------------------------------
Author: ASF GitHub Bot
Created on: 16/Dec/19 19:06
Start Date: 16/Dec/19 19:06
Worklog Time Spent: 10m
Work Description: y1chi commented on pull request #10387: [BEAM-8944]
Change to use unbounded worker threads in python sdk only…
URL: https://github.com/apache/beam/pull/10387
… if experiment flag is specified
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Issue Time Tracking
-------------------
Worklog Id: (was: 360433)
Remaining Estimate: 0h
Time Spent: 10m
> Python SDK harness performance degradation with UnboundedThreadPoolExecutor
> ---------------------------------------------------------------------------
>
> Key: BEAM-8944
> URL: https://issues.apache.org/jira/browse/BEAM-8944
> Project: Beam
> Issue Type: Test
> Components: sdk-py-harness
> Affects Versions: 2.18.0
> Reporter: Yichi Zhang
> Priority: Major
> Attachments: profiling.png, profiling_one_thread.png,
> profiling_twelve_threads.png
>
> Time Spent: 10m
> Remaining Estimate: 0h
>
> We are seeing a performance degradation for python streaming word count load
> tests.
>
> After some investigation, it appears to be caused by swapping the original
> ThreadPoolExecutor to UnboundedThreadPoolExecutor in sdk worker. Suspicion is
> that python performance is worse with more threads on cpu-bounded tasks.
>
> A simple test for comparing the multiple thread pool executor performance:
>
> {code:python}
> def test_performance(self):
> def run_perf(executor):
> total_number = 1000000
> q = queue.Queue()
> def task(number):
> hash(number)
> q.put(number + 200)
> return number
> t = time.time()
> count = 0
> for i in range(200):
> q.put(i)
> while count < total_number:
> executor.submit(task, q.get(block=True))
> count += 1
> print('%s uses %s' % (executor, time.time() - t))
> with UnboundedThreadPoolExecutor() as executor:
> run_perf(executor)
> with futures.ThreadPoolExecutor(max_workers=1) as executor:
> run_perf(executor)
> with futures.ThreadPoolExecutor(max_workers=12) as executor:
> run_perf(executor)
> {code}
> Results:
> <apache_beam.utils.thread_pool_executor.UnboundedThreadPoolExecutor object at
> 0x7fab400dbe50> uses 268.160675049
> <concurrent.futures.thread.ThreadPoolExecutor object at 0x7fab40096290> uses
> 79.904583931
> <concurrent.futures.thread.ThreadPoolExecutor object at 0x7fab400dbe50> uses
> 191.179054976
> ```
> Profiling:
> UnboundedThreadPoolExecutor:
> !profiling.png!
> 1 Thread ThreadPoolExecutor:
> !profiling_one_thread.png!
> 12 Threads ThreadPoolExecutor:
> !profiling_twelve_threads.png!
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