[
https://issues.apache.org/jira/browse/BEAM-8944?focusedWorklogId=430685&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-430685
]
ASF GitHub Bot logged work on BEAM-8944:
----------------------------------------
Author: ASF GitHub Bot
Created on: 05/May/20 13:28
Start Date: 05/May/20 13:28
Worklog Time Spent: 10m
Work Description: mxm commented on pull request #11590:
URL: https://github.com/apache/beam/pull/11590#issuecomment-624055135
Run Python Load Tests ParDo Flink Streaming
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
us...@infra.apache.org
Issue Time Tracking
-------------------
Worklog Id: (was: 430685)
Time Spent: 5h (was: 4h 50m)
> Python SDK harness performance degradation with UnboundedThreadPoolExecutor
> ---------------------------------------------------------------------------
>
> Key: BEAM-8944
> URL: https://issues.apache.org/jira/browse/BEAM-8944
> Project: Beam
> Issue Type: Bug
> Components: sdk-py-harness
> Affects Versions: 2.18.0
> Reporter: Yichi Zhang
> Priority: Critical
> Attachments: checkpoint-duration.png, profiling.png,
> profiling_one_thread.png, profiling_twelve_threads.png
>
> Time Spent: 5h
> 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!
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
