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https://issues.apache.org/jira/browse/BEAM-9085?focusedWorklogId=400788&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-400788
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ASF GitHub Bot logged work on BEAM-9085:
----------------------------------------
Author: ASF GitHub Bot
Created on: 10/Mar/20 15:58
Start Date: 10/Mar/20 15:58
Worklog Time Spent: 10m
Work Description: kamilwu commented on pull request #11092: [BEAM-9085]
Fix performance regression in SyntheticSource
URL: https://github.com/apache/beam/pull/11092
Beam was using `numpy.random.RandomState` during generation of Synthetic
input, which, starting from NumPy 1.17, is considered as a legacy generator.
This caused the slowdown on Python 3. The solution was using `random` module
instead of Numpy.
------------------------
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Issue Time Tracking
-------------------
Worklog Id: (was: 400788)
Time Spent: 3h 50m (was: 3h 40m)
> Performance regression in np.random.RandomState() skews performance test
> results across Python 2/3 on Dataflow
> --------------------------------------------------------------------------------------------------------------
>
> Key: BEAM-9085
> URL: https://issues.apache.org/jira/browse/BEAM-9085
> Project: Beam
> Issue Type: Bug
> Components: testing
> Reporter: Kamil Wasilewski
> Assignee: Kamil Wasilewski
> Priority: Major
> Time Spent: 3h 50m
> Remaining Estimate: 0h
>
> Tests show that the performance of core Beam operations in Python 3.x on
> Dataflow can be a few time slower than in Python 2.7. We should investigate
> what's the cause of the problem.
> Currently, we have one ParDo test that is run both in Py3 and Py2 [1]. A
> dashboard with runtime results can be found here [2].
> [1] sdks/python/apache_beam/testing/load_tests/pardo_test.py
> [2] https://apache-beam-testing.appspot.com/explore?dashboard=5678187241537536
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