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ASF GitHub Bot logged work on BEAM-9085: ---------------------------------------- Author: ASF GitHub Bot Created on: 10/Mar/20 16:24 Start Date: 10/Mar/20 16:24 Worklog Time Spent: 10m Work Description: kamilwu commented on issue #11092: [BEAM-9085] Fix performance regression in SyntheticSource URL: https://github.com/apache/beam/pull/11092#issuecomment-597180032 R: @tvalentyn I did many tests and gathered the results in one single document: https://docs.google.com/document/d/1AegjUCc5w4B90_rvR8WAfIeL65PHUi4oGYkodgPS0o0/edit?usp=sharing. The previous solution (https://github.com/apache/beam/pull/10885) didn't work very well on big `element_size`, which was most probably the cause of failures and slowdowns. Although this PR is still a bit slower than we have now using numpy 1.16 and Python 2.7, I'd rather avoid downgrading numpy via supplying an additional requirements.txt file to a Dataflow job. According to this document (https://numpy.org/neps/nep-0029-deprecation_policy.html), the support for numpy 1.16 will be dropped on Jan 13, 2021 - and this looks like a temporary solution. ---------------------------------------------------------------- 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: 400818) Time Spent: 4h (was: 3h 50m) > 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: 4h > 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 -- This message was sent by Atlassian Jira (v8.3.4#803005)