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Posted to issues@beam.apache.org by "ASF GitHub Bot (Jira)" <ji...@apache.org> on 2021/02/01 14:53:00 UTC
[jira] [Work logged] (BEAM-11707) Optimize WindmillStateCache CPU
usage
[ https://issues.apache.org/jira/browse/BEAM-11707?focusedWorklogId=545374&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-545374 ]
ASF GitHub Bot logged work on BEAM-11707:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 01/Feb/21 14:52
Start Date: 01/Feb/21 14:52
Worklog Time Spent: 10m
Work Description: scwhittle opened a new pull request #13862:
URL: https://github.com/apache/beam/pull/13862
… upon
reference invalidation instead of expensive set management. Reduce
operations of shared cache by caching per-key object sets locally and
flushing as groups to shared cache. Remove byte tracking which could
be racy based upon background evictions in favor of just iterating
for rendering the status page. Since we render only once every 10 minutes
by default this seems fine.
This also lets us capture more stats.
This previously showed up as 6% cpu on benchmarks and dropped to 1.25% with this change with throughput increase as well.
------------------------
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Issue Time Tracking
-------------------
Worklog Id: (was: 545374)
Remaining Estimate: 0h
Time Spent: 10m
> Optimize WindmillStateCache CPU usage
> -------------------------------------
>
> Key: BEAM-11707
> URL: https://issues.apache.org/jira/browse/BEAM-11707
> Project: Beam
> Issue Type: Bug
> Components: runner-dataflow
> Reporter: Sam Whittle
> Assignee: Sam Whittle
> Priority: P2
> Time Spent: 10m
> Remaining Estimate: 0h
>
> From profiling nexmark Query11 which has many unique tags per key, I observed that the WindmillStateCache cpu usage was 6% of CPU.
> The usage appears to be due to the invalidation set maintenance as well as many reads/inserts.
> The invalidation set is maintained so that if a key encounters an error processing or the cache token changes, we can invalidate all the entries for a key. Currently this is done by removing all entries for the key from the cache. Another alternative which appears much more CPU efficient is to instead leave the entries in the cache but make them unreachable. This can be done by having a per-key object that uses object equality as part of the cache lookup. Then to discard entries for the key, we start using a new per-key object. Cleanup of per-key objects can be done with a weak reference map.
> Another cost to the cache is that objects are grouped by window so that they are kept/evicted all at once. However currently when reading items into the cache, we fetch the window set and then lookup each tag in it. This could be cached for the key to avoid multiple cache lookups. Similarly for putting objects we lookup and insert each tag separately and then update the cache to update the weight for the per-window set. This could be done once after all updates for the window have been made.
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