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https://issues.apache.org/jira/browse/CASSANDRA-15213?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17012511#comment-17012511
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Jordan West commented on CASSANDRA-15213:
-----------------------------------------
I've updated [https://github.com/jrwest/cassandra/tree/jwest/15213] with the
changes you suggested. Regarding the extra bucket, I realized that even more
simply we could return {{bucketOffsets.length}} if the estimate is greater than
or equal to it – saving us an access in this case altogether.
Also added a very simple striping approach. The performance is about the same
(the runs are within each others' margin of error) and still significantly
better than before CASSANDRA-14281. From the memory consumption perspective,
however, 4 stripes ends up allocating more than 12MiB after startup. This isn't
surprising because with a single stripe (or pre CASSANDRA-14281) after startup
was a little more than 3MiB and we're quadrupling the size of each array.
However, these won't grow under contention like {{LongAdder[]}} would: memory
consumption is on the order of the number of histograms created not number
created and contention as before. I've found comparable performance using 2
stripes on my 4 core machine. I have an 8 core available to me I can test with
tomorrow. If the memory consumption is still a concern I can investigate
on-demand striping but I prefer the simpler approach and think the trade-offs
of 2-4 stripes is reasonable.
{code:java}
3.0 (4 core):
[java] Benchmark Mode Cnt
Score Error Units
[java] LatencyTrackingBench.benchInsertToDEHR thrpt 5
5848504.963 ± 147881.511 ops/s
[java] LatencyTrackingBench.benchLatencyMetricsWrite thrpt 5
1946154.537 ± 623185.290 ops/s
Trunk (4 core)
[java] Benchmark Mode Cnt
Score Error Units
[java] LatencyTrackingBench.benchInsertToDEHR thrpt 5
21887453.678 ± 2108481.285 ops/s
[java] LatencyTrackingBench.benchLatencyMetricsWrite thrpt 5
8908817.316 ± 115453.642 ops/s
15213 (4 core, linear search changes only):
[java] Benchmark Mode Cnt
Score Error Units
[java] LatencyTrackingBench.benchInsertToDEHR thrpt 5
24646022.304 ± 1052105.818 ops/s
[java] LatencyTrackingBench.benchLatencyMetricsWrite thrpt 5
9175928.594 ± 269984.204 ops/s
15213 (4 core, 4 stripes, striping and linear search):
[java] Benchmark Mode Cnt Score
Error Units
[java] LatencyTrackingBench.benchInsertToDEHR thrpt 5
18818181.576 ± 506997.366 ops/s
[java] LatencyTrackingBench.benchLatencyMetricsWrite thrpt 5
8895569.814 ± 154219.113 ops/s
{code}
> DecayingEstimatedHistogramReservoir Inefficiencies
> --------------------------------------------------
>
> Key: CASSANDRA-15213
> URL: https://issues.apache.org/jira/browse/CASSANDRA-15213
> Project: Cassandra
> Issue Type: Bug
> Components: Observability/Metrics
> Reporter: Benedict Elliott Smith
> Assignee: Jordan West
> Priority: Normal
> Fix For: 4.0-beta
>
>
> * {{LongAdder}} introduced to trunk consumes 9MiB of heap without user
> schemas, and this will grow significantly under contention and user schemas
> with many tables. This is because {{LongAdder}} is a very heavy class
> designed for single contended values.
> ** This can likely be improved significantly, without significant loss of
> performance in the contended case, by simply increasing the size of our
> primitive backing array and providing multiple buckets, with each thread
> picking a bucket to increment, or simply multiple backing arrays. Probably a
> better way still to do this would be to introduce some competition detection
> to the update, much like {{LongAdder}} utilises, that increases the number of
> backing arrays under competition.
> ** To save memory this approach could partition the space into chunks that
> are likely to be updated together, so that we do not need to duplicate the
> entire array under competition.
> * Similarly, binary search is costly and a measurable cost as a share of the
> new networking work (without filtering it was > 10% of the CPU used overall).
> We can compute an approximation floor(log2 n / log2 1.2) extremely cheaply,
> to save the random memory access costs.
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