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https://issues.apache.org/jira/browse/CASSANDRA-15213?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17014661#comment-17014661
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Jordan West edited comment on CASSANDRA-15213 at 1/13/20 9:27 PM:
------------------------------------------------------------------
Using {{addAndGet}} I see performance comparable to 3.0 and prior to
CASSANDRA-14281.
{codee}
15213 (4 core, 4 stripes, striping & linear search, addAndGet):
[java] Benchmark Mode Cnt
Score Error Units
[java] LatencyTrackingBench.benchInsertToDEHR thrpt 5
5742550.865 ± 256043.651 ops/s
[java] LatencyTrackingBench.benchLatencyMetricsWrite thrpt 5
1979885.731 ± 117381.276 ops/s
{/code}
Here is how I implemented update bucket in case I missed something
{code:java}
public void updateBucket(AtomicLongArray buckets, int index, long value)
{
int stripe = (int) (Thread.currentThread().getId() & (nStripes - 1));
buckets.addAndGet(stripedIndex(index, stripe), value);
}
{code}
was (Author: jrwest):
Using {{addAndGet}} I see performance comparable to 3.0 and prior to
CASSANDRA-14281.
{codee}
15213 (4 core, 4 stripes, striping & linear search, addAndGet):
[java] Benchmark Mode Cnt
Score Error Units
[java] LatencyTrackingBench.benchInsertToDEHR thrpt 5
5742550.865 ± 256043.651 ops/s
[java] LatencyTrackingBench.benchLatencyMetricsWrite thrpt 5
1979885.731 ± 117381.276 ops/s
{/code}
Here is how I implemented update bucket in case I missed something
{code:java}
public void updateBucket(AtomicLongArray buckets, int index, long value)
{
int stripe = (int) (Thread.currentThread().getId() & (nStripes - 1));
buckets.addAndGet(stripedIndex(index, stripe), value);
}
{/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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