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https://issues.apache.org/jira/browse/CASSANDRA-20250?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17928331#comment-17928331
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Benedict Elliott Smith commented on CASSANDRA-20250:
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This is about the shape of what I would have done, give or take [review
pending].
But... it turns out there's an {{onRemove}} method in {{FastThreadLocal}} that
we can use. It isn't guaranteed to be called, but it would be fine to use this
as our main mechanism. We can use more than one mechanism just fine, however.
I don't have a strong opinion about how to proceed. I encouraged the use of
PhantomReference, and I think it is still a very good mechanism for
guaranteeing we clean up when the system may be under pressure. It provides
much stronger guarantees than iteration based removal. But it would also
probably be fine to pair {{onRemove}} with iteration based removal.
> Optimize Counter, Meter and Histogram metrics using thread local counters
> -------------------------------------------------------------------------
>
> Key: CASSANDRA-20250
> URL: https://issues.apache.org/jira/browse/CASSANDRA-20250
> Project: Apache Cassandra
> Issue Type: New Feature
> Components: Observability/Metrics
> Reporter: Dmitry Konstantinov
> Assignee: Dmitry Konstantinov
> Priority: Normal
> Fix For: 5.x
>
> Attachments: 5.1_profile_cpu.html,
> 5.1_profile_cpu_without_metrics.html, 5.1_tl4_profile_cpu.html,
> Histogram_AtomicLong.png, async_profiler_cpu_profiles.zip,
> cpu_profile_insert.html, image-2025-02-18-23-22-19-983.png, jmh-result.json,
> vmstat.log, vmstat_without_metrics.log
>
>
> Cassandra has a lot of metrics collected, many of them are collected per
> table, so their instance number is multiplied by number of tables. From one
> side it gives a better observability, from another side metrics are not for
> free, there is an overhead associated with them:
> 1) CPU overhead: in case of simple CPU bound load: I already see like 5.5% of
> total CPU spent for metrics in cpu framegraphs for read load and 11% for
> write load.
> Example: [^cpu_profile_insert.html] (search by "codahale" pattern). The
> framegraph is captured using Async profiler build:
> async-profiler-3.0-29ee888-linux-x64
> 2) memory overhead: we spend memory for entities used to aggregate metrics
> such as LongAdders and reservoirs + for MBeans (String concatenation within
> object names is a major cause of it, for each table+metric name combination a
> new String is created)
> LongAdder is used by Dropwizard Counter/Meter and Histogram metrics for
> counting purposes. It has severe memory overhead + while has a better scaling
> than AtomicLong we still have to pay some cost for the concurrent operations.
> Additionally, in case of Meter - we have a non-optimal behaviour when we
> count the same things several times.
> The idea (suggested by [~benedict]) is to switch to thread-local counters
> which we can store in a common thread-local array to reduce memory overhead.
> In this way we can avoid concurrent update overheads/contentions and to
> reduce memory footprint as well.
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