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https://issues.apache.org/jira/browse/CASSANDRA-20250?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=18035363#comment-18035363
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Stefan Miklosovic commented on CASSANDRA-20250:
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EstimatedPartitionCount is really expensive right now because it looks into
statistics component every single time for every sstable and loads cardinality
data, then merges it ... terrible performance wise (deserializing it at least).
I would just ban this metric from querying right now until absolutely
necessary. I am not completely sure why there is a need to query estimated
number of rows in a table aggressively instead of doing that really just on
demand.
> 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: 5.1
>
> Attachments: 5.1_profile_cpu.html,
> 5.1_profile_cpu_without_metrics.html, 5.1_tl4_profile_cpu.html,
> CASSANDRA-20250_ci_summary.html, CASSANDRA-20250_results_details.tar.xz,
> Histogram_AtomicLong.png, async_profiler_cpu_profiles.zip,
> cas_reverse_graph_metrics.png, cpu_profile_insert.html,
> image-2025-02-18-23-22-19-983.png, jmh-result.json, vmstat.log,
> vmstat_without_metrics.log
>
> Time Spent: 11h 50m
> Remaining Estimate: 0h
>
> 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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