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https://issues.apache.org/jira/browse/CASSANDRA-8099?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16180444#comment-16180444
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Thomas Steinmaurer commented on CASSANDRA-8099:
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Are there any benchmarks V2.1 vs. V3.0 focusing on GC (object churn)? In our 9
node loadtest environment, after upgrading from 2.1.18 to 3.0.14, same load,
same infrastructure, same heap sizing etc., *GC suspension has doubled* (+
correlating CPU increase). According to JFR, allocation rate top class is
{{org.apache.cassandra.utils.btree.BTreeSearchIterator}}, especially in context
of compactions, e.g. for {{Rows.collectStats}}.
Checked out the code and I see e.g. commits
2457599427d361314dce4833abeb5cd4915d0b06 (some simplifications) and also
639d4b240c084900b6589222a0984babfc1890b1 (switch to BTree).
While the storage engine might now be more modern and code being most likely
easier to read, we got a really bad initial impression when switching from
2.1.18. It would be great if someone can share benchmarks focusing GC/CPU or if
https://issues.apache.org/jira/browse/CASSANDRA-13900 gets proper attention.
Thanks a lot.
> Refactor and modernize the storage engine
> -----------------------------------------
>
> Key: CASSANDRA-8099
> URL: https://issues.apache.org/jira/browse/CASSANDRA-8099
> Project: Cassandra
> Issue Type: Improvement
> Reporter: Sylvain Lebresne
> Assignee: Sylvain Lebresne
> Fix For: 3.0 alpha 1
>
> Attachments: 8099-nit
>
>
> The current storage engine (which for this ticket I'll loosely define as "the
> code implementing the read/write path") is suffering from old age. One of the
> main problem is that the only structure it deals with is the cell, which
> completely ignores the more high level CQL structure that groups cell into
> (CQL) rows.
> This leads to many inefficiencies, like the fact that during a reads we have
> to group cells multiple times (to count on replica, then to count on the
> coordinator, then to produce the CQL resultset) because we forget about the
> grouping right away each time (so lots of useless cell names comparisons in
> particular). But outside inefficiencies, having to manually recreate the CQL
> structure every time we need it for something is hindering new features and
> makes the code more complex that it should be.
> Said storage engine also has tons of technical debt. To pick an example, the
> fact that during range queries we update {{SliceQueryFilter.count}} is pretty
> hacky and error prone. Or the overly complex ways {{AbstractQueryPager}} has
> to go into to simply "remove the last query result".
> So I want to bite the bullet and modernize this storage engine. I propose to
> do 2 main things:
> # Make the storage engine more aware of the CQL structure. In practice,
> instead of having partitions be a simple iterable map of cells, it should be
> an iterable list of row (each being itself composed of per-column cells,
> though obviously not exactly the same kind of cell we have today).
> # Make the engine more iterative. What I mean here is that in the read path,
> we end up reading all cells in memory (we put them in a ColumnFamily object),
> but there is really no reason to. If instead we were working with iterators
> all the way through, we could get to a point where we're basically
> transferring data from disk to the network, and we should be able to reduce
> GC substantially.
> Please note that such refactor should provide some performance improvements
> right off the bat but it's not it's primary goal either. It's primary goal is
> to simplify the storage engine and adds abstraction that are better suited to
> further optimizations.
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