cassandra-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "Thomas Steinmaurer (JIRA)" <>
Subject [jira] [Commented] (CASSANDRA-8099) Refactor and modernize the storage engine
Date Tue, 26 Sep 2017 08:20:01 GMT


Thomas Steinmaurer commented on CASSANDRA-8099:

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
gets proper attention. Thanks a lot.

> Refactor and modernize the storage engine
> -----------------------------------------
>                 Key: CASSANDRA-8099
>                 URL:
>             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
> # 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.

This message was sent by Atlassian JIRA

To unsubscribe, e-mail:
For additional commands, e-mail:

View raw message