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From Edward Capriolo <>
Subject Re: reads and compression
Date Fri, 29 Nov 2013 20:49:39 GMT
The big * in the explanation: Smaller file size footprint leads to better
disk cache, however decompression adds work for the JVM to do and increases
the churn of objects in the JVM. Additionally compression block sizes might
be 4KB while for some use cases a small row may be 200bytes. This means
that internally a large block might be decompressed to get at the row
inside of it.

In many use cases compression is a performance win, but not necessarily in
all cases. In particular if you are already doing JVM performance tuning
issues to stop garbage collection pauses enabling compression could make
performance worse.

On Fri, Nov 29, 2013 at 6:29 AM, Artur Kronenberg <> wrote:

>  Hi John,
> I am trying again :)
> The way I understand it is that compression gives you the advantage of
> having to use way less IO and rather use CPU. The bottleneck of reads is
> usually the IO time you need to read the data from disk. As a figure, we
> had about 25 reads/s reading from disk, while we get up to 3000 reads/s
> when we have all of it in cache. So having good compression reduces the
> amount you have to read from disk. Rather you may spend a little bit more
> time decompressing data, but this data will be in cache anyways so it won't
> matter.
> Cheers
> On 29/11/13 01:09, John Sanda wrote:
>  This article[1] cites gains in read performance can be achieved when
> compression is enabled. The more I thought about it, even after reading the
> DataStax docs about reads[2], I realized I do not understand how
> compression improves read performance. Can someone provide some details on
> this?
>  Is the compression offsets map still used if compression is disabled for
> a table? If so what is its rate of growth like as compared to the growth of
> the map when compression is enabled?
>  [1] whats-new-in-cassandra-1-0-compression<>
> [2] about reads<>
>  Thanks
> - John

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