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From "Pavel Yaskevich (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (CASSANDRA-6474) Compaction strategy based on MinHash
Date Fri, 30 May 2014 18:17:02 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-6474?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14014044#comment-14014044
] 

Pavel Yaskevich commented on CASSANDRA-6474:
--------------------------------------------

We have tried MinHash + LCS while considering compaction strategies for our use-cases, the
problem with MinHash is it's pretty expensive to generate and doesn't really help much as
there is a way to split Memtable into multiple SSTable "on flush" (yet), otherwise compaction
would still do a lot of unnecessary data copying. 

> Compaction strategy based on MinHash
> ------------------------------------
>
>                 Key: CASSANDRA-6474
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-6474
>             Project: Cassandra
>          Issue Type: New Feature
>          Components: Core
>            Reporter: Yuki Morishita
>            Assignee: sankalp kohli
>              Labels: compaction
>             Fix For: 3.0
>
>
> We can consider an SSTable as a set of partition keys, and 'compaction' as de-duplication
of those partition keys.
> We want to find compaction candidates from SSTables that have as many same keys as possible.
If we can group similar SSTables based on some measurement, we can achieve more efficient
compaction.
> One such measurement is [Jaccard Distance|http://en.wikipedia.org/wiki/Jaccard_index],
> !http://upload.wikimedia.org/math/1/8/6/186c7f4e83da32e889d606140fae25a0.png!
> which we can estimate using technique called [MinHash|http://en.wikipedia.org/wiki/MinHash].
> In Cassandra, we can calculate and store MinHash signature when writing SSTable. New
compaction strategy uses the signature to find the group of similar SSTable for compaction
candidates. We can always fall back to STCS when such candidates are not exists.
> This is just an idea floating around my head, but before I forget, I dump it here. For
introduction to this technique, [Chapter 3 of 'Mining of Massive Datasets'|http://infolab.stanford.edu/~ullman/mmds/ch3.pdf]
is a good start.



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