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From "sankalp kohli (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (CASSANDRA-6474) Compaction strategy based on MinHash
Date Fri, 30 May 2014 17:05:03 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-6474?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14013938#comment-14013938
] 

sankalp kohli edited comment on CASSANDRA-6474 at 5/30/14 5:03 PM:
-------------------------------------------------------------------

I am not working on it. I might get time next week to work on it. Feel free to take it if
you like. 


was (Author: kohlisankalp):
I am not working on it. Feel free to take it. 

> 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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