[jira] [Updated] (CASSANDRA-2841) Always use even distribution for merkle tree with RandomPartitionner

["Sylvain Lebresne (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/CASSANDRA-2841?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Sylvain Lebresne updated CASSANDRA-2841:
----------------------------------------

    Attachment: 2841.patch

Patch is against 0.7.

> Always use even distribution for merkle tree with RandomPartitionner
> --------------------------------------------------------------------
>
>                 Key: CASSANDRA-2841
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-2841
>             Project: Cassandra
>          Issue Type: Improvement
>          Components: Core
>    Affects Versions: 0.7.0
>            Reporter: Sylvain Lebresne
>            Assignee: Sylvain Lebresne
>            Priority: Trivial
>              Labels: repair
>             Fix For: 0.7.7, 0.8.2
>
>         Attachments: 2841.patch
>
>
> When creating the initial merkle tree, repair tries to be (too) smart and use the key samples to "guide" the tree splitting. While this is a good idea for OPP where there is a good change the data distribution is uneven, you can't beat an even distribution for the RandomPartitionner. And a quick experiment even shows that the method used is significantly less efficient than an even distribution for the ranges of the merkle tree (that is, an even distribution gives a much better of distribution of the number of keys by range of the tree).
> Thus let's switch to an even distribution for RandomPartitionner. That 3 lines change alone amounts for a significant improvement of repair's precision.

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