Always use even distribution for merkle tree with RandomPartitionner
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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
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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