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From "Benedict (JIRA)" <j...@apache.org>
Subject [jira] [Comment Edited] (CASSANDRA-6933) Optimise Read Comparison Costs in collectTimeOrderedData
Date Thu, 27 Mar 2014 01:34:18 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-6933?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13948731#comment-13948731
] 

Benedict edited comment on CASSANDRA-6933 at 3/27/14 1:34 AM:
--------------------------------------------------------------

Okay, so with a little wikipedia and wolfram alpha help, we have:

lg N, lg 0.5N, lg 0.25N = sum lg2 (2 ^ m) for m in 1..lg2(N), _which is the same as_ sum(1..lg2(N)),
which 0.5(lg2(N))(1+lg2(N))

Whereas lg 1 + lg 2 + lg 3 ... + lg N _is actually_ sum (lg n) for n in 1..N, which is (lg
(1*2*3*4*5...*N)) / (lg 2)

So, for N = 100 we get an extra 1205 comparisons vs an extra 25.

... and I'll leave it there. if I've still messed it up, it's close enough!


was (Author: benedict):
Okay, so with a little wikipedia and wolfram alpha help, we have:

lg N, lg 0.5N, lg 0.25N = sum lg2 (2 ^ m) for m in 1..sqrt(N), _which is the same as_ sum(1..sqrt(N)),
which is sqrt(N).(sqrt(N)+1)/2. So extra comparisons are 0.5(N+sqrt(N))

Whereas lg 1 + lg 2 + lg 3 ... + lg N _is actually_ sum (lg n) for n in 1..N, which is (lg
(1*2*3*4*5...*N)) / (lg 2)

So, for N = 100 we get an extra 1205 comparisons vs an extra 55.

> Optimise Read Comparison Costs in collectTimeOrderedData
> --------------------------------------------------------
>
>                 Key: CASSANDRA-6933
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-6933
>             Project: Cassandra
>          Issue Type: Improvement
>          Components: Core
>            Reporter: Benedict
>            Assignee: Benedict
>            Priority: Minor
>              Labels: performance
>             Fix For: 2.1
>
>
> Introduce a new SearchIterator construct, which can be obtained from a ColumnFamily,
which permits efficiently iterating a subset of the cells in ascending order. Essentially,
it saves the previously visited position and searches from there, but also tries to avoid
searching the whole remaining space if possible.



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