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Date: Fri, 23 Oct 2009 17:57:59 +0000 (UTC)
From: "Jake Mannix (JIRA)" <jira@apache.org>
To: java-dev@lucene.apache.org
Subject: [jira] Commented: (LUCENE-1997) Explore performance of multi-PQ vs
 single-PQ sorting API
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    [ https://issues.apache.org/jira/browse/LUCENE-1997?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12769328#action_12769328 ] 

Jake Mannix commented on LUCENE-1997:
-------------------------------------

Mike, thanks for all the hard work on this - it's clearly far more work than anyone has spent yet on just doing the upgrade to the newer api, and that's appreciated.  

Am I wrong in thinking that these results are pretty ambiguous?  How often to people take the top 500 or top 1000 sorted hits?  If you don't focus on that case (that of looking for pages *50 through 100* of normal 10-per-page search results), there's a bunch of green, a bunch of red, both techniques are +/- 10-20% of each other?

Is that what everyone else sees of Mike's newest numbers here, or am I misreading them?

> Explore performance of multi-PQ vs single-PQ sorting API
> --------------------------------------------------------
>
>                 Key: LUCENE-1997
>                 URL: https://issues.apache.org/jira/browse/LUCENE-1997
>             Project: Lucene - Java
>          Issue Type: Improvement
>          Components: Search
>    Affects Versions: 2.9
>            Reporter: Michael McCandless
>            Assignee: Michael McCandless
>         Attachments: LUCENE-1997.patch, LUCENE-1997.patch, LUCENE-1997.patch, LUCENE-1997.patch
>
>
> Spinoff from recent "lucene 2.9 sorting algorithm" thread on java-dev,
> where a simpler (non-segment-based) comparator API is proposed that
> gathers results into multiple PQs (one per segment) and then merges
> them in the end.
> I started from John's multi-PQ code and worked it into
> contrib/benchmark so that we could run perf tests.  Then I generified
> the Python script I use for running search benchmarks (in
> contrib/benchmark/sortBench.py).
> The script first creates indexes with 1M docs (based on
> SortableSingleDocSource, and based on wikipedia, if available).  Then
> it runs various combinations:
>   * Index with 20 balanced segments vs index with the "normal" log
>     segment size
>   * Queries with different numbers of hits (only for wikipedia index)
>   * Different top N
>   * Different sorts (by title, for wikipedia, and by random string,
>     random int, and country for the random index)
> For each test, 7 search rounds are run and the best QPS is kept.  The
> script runs singlePQ then multiPQ, and records the resulting best QPS
> for each and produces table (in Jira format) as output.

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