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https://issues.apache.org/jira/browse/LUCENE-1997?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12769039#action_12769039
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Mark Miller commented on LUCENE-1997:
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Results from John Wang:
||Seg size||Query||Tot hits||Sort||Top N||QPS old||QPS new||Pct change||
|log|<all>|1000000|rand string|10|91.76|108.63|{color:green}18.4%{color}|
|log|<all>|1000000|rand string|25|92.39|106.79|{color:green}15.6%{color}|
|log|<all>|1000000|rand string|50|91.30|104.02|{color:green}13.9%{color}|
|log|<all>|1000000|rand string|500|86.16|63.27|{color:red}-26.6%{color}|
|log|<all>|1000000|rand string|1000|76.92|64.85|{color:red}-15.7%{color}|
|log|<all>|1000000|country|10|92.42|108.78|{color:green}17.7%{color}|
|log|<all>|1000000|country|25|92.60|106.26|{color:green}14.8%{color}|
|log|<all>|1000000|country|50|92.64|103.76|{color:green}12.0%{color}|
|log|<all>|1000000|country|500|83.92|50.30|{color:red}-40.1%{color}|
|log|<all>|1000000|country|1000|74.78|46.59|{color:red}-37.7%{color}|
|log|<all>|1000000|rand int|10|114.03|114.85|{color:green}0.7%{color}|
|log|<all>|1000000|rand int|25|113.77|112.92|{color:red}-0.7%{color}|
|log|<all>|1000000|rand int|50|113.36|109.56|{color:red}-3.4%{color}|
|log|<all>|1000000|rand int|500|103.90|66.29|{color:red}-36.2%{color}|
|log|<all>|1000000|rand int|1000|89.52|70.67|{color:red}-21.1%{color}|
> 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
>
>
> 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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