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https://issues.apache.org/jira/browse/LUCENE-1997?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Michael McCandless updated LUCENE-1997:
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Attachment: LUCENE-1997.patch
* Added inlined single PQ approach, for sorting by int, so now
sortBench.py runs trunk as baseline and inlined single PQ as
"new". It now only does the int sort. The external API now looks
like function queries DocValues (I added a simple
IntDocValueSource/IntDocValues, that has one method to get the int
for a given docID).
* Added random int field when building wiki index; you'll have to
remove existing wiki index. This is so we can test "more normal"
queries, sorting by int.
* Dropped number of threads during indexing to 1, and seeded the
Random, so that we all produce the same index
> 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, LUCENE-1997.patch, 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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