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https://issues.apache.org/jira/browse/LUCENE-1997?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12770978#action_12770978
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Yonik Seeley commented on LUCENE-1997:
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So if we're considering new comparator APIs, and the indirection seems to be
slowing things down... one thing to think about is how to eliminate that
indirection.
Even thinking about the multiPQ case - why should one need more than a single
PQ when dealing with primitives that don't depend on context (i.e. everything
except ord). If the comparator API had a way to set (or return) a primitive
value for a single docid, and then those were compared (either directly by the
PQ or via a callback), there wouldn't be an issue with reader transitions
(because you don't compare id vs id) and hence no need for multiple priority
queues. Avoiding the creation of intermediate Comparable objects also seems
desirable.
Perhaps do it how "score" is handled now... inlined into Entry? Should make
heap rebalancing faster (fewer callbacks, fewer array lookups).
> 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
>
>
> 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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