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https://issues.apache.org/jira/browse/LUCENE-1997?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12769221#action_12769221
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Mark Miller commented on LUCENE-1997:
-------------------------------------
bq. but how does this fit together.
Thats what Comparable FieldComparator#value is for - fillFields will grab all
those and load up FieldDoc fields - so the custom FieldComparator is tied into
it - it creates Comparable objects that can be compared by the native
compareTos.
{code}
/**
* Given a queue Entry, creates a corresponding FieldDoc
* that contains the values used to sort the given document.
* These values are not the raw values out of the index, but the internal
* representation of them. This is so the given search hit can be collated by
* a MultiSearcher with other search hits.
*
* @param entry The Entry used to create a FieldDoc
* @return The newly created FieldDoc
* @see Searchable#search(Weight,Filter,int,Sort)
*/
FieldDoc fillFields(final Entry entry) {
final int n = comparators.length;
final Comparable[] fields = new Comparable[n];
for (int i = 0; i < n; ++i) {
fields[i] = comparators[i].value(entry.slot);
}
//if (maxscore > 1.0f) doc.score /= maxscore; // normalize scores
return new FieldDoc(entry.docID, entry.score, fields);
}
{code}
> 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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