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From "Mark Miller (JIRA)" <>
Subject [jira] Commented: (LUCENE-1997) Explore performance of multi-PQ vs single-PQ sorting API
Date Fri, 23 Oct 2009 13:32:59 GMT


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.

   * 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);

> Explore performance of multi-PQ vs single-PQ sorting API
> --------------------------------------------------------
>                 Key: LUCENE-1997
>                 URL:
>             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/
> 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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