jpountz opened a new pull request, #13957:
URL: https://github.com/apache/lucene/pull/13957
`LeafSimScorer` is a specialization of a `SimScorer` for a given segment. It
doesn't add much value, but benchmarks suggest that it adds measurable overhead
to queries sorted by score.
Here is a `luceneutil` run with `-searchConcurrency 0` on `wikibigall`:
```
TaskQPS baseline StdDevQPS
my_modified_version StdDev Pct diff p-value
CountAndHighMed 148.80 (3.6%) 146.79
(3.3%) -1.4% ( -8% - 5%) 0.219
Prefix3 210.12 (3.4%) 208.12
(3.1%) -1.0% ( -7% - 5%) 0.355
OrNotHighLow 930.49 (2.9%) 922.26
(2.8%) -0.9% ( -6% - 4%) 0.326
CountOrHighMed 104.34 (1.6%) 103.50
(1.5%) -0.8% ( -3% - 2%) 0.099
CountAndHighHigh 48.93 (3.6%) 48.55
(3.4%) -0.8% ( -7% - 6%) 0.485
HighTermMonthSort 3011.98 (2.9%) 2989.18
(4.1%) -0.8% ( -7% - 6%) 0.498
TermDTSort 342.40 (7.1%) 340.02
(6.1%) -0.7% ( -13% - 13%) 0.741
CountOrHighHigh 49.93 (1.6%) 49.76
(1.2%) -0.3% ( -3% - 2%) 0.451
HighTermTitleSort 111.58 (2.3%) 111.22
(3.1%) -0.3% ( -5% - 5%) 0.710
OrNotHighHigh 308.36 (3.1%) 307.70
(3.3%) -0.2% ( -6% - 6%) 0.835
Fuzzy2 71.17 (1.6%) 71.07
(2.2%) -0.1% ( -3% - 3%) 0.824
OrHighLow 726.98 (1.6%) 727.36
(2.5%) 0.1% ( -4% - 4%) 0.939
HighTermDayOfYearSort 764.56 (3.8%) 765.85
(3.4%) 0.2% ( -6% - 7%) 0.882
OrNotHighMed 350.64 (3.4%) 351.46
(4.3%) 0.2% ( -7% - 8%) 0.848
Fuzzy1 75.46 (1.9%) 75.80
(1.8%) 0.5% ( -3% - 4%) 0.448
IntNRQ 139.45 (13.7%) 140.08
(14.5%) 0.5% ( -24% - 33%) 0.918
HighTermTitleBDVSort 15.35 (5.7%) 15.42
(5.5%) 0.5% ( -10% - 12%) 0.781
PKLookup 265.51 (2.5%) 267.01
(1.6%) 0.6% ( -3% - 4%) 0.389
AndHighLow 989.77 (1.9%) 995.39
(2.2%) 0.6% ( -3% - 4%) 0.387
CountTerm 7984.92 (3.9%) 8051.09
(5.0%) 0.8% ( -7% - 10%) 0.557
OrHighNotHigh 321.43 (2.7%) 324.15
(3.1%) 0.8% ( -4% - 6%) 0.357
OrMany 18.24 (2.4%) 18.45
(2.1%) 1.1% ( -3% - 5%) 0.107
Wildcard 117.97 (3.2%) 119.40
(3.2%) 1.2% ( -5% - 7%) 0.230
OrHighRare 269.54 (5.3%) 273.78
(6.5%) 1.6% ( -9% - 14%) 0.401
OrHighMed 219.25 (2.5%) 222.89
(2.7%) 1.7% ( -3% - 7%) 0.044
And2Terms2StopWords 151.65 (1.8%) 154.21
(1.6%) 1.7% ( -1% - 5%) 0.002
Or2Terms2StopWords 153.46 (3.1%) 156.15
(2.8%) 1.8% ( -4% - 7%) 0.061
Or3Terms 164.81 (2.4%) 168.57
(2.9%) 2.3% ( -2% - 7%) 0.007
MedTerm 610.37 (3.5%) 625.30
(3.7%) 2.4% ( -4% - 10%) 0.032
OrHighNotMed 417.48 (2.8%) 427.78
(3.1%) 2.5% ( -3% - 8%) 0.008
LowTerm 981.78 (2.8%) 1008.35
(3.8%) 2.7% ( -3% - 9%) 0.010
And3Terms 165.41 (1.8%) 170.05
(1.7%) 2.8% ( 0% - 6%) 0.000
AndStopWords 30.15 (3.0%) 31.07
(3.8%) 3.0% ( -3% - 10%) 0.005
HighTerm 455.84 (3.4%) 469.91
(4.0%) 3.1% ( -4% - 10%) 0.009
OrHighHigh 68.52 (1.7%) 70.69
(3.7%) 3.2% ( -2% - 8%) 0.000
OrHighNotLow 412.63 (2.8%) 427.86
(3.5%) 3.7% ( -2% - 10%) 0.000
OrStopWords 33.50 (3.8%) 34.75
(5.1%) 3.7% ( -4% - 13%) 0.009
AndHighMed 165.41 (1.9%) 171.81
(1.7%) 3.9% ( 0% - 7%) 0.000
AndHighHigh 72.22 (1.7%) 76.11
(1.4%) 5.4% ( 2% - 8%) 0.000
```
I could reproduce these small speedups with a low p-value across several
runs.
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