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https://issues.apache.org/jira/browse/LUCENE-9335?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17319882#comment-17319882
]
Zach Chen commented on LUCENE-9335:
-----------------------------------
I made the following changes, and actually still saw varying benchmark result
across runs (randomized queries?). I've listed them down below:
Changes in Boolean2ScorerSupplier.java (use DisjunctionSumScorer instead of
DisjunctionMaxScorer)
{code:java}
diff --git
a/lucene/core/src/java/org/apache/lucene/search/Boolean2ScorerSupplier.java
b/lucene/core/src/java/org/apache/lucene/search/Boolean2ScorerSupplier.java
index bdf085d4669..10478ab45bf 100644
--- a/lucene/core/src/java/org/apache/lucene/search/Boolean2ScorerSupplier.java
+++ b/lucene/core/src/java/org/apache/lucene/search/Boolean2ScorerSupplier.java
@@ -238,11 +238,34 @@ final class Boolean2ScorerSupplier extends ScorerSupplier
{
//
// However, as WANDScorer uses more complex algorithm and data
structure, we would like to
// still use DisjunctionSumScorer to handle exhaustive pure
disjunctions, which may be faster
- if (scoreMode == ScoreMode.TOP_SCORES || minShouldMatch > 1) {
+ boolean isPureDisjunction =
+ subs.get(Occur.FILTER).isEmpty()
+ && subs.get(Occur.MUST).isEmpty()
+ && subs.get(Occur.MUST_NOT).isEmpty();
+ // top-level boolean term query
+ boolean allTermScorers =
+ optionalScorers.stream().allMatch(scorer -> scorer instanceof
TermScorer);
+
+ if (isPureDisjunction && allTermScorers &&
isSimilarCost(optionalScorers) && minShouldMatch <= 1) {
+ return new DisjunctionSumScorer(weight, optionalScorers, scoreMode);
+ } else if (scoreMode == ScoreMode.TOP_SCORES || minShouldMatch > 1) {
return new WANDScorer(weight, optionalScorers, minShouldMatch,
scoreMode);
} else {
return new DisjunctionSumScorer(weight, optionalScorers, scoreMode);
}
}
}
+
+ private boolean isSimilarCost(List<Scorer> optionalScorers) {
+ long minCost = Long.MAX_VALUE;
+ long maxCost = Long.MIN_VALUE;
+ for (Scorer scorer : optionalScorers) {
+ long cost = scorer.iterator().cost();
+ minCost = Math.min(minCost, cost);
+ maxCost = Math.max(maxCost, cost);
+ }
+
+ // TODO heuristic based cost-similarity threshold
+ return maxCost / minCost < 2;
+ }
}
{code}
Changes in benchUtil.py to not verify counts
{code:java}
diff --git a/src/python/benchUtil.py b/src/python/benchUtil.py
index fb50033..3579f45 100644
--- a/src/python/benchUtil.py
+++ b/src/python/benchUtil.py
@@ -1203,7 +1203,7 @@ class RunAlgs:
cmpRawResults, heapCmp = parseResults(cmpLogFiles)
# make sure they got identical results
- cmpDiffs = compareHits(baseRawResults, cmpRawResults, self.verifyScores,
self.verifyCounts)
+ cmpDiffs = compareHits(baseRawResults, cmpRawResults, self.verifyScores,
False)
baseResults = collateResults(baseRawResults)
cmpResults = collateResults(cmpRawResults)
{code}
Benchmark result 1 with source wikimedium5m:
{code:java}
TaskQPS baseline StdDevQPS my_modified_version StdDev
Pct diff p-value
OrHighHigh 87.27 (4.9%) 51.12 (1.7%)
-41.4% ( -45% - -36%) 0.000
OrHighLow 624.55 (7.6%) 589.16 (8.1%)
-5.7% ( -19% - 10%) 0.022
OrHighMed 135.02 (3.7%) 129.51 (6.6%)
-4.1% ( -13% - 6%) 0.016
Wildcard 214.30 (3.3%) 209.33 (2.8%)
-2.3% ( -8% - 3%) 0.017
OrNotHighHigh 728.60 (8.5%) 713.53 (6.3%)
-2.1% ( -15% - 13%) 0.383
HighTerm 1195.98 (6.0%) 1174.51 (4.2%)
-1.8% ( -11% - 8%) 0.273
LowTerm 1757.60 (6.0%) 1728.64 (4.8%)
-1.6% ( -11% - 9%) 0.336
AndHighMed 231.78 (4.0%) 227.96 (3.7%)
-1.6% ( -8% - 6%) 0.175
Prefix3 196.03 (3.4%) 193.19 (3.5%)
-1.4% ( -8% - 5%) 0.180
Respell 59.52 (2.8%) 59.05 (2.7%)
-0.8% ( -6% - 4%) 0.362
MedTerm 1507.60 (5.3%) 1495.89 (3.3%)
-0.8% ( -8% - 8%) 0.580
BrowseDateTaxoFacets 11.04 (3.3%) 10.97 (2.8%)
-0.7% ( -6% - 5%) 0.462
BrowseMonthTaxoFacets 13.21 (3.4%) 13.12 (3.9%)
-0.7% ( -7% - 6%) 0.542
MedSloppyPhrase 67.05 (3.4%) 66.58 (4.0%)
-0.7% ( -7% - 6%) 0.544
IntNRQ 215.89 (4.2%) 214.39 (2.9%)
-0.7% ( -7% - 6%) 0.543
BrowseDayOfYearTaxoFacets 11.02 (3.2%) 10.96 (2.7%)
-0.6% ( -6% - 5%) 0.546
LowPhrase 193.14 (4.0%) 192.05 (4.5%)
-0.6% ( -8% - 8%) 0.678
BrowseDayOfYearSSDVFacets 27.80 (5.2%) 27.67 (5.5%)
-0.5% ( -10% - 10%) 0.781
OrHighNotLow 823.92 (6.1%) 820.15 (4.8%)
-0.5% ( -10% - 11%) 0.790
PKLookup 215.92 (3.9%) 215.02 (3.8%)
-0.4% ( -7% - 7%) 0.734
Fuzzy1 65.82 (7.8%) 65.58 (11.0%)
-0.4% ( -17% - 20%) 0.904
HighSloppyPhrase 42.05 (3.9%) 41.91 (3.3%)
-0.3% ( -7% - 7%) 0.771
MedSpanNear 155.25 (3.5%) 154.78 (3.3%)
-0.3% ( -6% - 6%) 0.779
AndHighHigh 84.97 (4.4%) 84.79 (3.0%)
-0.2% ( -7% - 7%) 0.857
LowSloppyPhrase 100.76 (3.6%) 100.55 (3.6%)
-0.2% ( -7% - 7%) 0.857
HighIntervalsOrdered 42.39 (3.4%) 42.34 (3.7%)
-0.1% ( -6% - 7%) 0.921
HighTermDayOfYearSort 210.65 (15.0%) 210.79 (11.5%)
0.1% ( -23% - 31%) 0.987
HighPhrase 468.21 (4.5%) 468.66 (4.0%)
0.1% ( -7% - 8%) 0.943
HighSpanNear 148.68 (3.6%) 148.94 (3.6%)
0.2% ( -6% - 7%) 0.880
OrNotHighMed 682.83 (7.2%) 684.51 (4.4%)
0.2% ( -10% - 12%) 0.896
OrHighNotMed 733.07 (7.3%) 736.52 (4.9%)
0.5% ( -10% - 13%) 0.811
OrHighNotHigh 638.40 (7.2%) 642.31 (4.5%)
0.6% ( -10% - 13%) 0.747
BrowseMonthSSDVFacets 31.42 (5.2%) 31.65 (2.6%)
0.7% ( -6% - 8%) 0.577
AndHighLow 923.45 (5.9%) 933.64 (5.3%)
1.1% ( -9% - 13%) 0.534
MedPhrase 347.33 (5.7%) 351.57 (3.3%)
1.2% ( -7% - 10%) 0.404
LowSpanNear 311.32 (6.2%) 315.13 (4.2%)
1.2% ( -8% - 12%) 0.466
Fuzzy2 59.05 (12.3%) 60.19 (10.6%)
1.9% ( -18% - 28%) 0.594
OrNotHighLow 851.87 (6.4%) 869.95 (5.6%)
2.1% ( -9% - 15%) 0.263
HighTermMonthSort 99.63 (12.7%) 102.07 (15.1%)
2.5% ( -22% - 34%) 0.578
HighTermTitleBDVSort 161.36 (16.9%) 165.53 (18.3%)
2.6% ( -27% - 45%) 0.643
TermDTSort 195.70 (11.5%) 201.16 (10.4%)
2.8% ( -17% - 27%) 0.420
WARNING: cat=OrHighHigh: hit counts differ: 13070+ vs 357939+
{code}
Benchmark result 2 with source wikimedium5m:
{code:java}
TaskQPS baseline StdDevQPS my_modified_version StdDev
Pct diff p-value
Fuzzy1 69.00 (13.7%) 64.57 (14.5%)
-6.4% ( -30% - 25%) 0.150
Fuzzy2 50.00 (15.2%) 47.36 (17.9%)
-5.3% ( -33% - 32%) 0.313
OrHighNotLow 780.22 (5.3%) 764.22 (3.9%)
-2.1% ( -10% - 7%) 0.165
OrHighLow 235.87 (3.5%) 232.57 (3.2%)
-1.4% ( -7% - 5%) 0.187
OrHighNotMed 741.20 (4.0%) 733.20 (4.9%)
-1.1% ( -9% - 8%) 0.450
OrNotHighHigh 850.77 (5.9%) 842.95 (5.3%)
-0.9% ( -11% - 10%) 0.606
IntNRQ 159.89 (1.5%) 158.54 (3.1%)
-0.8% ( -5% - 3%) 0.270
OrHighMed 161.49 (5.0%) 160.24 (6.0%)
-0.8% ( -11% - 10%) 0.659
MedTerm 1534.83 (4.9%) 1524.53 (5.7%)
-0.7% ( -10% - 10%) 0.691
LowTerm 1854.25 (6.1%) 1842.02 (5.7%)
-0.7% ( -11% - 11%) 0.725
Respell 67.40 (1.8%) 66.96 (2.5%)
-0.7% ( -4% - 3%) 0.346
Wildcard 222.44 (2.5%) 221.67 (2.3%)
-0.3% ( -5% - 4%) 0.645
Prefix3 213.45 (3.6%) 212.73 (3.9%)
-0.3% ( -7% - 7%) 0.776
OrHighHigh 62.29 (2.8%) 62.08 (2.5%)
-0.3% ( -5% - 5%) 0.700
BrowseDayOfYearSSDVFacets 27.76 (7.0%) 27.69 (6.9%)
-0.3% ( -13% - 14%) 0.897
HighSpanNear 108.87 (2.3%) 108.63 (3.8%)
-0.2% ( -6% - 6%) 0.820
BrowseMonthTaxoFacets 13.29 (2.3%) 13.30 (1.9%)
0.0% ( -4% - 4%) 0.940
LowSloppyPhrase 171.64 (2.8%) 171.88 (2.9%)
0.1% ( -5% - 6%) 0.875
PKLookup 217.98 (3.9%) 218.52 (3.6%)
0.2% ( -7% - 8%) 0.836
HighTerm 1392.99 (4.5%) 1396.79 (4.5%)
0.3% ( -8% - 9%) 0.847
HighIntervalsOrdered 29.54 (2.7%) 29.63 (2.6%)
0.3% ( -4% - 5%) 0.732
AndHighLow 797.99 (4.0%) 800.55 (3.7%)
0.3% ( -7% - 8%) 0.792
OrNotHighLow 885.90 (4.3%) 888.91 (5.4%)
0.3% ( -8% - 10%) 0.826
MedSloppyPhrase 65.81 (2.9%) 66.06 (3.1%)
0.4% ( -5% - 6%) 0.689
LowSpanNear 59.31 (2.6%) 59.55 (2.5%)
0.4% ( -4% - 5%) 0.609
BrowseDateTaxoFacets 11.19 (2.8%) 11.25 (2.8%)
0.5% ( -5% - 6%) 0.542
LowPhrase 170.61 (2.6%) 171.55 (2.6%)
0.6% ( -4% - 5%) 0.502
MedSpanNear 192.13 (3.2%) 193.32 (2.2%)
0.6% ( -4% - 6%) 0.469
BrowseDayOfYearTaxoFacets 11.20 (2.9%) 11.28 (2.9%)
0.7% ( -4% - 6%) 0.460
HighSloppyPhrase 82.88 (5.4%) 83.47 (5.5%)
0.7% ( -9% - 12%) 0.681
BrowseMonthSSDVFacets 31.59 (4.7%) 31.91 (2.0%)
1.0% ( -5% - 8%) 0.387
MedPhrase 138.53 (2.1%) 140.00 (2.7%)
1.1% ( -3% - 5%) 0.164
HighPhrase 294.46 (2.7%) 297.99 (2.3%)
1.2% ( -3% - 6%) 0.135
OrHighNotHigh 654.84 (5.4%) 663.25 (4.8%)
1.3% ( -8% - 12%) 0.427
HighTermMonthSort 175.68 (10.6%) 178.02 (11.3%)
1.3% ( -18% - 25%) 0.700
HighTermDayOfYearSort 301.64 (16.6%) 306.26 (17.4%)
1.5% ( -27% - 42%) 0.776
OrNotHighMed 694.25 (6.0%) 705.04 (5.6%)
1.6% ( -9% - 13%) 0.396
AndHighHigh 105.76 (2.4%) 107.44 (3.4%)
1.6% ( -4% - 7%) 0.087
TermDTSort 227.39 (12.5%) 232.52 (11.9%)
2.3% ( -19% - 30%) 0.559
AndHighMed 241.36 (2.9%) 246.82 (3.5%)
2.3% ( -4% - 8%) 0.026
HighTermTitleBDVSort 345.18 (13.3%) 361.51 (14.7%)
4.7% ( -20% - 37%) 0.286
{code}
> Add a bulk scorer for disjunctions that does dynamic pruning
> ------------------------------------------------------------
>
> Key: LUCENE-9335
> URL: https://issues.apache.org/jira/browse/LUCENE-9335
> Project: Lucene - Core
> Issue Type: Improvement
> Reporter: Adrien Grand
> Priority: Minor
>
> Lucene often gets benchmarked against other engines, e.g. against Tantivy and
> PISA at [https://tantivy-search.github.io/bench/] or against research
> prototypes in Table 1 of
> [https://cs.uwaterloo.ca/~jimmylin/publications/Grand_etal_ECIR2020_preprint.pdf].
> Given that top-level disjunctions of term queries are commonly used for
> benchmarking, it would be nice to optimize this case a bit more, I suspect
> that we could make fewer per-document decisions by implementing a BulkScorer
> instead of a Scorer.
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