Lucene nightly benchmarks
Each night, an automated Python tool checks out the Lucene/Solr trunk source code and runs multiple benchmarks: indexing the entire Wikipedia English export three times (with different settings / document sizes); running a near-real-time latency test; running a set of "hardish" auto-generated queries and tasks. The tests take around 2.5 hours to run, and the results are verified against the previous run and then added to the graphs linked below.The goal is to spot any long-term regressions (or, gains!) in Lucene's performance that might otherwise accidentally slip past the committers, hopefully avoiding the fate of the boiling frog.
See more details in this blog post.
See pretty flame charts from Java Flight Recorder profiling at blunders.io.
Indexing:
Indexing throughput
Analyzers throughput
Near-real-time refresh latency
GB/hour for medium docs index
GB/hour for medium docs index with vectors
GB/hour for medium docs index with int8 quantized vectors
GB/hour for big docs index
Indexing JIT/GC times
Index disk usage
BooleanQuery:
+high-freq +high-freq
+high-freq +medium-freq
high-freq high-freq
high-freq medium-freq
+high-freq +(medium-freq medium-freq)
+medium-freq +(high-freq high-freq)
Disjunction of 2 regular terms and 2 stop words
Conjunction of 2 regular terms and 2 stop words
Disjunction of 2 or more stop words
Conjunction of 2 or more stop words
Disjunction of 3 terms
Conjunction of 3 terms
Disjunction of a very frequent term and a very rare term
CombinedFieldsQuery:
Combined high-freq
Combined high-freq medium-freq
Combined high-freq high-freq
Proximity queries:
Exact phrase
Sloppy (~4) phrase
Span near (~10)
Ordered intervals (MAXWIDTH/10)
FuzzyQuery:
Edit distance 1
Edit distance 2
Count:
Count(Term)
Count(Phrase)
Count(+high-freq +high-freq)
Count(+high-freq +med-freq)
Count(high-freq high-freq)
Count(high-freq med-freq)
Other queries:
TermQuery
Respell (DirectSpellChecker)
Primary key lookup
WildcardQuery
PrefixQuery (3 leading characters)
Numeric range filtering on last-modified-datetime
VectorSearch (approximate KNN float 768-dimension vector search from word embeddings)
Faceting:
Term query + date hierarchy
All dates hierarchy
All dates hierarchy (doc values)
All months
All months (doc values)
All dayOfYear
All dayOfYear (doc values)
medium-freq-term +dayOfYear taxo facets
high-freq medium-freq +dayOfYear taxo facets
+high-freq +high-freq +dayOfYear taxo facets
+high-freq +medium-freq +dayOfYear taxo facets
Random labels chosen from each doc
Random labels chosen from each doc (doc values)
NAD high cardinality faceting
Sorting (on TermQuery):
Date/time (long, high cardinality)
Title (string, high cardinality)
Month (string, low cardinality)
Day of year (int, medium cardinality)
Grouping (on TermQuery):
100 groups
10K groups
1M groups
1M groups (two pass block grouping)
1M groups (single pass block grouping)
Others:
Stored fields geonames benchmarks
GC/JIT metrics during search benchmarks
Geo spatial benchmarks
Sparse vs dense doc values performance on NYC taxi ride corpus
"gradle -p lucene test" and "gradle precommit" time in lucene
CheckIndex time
Lucene GitHub pull-request counts
[last updated: 2024-09-18 06:32:59.019226; send questions to Mike McCandless]