GenericVectorBuilder

Benchmark

Summary and all runs

Vector engine benchmark: eshoponweb

Run started 6 Oct 2026, 13:06 UTC. Data: eShopOnWeb, 524 vectors. Queries: 20 labelled questions.

Used by: published-2026-10-08 (v7, basis)

The runs of v7 were also used by the set blocked-2026-10-06-v7, which was not published; its verdict, design/verdicts/v7-verdict.md, holds the word BLOCK.

sources
  • file design/verdicts/v7-verdict.md#BLOCK = BLOCK
  • consolidated consolidated:reuse[session=v7].verdict = design/verdicts/v7-verdict.md
  • consolidated consolidated:reuse[session=v7].blockedFolders[0] = blocked-2026-10-06-v7

Machine: CPU Intel(R) Xeon(R) CPU E5-1620 v3 @ 3.50GHz; Logical CPUs 8; RAM (GiB) 62.7; OS Ubuntu 24.04.5 LTS, kernel 6.8.0-142-generic; Governor performance; Partition client 0-1,4-5; engines 2-3,6-7; Release build; Exact mode seconds 60.

Warm-up, as this run's notes record it: Warm-up and settle check, untimed, right before every timed pass: the pass's own search at the pass's own number of searchers for at least 15 s and at least 20 searches (at most 120 s), read in windows of at least 2 s and 100 searches; then a 3 s trial of the same pass.

Clock, as this run's notes record it: CPU clock pinned for the run: turbo off (intel_pstate/no_turbo 0 -> 1), so every CPU's clock is held at its ceiling of 3500 MHz whatever the engine runs [Correction 5]

Enginep50 (ms)Searches per second, one searcherSearches per second, eight searchers at onceExact mode p50 (ms)Client CPU per search, one searcher (ms)Recall in hitsFlags
Chroma2.11469777-0.48200 of 200
ClickHouse4.971923756.670.87199 of 200
DuckDB3.672705764.215.02200 of 200
Elasticsearch1.337392,6711.280.46200 of 200Ix
MariaDB0.641,5465,9111.640.49200 of 200
Milvus2.344051,255-0.57200 of 200
MongoDB Atlas Local1.416912,0661.240.31200 of 200Bz
OpenSearch2.164511,3462.750.47200 of 200
Oracle 23ai Free0.931,0512,8022.480.88200 of 200
Qdrant (HNSW)0.921,0713,4520.890.73200 of 200
Qdrant (exact)0.891,1113,274-0.74200 of 200
Redis
redis holds its data in memory: its saved docs page says 'Redis is an in-memory but persistent on disk database', and its compose file sets save "300 1" and appendonly no.
sources
  • doc doc:design/engine-docs/redis-faq-2026-10-07.html#Redis is an in-memory but persistent on disk database = Redis is an in-memory but persistent on disk database
  • file deploy/engines/redis.compose.yaml#--save "300 1" = --save "300 1"
  • file deploy/engines/redis.compose.yaml#--appendonly no = --appendonly no
0.402,5275,1200.380.57200 of 200
SQL Server 20254.06241612-1.22200 of 200
SQL Server 2025 + DiskANN3.602716913.741.02193 of 200
Typesense4.182355755.660.52200 of 200
Vespa1.974921,5261.960.96200 of 200
Weaviate5.36166305-0.57200 of 200
pgvector0.881,1073,9342.260.69200 of 200
sqlite-vec1.925101,1421.912.07200 of 200

Engines are listed in alphabetical order. The table does not rank them.

A dash means the table has no figure there.

The small markers after an engine name are flags. Hover a marker for its evidence, or read the list below the tables.

  • Bz busy-box CPUs outside the benchmark were busy during the timed pass. The evidence gives the figure.
  • Ix index-not-ready The engine did not report a finished index after the load or after the searches.
Evidence behind the flags
  • Elasticsearch index-not-ready afterLoad: the engine's index state read not ready, 0 of 524 vectors indexed
  • Elasticsearch index-not-ready afterSearch: the engine's index state read not ready, 0 of 524 vectors indexed
  • MongoDB Atlas Local busy-box exact: CPUs busy outside the benchmark 0.307

Full results

This report is printed as the run wrote it, except for any sentence a note above it says was left out. This page does not check the engine texts in it. The summary page's facts table gives the label of each fact it uses.

Run 2026-10-06T13:06:19Z (run-all). Command line:

~/gvb-work/lanes/v7-final/src/GenericVectorBuilder.Bench/bin/Release/net10.0/GenericVectorBuilder.Bench.dll run-all --pipeline eshoponweb --queries golden --seed 701 --out ~/ForClaude/GenericVectorBuilder/bench-results
engineindexload rows/sp50 msp95 msp99 msQPS@1QPS@8client CPU ms/search@1client CPU ms/search@8recall@10nDCG@10RAMdisk
clickhousevector_similarity HNSW cosineDistance, quantization bf16, M=16 ef_construction=128, hnsw_candidate_list_size_for_search=256, rescoring off; exact mode = full scan with skip indexes off3,1964.977.408.14191.6374.80.871.060.9950.528894 MiB4.21 GiB
vespaHNSW float32 tensor, prenormalized-angular (cosine), max-links-per-node=16, neighbors-to-explore-at-insert=128; search targetHits=top, ef=100 via exploreAdditionalHits; exact mode = approximate:false; vectors held in memory2761.972.422.74491.51526.20.960.881.0000.5182.93 GiB29.63 MiB
oracleHNSW in-memory neighbor graph NEIGHBORS=16 EFCONSTRUCTION=128, EFSEARCH=100 per query, cosine; exact mode = FETCH EXACT FIRST (full scan); Oracle Free caps itself at 2 CPUs (cpu_count 2 in V$PARAMETER, edition FREE in V$INSTANCE, 8 host CPUs in V$OSSTAT NUM_CPUS; the 2 CPU thread limit is Oracle's documented Free edition limit)1,7290.931.121.781050.92802.20.880.641.0000.5182.13 GiB0 B
elasticsearchHNSW float32, no quantization, m=16, ef_construction=128, cosine; search k=top, num_candidates=100; 1 shard, 0 replicas; force-merged to one segment after the load (at 1,024 dimensions a segment under 1,043 vectors gets no graph)4521.331.511.78739.52671.30.460.501.0000.5182.68 GiB2.39 MiB
sqlexact VECTOR_DISTANCE cosine, no vector index (full scan)5874.064.586.06241.0611.61.221.181.0000.518529.9 MiB5.15 MiB
pgvectorHNSW vector_cosine_ops m=16 ef_construction=128, hnsw.ef_search=100 per query, float32 vector(n), cosine; exact mode = same query with index scans off (sequential scan)6050.881.041.311107.13933.90.690.541.0000.518100.5 MiB7.57 MiB
sqlitevecvec0 brute-force scan, no ANN index (exact), float32, cosine distance, default chunk_size=1024; score = 1 - cosine distance; searches run concurrently, one WAL reader connection per searcher (opened as searchers arrive, at most 32), writes run one at a time and may overlap searches4,0771.922.182.34509.91141.92.073.491.0000.518-9.72 MiB
qdrantexact scan: the builder's sink sends exact=true on every search, so no HNSW graph is used whether or not Qdrant has built one (see the index state)5,0330.891.021.401111.23273.80.740.531.0000.51890.19 MiB196.08 MiB
opensearchfaiss HNSW float32, no compression, m=16, ef_construction=128, cosinesimil; search k=top, ef_search=100; 1 shard, 0 replicas; graph built at any segment size (approximate_threshold=0); force-merged to one segment after the load3692.162.503.45450.91346.30.470.511.0000.5182.67 GiB9.66 MiB
qdrant-hnswHNSW m=16 ef_construct=100, hnsw_ef=server default, cosine; indexing_threshold_kb 1 and full_scan_threshold_kb 10 (server defaults are 10,000 each) so a small collection builds and walks its graph7,0940.921.051.411071.53451.90.730.511.0000.51837.06 MiB164.1 MiB
redisHNSW TYPE FLOAT32 M=16 EF_CONSTRUCTION=128, EF_RUNTIME=100 per query, cosine; exact mode = FLAT index built on first exact query12,4050.400.490.722526.95120.10.570.361.0000.518254.2 MiB0 B
milvusHNSW M=16 efConstruction=128, ef=100, metric COSINE, Strong consistency searches; approximate only (no exact mode)1,1552.342.903.91405.41254.70.570.641.0000.518223.6 MiB10.93 MiB
mariadbVECTOR INDEX (HNSW variant) DISTANCE=cosine, M=16 (no ef_construction setting exists), mhnsw_ef_search=100 per statement (the ef 100 most engines here use, so the search effort matches; MariaDB's own default is 20; recall@10 at ef 100 falls as the set grows (random 1024-dimension vectors, measured 2026-10-04: 0.99 at 524, 0.89 to 0.92 at 2,000; an earlier run gave about 0.09 at 100,000 [Correction 4])), mhnsw_max_cache_size 4G; exact mode = IGNORE INDEX full scan1,0100.640.720.861546.45911.50.490.381.0000.518159.5 MiB22.01 MiB
weaviateHNSW maxConnections(M)=16 efConstruction=128, ef=-1 (dynamic: limit x 8 clamped 100..500), cosine, no quantization; approximate only (no exact mode)1,0045.3610.711.8165.6304.90.570.621.0000.518175.9 MiB2.96 MiB
mongodbvectorSearch index, HNSW maxEdges=16 numEdgeCandidates=128, float32 binData, cosine, numCandidates=20x hits (min 100); exact mode = $vectorSearch exact:true3,5451.411.661.95691.02066.00.310.291.0000.518882.5 MiB171.38 KiB
chromaHNSW M=16 ef_construction=128, ef_search=100 (Chroma default), cosine; approximate only (no exact mode)6682.112.382.65468.9777.10.480.481.0000.51879.52 MiB414.16 KiB
typesenseHNSW float32 (hnswlib), m=16, ef_construction=128, cosine; search k=top, ef=100; exact mode = filter ordinal:>=0 with flat_search_cutoff; index held in memory9984.184.605.54235.2575.00.520.591.0000.518114.6 MiB9.42 MiB
sql-diskannDiskANN (preview) via VECTOR_SEARCH, cosine, build {"StartId":"306", "L":"48", "M":"8", "R":"48"}; the exact mode scans the same table6173.604.064.68271.5690.71.021.090.9650.526530.6 MiB4.52 MiB
duckdbHNSW (vss extension) FLOAT[n] metric=cosine m=16 ef_construction=128, ef_search=100 per connection, persistent (hnsw_enable_experimental_persistence=true, checkpoint_threshold=256MB); exact mode = array_cosine_similarity sequential scan; score = 1 - cosine distance; searches run concurrently, one connection per searcher (opened as searchers arrive, at most 32), writes run one at a time and never overlap a search1,5273.674.065.26269.6576.15.026.911.0000.518-2.9 MiB

Details per target

ClickHouse clickhouse
Vespa vespa
Oracle 23ai Free oracle
Elasticsearch elasticsearch
SQL Server 2025 sql
pgvector
sqlite-vec sqlitevec
Qdrant (exact) qdrant
OpenSearch opensearch
Qdrant (HNSW) qdrant-hnsw
Redis redis
Milvus milvus
MariaDB mariadb
Weaviate weaviate
MongoDB Atlas Local mongodb
Chroma chroma
Typesense typesense
SQL Server 2025 + DiskANN sql-diskann
DuckDB duckdb

Notes

Raw files

results.md is kept unedited. It still holds the sentences this page leaves out of its copy. Count: 2

The raw files below hold the recorded statements that the corrections above refer to, as written.