GenericVectorBuilder

Benchmark

Summary and all runs

Vector engine benchmark: eshoponweb

Run started 6 Oct 2026, 14:27 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
Chroma2.11469777-0.47200 of 200
ClickHouse4.941964286.660.88193 of 200
DuckDB3.682695764.205.03200 of 200
Elasticsearch1.337392,6691.280.46200 of 200Ix
MariaDB0.621,5785,9201.630.48200 of 200
Milvus2.324091,258-0.56200 of 200
MongoDB Atlas Local1.416932,1151.260.31200 of 200Bz
OpenSearch1.994911,5162.690.46200 of 200
Oracle 23ai Free0.931,0422,6552.490.89200 of 200
Qdrant (HNSW)0.921,0773,4400.890.74200 of 200
Qdrant (exact)0.881,1143,280-0.73200 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
0.392,5375,0670.380.56200 of 200
SQL Server 20254.02243602-1.22200 of 200
SQL Server 2025 + DiskANN3.692636823.751.03193 of 200
Typesense4.202355745.680.53200 of 200
Vespa1.954961,5061.940.94200 of 200
Weaviate5.36167305-0.61200 of 200
pgvector0.871,1264,0012.210.69200 of 200
sqlite-vec1.915141,1361.912.04200 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 0.317

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 2026-10-06T14:27:24Z (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 702 --out ~/ForClaude/GenericVectorBuilder/bench-results
engineindexload rows/sp50 msp95 msp99 msQPS@1QPS@8client CPU ms/search@1client CPU ms/search@8recall@10nDCG@10RAMdisk
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 load3061.992.292.78490.81516.00.460.501.0000.5182.57 GiB9.48 MiB
redisHNSW TYPE FLOAT32 M=16 EF_CONSTRUCTION=128, EF_RUNTIME=100 per query, cosine; exact mode = FLAT index built on first exact query11,7360.390.490.702536.65067.30.560.371.0000.518253.7 MiB0 B
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 graph4,6400.921.031.401076.63440.10.740.521.0000.51838.41 MiB164.1 MiB
weaviateHNSW maxConnections(M)=16 efConstruction=128, ef=-1 (dynamic: limit x 8 clamped 100..500), cosine, no quantization; approximate only (no exact mode)1,0015.3610.511.6166.7305.50.610.621.0000.51871.18 MiB2.96 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)6,9190.881.021.391113.83280.10.730.531.0000.51835.56 MiB196.08 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 scan1,0350.620.710.851578.05919.70.480.381.0000.518170.4 MiB22.01 MiB
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 memory2381.952.422.88496.01505.70.940.881.0000.5182.8 GiB0 B
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)1,9550.931.121.871042.12655.30.890.651.0000.5182.16 GiB0 B
milvusHNSW M=16 efConstruction=128, ef=100, metric COSINE, Strong consistency searches; approximate only (no exact mode)1,1782.322.893.83408.51258.00.560.641.0000.518213 MiB10.86 MiB
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 off2,1604.946.007.74195.8428.50.881.060.9650.519892.7 MiB2.57 GiB
sqlexact VECTOR_DISTANCE cosine, no vector index (full scan)5864.024.576.02242.9602.21.221.201.0000.518532.2 MiB5.15 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 search1,4863.684.075.27269.1576.05.036.901.0000.518-2.9 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)6090.871.041.291125.74001.30.690.541.0000.51899.93 MiB7.56 MiB
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)4161.331.521.76739.42668.90.460.491.0000.5182.61 GiB2.39 MiB
sql-diskannDiskANN (preview) via VECTOR_SEARCH, cosine, build {"StartId":"306", "L":"48", "M":"8", "R":"48"}; the exact mode scans the same table6113.694.205.57263.3682.31.031.110.9650.526529.9 MiB4.52 MiB
chromaHNSW M=16 ef_construction=128, ef_search=100 (Chroma default), cosine; approximate only (no exact mode)8202.112.382.63468.9777.20.470.471.0000.51844.93 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 memory9914.204.565.08234.5574.00.530.591.0000.518117.5 MiB0 B
mongodbvectorSearch index, HNSW maxEdges=16 numEdgeCandidates=128, float32 binData, cosine, numCandidates=20x hits (min 100); exact mode = $vectorSearch exact:true2,8961.411.661.99693.42115.30.310.281.0000.518646.8 MiB148.12 KiB
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 searches4,3381.912.132.36514.31136.42.043.511.0000.518-9.72 MiB

Details per target

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

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.