A benchmarking tool for vector databases, written in Rust. Measures upload throughput, search QPS, latency percentiles (p50/p95/p99), and recall for vector search engines.
| Engine | Client Library | Protocol | Distance Metrics | Metadata Filters |
|---|---|---|---|---|
| Redis (RediSearch) | redis 1.3 |
Redis protocol | L2, Cosine, IP | Yes |
| VectorSets | redis 1.3 |
Redis protocol | L2, Cosine | Yes |
| Elasticsearch | elasticsearch 8.15 |
HTTP/REST | L2, Cosine | Yes |
| OpenSearch | opensearch 2.4 |
HTTP/REST | L2, Cosine | Yes ******* |
| Qdrant | qdrant-client 1.18 |
gRPC | L2, Cosine, Dot | Yes |
| PgVector | postgres 0.19 + pgvector 0.4 |
PostgreSQL | L2, Cosine | Yes |
| Weaviate | tonic 0.12 / prost 0.13 (gRPC) + reqwest (REST) |
gRPC (search) + HTTP/REST (schema) ** | L2, Cosine, Dot | Yes |
| Milvus | reqwest (REST API v2) |
HTTP/REST | L2, Cosine, IP | Yes |
| MongoDB (Atlas Search) | mongodb 3 (sync) |
MongoDB protocol | Euclidean, Cosine, Dot | Yes ******** |
| Valkey (Valkey Search) | redis 1.3 * |
RESP protocol | L2, Cosine, IP | Yes |
| Turbopuffer | turbopuffer-client 0.0.4 |
HTTP/REST (cloud) | Cosine, Euclidean | Yes |
| Dragonfly (Dragonfly Search) | redis 1.3 |
RESP protocol | L2, Cosine, IP | Yes *** |
| Vertex AI (Vector Search) | reqwest (Vertex AI REST v1) |
HTTP/REST (cloud) | L2, Cosine, Dot | Yes **** |
| Chroma | reqwest (Chroma v2 REST API) |
HTTP/REST | L2, Cosine, IP | Yes ***** |
| KiviDB | redis 1.3 |
RESP protocol | L2, Cosine, IP | Yes ****** |
* Valkey client note: Valkey GLIDE has no published Rust crate (valkey-io/valkey-glide#828, closed NOT_PLANNED). The GLIDE maintainers recommend using redis-rs for Rust and upstream their improvements to it. The redis crate works with Valkey since it speaks the same RESP protocol.
** Weaviate protocol note: Vector search runs over Weaviate's gRPC API (port 50051) by default — the high-throughput query path used by the official clients (packed binary vectors). Schema management, upload, and search-time ef tuning use the REST API (v1). Filtered searches also run over gRPC — metadata filter conditions are translated to the gRPC Filters message. The tool falls back to the slower GraphQL-over-HTTP search path only when a filter condition can't be expressed in gRPC or when WEAVIATE_USE_GRAPHQL is set. Override the gRPC port with WEAVIATE_GRPC_PORT.
*** Dragonfly note: Uses Dragonfly Search (Beta), the RediSearch-compatible FT.* subset Dragonfly ships (FT.CREATE/FT.SEARCH/FT.INFO/FT.DROPINDEX, VECTOR FLAT/HNSW, *=>[KNN k @field $blob AS score]). This engine supports vector KNN + metadata filtering — Dragonfly Search implements RediSearch TAG/NUMERIC/TEXT hybrid filtered KNN ((prefilter)=>[KNN...], verified live against df-v1.38.1), so the engine indexes the dataset's metadata schema and applies filter conditions exactly like Redis/Valkey: keyword/int/float/bool/datetime/uuid datatypes, match/match_any/range, and AND/OR/nested boolean. GEO is the one unsupported filter type (Dragonfly's geo-query parser rejects the $param placeholders the shared RediSearch builder emits, like Chroma/Milvus). No mixed (search+update) workload, no quantization. Dragonfly Search supports only the float32 vector type, so vectors are always encoded as FLOAT32. Runs over the RESP protocol via Docker; set the host port with DRAGONFLY_PORT (default 6385). Upload concurrency/batch come from the engine config but can be overridden at runtime with DRAGONFLY_UPLOAD_PARALLEL / DRAGONFLY_UPLOAD_BATCH_SIZE (env takes precedence) — managed Dragonfly Cloud resets connections under the default 100-thread upload burst on larger-dimensional datasets, so cloud runs set DRAGONFLY_UPLOAD_PARALLEL=16; search throughput is unaffected by upload concurrency.
**** Vertex AI note: Uses Vertex AI Vector Search (Google Cloud) — a STREAM_UPDATE tree-AH index streamed with upsertDatapoints, queried with findNeighbors. Cloud-only (no local server), like Turbopuffer. Metadata filters are supported: on upload, string/labels fields become categorical restricts and int/float fields become numericRestricts on each datapoint; on query, match/range conditions translate to Vertex query restrictions over REST and both gRPC transports. Vertex restrictions AND across fields and OR within a field's allowList, so an and of per-field conditions maps directly — but a filter Vertex cannot express (cross-field or, nested boolean, a numeric match_any IN-list, or geo) is a hard error rather than a silently partial filter. Mixed workload (--update-search-ratio) is supported: each worker interleaves S findNeighbors searches with U single-datapoint upsertDatapoints updates, reporting search recall/latency alongside update RPS/latency. Required: VERTEX_PROJECT; auth is VERTEX_ACCESS_TOKEN if set, else gcloud auth print-access-token. Optional: VERTEX_REGION (default us-central1), VERTEX_MACHINE_TYPE (default e2-standard-16), VERTEX_DEPLOY_TIMEOUT_SECS (default 3600), and index-tuning knobs VERTEX_APPROX_NEIGHBORS / VERTEX_LEAF_EMBEDDING_COUNT / VERTEX_LEAF_SEARCH_PERCENT. Deploying an index takes tens of minutes; to skip the create+deploy step, point at an already-deployed index with VERTEX_INDEX, VERTEX_INDEX_ENDPOINT, and VERTEX_DEPLOYED_INDEX_ID (in that case the tool leaves those resources in place on cleanup). Query-time recall/latency is tuned per search config via search_params.fraction_leaf_nodes_to_search_override (0..1) and num_candidates (→ approximateNeighborCount). A config's num_candidates is honored (clamped to top, which Vertex requires as the floor); when it's unset the query runs at the index's own configured approximateNeighborsCount sent explicitly — never Vertex's silent 0 "use index default" sentinel — and the effective knobs are logged per config (Vertex effective search knobs: approximateNeighborCount=… (config|index-default), …) so a sweep point is honestly labeled rather than silently measured at the default (fairness gate, #200). Upload streams upsertDatapoints concurrently (upload_params.parallel); each upsertDatapoints request is bounded by payload size, so very wide datasets may need a smaller batch_size than the default 1000 to stay under the request limit. Batch ingest (experimental, #187): setting VERTEX_GCS_STAGING_BUCKET switches the index to BATCH_UPDATE and, on upload, stages every datapoint to a JSONL object under gs://<bucket>/vdbb-batch/<display-name>/ and triggers a single index rebuild via contentsDeltaUri — avoiding the per-project streaming write quota on large corpora (VERTEX_BATCH_THRESHOLD, default 100000, is the recommended cross-over size). This path was live-validated (1000×8d ingested to vectorsCount=1000 in ~1 min on a fresh BATCH_UPDATE index); it ships behind the opt-in env var, so leave the bucket unset for the default streaming ingest.
***** Chroma note: Uses Chroma (OSS) via its v2 REST API — a collection of records (ids + embeddings + scalar metadatas) queried with query + a where document. Metadata filters map directly onto the canonical model: match → $eq, match_any → $in, range → $gte/$gt/$lte/$lt, and AND / OR / nested boolean to Chroma's native $and / $or (which nest arbitrarily). Supported datatypes: keyword, int, float, bool, uuid, datetime (stored as epoch-seconds int, like Milvus, so numeric range operators apply). Full-text ({match:{text}}) is supported via where_document $contains — the text-typed field's value is uploaded as each record's Chroma document. NOT supported by Chroma's metadata engine (those conditions are dropped, like Dragonfly's documented limits): geo-radius and multi-valued labels arrays (Chroma metadata values are scalar only). Runs over HTTP/REST via Docker; set the host port with CHROMA_PORT (default 8000, test compose maps 8003), and optionally CHROMA_COLLECTION / CHROMA_TENANT / CHROMA_DATABASE. Distance space (l2/cosine/ip) is set per collection from the dataset metric.
****** KiviDB note: KiviDB is a Redis-wire-compatible (RESP2) data store that implements a RediSearch-compatible FT.* subset (FT.CREATE/FT.SEARCH/FT.INFO/FT.DROPINDEX, VECTOR HNSW/FLAT, *=>[KNN k @field $blob AS score]). Supports vector KNN + metadata filtering: TAG/NUMERIC/TEXT datatypes, match/match_any/range (inclusive and exclusive bounds), and AND/OR/nested boolean, all verified live against filtered ground truth. KiviDB genuinely pre-filters (measured recall ~1.0 down to ~2% filter selectivity), so its filtered numbers are meaningful. KiviDB's FT.CREATE supports only the float32 vector type.
The filter path is not the shared RediSearch builder used by Redis/Valkey/Dragonfly — KiviDB's FT.SEARCH diverges from RediSearch in ways that would otherwise produce silently wrong recall (issue #205), so the engine has its own emitter (kividb_filter in kividb.rs, where every divergence is documented with the measurement behind it). The headline one: KiviDB does not substitute $param placeholders in a filter expression, in any query form — a plain FT.SEARCH fails identically to a hybrid one, so this is not a prefilter-specific quirk — and all filter values are therefore inlined as literals. It also does not accept RediSearch backslash escaping, RediSearch's ( exclusive-range marker, or - negation, and it reads a spaced intra-brace TAG-OR (@f:{a | b}) as match-all.
Not supported on KiviDB, and therefore a hard error rather than a silently dropped condition (following vertex.rs; a dropped clause would run kNN over the whole corpus and publish a recall for a filter that was never applied):
- GEO — KiviDB's index schema has no GEO field type at all, so the field is never indexed (unlike Dragonfly, which has a GEO type but rejects this tool's
$paramgeo bounds at the query-parser level). - Multi-valued
labelsarrays — a KiviDB TAG value is atomic, never split on a separator (FT.CREATErejects theSEPARATORmodifier, and@f:{b}does not match a storeda;b;c), somatch_anyover a labels field could only ever match the whole joined string. - Multi-term or non-alphanumeric full-text terms — KiviDB's TEXT matcher handles a single alphanumeric term per
@field:(...)clause and returns zero documents otherwise. - TAG filter values containing
|,@,(or)— inexpressible with no escaping mechanism.|is always the TAG-OR separator (under-match),@degrades the whole query to match-all (over-match), and(/)are parsed as grouping as soon as the clause sits inside a group, which every non-trivial filter does (@t:{zz(zz} @u:{blue}returns 0;(@t:{zz)zz})returns the whole corpus). This is a query-side limit only: a stored value containing one of these is inert — measured, a storeda|bis not split, and a corpus seeded with such values returns identical counts to a clean one for every clause shape the builder emits — so uploads are never rejected for it. That matters in practice: h-and-m stores a paren inprod_nameon 3,900 documents and benchmarks correctly, because none of the 586 distinct filter values in its shipped queries contains one.
The first two are decidable from the dataset schema, so KiviDB rejects them in the configure phase — before the index is created and before the corpus is read — leaving no partial upload, no orphan result file and no populated keyspace behind.
Which shipped datasets this excludes. 3 of the 53 datasets in
datasets/datasets.jsoncannot run on KiviDB at all and now abort instead of producing a result file:random-geo-radius-100-angular-filtersandrandom-geo-radius-2048-angular-filters(geo), andarxiv-titles-384-angular-filters(multi-valuedlabels— the only shipped dataset that declares alabelsfield). Before this was fixed these three did run and published a recall near 0.0, which was visibly wrong; they now publish nothing at all, and no result file records the rejection, so--plotsimply shows a gap for KiviDB on those datasets. If you are sweeping KiviDB across everything, either exclude the three from--datasets(arxiv-titles-384-angular-no-filtersis the pure-KNN twin of the arxiv one) or pass--exit-on-error falseso the rest of the sweep continues. Every other dataset — including all filtered ones — is unaffected.
One real protocol difference worth knowing: KiviDB's FT.INFO does not expose RediSearch's num_docs/percent_indexed — it reports HNSW graph state directly instead (hnsw_live_count, hnsw_compaction_in_progress), because it builds each vector's HNSW entry synchronously inside the HSET that stores it (no async backfill phase exists to report progress on); wait_for_indexing polls those fields instead and returns immediately as a result. RESP2 only (no RESP3 opt-in). Set the host port with KIVIDB_PORT (default 6380 — KiviDB's own default listen port, not Redis's 6379).
******** MongoDB note — which knobs MongoDB actually honours: MongoDB Vector Search exposes build-time HNSW tuning and no query-time ef.
collection_params.hnsw_config is forwarded into the vectorSearch index as the hnswOptions sub-document — MongoDB's own spelling, since it rejects the HNSW-generic names outright (unrecognized fields ["m", "efConstruction"]):
| benchmark key | MongoDB key | server-enforced bounds |
|---|---|---|
M |
hnswOptions.maxEdges |
[16..64] |
EF_CONSTRUCTION |
hnswOptions.numEdgeCandidates |
[100..3200] |
Values are forwarded verbatim, never clamped: an out-of-range value fails index creation loudly rather than silently benchmarking a different index. Note the server elides default-valued options when reporting a definition back, so {maxEdges:16, numEdgeCandidates:100} (the defaults) is indistinguishable from an untuned index.
At query time, numCandidates is the only recall/latency dial — there is no ef/efSearch, and $vectorSearch silently ignores unknown stage fields, so a stray search_params.ef is a no-op rather than an error. The benchmark sends numCandidates = top × search_params.num_candidates, i.e. num_candidates is a multiplier here, unlike elasticsearch.rs and vertex.rs where the same key is an absolute count. experiments/configurations/mongodb-single-node.json therefore sweeps M × EF_CONSTRUCTION at build time and num_candidates at query time; it previously swept a search_params.ef that MongoDB never honoured (issue #216).
******* OpenSearch note: Connection is set with OPENSEARCH_PORT (default 9200), OPENSEARCH_INDEX (default bench), OPENSEARCH_TIMEOUT seconds (default 300), and OPENSEARCH_USER / OPENSEARCH_PASSWORD.
Shard count is read from collection_params.number_of_shards. Leave it unset to inherit the cluster default — but note that default is 1 on open-source OpenSearch and historically 5 on Amazon OpenSearch Service, and shard count materially changes vector indexing speed and precision, so a run that leaves it unset is not comparable across engine versions or deployments. The effective value is printed per config (OpenSearch: HNSW { … }, number_of_shards: 5|cluster-default), and a present-but-non-integer value is a hard error rather than a silent fallback to the default.
Every OpenSearch config shipped in this (Rust) tree pins it explicitly, so no published run inherits a default (#211). The resolved value is also written into every result file as params.number_of_shards ("cluster-default" if a custom config left it unpinned), so a run can be audited without reverse-engineering its config name. The legacy v0/ Python tree is not pinned and cannot be: v0/engine/clients/opensearch/configure.py hardcodes its index settings and ignores number_of_shards entirely, while v0/engine/clients/elasticsearch/configure.py pins 1 — so v0's own ES-vs-OS pairing still has the #211 asymmetry.
| Config file | number_of_shards |
Use for |
|---|---|---|
experiments/configurations/opensearch-single-node.json |
1 |
Open-source / single-node OpenSearch. Matches elasticsearch.rs, which pins ES_NUMBER_OF_SHARDS = 1, so this is the ES-vs-OS head-to-head pairing (see the caveat below). |
experiments/configurations/opensearch-5-shard.json |
5 |
A 5-shard index — the per-index default Amazon OpenSearch Service inherited from Elasticsearch and still applies on legacy/ES-derived domains. Same HNSW sweep, config names prefixed opensearch-5-shard-. |
The 5-shard file is named for what it does, not for a deployment: a modern Amazon OpenSearch Service domain defaults to 1 shard per index, not 5, so "managed" would have been a claim about someone else's default that is no longer reliably true. Run it on a managed domain if you want to reproduce a legacy default, and set number_of_shards to whatever your own domain actually reports if you want to model it.
Do not compare opensearch-5-shard-* numbers against Elasticsearch. elasticsearch.rs always builds a 1-shard index, so a 5-shard OpenSearch result against an ES result differs in shard count as well as engine and is not a head-to-head. Pair ES only with opensearch-single-node.json. Even that pairing is not yet fully matched: ES still bulk-loads with refresh_interval: "10s" while OpenSearch uses -1, so ES pays refresh work during ingest that OpenSearch does not — tracked in #240.
Pick one file per run — they are exact and survive future renames:
--engines-file experiments/configurations/opensearch-single-node.json
--engines-file experiments/configurations/opensearch-5-shard.json
Avoid selecting by glob here: --engines 'opensearch-m-*' matches only 6 of the 7 sweep points — it silently drops the opensearch-default (m=16, ef_construction=100) baseline, whose name lacks the opensearch-m- prefix — and a bare --engines 'opensearch-*' matches both files, sweeping two different shard counts into one report.
Retries against a managed domain. Amazon OpenSearch Service returns states an open-source single-node cluster never produces: HTTP 429 on bulk (its knn.algo_param.index_thread_qty defaults to 1, so HNSW graph building is single-threaded and cannot drain as fast as a parallel uploader pushes), 400 snapshot_in_progress_exception on delete (automated snapshots cannot be disabled), 503 process_cluster_event_timeout_exception on create, and 502/504 from the front door on force-merge. All four paths retry with jittered exponential backoff, tunable with:
| Variable | Default | Applies to |
|---|---|---|
OPENSEARCH_BULK_MAX_RETRIES |
8 |
bulk upload |
OPENSEARCH_BULK_RETRY_BASE_MS |
500 |
bulk upload |
OPENSEARCH_INDEX_OP_MAX_RETRIES |
10 |
create / delete / refresh / force-merge |
OPENSEARCH_INDEX_OP_RETRY_BASE_MS |
2000 |
create / delete / refresh / force-merge |
OPENSEARCH_SEARCH_MAX_RETRIES |
5 |
search |
OPENSEARCH_SEARCH_RETRY_BASE_MS |
50 |
search |
OPENSEARCH_SEARCH_RETRY_BUDGET_MS |
2000 |
search (total wall-clock ceiling per query) |
Search retries are deliberately short and additionally bounded by a wall-clock budget, because a single transport attempt can block for OPENSEARCH_TIMEOUT. Backoff is excluded from the reported latency: a retried query is billed only for the attempt that produced its result, so the percentiles stay comparable with engines that do not retry. Queries that needed a retry are counted and reported separately (⚠ N of M search queries succeeded only after a retry), since clean latency figures would otherwise hide that the server was shedding load.
Dropped queries are handled uniformly across all engines, not here: failed_queries is recorded in the results JSON and warned about, and --fail-on-dropped-queries makes it fatal. See that flag's help for why it is off by default.
Runbook: benchmarking against Vertex AI
# 1. Auth + enable the API (once per project).
gcloud config set project <your-project>
gcloud services enable aiplatform.googleapis.com
export VERTEX_PROJECT=<your-project>
export VERTEX_REGION=us-central1
# VERTEX_MACHINE_TYPE defaults to e2-standard-16 (the smallest type the default
# shard size accepts — e2-standard-2 is rejected at deploy).
# 2. Full run — creates + DEPLOYS a fresh index (slow, ~30-40 min), uploads,
# searches, then tears the resources back down.
vector-db-benchmark --engines vertex-default --datasets random-100 --skip-if-exists false
# 3. Fast iteration — reuse an already-deployed index and skip the deploy.
# (grab the ids the first run printed; cleanup then LEAVES them in place)
export VERTEX_INDEX=projects/P/locations/us-central1/indexes/ID
export VERTEX_INDEX_ENDPOINT=projects/P/locations/us-central1/indexEndpoints/EID
export VERTEX_DEPLOYED_INDEX_ID=vdb_benchmark_deployed
vector-db-benchmark --engines vertex-default --datasets random-100 --skip-if-exists falseThe gated integration_vertex test drives this same flow and asserts a recall floor; it self-skips unless VERTEX_PROJECT is set:
VERTEX_PROJECT=<your-project> \
cargo test --test integration_vertex --release -- --nocapturedocker run --rm --network=host redis/vector-db-benchmark:latest \
--host localhost --engines 'redis-single*' --datasets glove-25-angular
(...)
============================================================
Running experiment: redis-single-node - glove-25-angular
============================================================
Experiment stage: Configure
Using algorithm hnsw with config {'M': 16, 'EF_CONSTRUCTION': 128}
Experiment stage: Upload
Reading dataset from datasets/glove-25-angular/...
Read 1183514 vectors (25d) in 0.82s
Upload time: 12.3s (96,220 records/sec)
Experiment stage: Search
Running search 0: ef=128, parallel=4
→ QPS: 3214.5, Precision: 0.9785
Running search 1: ef=128, parallel=8
→ QPS: 5891.2, Precision: 0.9785
Experiment stage: Done
# Show help
docker run --rm redis/vector-db-benchmark:latest --help
# List available datasets and engines
docker run --rm redis/vector-db-benchmark:latest --describe datasets
docker run --rm redis/vector-db-benchmark:latest --describe engines
# Run a benchmark against a local Redis instance
docker run --rm --network=host \
-v $(pwd)/datasets:/code/datasets \
-v $(pwd)/results:/code/results \
redis/vector-db-benchmark:latest \
--host localhost --engines 'redis-single*' --datasets glove-25-angular# Start Redis
docker run -d --name redis -p 6379:6379 redis:8.8.0
# Run benchmark
docker run --rm --network=host \
-v $(pwd)/datasets:/code/datasets \
-v $(pwd)/results:/code/results \
redis/vector-db-benchmark:latest \
--host localhost --engines redis-docker-test --datasets random-100
# Clean up
docker stop redis && docker rm redis# Full integration test (downloads h-and-m dataset ~200MB)
make docker-integration
# Fast smoke test (uses random-100 dataset baked into image, 228KB)
make docker-integration-fast# Prerequisites: Rust toolchain, libhdf5-dev, pkg-config
cargo build --release --bin vector-db-benchmark
# Run
./target/release/vector-db-benchmark --help
./target/release/vector-db-benchmark --describe datasets
./target/release/vector-db-benchmark \
--host localhost --engines 'redis-single*' --datasets glove-25-angularUsage: vector-db-benchmark [OPTIONS]
Options:
--engines <PATTERN> Engine config patterns (wildcards supported) [default: *]
--engines-file <PATH> Path to JSON file with custom engine configs
--allow-partial-configs Run even if some experiments/configurations/*.json
failed to load (off by default; see "Engine
Configurations" below)
--datasets <PATTERN> Dataset patterns (wildcards supported) [default: *]
--host <HOST> Redis/engine host [default: localhost]
--parallels <N,N,...> Filter by parallel thread counts
--ef-runtime <N,N,...> Filter by ef runtime values
--skip-upload Skip the upload phase
--skip-search Skip the search phase
--skip-if-exists Skip if results already exist
--exit-on-error Stop on first error
--timeout <SECS> Timeout in seconds [default: 86400]
--update-search-ratio <U:S> Mixed benchmark: interleave U updates per S searches
--describe <TYPE> Describe available 'datasets' or 'engines'
-v, --verbose Verbose output for --describe
--plot <OUTPUT.svg> Render a QPS-vs-precision trade-off chart from results/
-h, --help Print help
Each engine config gets its own RediSearch index and keyspace, derived from the
config name: index "<base>:<config>" (base idx) with docs keyed
"<config>:<id>". This lets an M×EF_CONSTRUCTION sweep run all its configs
against one server and coexist, so you can upload every config once and then
search each in a later --skip-upload pass — each config reads its own graph, and
memory is reported per-index via FT.INFO (issue #151-4).
REDIS_INDEX_NAME/VALKEY_INDEX_NAME/DRAGONFLY_INDEX_NAME/KIVIDB_INDEX_NAMEnow set the base namespace, not the whole index name; the config name is always appended. Set<VAR>_EXACT=1to use the base verbatim (single-config "point at an out-of-band index" case — combining it with >1 config for that engine is a startup error).- Indexes/keys written by any pre-#151-4 binary are incompatible — re-upload.
--skip-uploadagainst a missing/mismatched index now hard-errors instead of silently writing arecall 0.0file. - Two-phase coexistence sweep:
… --keep-data(upload + search all configs, keep the data), then… --skip-upload --keep-data --skip-if-exists false(search each against its own index). Per-config prefixing stores N copies of otherwise-identical sweep docs, so keyspace bytes scale ×N — the intended trade for isolation. - Shared-corpus (upload-once / build-many) mode — Redis, opt-in (#188): for a
sweep over one dataset where only the index params (M / EF_construction)
differ, the corpus is identical across configs, so re-uploading it per config is
wasted work (N× ingest — dominant at 10M+). Set
REDIS_KEY_PREFIX=<shared>:to make all configs share ONE corpus keyspace: the first config uploads it, and every later config skips the re-upload (detected by a corpus key-count check) and just builds its own per-config index over the shared docs (the index name stays per-config). In this mode the index is dropped withoutDDso the shared corpus survives across configs; flush the DB (or the shared prefix) when the sweep finishes. Unset (the default) keeps full per-config isolation — no behavior change.
VSIM FILTER has no date type and its comparison operators are numeric, so since
PR #230 a datetime metadata value is stored as an epoch-seconds number on
VADD … SETATTR, and range/equality filters compare against epoch numbers. A
corpus uploaded by any pre-#230 binary holds ISO-8601 strings instead, and
VSIM coerces a non-numeric attribute to 0 in a numeric comparison — so a
--skip-upload search against such a corpus silently returns the wrong documents
(a two-sided range matches nothing → recall 0; a one-sided lt/lte matches
everything). Re-upload before searching.
Unlike Redis/Valkey — where --skip-upload against a missing or mismatched index
hard-errors (redis_utils::ensure_index_exists) — VectorSets has no such
guard: it uses a single hardcoded key (idx), so there is not even a name
change for the tool to detect. The stale corpus is found, the run succeeds, and
only the recall number is wrong.
Render a QPS-vs-precision trade-off plot (SVG, no dependencies) from existing *-summary.json results — one colored series per engine, filtered by --engines/--datasets:
# Compare all engines on one dataset
vector-db-benchmark --plot tradeoff.svg --engines '*' --datasets glove-100-angularConcurrency model. A search config's parallel: N (and the --parallels filter) runs N OS threads that share a single in-memory copy of the dataset and query set — not N processes. Raising parallel from 1 to 100 adds ~N worker threads and N engine connections (on the order of tens to low-hundreds of MB), not N× the dataset. Each worker accumulates only its own small latency/quality samples; nothing per-query is retained beyond scalar metrics.
Peak client memory ≈ raw dataset size, during upload. The client loads the whole dataset into RAM for the upload phase — roughly vector_count × dim × 4 bytes (e.g. cohere-768-1M ≈ 1M × 768 × 4 ≈ 3 GB, plus reader/allocator overhead) — then frees it before the search phase. Search holds only the (far smaller) query set plus the per-thread sample buffers, so search-phase memory is largely independent of parallel. A client with RAM ≥ ~2× the raw dataset size runs comfortably; increasing search concurrency does not materially change client memory.
If you saw the client machine hang/OOM specifically when the client count rose (e.g. to 100) — as reported for the original Python
vector-db-benchmark, which used a process-per-client model that copied the dataset into every worker process — this Rust implementation does not reproduce that: workers are threads sharing one copy, and the uploaded vectors are released before searching. Size the client for the upload peak above, not for the client count.
The --update-search-ratio flag enables mixed workload benchmarks that interleave vector updates with searches. This measures how search performance is affected by concurrent write operations.
# 1 update per 10 searches
vector-db-benchmark --engines redis-docker-test --datasets random-100 \
--update-search-ratio 1:10
# 1 update per 5 searches (heavier write load)
vector-db-benchmark --engines vectorsets-docker-test --datasets h-and-m-2048-angular \
--update-search-ratio 1:5The ratio format is U:S where U = number of updates and S = number of searches per cycle. Each worker thread performs S searches followed by U updates in a loop.
Supported engines: Redis, VectorSets, Valkey
Results JSON includes separate metrics for both operation types:
{
"results": {
"rps": 5891.2,
"mean_precision_at_returned": 0.9785,
"mean_recall": 0.9785,
"p50_time": 0.00032,
"p95_time": 0.00089,
"p99_time": 0.00142,
"update_rps": 589.1,
"update_mean_time": 0.00045,
"update_p50_time": 0.00041,
"update_p95_time": 0.00098,
"update_p99_time": 0.00156,
"update_search_ratio": "1:10"
}
}Omitting the flag preserves the standard search-only benchmark behavior.
Multi-tenancy benchmarks model many tenants sharing one index: every search is scoped to a single tenant via an exact keyword-equality filter on a tenant field (schema: { "<field>": "keyword" } — the field is named a in random-768-100-tenants and tenant in random-768-25-tenants), and recall is measured against the nearest neighbours within that tenant only. This reuses the standard keyword-TAG filter path (no engine-specific code) and mirrors upstream qdrant/vector-db-benchmark's random-768-*-tenants scenario. Two are registered: random-768-100-tenants (1M points over 100 tenants) downloads, and is the reproducible one to benchmark with — note its tenant field is named a (schema: { "a": "keyword" }), matching the published tarball; random-768-25-tenants is a locally-generated placeholder using a tenant field. The per-query filter looks like {"and":[{"tenant":{"match":{"value":"tenant_7"}}}]} for the 25-tenant fixture, and {"and":[{"a":{"match":{"value":"WLRCI"}}}]} for the downloadable 100-tenant set (whose tenant ids are random 5-character strings). Because ground truth is tenant-local, recall is a strong isolation signal — a leaked cross-tenant document displaces a correct neighbour and lowers recall — and the tests assert exact per-query recall (== 1.0 against an exact search, one query per tenant), so any single cross-tenant leak fails the check. Redis and Valkey are covered end-to-end (over both RESP2 and RESP3) by the test_binary_{redis,valkey}_tenancy integration tests. (Recall is necessary but not by itself sufficient to prove zero leakage; strict per-id membership checking is a possible future hardening.)
Most datasets are automatically downloaded on first use. The image includes random-100 (228KB) for quick smoke tests. (Exception: random-768-25-tenants is a locally-generated placeholder with no public download link — use the downloadable random-768-100-tenants instead; see the Multi-tenancy section.)
| Dataset | Dimensions | Train size | Test size | Neighbors | Distance |
|---|---|---|---|---|---|
| LAION Image Embeddings (512D) | |||||
| LAION-1M: subset of LAION 400M English (image embedings) | 512 | 1,000,000 | 10,000 | 100 | Cosine |
| LAION-10M: subset of LAION 400M English (image embedings) | 512 | 10,000,000 | 10,000 | 100 | Cosine |
| LAION-20M: subset of LAION 400M English (image embedings) | 512 | 20,000,000 | 10,000 | 100 | Cosine |
| LAION-40M: subset of LAION 400M English (image embedings) | 512 | 40,000,000 | 10,000 | 100 | Cosine |
| LAION-100M: subset of LAION 400M English (image embedings) | 512 | 100,000,000 | 10,000 | 100 | Cosine |
| LAION-200M: subset of LAION 400M English (image embedings) | 512 | 200,000,000 | 10,000 | 100 | Cosine |
| LAION-400M: from LAION 400M English (image embedings) | 512 | 400,000,000 | 10,000 | 100 | Cosine |
| LAION Image Embeddings (768D) | |||||
| LAION-1M: 768D image embeddings | 768 | 1,000,000 | 10,000 | 100 | Cosine |
| LAION-1B: 768D image embeddings | 768 | 1,000,000,000 | 10,000 | 100 | Cosine |
| Standard Benchmarks | |||||
| GloVe-25: Word vectors | 25 | 1,183,514 | 10,000 | 100 | Cosine |
| GloVe-100: Word vectors | 100 | 1,183,514 | 10,000 | 100 | Cosine |
| Deep Image-96: CNN image features | 96 | 9,990,000 | 10,000 | 100 | Cosine |
| GIST-960: Image descriptors | 960 | 1,000,000 | 1,000 | 100 | L2 |
| Text and Knowledge Embeddings | |||||
| DBpedia OpenAI-1M: Knowledge embeddings | 1,536 | 1,000,000 | 10,000 | 100 | Cosine |
| DBpedia OpenAI-100K: Knowledge embeddings | 1,536 | 100,000 | 5,000 | 10 | Cosine |
| LAION Small CLIP: Small CLIP embeddings | 512 | 100,000 | 1,000 | 100 | Cosine |
| Sparse Vectors (learned/lexical sparse embeddings — Qdrant is the only engine with a sparse path) | |||||
| MS MARCO Sparse-100K: SPLADE-style sparse embeddings | sparse | 100,000 | 6,980 | 10 | Dot |
| MS MARCO Sparse-1M: SPLADE-style sparse embeddings | sparse | 1,000,000 | 6,980 | 10 | Dot |
| Yandex Datasets | |||||
| Yandex T2I: Text-to-image embeddings | 200 | 1,000,000 | 100,000 | 100 | Dot |
| Random and Synthetic | |||||
| Random-100: Small synthetic dataset | 100 | 100 | 9 | 9 | Cosine |
| Random-100-Euclidean: Small synthetic dataset | 100 | 100 | 9 | 9 | L2 |
| Filtered Search Datasets | |||||
| H&M-2048: Fashion product embeddings (with filters) | 2,048 | 105,542 | 10,000 | ≤ 25 † | Cosine |
| H&M-2048: Fashion product embeddings (no filters) | 2,048 | 105,542 | 10,000 | 10 | Cosine |
| ArXiv-384: Academic paper embeddings (with filters) | 384 | 2,205,995 | 10,000 | 100 | Cosine |
| ArXiv-384: Academic paper embeddings (no filters) | 384 | 2,205,995 | 10,000 | 100 | Cosine |
| Random Match Keyword-100: Synthetic keyword matching (with filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
| Random Match Keyword-100: Synthetic keyword matching (no filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
| Random Match Int-100: Synthetic integer matching (with filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
| Random Match Int-100: Synthetic integer matching (no filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
| Random Range-100: Synthetic range queries (with filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
| Random Range-100: Synthetic range queries (no filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
| Random Geo Radius-100: Synthetic geo queries (with filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
| Random Geo Radius-100: Synthetic geo queries (no filters) | 100 | 1,000,000 | 10,000 | 100 | Cosine |
| Random Match Keyword-2048: Large synthetic keyword matching (with filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
| Random Match Keyword-2048: Large synthetic keyword matching (no filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
| Random Match Int-2048: Large synthetic integer matching (with filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
| Random Match Int-2048: Large synthetic integer matching (no filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
| Random Range-2048: Large synthetic range queries (with filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
| Random Range-2048: Large synthetic range queries (no filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
| Random Geo Radius-2048: Large synthetic geo queries (with filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
| Random Geo Radius-2048: Large synthetic geo queries (no filters) | 2,048 | 100,000 | 1,000 | 100 | Cosine |
| Random Match Keyword Small Vocab-256: Small vocabulary keyword matching (with filters) | 256 | 1,000,000 | 10,000 | 100 | Cosine |
| Random Match Keyword Small Vocab-256: Small vocabulary keyword matching (no filters) | 256 | 1,000,000 | 10,000 | 100 | Cosine |
| Multi-Tenancy (many tenants share one index; every query scoped to one tenant) | |||||
Random-768-100-tenants: 100 tenants, per-tenant scoped queries (tenant field a) |
768 | 1,000,000 | 200 | 25 | Cosine |
† The "Neighbors" column is the ground-truth width, and it bounds what recall can mean. H&M-2048 (with filters) is not uniform: its 10,000 queries carry between 1 and 25 true neighbours (mean 23.4; 931 queries have fewer than 25), because a filtered query only has as many true neighbours as the filter admits. Our mean_recall divides by the neighbours that actually exist, so a 1-neighbour query can still score 1.0; upstream qdrant/vector-db-benchmark always divides by top, so the same query caps at 1/top. A run's metrics_schema.ground_truth block reports the measured profile and the resulting ceiling — at top: 100 H&M's ceiling is 0.233, so a calibration_precision above that is unreachable by construction. Configs that do not set top derive it from the ground-truth row width (25 here, 10 for the no-filters variant), which is why this matters mostly when top is set explicitly.
The sparse-vector, hybrid (dense+sparse fusion), and multi-datatype filter code
paths ship with locally-generated synthetic datasets — small, deterministic
(fixed-seed) fixtures with no public download link. (For sparse vectors at
realistic scale, prefer the downloadable msmarco-sparse-100K / -1M in the
table above; the synthetic fixture exists to exercise the code path fast. Both
work: a sparse dataset's ground truth is read from either neighbours.jsonl —
one JSON array of ids per query line, what the generator writes — or the binary
results.gt those downloads ship.)
Generate them once with:
cargo run --release --bin generate-dataset # writes into ./datasets
# or a subset / custom location:
cargo run --release --bin generate-dataset -- --only sparse --out-dir /tmp/dsThis writes four datasets under datasets/ (each in the exact on-disk layout
its reader expects), registered in datasets/datasets.json:
| Dataset | Type | Dims | Distance | Layout |
|---|---|---|---|---|
synthetic-sparse-300 |
sparse |
300 | dot | data.csr + queries.csr + neighbours.jsonl (dot/MIPS ground truth) |
synthetic-hybrid-16 |
hybrid |
16 | l2 | vectors.npy + queries.npy + data.csr + queries.csr + shared neighbours.jsonl |
synthetic-filter-32 |
tar |
32 | l2 | vectors.npy + payloads.jsonl + tests.jsonl (per-query conditions + filtered GT) |
synthetic-selectivity-32 |
tar |
32 | l2 | vectors.npy + payloads.jsonl + tests.jsonl (one rank < K range query per selectivity rung) |
synthetic-filter-32's per-query conditions rotate through keyword, int,
bool and datetime filters (schema color:keyword, size:int, flag:bool, ts:datetime),
each with ground truth brute-forced over only the matching documents, so a high
recall proves the engine actually applied the filter. synthetic-selectivity-32
(2000 docs) instead holds one rank < K range query per rung of a selectivity
ladder (1% / 2% / 5% / 10% / 25% / 50% / 90%), each row annotated with its
selectivity / n_matching, so recall/latency can be reported as a function of
filter selectivity. The generated files are git-ignored — regenerate them on any
checkout with the command above.
Across the filtering engines, metadata conditions support the datatypes
keyword, int, float, bool, datetime (ISO-8601 range),
uuid, geo-radius, and full-text; the compositions match (exact),
match_any (IN-set), range, and boolean AND, OR, and
nested/grouped trees (e.g. (A∧B)∨(C∧D)); plus multi-tenancy (per-tenant
scoped filters). Not every engine supports every feature natively — see each
engine's note above for its exceptions (e.g. Dragonfly is KNN-only; Chroma has no
geo/full-text/array metadata; Vertex errors on cross-field or/nested/geo). Each
(engine × feature) combination is covered by an end-to-end tests/integration_*
recall test that scores against filtered brute-force ground truth, so an engine
that silently drops or mis-applies a filter fails its test.
Example runs against a generated dataset (start the engine first, e.g.
docker compose -f tests/docker-compose.test.yml up -d qdrant redis):
# Sparse (Qdrant):
cargo run --release --bin vector-db-benchmark -- \
--engines qdrant-default --datasets synthetic-sparse-300
# Hybrid dense+sparse fusion (Qdrant):
cargo run --release --bin vector-db-benchmark -- \
--engines qdrant-hybrid --datasets synthetic-hybrid-16
# Filter datatypes (Redis):
cargo run --release --bin vector-db-benchmark -- \
--engines redis-docker-test --datasets synthetic-filter-32Engine configurations live in experiments/configurations/. Each JSON file defines one or more experiment configurations specifying the engine, index parameters, search parameters, and upload parallelism.
Two rules are enforced at load time, and breaking either aborts the run rather than
quietly changing what gets measured. --describe engines is deliberately exempt from the
second one: its job is to diagnose the configuration directory, so it lists the unloadable
files first and then shows the configurations that did load. (A duplicate name aborts
--describe too — there is nothing useful to show when one name means two things.)
namemust be unique across every file in the directory, not just within one file. The name is what--enginesselects and what the result JSON is keyed by, so two definitions sharing a name would mean the reported number came from whichever one happened to load last. A duplicate is an error naming both definitions (#239). The same rule applies to dataset names indatasets/datasets.json.- Every
*.jsonin the directory must parse.serderejects a whole file on one bad entry, so a single typo removes every configuration that file defines — and under a wildcard--engines(the default is*) the sweep would just get smaller and still exit 0. Pass--allow-partial-configsto run anyway; the run then records the offending files underskipped_config_filesin every summary JSON it writes.
Example (redis-docker-test.json):
[
{
"name": "redis-docker-test",
"engine": "redis",
"connection_params": {},
"collection_params": {
"hnsw_config": { "M": 16, "EF_CONSTRUCTION": 128 }
},
"search_params": [
{ "parallel": 1, "search_params": { "ef": 128 } }
],
"upload_params": { "parallel": 8 }
}
]Use --engines with wildcard patterns to select configurations:
vector-db-benchmark --engines 'redis-single*' --datasets 'glove*'
vector-db-benchmark --engines 'vectorsets*' --datasets 'h-and-m*'
vector-db-benchmark --engines 'elasticsearch*' --datasets 'glove*'
vector-db-benchmark --engines 'qdrant*' --datasets 'deep-image*'Or provide a custom file with --engines-file:
vector-db-benchmark --engines-file my_engines.json --datasets glove-25-angularexperiments/configurations/qdrant-hybrid.json runs Qdrant's server-side
reciprocal-rank fusion (RRF) of a dense-vector prefetch and a sparse-vector
prefetch. It requires a type: "hybrid" dataset — running it against an
ordinary dense dataset silently degrades to a plain dense search (there is no
sparse vector to fuse). A hybrid dataset directory must contain all of:
vectors.npy # dense document vectors (npy, row i == point id i)
queries.npy # dense query vectors (npy)
data.csr # sparse document vectors (binary CSR, row-aligned with vectors.npy)
queries.csr # sparse query vectors (binary CSR, row-aligned with queries.npy)
neighbours.jsonl # fused ground truth: one JSON array of ids per query line
Register it in datasets/datasets.json with "type": "hybrid" and the dense
vector_size/distance. The end-to-end hybrid path (collection with a named
dense + named sparse vector, dual-vector upsert, and RRF fusion) is covered
by tests/integration_qdrant.rs::test_binary_qdrant_hybrid, which also
generates a tiny hybrid fixture you can consult for the exact layout.
Datasets are configured in datasets/datasets.json. The tool automatically downloads datasets on first use if a download link is provided.
The quickest way to install all dependencies (Linux/macOS):
make setup # installs libhdf5, pkg-config, and Rust toolchainOr install manually:
- Rust toolchain (install via rustup)
- libhdf5-dev and pkg-config
# Ubuntu/Debian
sudo apt-get install libhdf5-dev pkg-config
# macOS
brew install hdf5 pkg-configmake build # Build release binary
make test # Run unit tests
make check # Clippy + rustfmt
make docker-build # Build Docker imageEach engine has a dedicated integration test that runs against a Docker container:
make integration-test # Redis 8.8.0 (default)
make integration-test-elasticsearch # Elasticsearch 9.4.3
make integration-test-opensearch # OpenSearch 3.7.0
make integration-test-pgvector # PgVector (PostgreSQL 18)
make integration-test-qdrant # Qdrant v1.18.2
make integration-test-weaviate # Weaviate 1.38.2
make integration-test-milvus # Milvus v2.6.19
make integration-test-mongodb # MongoDB Atlas Local 8.0.17
make integration-test-valkey # Valkey Bundle (latest)Each target starts the engine via docker compose -f tests/docker-compose.test.yml, runs the tests, then stops the container.
The untrusted dataset parsers (sparse CSR, NPY, JSONL, and metadata/JSON readers) are fuzzed with cargo-fuzz / libFuzzer to ensure malformed input returns Err instead of panicking/overflowing/OOMing. Run locally with a nightly toolchain:
cargo +nightly fuzz run sparse_reader -- -max_total_time=60 -rss_limit_mb=2048A nightly GitHub Actions workflow (.github/workflows/fuzz.yml) fuzzes each parser at higher effort. See fuzz/README.md for details.
Turbopuffer is cloud-only and requires an API key:
TURBOPUFFER_API_KEY=your-key ./target/release/vector-db-benchmark \
--engines 'turbopuffer*' --datasets random-100src/
lib.rs # Library: readers, data formats
readers/ # HDF5, NPY, JSONL, compound readers
bin/
vector_db_benchmark/
main.rs # CLI entry point
config.rs # Configuration loading
dataset.rs # Dataset resolution and reading
experiment.rs # Experiment runner with calibration
engine/
mod.rs # Engine trait and factory
redis.rs # Redis (RediSearch) engine
vectorsets.rs # VectorSets engine
elasticsearch.rs # Elasticsearch engine
opensearch.rs # OpenSearch engine
qdrant.rs # Qdrant engine (gRPC)
pgvector.rs # PgVector engine (PostgreSQL)
weaviate_grpc.rs # Weaviate gRPC client (generated from vendored v1 protos)
weaviate.rs # Weaviate engine (gRPC search + REST schema)
milvus.rs # Milvus engine (REST)
mongodb_engine.rs # MongoDB Atlas Search engine
valkey.rs # Valkey engine (RESP protocol)
kividb.rs # KiviDB engine (RESP protocol)
turbopuffer.rs # Turbopuffer engine (cloud API)
redis_utils.rs # Shared utils for Redis-protocol engines
experiments/configurations/ # Engine configuration JSON files
datasets/datasets.json # Dataset definitions
tests/
docker-compose.test.yml # Docker services for integration tests
integration_redis.rs # Redis integration tests
integration_elasticsearch.rs # Elasticsearch integration tests
integration_opensearch.rs # OpenSearch integration tests
integration_pgvector.rs # PgVector integration tests
integration_qdrant.rs # Qdrant integration tests
integration_weaviate.rs # Weaviate integration tests
integration_milvus.rs # Milvus integration tests
integration_mongodb.rs # MongoDB integration tests
integration_valkey.rs # Valkey integration tests