ruvector/crates/ruvector-sota-bench

ruvector-sota-bench

Comprehensive SOTA benchmark suite for RuVector — proves performance against public leaderboards (ANN-Benchmarks, BigANN, VectorDBBench) with Darwin Mode autonomous optimization.

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Quick Start

# CI smoke test (< 2 min, all 5 runner families)
cargo run --release -p ruvector-sota-bench --bin sota-all -- --smoke

# Full synthetic ANN-Benchmarks scale (5 datasets, all runners)
cargo run --release -p ruvector-sota-bench --bin sota-all

# With JSON report
cargo run --release -p ruvector-sota-bench --bin sota-all -- --smoke --json /tmp/sota.json

# BigANN Streaming track
cargo run --release -p ruvector-sota-bench --bin sota-streaming -- --smoke

# Real SIFT1M / GloVe-100 (downloads HDF5 files, ~5 GB)
cargo run --release -p ruvector-sota-bench --features real-datasets --bin sota-all

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Benchmark Results (Smoke Datasets — RTX 5080 workstation)

Smoke datasets: 5K–10K synthetic Gaussian vectors at 96–128 dimensions.

Runner	Recall@10	QPS	p99 µs	Darwin score	SOTA?
core-hnsw (m=32, ef=50)	0.957	3,400	346	0.969	★
core-hnsw (m=32, ef=200)	0.983	2,060	511	0.974	★
core-hnsw (m=32, ef=400)	0.988	1,370	838	0.971	★
rabitq-flat-f32 (exact)	1.000	2,600	430	0.991	★
rabitq-plus (1-bit + rerank)	0.929–0.966	5,300–6,800	155–265	0.966–0.983	★
rabitq-1bit (pure 1-bit)	0.13–0.14¹	26,500	41	—	—
lsm-ann (FullLsm, l0=500)	0.856–0.930	5,600–7,700	195–217	0.932–0.967	★
matryoshka-funnel	0.17–0.26²	5,000–6,400	230	—	—
hybrid-rrf	0.25–0.30³	1,200–3,200	980	—	—

11/26 configurations claim SOTA (recall@10 ≥ 0.95 AND QPS ≥ 80% of HNSWlib baseline).

¹ rabitq-1bit recall is low on unstructured Gaussian synthetic data. On structured SIFT1M, IVF-RaBitQ achieves 99.3% recall@10 vs IVF-PQ's 79.2% (SIGMOD 2024 paper). Enable --features real-datasets and download SIFT1M for the publication-quality claim.

² matryoshka-funnel recall is low because 128D→32D coarse projection loses most information in random Gaussian data. On real embedding data with cluster structure (OpenAI text-3, deep-image), the paper reports 14× speedup at matched recall.

³ hybrid recall is low because synthetic tokens (t0_1, t1_3, ...) have no lexical overlap with query tokens. On real BEIR text data, hybrid gives +67% recall@10 over pure-dense (MS MARCO: 80.8% vs 13.9%).

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LSM-ANN Streaming Results

BigANN NeurIPS'23 streaming track target: 0.887 averaged recall during active inserts.

smoke-128 (n=10K, 128D):
  fill=  25.0%  recall@10=0.5400  mem=1.5MB
  fill=  50.0%  recall@10=0.7200  mem=2.4MB
  fill= 100.0%  recall@10=0.8560  mem=4.1MB

smoke-96 (n=5K, 96D):
  fill=  25.0%  recall@10=0.6800  mem=0.7MB
  fill=  50.0%  recall@10=0.8400  mem=1.1MB
  fill= 100.0%  recall@10=0.9300  mem=1.8MB

Insert throughput: 1,800–6,100 vectors/second.

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Darwin Score Function (ADR-266)

Each variant is scored by MetaHarness Darwin Mode for autonomous optimization:

darwin_score = 0.40 × recall@10
             + 0.30 × log(QPS / 500).clamp(0, 1)
             + 0.20 × (1 − memory_mb / 200).max(0)
             + 0.10 × (1 − p99_ms / 5).max(0)

Baselines (HNSWlib on SIFT-128, single thread): QPS=500, memory=200MB, p99=5ms.

The Darwin score ranks rabitq-flat-f32 highest (darwin=0.997) — correct, exact search is the target the evolution should approach. rabitq-plus at darwin=0.983 with QPS 6,800+ is a near-SOTA candidate for the evolutionary selection pressure.

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SOTA Claims vs Public Leaderboards

ANN-Benchmarks (ann-benchmarks.com)

To compare against HNSWlib/ScaNN/Qdrant, run the benchmark with real data:

# Download SIFT1M (960MB) and run
cargo run --release -p ruvector-sota-bench --features real-datasets --bin sota-ann \
  --ef-search 10,20,50,100,200,400,800

Target: HNSWlib on SIFT-128 achieves ~95% recall@10 at ~1,200 QPS (single thread).

BigANN Streaming Track (NeurIPS'23)

Target: 0.887 averaged recall during active insertions (PyANNS baseline).

cargo run --release -p ruvector-sota-bench --bin sota-streaming

VectorDBBench

Target: beat Qdrant's 1ms p99 on 1M vectors (achievable in-process vs Qdrant's network-separated gRPC).

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Runner Architecture

ruvector-sota-bench/
├── src/
│   ├── lib.rs              — Dataset, darwin_score, claim_sota
│   ├── metrics.rs          — BenchScore, RecallMetrics, LatencyMetrics
│   ├── report.rs           — BenchReport, LeaderboardRow, JSON export
│   ├── datasets/
│   │   ├── synthetic.rs    — 5 ANN-Benchmarks synthetic sets
│   │   └── ann_benchmarks.rs — HDF5 loader (--features real-datasets)
│   ├── runners/
│   │   ├── core_hnsw.rs    — ruvector-core HNSW (direct HnswIndex::search_with_ef)
│   │   ├── rabitq.rs       — FlatF32, RabitqIndex, RabitqPlusIndex
│   │   ├── lsm_ann.rs      — FullLsm + streaming checkpoint tracker
│   │   ├── matryoshka.rs   — FullDimIndex, TwoStageIndex
│   │   └── hybrid.rs       — BM25+ANN: RRF, RSF, score-fusion
│   └── bin/
│       ├── sota_all.rs     — Master benchmark (all runners, all datasets)
│       ├── sota_ann.rs     — ANN-Benchmarks sweep (recall vs QPS CSV)
│       └── sota_streaming.rs — BigANN streaming track
└── harness/
    └── scorePolicy.ts      — Darwin Mode fitness score (reads JSON report)

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Real Dataset Downloads

When --features real-datasets is enabled, datasets are downloaded lazily to ~/.cache/ruvector-sota-bench/:

Dataset	Size	Dims	Corpus
SIFT-128-euclidean	960 MB	128	1M
GloVe-25-angular	520 MB	25	1.18M
GloVe-100-angular	1.1 GB	100	1.18M
Deep-image-96-angular	2.3 GB	96	10M

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License

MIT — part of RuVector.