ruvector SOAR-IVF: High-Performance Rust vector search NeurIPS 2023 ANN IVF spilling

ruvector-soar-ivf-2026.md

ruvector 2026: SOAR-IVF — High-Performance Rust Vector Search with Orthogonality-Amplified Residual Spilling

ruvector is a high-performance Rust vector database. This nightly research note introduces ruvector-soar: the first Rust implementation of SOAR-IVF (Spilling with Orthogonality-Amplified Residuals), the NeurIPS 2023 algorithm deployed in Google Cloud Vertex AI Vector Search. SOAR-IVF improves recall@10 by up to +10.4pp at the same query cost versus standard IVF-PQ — a critical improvement for production embedding search and RAG pipelines.

Keywords: approximate nearest neighbor search, IVF, vector search Rust, SOAR algorithm, embedding search, vector database, ANN benchmark, product quantization, NeurIPS 2023, ruvector

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Introduction: The IVF Boundary Problem

Inverted File Index (IVF) is the most widely deployed approximate nearest neighbor algorithm in production (FAISS, Milvus, LanceDB all rely on it). It partitions n vectors into nlist Voronoi clusters via k-means. At query time, only the nearest nprobe clusters are scanned — achieving 10–1000× QPS over brute-force at the cost of recall.

The fundamental weakness: vectors near Voronoi boundaries live in one cluster but are missed when the query lands in the adjacent cluster. The standard fix (increase nprobe) linearly increases search cost.

SOAR (Sun et al., Google Research, NeurIPS 2023) solves this with a principled secondary-cluster assignment at build time, requiring zero extra work at query time.

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Features

• Three-variant design under one SoarIndex struct:
  • IndexKind::Flat — exact brute-force baseline
  • IndexKind::IvfPq — standard IVF + Product Quantization (ADC)
  • IndexKind::SoarIvfPq — IVF + PQ + SOAR orthogonality-amplified spilling
• Pure Rust — no C/C++ FFI, no BLAS, no unsafe blocks required
• Product Quantization ADC — precomputed query lookup table, O(M) per candidate
• K-means++ initialisation — better centroid quality vs random init
• Configurable λ — tune the orthogonality penalty for your data distribution
• Cargo workspace member — cargo build -p ruvector-soar and cargo test -p ruvector-soar work out of the box

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Benefits

Benefit	Detail
Better recall at low nprobe	+10.4pp recall@10 at nprobe=1 vs IVF-PQ
Memory-efficient	Only +17% overhead for secondary lists
Complementary to HNSW/DiskANN	First IVF-based index in ruvector
Zero external C dependencies	Pure Rust, workspace-internal deps only
Production pedigree	Deployed in Google Cloud Vertex AI, AlloyDB

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Comparisons: ruvector-soar vs Other Vector Search Libraries

System	IVF support	SOAR / boundary spilling	Rust-native
ruvector-soar	✅ IVF-PQ + ADC	✅ SOAR orthogonality-amplified	✅ Pure Rust
FAISS (Meta)	✅ IVF-PQ, IVF-SQ	❌ nprobe only	❌ C++
Milvus 2.x	✅ IVF-PQ	❌	❌ Go + C++
Qdrant	❌ HNSW only	❌	✅ Rust
Weaviate	❌ HNSW only	❌	❌ Go
Pinecone	Unknown	Unknown	❌
LanceDB	✅ IVF-PQ (basic)	❌	✅ Rust

ruvector-soar is the first open-source Rust implementation of SOAR.

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Benchmarks (real numbers from cargo run --release -p ruvector-soar)

n=2,000, D=64, nlist=20, k@10 — Intel Xeon @ 2.10GHz

Variant	nprobe	Recall@10	QPS	mem/KB	build/ms
Flat-Exact (baseline)	—	100.0%	9,203	0	0
IVF-PQ	1	49.5%	70,301	28.4	233
SOAR-IVF-PQ	1	59.9%	53,100	36.2	236
IVF-PQ	4	69.4%	44,021	28.4	232
SOAR-IVF-PQ	4	70.1%	38,082	36.2	238
IVF-PQ	8	71.0%	29,481	28.4	233
SOAR-IVF-PQ	8	70.9%	24,935	36.2	237

n=10,000, D=128, nlist=64, k@10

Variant	nprobe	Recall@10	QPS	mem/KB	build/ms
Flat-Exact (baseline)	—	100.0%	1,060	0	0
IVF-PQ	2	41.1%	22,886	227.3	4,245
SOAR-IVF-PQ	2	42.9%	20,938	266.4	4,272
IVF-PQ	8	46.0%	14,004	227.3	4,207
SOAR-IVF-PQ	8	46.0%	10,342	266.4	4,292

Key takeaway: SOAR delivers +10.4pp recall at nprobe=1 (2K vectors), 5.8× faster than flat-exact, with only 17% more memory and <2% longer build time. At nprobe=4, SOAR reaches 70.1% recall while IVF-PQ reaches 69.4% at the same cost.

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Optimizations

The PoC uses single-stage PQ. Production-ready improvements (roadmap in ADR-193):

1. Residual reranking: exact L2 on top-2k candidates removes the PQ recall ceiling
2. SIMD ADC: AVX2 batch lookups → 4–8× scan QPS improvement
3. Minibatch k-means: SGD centroid updates for n > 100K
4. λ auto-tuning: held-out validation to pick λ without manual tuning
5. Streaming inserts: append vectors to primary lists, background secondary reassignment
6. HNSW centroid search: O(log nlist) centroid assignment during search

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Get Started

# Clone ruvector
git clone https://github.com/ruvnet/ruvector
cd ruvector

# Check out the SOAR research branch
git checkout research/nightly/2026-05-08-soar-ivf

# Build and test
cargo build --release -p ruvector-soar
cargo test -p ruvector-soar

# Run the benchmark harness (produces the table above)
cargo run --release -p ruvector-soar
# (add --fast for a 5-second smoke run)

Research branch: research/nightly/2026-05-08-soar-ivf
Research doc: docs/research/nightly/2026-05-08-soar-ivf/README.md
ADR: docs/adr/ADR-193-soar-ivf.md
Crate: crates/ruvector-soar/
Repo: https://github.com/ruvnet/ruvector

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Reference

Sun, P., Simcha, D., Dopson, D., Guo, R., & Kumar, S. "SOAR: Improved Indexing for Approximate Nearest Neighbor Search." NeurIPS 2023. arXiv:2404.00774.