d803bfe2b1
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
160 lines
4.8 KiB
Markdown
160 lines
4.8 KiB
Markdown
# rUvector Performance Benchmarks
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**Date:** November 25, 2025
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**Test Environment:** Linux 4.4.0, Rust 1.91.1
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---
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## ⚠️ Important Disclaimer
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**This document contains internal rUvector benchmark results only.**
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The previous version of this document made unfounded performance claims comparing rUvector to other vector databases (e.g., "100-4,400x faster than Qdrant"). These claims were based on fabricated data and hardcoded multipliers in test code, not actual comparative benchmarks.
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**We have removed all false comparison claims.** This document now only reports verified rUvector internal benchmark results.
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---
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## Verified rUvector Benchmark Results
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### 1. Distance Metrics Performance (SimSIMD + AVX2)
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rUvector uses SimSIMD with custom AVX2 intrinsics for SIMD-optimized distance calculations:
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| Dimensions | Euclidean | Cosine | Dot Product |
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|------------|-----------|--------|-------------|
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| **128D** | 25 ns | 22 ns | 22 ns |
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| **384D** | 47 ns | 42 ns | 42 ns |
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| **768D** | 90 ns | 78 ns | 78 ns |
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| **1536D** | 167 ns | 135 ns | 135 ns |
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**Batch Processing (1000 vectors × 384D):** 278 µs total = **3.6M distance ops/sec**
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### 2. HNSW Search Performance
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Benchmarked with 1,000 vectors, 128 dimensions:
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| k (neighbors) | Latency | QPS Equivalent |
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|---------------|---------|----------------|
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| **k=1** | 45 µs | 22,222 QPS |
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| **k=10** | 61 µs | 16,393 QPS |
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| **k=100** | 165 µs | 6,061 QPS |
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### 3. rUvector Internal Scaling Tests
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#### 10,000 Vectors, 384 Dimensions
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| Configuration | Insert (ops/s) | Search QPS | p50 Latency |
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|---------------|----------------|------------|-------------|
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| **rUvector** | 34,435,442 | 623 | 1.57 ms |
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| **rUvector (quantized)** | 29,673,943 | 742 | 1.34 ms |
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#### 50,000 Vectors, 384 Dimensions
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| Configuration | Insert (ops/s) | Search QPS | p50 Latency |
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|---------------|----------------|------------|-------------|
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| **rUvector** | 16,697,377 | 113 | 8.71 ms |
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| **rUvector (quantized)** | 35,065,891 | 143 | 6.86 ms |
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### 4. Quantization Performance
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#### Scalar Quantization (4x compression)
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| Operation | 384D | 768D | 1536D |
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|-----------|------|------|-------|
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| Encode | 605 ns | 1.27 µs | 2.11 µs |
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| Decode | 493 ns | 971 ns | 1.89 µs |
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| Distance | 64 ns | 127 ns | 256 ns |
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#### Binary Quantization (32x compression)
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| Operation | 384D | 768D | 1536D |
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|-----------|------|------|-------|
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| Encode | 625 ns | 1.27 µs | 2.5 µs |
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| Decode | 485 ns | 970 ns | 1.9 µs |
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| Hamming Distance | 33 ns | 65 ns | 128 ns |
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**Compression Ratios:**
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- Scalar (int8): **4x** memory reduction
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- Product Quantization: **8-16x** memory reduction
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- Binary: **32x** memory reduction (with ~10% recall loss)
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---
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## Architecture
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### rUvector
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| Component | Technology | Benefit |
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|-----------|------------|---------|
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| Core | Rust + NAPI-RS | Zero-overhead bindings |
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| Distance | SimSIMD + AVX2/AVX-512 | 4-16x faster than scalar |
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| Index | hnsw_rs | O(log n) search |
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| Storage | redb (memory-mapped) | Zero-copy I/O |
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| Concurrency | DashMap + RwLock | Lock-free reads |
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| WASM | wasm-bindgen | Browser support |
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---
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## Features
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| Feature | rUvector |
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|---------|----------|
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| **HNSW Index** | ✅ |
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| **Cosine/Euclidean/DotProduct** | ✅ |
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| **Scalar Quantization** | ✅ |
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| **Product Quantization** | ✅ |
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| **Binary Quantization** | ✅ |
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| **Filtered Search** | ✅ |
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| **Hybrid Search (BM25)** | ✅ |
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| **MMR Diversity** | ✅ |
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| **Hypergraph Support** | ✅ |
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| **Neural Hashing** | ✅ |
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| **Conformal Prediction** | ✅ |
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| **AgenticDB API** | ✅ |
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| **Browser/WASM** | ✅ |
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---
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## Use Cases
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### rUvector is ideal for:
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- **Embedded/Edge deployment** - Single binary, no external dependencies
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- **Low-latency requirements** - Sub-millisecond search times
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- **Browser/WASM** - Need vector search in frontend
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- **AI Agent integration** - AgenticDB API, hypergraphs, causal memory
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- **Research/experimental** - Neural hashing, TDA, learned indexes
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---
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## Reproducing Benchmarks
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```bash
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# rUvector Rust benchmarks
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cargo bench -p ruvector-core --bench hnsw_search
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cargo bench -p ruvector-core --bench distance_metrics
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cargo bench -p ruvector-core --bench quantization_bench
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```
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## References
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- [rUvector Repository](https://github.com/ruvnet/ruvector)
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- [SimSIMD SIMD Library](https://github.com/ashvardanian/SimSIMD)
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- [hnsw_rs Rust Implementation](https://github.com/jean-pierreBoth/hnswlib-rs)
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---
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## Note on Comparisons
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**We do not currently have verified comparative benchmarks against other vector databases.**
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If you need to compare rUvector with other solutions, please run your own benchmarks in your specific environment with your specific workload. Performance characteristics vary significantly based on:
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- Vector dimensions and count
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- Search parameters (k, ef_search)
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- Hardware configuration
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- Dataset distribution
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- Query patterns
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We welcome community contributions of fair, reproducible comparative benchmarks.
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