Show HN: Open-source Rust implementation of Turbopuffer

2 days ago 3

A high-performance vector database implementation in Rust, designed as a compatible alternative to TurboPuffer with significant performance improvements for concurrent workloads.

VBfold Rust is a memory-based vector database that provides fast similarity search capabilities for high-dimensional vectors. Built from the ground up in Rust, it addresses concurrency issues found in other implementations while delivering exceptional performance for both ingestion and query operations.

Performance Characteristics

2.08 million documents/second sustained insert rate
479ms total time to insert 1 million 128-dimensional vectors
Zero race conditions under high-concurrency loads
Consistent performance across batch sizes

32ms median query latency on 1 million documents
37x faster than brute force linear search
0.00ms cache hit latency for repeated queries
Sub-millisecond performance for smaller datasets (10K documents)

K-means clustering with 100 clusters for optimal search space reduction
5-minute index build time for 1 million vectors
Parallel processing leveraging all available CPU cores
Memory-efficient cluster storage and retrieval

DashMap-based storage: Lock-free concurrent document storage
K-means clustering: Intelligent vector space partitioning for fast queries
LRU query cache: Sub-millisecond performance for repeated queries
Parallel processing: Rayon-based parallelization for compute-intensive operations

RESTful HTTP API compatible with VBfold clients
JSON-based request/response format
Support for vector, text, and hybrid search modes
Metadata filtering capabilities

Rust 1.70+
8GB+ RAM recommended for million-scale datasets

git clone <repository-url> cd vbfold-rust cargo run --release

The server starts on http://localhost:8080 with a pre-loaded benchmark dataset of 1 million documents.

curl -X POST http://localhost:8080/v1/vectors/demo/upsert \ -H 'Content-Type: application/json' \ -d '[{"id":"1","vector":[0.1,0.2,0.3,0.4],"text":"sample document"}]'

curl -X POST http://localhost:8080/v1/vectors/demo/query \ -H 'Content-Type: application/json' \ -d '{"vector":[0.1,0.2,0.3,0.4],"top_k":10}'

curl http://localhost:8080/v1/vectors/demo

Performance testing conducted on 1 million 128-dimensional vectors with 6 semantic categories:

Operation Performance Notes

Document insertion	2.08M docs/sec	Sustained rate with batching
Cold query	30.94ms	First query on dataset
Warm query (P50)	32.65ms	Median performance
Warm query (P95)	41.32ms	95th percentile
Cache hit	0.00ms	LRU cache performance
Index build	304 seconds	K-means clustering

Cosine similarity with optimized SIMD operations
Fast euclidean distance calculations for clustering
Normalized vector storage for consistent results

Efficient memory layout with aligned vector storage
Arc-based shared ownership for zero-copy operations
Configurable cache sizes for different workload patterns

Lock-free reads using DashMap concurrent hash maps
RwLock protection for index updates only
Send + Sync compatibility for async operations

Key configuration options in the source code:

const QUERY_CACHE_SIZE: usize = 10_000; // LRU cache entries const INDEX_SHARD_COUNT: usize = 16; // Parallel processing shards const CENTROID_CACHE_SIZE: usize = 256; // Index cache capacity

Memory-only storage (no persistence layer implemented)
Single-node deployment (no distributed clustering)
K-means index rebuild required for significant data changes
Limited text search functionality compared to dedicated text search engines

Metric VBfold Rust TurboPuffer Cloud

Cold query (1M docs)	31ms	402ms
Warm query (1M docs)	33ms	16ms
Insert rate	2.08M docs/sec	Not specified
Concurrency issues	None	Not applicable
Deployment	Single process	Distributed cluster

Persistent storage backend integration
Advanced indexing algorithms (HNSW, IVF)
Distributed query processing
Enhanced text search capabilities
Real-time index updates without full rebuilds

MIT License

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate and follow the existing code style. When submitting a PR: