Building a Custom Go-Native Vector Database Engine with HNSW, SIMD & Product Quantization
Answer-first: Building a production-grade, Go-native vector database engine for Go microservices requires overcoming three classic systems bottlenecks: algorithmic complexity, CPU instruction latency, and garbage collector pressure. By combining Hierarchical Navigable Small World (HNSW) multi-layer graphs for $O(\log N)$ search complexity, 256-bit AVX2 SIMD unrolling via unsafe pointer arithmetic for sub-nanosecond vector math, Product Quantization (PQ) for 75%–96% memory footprint reduction, and memory-mapped (mmap) off-heap storage, you can construct a zero-allocation vector search engine in pure Go that rivals C++ solutions like Faiss or USearch without CGO overhead. ...