Why HNSW is not the answer and disk-based alternatives might be more practical
(pgvecto.rs)
HNSW (Hierarchical Navigable Small World) has become the go-to algorithm for many vector databases. Its multi-layered graph structure and ability to efficiently navigate vector embeddings make it particularly appealing. However, despite its apparent advantages, HNSW may not be the optimal solution for large-scale and dynamic vector similarity search. In this blog post, we challenge the dominance of HNSW and explore why disk-based alternatives, such as IVF (Inverted File Index), might be more practical for massive datasets.
HNSW (Hierarchical Navigable Small World) has become the go-to algorithm for many vector databases. Its multi-layered graph structure and ability to efficiently navigate vector embeddings make it particularly appealing. However, despite its apparent advantages, HNSW may not be the optimal solution for large-scale and dynamic vector similarity search. In this blog post, we challenge the dominance of HNSW and explore why disk-based alternatives, such as IVF (Inverted File Index), might be more practical for massive datasets.