HFresh: Memory-Efficient Vector Search

HFresh is Weaviate’s disk-based vector index for applications that need to reduce memory consumption and support large, mutable datasets while accepting lower query throughput than in-memory HNSW. It partitions vectors into disk-resident postings, uses a compact HNSW centroid index in memory to identify relevant postings, and limits disk reads through a two-stage search that rescoring candidates with original vectors preserves ranking quality. HFresh applies RQ8 quantization to centroids and RQ1 compression to postings, substantially reducing memory and storage requirements, while asynchronous split, merge, and reassignment operations continuously rebalance partitions without full rebuilds. It also supports filtered search through bitmap allow lists, switching to direct exact search for highly selective filters and using posting-aware routing for broader filters. Benchmarks on a one-million-vector dataset showed HFresh using 239 MB of Go heap versus 6.67 GB for uncompressed HNSW, though HNSW retained higher throughput at similar recall; a separate test successfully built an HFresh index for one billion 256-dimensional vectors. Introduced as a technical preview in Weaviate 1.36 and generally available in 1.38, HFresh is aimed at workloads where memory efficiency and scale outweigh the need for the lowest latency.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.