Hierarchical navigable small world, or HNSW, is a graph-based ANN algorithm that combines navigable small worlds

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HNSW (Hierarchical Navigable Small World) is a graph-based approximate nearest neighbor algorithm that combines hierarchical layers with navigable small world networks to enable fast, scalable vector search. Unlike traditional KNN searches that become impractical with high-dimensional data, HNSW provides superior performance through its multi-layer structure and efficient graph navigation. The algorithm excels in applications like recommendation engines, similarity search, and AI workloads, offering better speed-accuracy tradeoffs than alternatives like LSH, IVF, and KD-Trees. While HNSW requires more memory and computational resources during indexing, proper parameter tuning (M, efConstruction, efSearch) can optimize the balance between accuracy and performance for specific use cases.

How HNSW algorithms can improve search

What is a hierarchical navigable small world (HNSW)?

How does HNSW compare to other ANN approaches?

What are the tradeoffs and challenges with HNSW?

Redis supports scalable and high-performance HNSW search