New benchmarks for approximate nearest neighbors

Erik Bernhardsson, author of Spotify's Annoy library, presents updated benchmarks for approximate nearest neighbor (ANN) search algorithms. The ANN-benchmarks project now features Dockerized algorithms and pre-computed datasets for reproducible comparisons. Across multiple datasets (GloVe, SIFT, Fashion-MNIST, GIST), HNSW from NMSLIB consistently ranks first — over 10x faster than Annoy — followed by KGraph, SW-graph, FAISS-IVF, and Annoy. Graph-based algorithms dominate, while LSH-based approaches like FALCONN have regressed. The author calls for all future ANN research papers to benchmark against these standard libraries.