Billion-scale vector search with Vespa - part two

The blog post delves into the trade-offs involved in billion-scale vector search using Vespa, focusing on the balance between serving performance and accuracy in approximate nearest neighbor search. It discusses the use of the HNSW algorithm in Vespa to optimize vector search by employing the Microsoft SPACEV-1B dataset, which comprises over a billion int8 precision vectors. The experiment involves transforming the dataset into a binarized form, utilizing hamming distance for search, and evaluating the impact on performance and resource utilization with various HNSW parameters. The post highlights how enabling HNSW indexing can significantly reduce write throughput and increase memory usage, yet offers substantial speed improvements in search latency compared to brute force methods. Additionally, it explores the use of multithreaded search to mitigate latency challenges in exact nearest neighbor searches, and posits that while approximate methods show substantial speed gains, the choice between exact and approximate search depends heavily on specific use case requirements. The discussion sets the stage for future exploration of hybrid approaches to further optimize memory usage and search efficiency.