Querying 3B Vectors (vickiboykis.com)

A recent exploration into querying 3 billion vectors, sparked by a discussion with Google AI’s Jeff Dean, highlighted significant challenges and advancements in the arena of vector searches, commonly used for identifying semantically similar items in applications such as search and recommendations. The analysis began with a naive implementation that tested basic dot product calculations between large sets of document and query vectors, ultimately revealing the limitations of initial approaches that were inefficient even with reduced vector counts. The findings demonstrated that optimizing the dot product calculations through vectorization dramatically improved execution speed; for example, processing 3 million vectors took only around 12 seconds compared to earlier methods. However, operating at the scale of 3 billion vectors introduced critical issues, particularly concerning memory constraints. The memory requirement for handling such massive datasets could reach up to 8.6 TB, prompting the need for more sophisticated methods such as batch processing, on-disk comparisons, and potential programming optimizations in languages like Rust or C. This case underscores the importance of clearly defining requirements and constraints in large-scale AI/ML projects, as technical complexities often arise beyond the coding challenges themselves.