An In-Depth Study of Filter-Agnostic Vector Search on a PostgreSQL Database System: [Experiments and Analysis]

Researchers conducted the first comprehensive study of filter-agnostic vector search algorithms in a production PostgreSQL database system, revealing that real-world performance differs significantly from isolated library testing. The study found that system-level overheads often outweigh theoretical algorithmic benefits, with clustering-based approaches like ScaNN often outperforming graph-based methods like NaviX/ACORN in practice.

• →Vector search performance in production databases differs fundamentally from isolated library benchmarks due to system-level overheads.
• →Graph-based algorithms like NaviX/ACORN can incur prohibitive filter checks and system costs that cancel out their theoretical advantages.
• →Clustering-based indexes such as ScaNN often perform better than graph-based approaches in real-world database environments.
• →Optimal algorithm selection depends on workload characteristics and underlying data access costs rather than distance computation costs alone.
• →The research provides practical guidelines for implementing filtered vector search in enterprise-grade database systems.