Approximate similarity queries on high-dimensional vector indices have become the cornerstone for many critical online services. An increasing need for more sophisticated vector queries requires integrating vector search systems with relational databases. However, high-dimensional vector indices do not exhibit monotonicity, a critical property of conventional indices. The lack of monotonicity forces existing vector systems to rely on monotonicity-preserving tentative indices, set up temporarily for a target vector’s TopK nearest neighbors, to facilitate queries. This leads to suboptimal performance due to the difficulty to predict the optimal K.<\/p>\n
This paper presents VBase, a system that efficiently supports complex queries of both approximate similarity search and relational operators. VBase identifies a common property, relaxed monotonicity, to unify two seemingly incompatible systems. This common property allows VBase to circumvent the constraints of a TopK-only interface to achieve significantly higher efficiency, while provably preserving the semantics of TopK-based solutions. Evaluation results show VBase offers up to three orders-of-magnitude higher performance than state-of-the-art vector systems on complex online vector queries. VBase further enables analytical similarity queries that previous vector systems do not, and shows 7,000\u00d7 speedup with 99.9% accuracy of exact queries.<\/p>\n","protected":false},"excerpt":{"rendered":"
Approximate similarity queries on high-dimensional vector indices have become the cornerstone for many critical online services. An increasing need for more sophisticated vector queries requires integrating vector search systems with relational databases. However, high-dimensional vector indices do not exhibit monotonicity, a critical property of conventional indices. The lack of monotonicity forces existing vector systems to 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