OneSparse: A Unified System for Multi-index Vector Search - Chen, Yaoqi; Zheng, Ruicheng; Chen, Qi; Xu, Shuotao; Zhang, Qianxi; Wu, Xue; Han, Weihao; Yuan, Hua; Li, Mingqin; Wang, Yujing; Li, Jason; Yang, Fan; et al. (2024 The Web Conference, 2024)
Multi-index vector search has become the cornerstone for many applications, such as recommendation systems. Efficient search in such a multi-modal hybrid vector space is challenging since no single index design performs well for all kinds of vector data. Existing approaches to processing multi-index hybrid queries either suffer from algorithmic limitations or processing inefficiency. In this paper, we propose OneSparse, a unified multi-vector index query system that incorporates multiple posting-based vector indices, which enables highly efficient retrieval of multi-modal data-sets. OneSparse introduces a novel multi-index query engine design of inter-index intersection push-down. It also optimizes the vector posting format to expedite multi-index queries. Our experiments show OneSparse achieves more than 6× search performance improvement while maintaining comparable accuracy. OneSparse has already been integrated into Microsoft online web search and advertising systems with 5 × + latency gain for Bing web search and 2.0% Revenue Per Mille (RPM) gain for Bing sponsored search.