OneSparse: A Unified System for Multi-index Vector Search
Authors: 
Yaoqi Chen, Ruicheng Zheng, Qi Chen, Shuotao Xu, Qianxi Zhang, Xue Wu, Weihao Han, Hua Yuan, Mingqin Li, Yujing Wang, Jason Li, Fan Yang, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang, Mao YangAuthors Info & Claims
Yaoqi Chen, Ruicheng Zheng, Qi Chen, Shuotao Xu, Qianxi Zhang, Xue Wu, Weihao Han, Hua Yuan, Mingqin Li, Yujing Wang, Jason Li, Fan Yang, Hao Sun, Weiwei Deng, Feng Sun, Qi Zhang, Mao YangAuthors Info & ClaimsPages 393 - 402
Abstract
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 6x search performance improvement while maintaining comparable accuracy. OneSparse has already been integrated into Microsoft online web search and advertising systems with 5x+ latency gain for Bing web search and 2.0% Revenue Per Mille (RPM) gain for Bing sponsored search.
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May 2024
1928 pages
ISBN:9798400701726
DOI:10.1145/3589335
- General Chairs:
- Tat-Seng Chua,
- Chong-Wah Ngo,
- Program Chairs:
- Ravi Kumar,
- Hady W. Lauw,
- Roy Ka-Wei Lee
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Published: 13 May 2024
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