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ACM Manag. Data"],"published-print":{"date-parts":[[2024,3,12]]},"abstract":"<jats:p>High-dimensional vector similarity search (HVSS) is gaining prominence as a powerful tool for various data science and AI applications. As vector data scales up, in-memory indexes pose a significant challenge due to the substantial increase in main memory requirements. A potential solution involves leveraging disk-based implementation, which stores and searches vector data on high-performance devices like NVMe SSDs. However, implementing HVSS for data segments proves to be intricate in vector databases where a single machine comprises multiple segments for system scalability. In this context, each segment operates with limited memory and disk space, necessitating a delicate balance between accuracy, efficiency, and space cost. Existing disk-based methods fall short as they do not holistically address all these requirements simultaneously. In this paper, we present Starling, an I\/O-efficient disk-resident graph index framework that optimizes data layout and search strategy within the segment. It has two primary components: (1) a data layout incorporating an in-memory navigation graph and a reordered disk-based graph with enhanced locality, reducing the search path length and minimizing disk bandwidth wastage; and (2) a block search strategy designed to minimize costly disk I\/O operations during vector query execution. Through extensive experiments, we validate the effectiveness, efficiency, and scalability of Starling. On a data segment with 2GB memory and 10GB disk capacity, Starling can accommodate up to 33 million vectors in 128 dimensions, offering HVSS with over 0.9 average precision and top-10 recall rate, and latency under 1 millisecond. The results showcase Starling's superior performance, exhibiting 43.9x higher throughput with 98% lower query latency compared to state-of-the-art methods while maintaining the same level of accuracy.<\/jats:p>","DOI":"10.1145\/3639269","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T18:51:32Z","timestamp":1711479092000},"page":"1-27","source":"Crossref","is-referenced-by-count":47,"title":["Starling: An I\/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3806-1012","authenticated-orcid":false,"given":"Mengzhao","family":"Wang","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7384-7212","authenticated-orcid":false,"given":"Weizhi","family":"Xu","sequence":"additional","affiliation":[{"name":"Zilliz, Redwood City, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5792-9994","authenticated-orcid":false,"given":"Xiaomeng","family":"Yi","sequence":"additional","affiliation":[{"name":"Zhejiang Lab, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3560-436X","authenticated-orcid":false,"given":"Songlin","family":"Wu","sequence":"additional","affiliation":[{"name":"Tongji University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5383-9750","authenticated-orcid":false,"given":"Zhangyang","family":"Peng","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8082-7398","authenticated-orcid":false,"given":"Xiangyu","family":"Ke","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3816-8450","authenticated-orcid":false,"given":"Yunjun","family":"Gao","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8040-6809","authenticated-orcid":false,"given":"Xiaoliang","family":"Xu","sequence":"additional","affiliation":[{"name":"Hangzhou Dianzi University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6895-8185","authenticated-orcid":false,"given":"Rentong","family":"Guo","sequence":"additional","affiliation":[{"name":"Zilliz, Redwood City, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1713-8696","authenticated-orcid":false,"given":"Charles","family":"Xie","sequence":"additional","affiliation":[{"name":"Zilliz, Redwood City, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2018. 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