Disk-Resident Graph ANN Search: An Experimental Evaluation
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914363218067456 |
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| author | Chen, Xiaoyu Qu, Jinxiu Song, Yitong Lu, Shuhang Li, Huiling Jiang, Minghui Zhou, Wei Xu, Jianliang Zhou, Xuanhe Wu, Fan |
| author_facet | Chen, Xiaoyu Qu, Jinxiu Song, Yitong Lu, Shuhang Li, Huiling Jiang, Minghui Zhou, Wei Xu, Jianliang Zhou, Xuanhe Wu, Fan |
| contents | As data volumes grow while memory capacity remains limited, disk-resident graph-based approximate nearest neighbor (ANN) methods have become a practical alternative to memory-resident designs, shifting the bottleneck from computation to disk I/O. However, since their technical designs diverge widely across storage, layout, and execution paradigms, a systematic understanding of their fundamental performance trade-offs remains elusive. This paper presents a comprehensive experimental study of disk-resident graph-based ANN methods. First, we decompose such systems into five key technical components, i.e., storage strategy, disk layout, cache management, query execution, and update mechanism, and build a unified taxonomy of existing designs across these components. Second, we conduct fine-grained evaluations of representative strategies for each technical component to analyze the trade-offs in throughput, recall, and resource utilization. Third, we perform comprehensive end-to-end experiments and parameter-sensitivity analyses to evaluate overall system performance under diverse configurations. Fourth, our study reveals several non-obvious findings: (1) vector dimensionality fundamentally reshapes component effectiveness, necessitating dimension-aware design; (2) existing layout strategies exhibit surprisingly low I/O utilization (less than or equal to 15%); (3) page size critically affects feasibility and efficiency, with smaller pages preferred when layouts are carefully optimized; and (4) update strategies present clear workload-dependent trade-offs between in-place and out-of-place designs. Based on these findings, we derive practical guidelines for system design and configuration, and outline promising directions for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_01779 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Disk-Resident Graph ANN Search: An Experimental Evaluation Chen, Xiaoyu Qu, Jinxiu Song, Yitong Lu, Shuhang Li, Huiling Jiang, Minghui Zhou, Wei Xu, Jianliang Zhou, Xuanhe Wu, Fan Databases As data volumes grow while memory capacity remains limited, disk-resident graph-based approximate nearest neighbor (ANN) methods have become a practical alternative to memory-resident designs, shifting the bottleneck from computation to disk I/O. However, since their technical designs diverge widely across storage, layout, and execution paradigms, a systematic understanding of their fundamental performance trade-offs remains elusive. This paper presents a comprehensive experimental study of disk-resident graph-based ANN methods. First, we decompose such systems into five key technical components, i.e., storage strategy, disk layout, cache management, query execution, and update mechanism, and build a unified taxonomy of existing designs across these components. Second, we conduct fine-grained evaluations of representative strategies for each technical component to analyze the trade-offs in throughput, recall, and resource utilization. Third, we perform comprehensive end-to-end experiments and parameter-sensitivity analyses to evaluate overall system performance under diverse configurations. Fourth, our study reveals several non-obvious findings: (1) vector dimensionality fundamentally reshapes component effectiveness, necessitating dimension-aware design; (2) existing layout strategies exhibit surprisingly low I/O utilization (less than or equal to 15%); (3) page size critically affects feasibility and efficiency, with smaller pages preferred when layouts are carefully optimized; and (4) update strategies present clear workload-dependent trade-offs between in-place and out-of-place designs. Based on these findings, we derive practical guidelines for system design and configuration, and outline promising directions for future research. |
| title | Disk-Resident Graph ANN Search: An Experimental Evaluation |
| topic | Databases |
| url | https://arxiv.org/abs/2603.01779 |