Disk-Resident Graph ANN Search: An Experimental Evaluation

2026-03-02

AI Summary

This work addresses the I/O bottleneck in disk-resident graph-based approximate nearest neighbor (ANN) search under memory-constrained settings, where existing approaches lack a systematic understanding of storage, layout, and execution strategies. The authors propose a unified taxonomy that decomposes such systems into five core components: storage strategy, disk layout, cache management, query execution, and update mechanism. Through fine-grained evaluation and end-to-end experiments, they uncover several non-intuitive insights: vector dimensionality significantly impacts component effectiveness; current disk layouts exhibit I/O utilization consistently below 15%; smaller page sizes outperform larger ones under optimized layouts; and update strategies must be tailored to specific workloads. These findings provide empirical foundations and practical design guidelines for building efficient disk-based ANN systems.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Social Networks and Social Media: Social mining and social search on the Web

Abstract

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.

Problem

• disk-resident
• graph-based ANN
• performance trade-offs
• I/O bottleneck
• approximate nearest neighbor

Innovation

• disk-resident ANN
• graph-based indexing
• I/O efficiency
• systematic evaluation
• dimension-aware design