In-memory graph-based approximate nearest neighbor (ANN) search has superior search performance but incurs significant memory footprint. Disk-based methods reduce memory usage but suffer from high disk access latency. A common challenge is how to achieve low-latency search while significantly reducing memory footprint. In this paper, we propose SkipDisk, a disk-memory hybrid ANN search that significantly reduces memory footprint while achieving search latency comparable to or lower than in-memory method HNSW. By analyzing existing disk-based methods, we observed that disk access remains the primary bottleneck, and existing lower bound based filtering methods are two loose to effectively reduce disk access. Therefore, we design SkipDisk to achieve tight lower bound with low memory footprint to reduce the search latency. First, we design a dedicated pivot for each point to improve the lower bound of the triangle inequality for effective filtering. We further design an estimation-based approach based on this lower bound. Second, to reduce the memory footprint, we employ a three-level data pruning strategy to preserve informative data in memory. Third, to further reduce search latency, we design an asynchronous I/O strategy based on the decoupling of in-memory search and disk access by storing neighbor nodes in memory. Experiments show that our method achieves a latency of 85 of HNSW's latency with approximately 10 memory footprint, and a latency to 63 of HNSW's with a slightly higher memory footprint of around 20.

翻译：内存图结构近似最近邻搜索具有优越的搜索性能，但会占用大量内存。基于磁盘的方法减少了内存使用，却面临高磁盘访问延迟。如何在大幅降低内存占用的同时实现低延迟搜索成为共同挑战。本文提出SkipDisk——一种磁盘-内存混合近似最近邻搜索方法，能在显著降低内存占用的同时，实现与内存方法HNSW相当或更低的搜索延迟。通过分析现有基于磁盘的方法，我们观察到磁盘访问仍是主要瓶颈，而现有基于下界的过滤方法过于宽松，无法有效减少磁盘访问。为此，我们设计SkipDisk以紧凑内存占用实现严格下界，从而降低搜索延迟。首先，我们为每个点设计专属中心点，以提升三角不等式的下界紧致性实现高效过滤，并基于此下界进一步设计了估计方法。其次，为降低内存占用，我们采用三级数据剪枝策略，在内存中保留信息量丰富的数据。第三，通过将邻居节点存储于内存，实现内存搜索与磁盘访问的解耦，设计异步I/O策略进一步降低搜索延迟。实验表明，本方法在内存占用约为HNSW的10%时，延迟为其85%；当内存占用略升至约20%时，延迟降至HNSW的63%。