The research paper presents PageANN, a novel framework engineered to overcome the severe latency and scalability limitations facing existing **disk-based Approximate Nearest Neighbor Search (ANNS)** methods used in vector databases. Current systems suffer from inefficient search paths and a crucial misalignment between logical graph node size and the **physical I/O granularity of Solid-State Drives (SSDs)**. PageANN introduces a core innovation: a **page-node graph structure** that directly maps logical graph nodes to physical SSD pages, significantly shortening I/O traversal paths and maximizing data utility during retrieval. This is supported by a co-designed **disk data layout** that embeds compressed neighbor vectors within each page and a dynamic **memory management strategy** utilizing lightweight indexing for fast query routing. According to experimental results, PageANN consistently **outperforms state-of-the-art techniques**, achieving substantial gains in throughput and latency across diverse datasets and memory constraints while maintaining comparable recall accuracy.

Source:

https://arxiv.org/pdf/2509.25487