Accelerating Vector Search at Scale: BAM-ANN with Batch-Aware Memory-Disk Hybrid Indexing

With the rapid advancement of Large Language Models (LLMs), Approximate Nearest Neighbor Search (ANNS) in vector databases has become a cornerstone of modern AI infrastructure. ANNS excels at efficiently retrieving similar objects from billion-scale datasets, achieving remarkable success in delivering fast, high-recall searches. However, in-memory ANNS algorithms are prohibitively expensive when scaling to large datasets, creating a growing need for cost-effective memory-disk hybrid solutions. This paper introduces BAM-ANN, an efficient graph-based memory-disk hybrid solution for batch query processing. Specifically, we introduce three key innovations: (1) a multi-tiered indexing strategy with iterative balanced clustering to efficiently manage billion-scale data under limited memory resources, (2) a learning-driven pruning mechanism that eliminates redundant I/O and computations while preserving high accuracy, and (3) a batch-aware query organization scheme that further accelerates the search process. Experimental results demonstrate that BAM-ANN achieves 90% recall@100 in approximately one millisecond, outperforming state-of-the-art solutions like SPANN and DiskANN by 1.25x to 2.5x in speed.