Multiple Index Merge for Approximate Nearest Neighbor Search

TL;DR

This paper introduces an efficient method for merging multiple approximate nearest neighbor indexes, significantly speeding up search and merging processes while maintaining high accuracy in large-scale high-dimensional data.

Contribution

We propose a novel reverse neighbor sliding merge (RNSM) technique and merge order selection (MOS) to improve multi-index AKNN search efficiency and scalability.

Findings

Up to 5.48× speedup over existing merge methods

Achieves 9.92× faster index reconstruction

Scales efficiently to 100 million vectors

Abstract

Approximate $k$ nearest neighbor (AKNN) search in high-dimensional space is a foundational problem in vector databases with widespread applications. Among the numerous AKNN indexes, Proximity Graph-based indexes achieve state-of-the-art search efficiency across various benchmarks. However, their extensive distance computations of high-dimensional vectors lead to slow construction and substantial memory overhead. The limited memory capacity often prevents building the entire index at once when handling large-scale datasets. A common practice is to build multiple sub-indexes separately. However, directly searching on these separated indexes severely compromises search efficiency, as queries cannot leverage cross-graph connections. Therefore, efficient graph index merging is crucial for multi-index searching. In this paper, we focus on efficient two-index merging and the merge order of multiple indexes for AKNN search. To achieve this, we propose a reverse neighbor sliding merge (RNSM) that exploits structural information to boost merging efficiency. We further investigate merge order selection (MOS) to reduce the merging cost by eliminating redundant merge operations. Experiments show that our approach yields up to a 5.48$\times$ speedup over existing index merge methods and 9.92$\times$ speedup over index reconstruction, while maintaining expected superior search performance. Moreover, our method scales efficiently to 100 million vectors with 50 partitions, maintaining consistent speedups.