Work Sharing and Offloading for Efficient Approximate Threshold-based Vector Join

TL;DR

This paper introduces a unified framework for approximate vector joins that enhances efficiency and robustness through soft work sharing, a merged index, and an adaptive hybrid search, outperforming existing methods.

Contribution

It proposes a novel framework combining soft work sharing, merged indexing, and adaptive hybrid search for more efficient approximate vector joins.

Findings

Significant efficiency improvements over state-of-the-art methods.

Enhanced robustness for out-of-distribution queries.

Demonstrated effectiveness on eight diverse datasets.

Abstract

Vector joins - finding all vector pairs between a set of query and data vectors whose distances are below a given threshold - are fundamental to modern vector and vector-relational database systems that power multimodal retrieval and semantic analytics. Existing state-of-the-art approach exploits work sharing among similar queries but still suffers from redundant index traversals and excessive distance computations. We propose a unified framework for efficient approximate vector joins that (1) introduces soft work sharing to reuse traversal results beyond the join results of previous queries, (2) builds a merged index over both query and data vectors to further speedup graph explorations, and (3) improves robustness for out-of-distribution queries through an adaptive hybrid search strategy. Experiments on eight datasets demonstrate substantial improvements in efficiency-recall trade-off over the state of the art.