GateANN: I/O-Efficient Filtered Vector Search on SSDs

TL;DR

GateANN is an SSD-based graph approximate nearest neighbor search system that significantly reduces I/O by decoupling graph traversal from vector retrieval, enabling efficient filtered searches with fewer SSD reads.

Contribution

GateANN introduces graph tunneling, a novel approach that decouples traversal from vector retrieval, reducing I/O and improving performance in SSD-based filtered vector search.

Findings

Reduces SSD reads by up to 10x

Improves throughput by up to 7.6x

Supports filtered search without index rebuilds

Abstract

We present GateANN, an I/O-efficient SSD-based graph ANNS system that supports filtered vector search on an unmodified graph index. Existing SSD-based systems either waste I/O by post-filtering, or require expensive filter-aware index rebuilds. GateANN avoids both by decoupling graph traversal from vector retrieval. Our key insight is that traversing a node requires only its neighbor list and an approximate distance, neither of which needs the full-precision vector on SSD. Based on this, GateANN introduces graph tunneling. It checks each node's filter predicate in memory before issuing I/O and routes through non-matching nodes entirely in memory, preserving graph connectivity without any SSD read for non-matching nodes. Our experimental results show that it reduces SSD reads by up to 10x and improves throughput by up to 7.6x.