# Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines

## TL;DR

This paper presents a novel engineering approach to enable full-text search engines to perform fast and exact nearest neighbor search in Hamming space, significantly improving speed over existing methods.

## Contribution

It revisits and combines three techniques from information retrieval to adapt full-text search engines for efficient NNS in binary codes, a novel integration.

## Key findings

- Achieves significant speed-ups over state-of-the-art term match approaches
- Enables efficient and exact NNS in Hamming space within full-text search engines
- Supports multi-model search and reduces memory consumption

## Abstract

A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines. Compared with other NNS systems, such solutions are capable of effectively reducing main memory consumption, coherently supporting multi-model search and being immediately ready for production deployment. In this paper, we continue the journey to explore specifically how to empower full-text search engines with fast and exact NNS in Hamming space (i.e., the set of binary codes). By revisiting three techniques (bit operation, subs-code filtering and data preprocessing with permutation) in information retrieval literature, we develop a novel engineering solution for full-text search engines to efficiently accomplish this special but important NNS task. In the experiment, we show that our proposed approach enables full-text search engines to achieve significant speed-ups over its state-of-the-art term match approach for NNS within binary codes.