Benchmarking RL-Enhanced Spatial Indices Against Traditional, Advanced, and Learned Counterparts

TL;DR

This paper introduces a comprehensive benchmark for reinforcement learning-enhanced spatial indices, evaluating their performance against traditional, advanced, and learned indices across multiple datasets and workloads.

Contribution

It provides the first modular, extensible benchmarking framework for RLESIs, enabling consistent comparison and analysis of their practical benefits and limitations.

Findings

RLESIs can reduce query latency with tuning

They underperform compared to learned and advanced indices in efficiency

High tuning costs and limited generalization hinder practical use

Abstract

Reinforcement learning has recently been used to enhance index structures, giving rise to reinforcement learning-enhanced spatial indices (RLESIs) that aim to improve query efficiency during index construction. However, their practical benefits remain unclear due to the lack of unified implementations and comprehensive evaluations, especially in disk-based settings. We present the first modular and extensible benchmark for RLESIs. Built on top of an existing spatial index library, our framework decouples index training from building, supports parameter tuning, and enables consistent comparison with traditional, advanced, and learned spatial indices. We evaluate 12 representative spatial indices across six datasets and diverse workloads, including point, range, kNN, spatial join, and mixed read/write queries. Using latency, I/O, and index statistics as metrics, we find that while RLESIs can reduce query latency with tuning, they consistently underperform learned spatial indices and advanced variants in both query efficiency and index build cost. These findings highlight that although RLESIs offer promising architectural compatibility, their high tuning costs and limited generalization hinder practical adoption.