AI-Driven Research for Databases

TL;DR

This paper introduces an automated approach using AI to design evaluators that assess database system optimizations, enabling the discovery of novel, high-performance algorithms through co-evolution.

Contribution

It proposes a co-evolution method for automating evaluator design, overcoming the evaluation bottleneck in AI-driven research for databases.

Findings

Automated evaluators enabled discovery of algorithms with up to 6.8x lower latency.

Co-evolving evaluators with solutions improves optimization effectiveness.

Demonstrated success in buffer management, query rewriting, and index selection.

Abstract

As the complexity of modern workloads and hardware increasingly outpaces human research and engineering capacity, existing methods for database performance optimization struggle to keep pace. To address this gap, a new class of techniques, termed AI-Driven Research for Systems (ADRS), uses large language models to automate solution discovery. This approach shifts optimization from manual system design to automated code generation. The key obstacle, however, in applying ADRS is the evaluation pipeline. Since these frameworks rapidly generate hundreds of candidates without human supervision, they depend on fast and accurate feedback from evaluators to converge on effective solutions. Building such evaluators is especially difficult for complex database systems. To enable the practical application of ADRS in this domain, we propose automating the design of evaluators by co-evolving them with the solutions. We demonstrate the effectiveness of this approach through three case studies optimizing buffer management, query rewriting, and index selection. Our automated evaluators enable the discovery of novel algorithms that outperform state-of-the-art baselines (e.g., a deterministic query rewrite policy that achieves up to 6.8x lower latency), demonstrating that addressing the evaluation bottleneck unlocks the potential of ADRS to generate highly optimized, deployable code for next-generation data systems.