QUITE: A Query Rewrite System Beyond Rules with LLM Agents

2025-06-09

AI Summary

Existing SQL query rewriting approaches rely on predefined rules, encountering three key bottlenecks: difficulty in discovering effective rules, poor generalizability, and inability to express complex optimization logic—leading to narrow coverage and frequent performance regressions. This paper proposes the first training-free, feedback-aware LLM-based multi-agent framework for dynamic query rewriting. It integrates a finite-state-machine-driven workflow controller, a database execution feedback loop, a rewriting middleware layer, and prompt injection techniques to ensure both semantic equivalence and high-performance execution. Experimental results demonstrate that, compared to state-of-the-art methods, our approach achieves up to a 35.8% reduction in query execution time and a 24.1% improvement in rewriting success rate. Moreover, it significantly broadens support for complex query patterns and diverse rewriting strategies.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Search and Optimization: Learning to Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Query rewrite transforms SQL queries into semantically equivalent forms that run more efficiently. Existing approaches mainly rely on predefined rewrite rules, but they handle a limited subset of queries and can cause performance regressions. This limitation stems from three challenges of rule-based query rewrite: (1) it is hard to discover and verify new rules, (2) fixed rewrite rules do not generalize to new query patterns, and (3) some rewrite techniques cannot be expressed as fixed rules. Motivated by the fact that human experts exhibit significantly better rewrite ability but suffer from scalability, and Large Language Models (LLMs) have demonstrated nearly human-level semantic and reasoning abilities, we propose a new approach of using LLMs to rewrite SQL queries beyond rules. Due to the hallucination problems in LLMs, directly applying LLMs often leads to nonequivalent and suboptimal queries. To address this issue, we propose QUITE (query rewrite), a training-free and feedback-aware system based on LLM agents that rewrites SQL queries into semantically equivalent forms with significantly better performance, covering a broader range of query patterns and rewrite strategies compared to rule-based methods. Firstly, we design a multi-agent framework controlled by a finite state machine (FSM) to equip LLMs with the ability to use external tools and enhance the rewrite process with real-time database feedback. Secondly, we develop a rewrite middleware to enhance the ability of LLMs to generate optimized query equivalents. Finally, we employ a novel hint injection technique to improve execution plans for rewritten queries. Extensive experiments show that QUITE reduces query execution time by up to 35.8% over state-of-the-art approaches and produces 24.1% more rewrites than prior methods, covering query cases that earlier systems did not handle.

Problem

• Overcoming limitations of rule-based SQL query rewrite systems
• Using LLMs to rewrite SQL queries beyond fixed rules
• Ensuring semantic equivalence and performance in LLM-based query rewrites

Innovation

• LLM agents rewrite SQL queries beyond rules
• Multi-agent framework with real-time feedback
• Hint injection improves execution plans