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Large Language Models in Database Management System Optimization: A Survey
Journal article   Peer reviewed

Large Language Models in Database Management System Optimization: A Survey

Sikha Bagui, Marta Malagutti, Riccardo Morelli and Michael Tamascelli
ACM transactions on intelligent systems and technology, Vol.online ahead of print
07/08/2026
DOI: https://doi.org/10.1145/3829078

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Abstract

Query optimization Large Language Models (LLMs) DBMS Optimization Index Design Knob Tuning SQL-to-SQL Transformation Query Plan Optimization LLM-driven Optimization LLM Optimization Taxonomy Retrieval Augmented Generalization (RAG) Engineering Management Information Systems
Large language models have demonstrated remarkable capabilities in reasoning, code generation, and knowledge integration, making them increasingly relevant across a range of data-intensive applications. Their potential to interpret natural language, synthesize domain knowledge, and adapt to complex tasks has recently motivated research on their use within database management systems to enhance performance and automation. This survey reviews how these models have been employed to support core optimization tasks in modern databases, including query processing, system tuning, indexing, and performance diagnostics. We examine the methodological foundations, such as fine-tuning and RAG, and analyze how these approaches complement or extend traditional optimization components. The survey concludes by identifying key limitations and research gaps, outlining opportunities for advancing the integration of large language models into database system optimization.

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