探索大模型与数据库结合的挑战，揭示当前系统三大瓶颈。
Research Challenges in Relational Database Management Systems for LLM Queries
- 对比开源与企业平台，测试五类查询表现
- 发现输出控制、资源利用和查询规划三大问题
- 提出初步优化方案，验证深度融合可行性
大型语言模型（LLMs）已广泛应用于文本摘要、情感分析和自动问答等任务。近期，LLMs被集成到关系型数据库管理系统中以增强查询能力并支持高级数据处理。亚马逊、达摩院、谷歌和雪花公司等已提供在SQL中直接调用LLM的功能，称为LLM查询，以提升数据洞察力。然而，当前开源方案功能有限且性能较差。本文对两个开源系统和一个企业级平台进行了早期探索，通过五类代表性查询揭示了现有SQL调用LLM集成在功能、性能和可扩展性方面的局限性。我们识别出三大核心问题：结构化输出约束、资源利用率优化以及查询计划改进。我们实现了初步解决方案，并观察到在支持基于LLM的SQL查询方面取得进展。这些初步成果表明，紧密集成LLM与数据库管理系统是实现高效可扩展的LLM查询处理的关键。
原文摘要 · Abstract (English)
Large language models (LLMs) have become essential for applications such as text summarization, sentiment analysis, and automated question-answering. Recently, LLMs have also been integrated into relational database management systems to enhance querying and support advanced data processing. Companies such as Amazon, Databricks, Google, and Snowflake offer LLM invocation directly within SQL, denoted as LLM queries, to boost data insights. However, open-source solutions currently have limited functionality and poor performance. In this work, we present an early exploration of two open-source systems and one enterprise platform, using five representative queries to expose functional, performance, and scalability limits in today's SQL-invoked LLM integrations. We identify three main issues: enforcing structured outputs, optimizing resource utilization, and improving query planning. We implemented initial solutions and observed improvements in accommodating LLM powered SQL queries. These early gains demonstrate that tighter integration of LLM+DBMS is the key to scalable and efficient processing of LLM queries.
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