Title: Training-Free Query Optimization via LLM-Based Plan Similarity Show Chinese title

Title: 无需训练的查询优化通过基于大语言模型的计划相似性

Abstract: Large language model (LLM) embeddings offer a promising new avenue for database query optimization. In this paper, we explore how pre-trained execution plan embeddings can guide SQL query execution without the need for additional model training. We introduce LLM-PM (LLM-based Plan Mapping), a framework that embeds the default execution plan of a query, finds its k nearest neighbors among previously executed plans, and recommends database hintsets based on neighborhood voting. A lightweight consistency check validates the selected hint, while a fallback mechanism searches the full hint space when needed. Evaluated on the JOB-CEB benchmark using OpenGauss, LLM-PM achieves an average speed-up of 21% query latency reduction. This work highlights the potential of LLM-powered embeddings to deliver practical improvements in query performance and opens new directions for training-free, embedding-based optimizer guidance systems. Show Chinese abstract

Abstract: 大型语言模型（LLM）嵌入为数据库查询优化提供了有前景的新途径。 在本文中，我们探讨了预训练执行计划嵌入如何在不需要额外模型训练的情况下指导SQL查询执行。 我们引入了LLM-PM（基于LLM的计划映射），这是一个框架，它嵌入查询的默认执行计划，在之前执行的计划中找到其k个最近邻，并根据邻域投票推荐数据库提示集。 一种轻量级的一致性检查验证所选提示，而在需要时，回退机制会搜索完整的提示空间。 在使用OpenGauss的JOB-CEB基准上进行评估，LLM-PM实现了平均21%的查询延迟减少。 这项工作突显了基于LLM的嵌入在提升查询性能方面的实际改进潜力，并为无需训练的、基于嵌入的优化器指导系统开辟了新的方向。