R-Bot: An LLM-based Query Rewrite System

• URL: http://arxiv.org/abs/2412.01661v1
• Date: Mon, 02 Dec 2024 16:13:04 GMT
• Title: R-Bot: An LLM-based Query Rewrite System
• Authors: Zhaoyan Sun, Xuanhe Zhou, Guoliang Li,
• Abstract summary: We propose R-Bot, a query rewrite system based on machine learning.<n>We first design a multi-source rewrite evidence preparation pipeline to generate query rewrite evidences.<n>We then propose a hybrid-semantics retrieval method that combines structural and semantic analysis.<n>We conduct comprehensive experiments on widely used benchmarks, and demonstrate the superior performance of our system.
• Score: 15.46599915198438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Query rewrite is essential for optimizing SQL queries to improve their execution efficiency without changing their results. Traditionally, this task has been tackled through heuristic and learning-based methods, each with its limitations in terms of inferior quality and low robustness. Recent advancements in LLMs offer a new paradigm by leveraging their superior natural language and code comprehension abilities. Despite their potential, directly applying LLMs like GPT-4 has faced challenges due to problems such as hallucinations, where the model might generate inaccurate or irrelevant results. To address this, we propose R-Bot, an LLM-based query rewrite system with a systematic approach. We first design a multi-source rewrite evidence preparation pipeline to generate query rewrite evidences for guiding LLMs to avoid hallucinations. We then propose a hybrid structure-semantics retrieval method that combines structural and semantic analysis to retrieve the most relevant rewrite evidences for effectively answering an online query. We next propose a step-by-step LLM rewrite method that iteratively leverages the retrieved evidences to select and arrange rewrite rules with self-reflection. We conduct comprehensive experiments on widely used benchmarks, and demonstrate the superior performance of our system, R-Bot, surpassing state-of-the-art query rewrite methods.

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