CrackSQL: A Hybrid SQL Dialect Translation System Powered by Large Language Models

• URL: http://arxiv.org/abs/2504.00882v1
• Date: Tue, 01 Apr 2025 15:11:03 GMT
• Title: CrackSQL: A Hybrid SQL Dialect Translation System Powered by Large Language Models
• Authors: Wei Zhou, Yuyang Gao, Xuanhe Zhou, Guoliang Li,
• Abstract summary: Crack is the first hybrid SQL dialect translation system that combines rule and LLM-based methods to overcome limitations.<n>Crack supports three translation modes and offers multiple deployment options including a web console interface, a PyPI package, and a command-line prompt.
• Score: 20.718779783349984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dialect translation plays a key role in enabling seamless interaction across heterogeneous database systems. However, translating SQL queries between different dialects (e.g., from PostgreSQL to MySQL) remains a challenging task due to syntactic discrepancies and subtle semantic variations. Existing approaches including manual rewriting, rule-based systems, and large language model (LLM)-based techniques often involve high maintenance effort (e.g., crafting custom translation rules) or produce unreliable results (e.g., LLM generates non-existent functions), especially when handling complex queries. In this demonstration, we present CrackSQL, the first hybrid SQL dialect translation system that combines rule and LLM-based methods to overcome these limitations. CrackSQL leverages the adaptability of LLMs to minimize manual intervention, while enhancing translation accuracy by segmenting lengthy complex SQL via functionality-based query processing. To further improve robustness, it incorporates a novel cross-dialect syntax embedding model for precise syntax alignment, as well as an adaptive local-to-global translation strategy that effectively resolves interdependent query operations. CrackSQL supports three translation modes and offers multiple deployment and access options including a web console interface, a PyPI package, and a command-line prompt, facilitating adoption across a variety of real-world use cases

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