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

翻译：方言翻译在实现异构数据库系统间的无缝交互中起着关键作用。然而，由于语法差异和细微的语义变化，在不同方言间（例如从PostgreSQL到MySQL）翻译SQL查询仍然是一项具有挑战性的任务。现有方法包括手动重写、基于规则的系统以及基于大型语言模型（LLM）的技术，通常涉及较高的维护成本（例如，制定自定义翻译规则）或产生不可靠的结果（例如，LLM生成不存在的函数），尤其是在处理复杂查询时。在本演示中，我们介绍CrackSQL，首个结合规则与基于LLM方法的混合式SQL方言翻译系统，以克服这些局限性。CrackSQL利用LLM的适应性来最小化人工干预，同时通过基于功能的查询处理对冗长复杂的SQL进行分段，从而提高翻译准确性。为进一步提升鲁棒性，它引入了一种新颖的跨方言语法嵌入模型以实现精确的语法对齐，以及一种自适应局部到全局的翻译策略，能有效解决相互依赖的查询操作。CrackSQL支持三种翻译模式，并提供多种部署和访问选项，包括Web控制台界面、PyPI软件包和命令行提示符，便于在各种实际应用场景中采用。