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

arXiv: https://arxiv.org/abs/2504.00882 · HTML: https://mlsanity.com/paper/2504.00882/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 6 references that are in the map.
- First posted on arXiv 1 Apr 2025.

## Built on by (most cited first, 4 of 4)

- [LLM/Agent-as-Data-Analyst: A Survey](https://mlsanity.com/paper/2509.23988/) (2025)
- [PARROT: A Benchmark for Evaluating LLMs in Cross-System SQL Translation](https://mlsanity.com/paper/2509.23338/) (2025)
- [Specification Portability Across LLM Development Agents: Cross-Agent Compatibility in Specification-Driven Software Migration](https://mlsanity.com/paper/2608.21208/) (2026)
- [A Hybrid Dependency-Aware Framework for Task Decomposition and Dynamic Agent Generation in Oracle-to-PostgreSQL Migration](https://mlsanity.com/paper/2609.14413/) (2026)

## Abstract (arXiv)

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