SQLGovernor: An LLM-powered SQL Toolkit for Real World Application
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914037808234496 |
|---|---|
| author | Jiang, Jie Shen, Siqi Xie, Haining Li, Yang Shen, Yu Huang, Danqing Qian, Bo Wu, Yinjun Zhang, Wentao Cui, Bin Chen, Peng |
| author_facet | Jiang, Jie Shen, Siqi Xie, Haining Li, Yang Shen, Yu Huang, Danqing Qian, Bo Wu, Yinjun Zhang, Wentao Cui, Bin Chen, Peng |
| contents | SQL queries in real world analytical environments, whether written by humans or generated automatically often suffer from syntax errors, inefficiency, or semantic misalignment, especially in complex OLAP scenarios. To address these challenges, we propose SQLGovernor, an LLM powered SQL toolkit that unifies multiple functionalities, including syntax correction, query rewriting, query modification, and consistency verification within a structured framework enhanced by knowledge management. SQLGovernor introduces a fragment wise processing strategy to enable fine grained rewriting and localized error correction, significantly reducing the cognitive load on the LLM. It further incorporates a hybrid self learning mechanism guided by expert feedback, allowing the system to continuously improve through DBMS output analysis and rule validation. Experiments on benchmarks such as BIRD and BIRD CRITIC, as well as industrial datasets, show that SQLGovernor consistently boosts the performance of base models by up to 10%, while minimizing reliance on manual expertise. Deployed in production environments, SQLGovernor demonstrates strong practical utility and effective performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08575 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SQLGovernor: An LLM-powered SQL Toolkit for Real World Application Jiang, Jie Shen, Siqi Xie, Haining Li, Yang Shen, Yu Huang, Danqing Qian, Bo Wu, Yinjun Zhang, Wentao Cui, Bin Chen, Peng Databases SQL queries in real world analytical environments, whether written by humans or generated automatically often suffer from syntax errors, inefficiency, or semantic misalignment, especially in complex OLAP scenarios. To address these challenges, we propose SQLGovernor, an LLM powered SQL toolkit that unifies multiple functionalities, including syntax correction, query rewriting, query modification, and consistency verification within a structured framework enhanced by knowledge management. SQLGovernor introduces a fragment wise processing strategy to enable fine grained rewriting and localized error correction, significantly reducing the cognitive load on the LLM. It further incorporates a hybrid self learning mechanism guided by expert feedback, allowing the system to continuously improve through DBMS output analysis and rule validation. Experiments on benchmarks such as BIRD and BIRD CRITIC, as well as industrial datasets, show that SQLGovernor consistently boosts the performance of base models by up to 10%, while minimizing reliance on manual expertise. Deployed in production environments, SQLGovernor demonstrates strong practical utility and effective performance. |
| title | SQLGovernor: An LLM-powered SQL Toolkit for Real World Application |
| topic | Databases |
| url | https://arxiv.org/abs/2509.08575 |