The authors tested D-Bot on real benchmarks, including 539 anomalies of six typical applications. The results demonstrated that D-Bot could effectively analyze the root causes of unseen anomalies and significantly outperformed traditional methods and vanilla models like GPT-4. The system's ability to quickly and accurately diagnose database issues could revolutionize the role of DBAs, making their work more efficient and less tedious.
Key takeaways:
- The paper proposes D-Bot, a large language model-based database diagnosis system that can automatically acquire knowledge from diagnosis documents and generate a diagnosis report in a significantly shorter time than a DBA.
- D-Bot's techniques include offline knowledge extraction from documents, automatic prompt generation, root cause analysis using a tree search algorithm, and a collaborative mechanism for complex anomalies with multiple root causes.
- The system was tested on real benchmarks, including 539 anomalies of six typical applications, and it was found to effectively analyze the root causes of unseen anomalies.
- D-Bot significantly outperforms traditional methods and vanilla models like GPT-4.