Database administrators (DBAs) play an important role in managing database systems. However, it is hard and tedious for DBAs to manage vast database instances and give timely response (waiting for hours is intolerable in many online cases). In addition, existing empirical methods only support limited diagnosis scenarios, which are also labor-intensive to update the diagnosis rules for database version updates. Recently large language models (LLMs) have shown great potential in various fields. Thus, we propose D-Bot , an LLM-based database diagnosis system that can automatically acquire knowledge from diagnosis documents, and generate reasonable and well-founded diagnosis report (i.e., identifying the root causes and solutions) within acceptable time (e.g., under 10 minutes compared to hours by a DBA). The techniques in D-Bot include ( i ) offline knowledge extraction from documents, ( ii ) automatic prompt generation (e.g., knowledge matching, tool retrieval), ( iii ) root cause analysis using tree search algorithm, and ( iv ) collaborative mechanism for complex anomalies with multiple root causes. We verify D-Bot on real benchmarks (including 539 anomalies of six typical applications), and the results show D-Bot can effectively identify root causes of unseen anomalies and significantly outperforms traditional methods and vanilla models like GPT-4.

Query rewriting is a challenging task that transforms a SQL query to improve its performance while maintaining its result set. However, it is difficult to rewrite SQL queries, which often involve complex logical structures, and there are numerous candidate rewrite strategies for such queries, making it an NP-hard problem. Existing databases or query optimization engines adopt heuristics to rewrite queries, but these approaches may not be able to judiciously and adaptively apply the rewrite rules and may cause significant performance regression in some cases (e.g., correlated subqueries may not be eliminated). To address these limitations, we introduce LearnedRewrite, a query rewrite system that combines traditional and learned algorithms (i.e., Monte Carlo tree search + hybrid estimator) to rewrite queries. We have implemented the system in Calcite, and experimental results demonstrate LearnedRewrite achieves superior performance on three real datasets.