# D-Bot: Database Diagnosis System using Large Language Models

arXiv: https://arxiv.org/abs/2312.01454 · HTML: https://mlsanity.com/paper/2312.01454/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 69 references that are in the map.
- First posted on arXiv 3 Dec 2023.

## Builds on (most cited first, 28 of 28)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://mlsanity.com/paper/2211.12588/) (2022)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [PAL: Program-aided Language Models](https://mlsanity.com/paper/2211.10435/) (2022)
- [AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors](https://mlsanity.com/paper/2308.10848/) (2023)
- [Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping](https://mlsanity.com/paper/2002.06305/) (2020)
- [A Survey of Hallucination in Large Foundation Models](https://mlsanity.com/paper/2309.05922/) (2023)
- [Effective Long-Context Scaling of Foundation Models](https://mlsanity.com/paper/2309.16039/) (2023)
- [Tool Learning with Foundation Models](https://mlsanity.com/paper/2304.08354/) (2023)
- [LooGLE: Can Long-Context Language Models Understand Long Contexts?](https://mlsanity.com/paper/2311.04939/) (2023)
- [WebCPM: Interactive Web Search for Chinese Long-form Question Answering](https://mlsanity.com/paper/2305.06849/) (2023)
- [Apache Calcite: A Foundational Framework for Optimized Query Processing Over Heterogeneous Data Sources](https://mlsanity.com/paper/1802.10233/) (2018)
- [DB-BERT: a Database Tuning Tool that "Reads the Manual"](https://mlsanity.com/paper/2112.10925/) (2021)
- [Actionable and Interpretable Fault Localization for Recurring Failures in Online Service Systems](https://mlsanity.com/paper/2207.09021/) (2022)
- [LLM As DBA](https://mlsanity.com/paper/2308.05481/) (2023)
- [Systematic definition and classification of data anomalies in DBMS (English Version)](https://mlsanity.com/paper/2110.14230/) (2021)

## Built on by (most cited first, 28 of 28)

- [Large Language Model-Based Agents for Software Engineering: A Survey](https://mlsanity.com/paper/2409.02977/) (2024)
- [Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems](https://mlsanity.com/paper/2504.01990/) (2025)
- [SkillForge: Forging Domain-Specific, Self-Evolving Agent Skills in Cloud Technical Support](https://mlsanity.com/paper/2604.08618/) (2026)
- [In-depth Analysis of Graph-based RAG in a Unified Framework](https://mlsanity.com/paper/2503.04338/) (2025)
- [Flow-of-Action: SOP Enhanced LLM-Based Multi-Agent System for Root Cause Analysis](https://mlsanity.com/paper/2502.08224/) (2025)
- [A Survey of Data Agents: Emerging Paradigm or Overstated Hype?](https://mlsanity.com/paper/2510.23587/) (2025)
- [mABC: multi-Agent Blockchain-Inspired Collaboration for root cause analysis in micro-services architecture](https://mlsanity.com/paper/2404.12135/) (2024)
- [R-Bot: An LLM-based Query Rewrite System](https://mlsanity.com/paper/2412.01661/) (2024)
- [NAT-NL2GQL: A Novel Multi-Agent Framework for Translating Natural Language to Graph Query Language](https://mlsanity.com/paper/2412.10434/) (2024)
- [Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems](https://mlsanity.com/paper/2507.01599/) (2025)
- [SAGE: A Framework of Precise Retrieval for RAG](https://mlsanity.com/paper/2503.01713/) (2025)
- [LLM-Enhanced Data Management](https://mlsanity.com/paper/2402.02643/) (2024)
- [DBAIOps: A Reasoning LLM-Enhanced Database Operation and Maintenance System using Knowledge Graphs](https://mlsanity.com/paper/2508.01136/) (2025)
- [Graph of States: Solving Abductive Tasks with Large Language Models](https://mlsanity.com/paper/2603.21250/) (2026)
- [RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems](https://mlsanity.com/paper/2503.04252/) (2025)
- [Query Performance Explanation through Large Language Model for HTAP Systems](https://mlsanity.com/paper/2412.01709/) (2024)
- [ENCO: Life-Cycle Management of Enterprise-Grade Copilots](https://mlsanity.com/paper/2412.06099/) (2024)
- [LLMIA: An Out-of-the-Box Index Advisor via In-Context Learning with LLMs](https://mlsanity.com/paper/2503.07884/) (2025)
- [LLM-Based Test Case Generation in DBMS through Monte Carlo Tree Search](https://mlsanity.com/paper/2603.21530/) (2026)
- [PLM4NDV: Minimizing Data Access for Number of Distinct Values Estimation with Pre-trained Language Models](https://mlsanity.com/paper/2504.00608/) (2025)
- [LLM4Hint: Leveraging Large Language Models for Hint Recommendation in Offline Query Optimization](https://mlsanity.com/paper/2507.03384/) (2025)
- [A Case for Agentic Tuning: From Documentation to Action in PostgreSQL](https://mlsanity.com/paper/2605.19988/) (2026)
- [SkillEvo: Self-Renewing Evolution Gradients from Multi-Turn Interaction Feedback](https://mlsanity.com/paper/2608.13120/) (2026)
- [Towards Agentic Cloud Engineering: Graph and Loop Engineering with a Zero-Trust Agent Harness](https://mlsanity.com/paper/2609.00050/) (2026)
- [What Stops a Small Language Model From Driving a Database Agent](https://mlsanity.com/paper/2609.21341/) (2026)
- [Data Agents: Agentic Data Systems](https://mlsanity.com/paper/2609.24137/) (2026)
- [Where Root Cause Analysis Fails: A Retrieval-Reranking Decomposition](https://mlsanity.com/paper/2609.36686/) (2026)
- [Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretability](https://mlsanity.com/paper/2610.01168/) (2026)

## Abstract (arXiv)

Database administrators (DBAs) play an important role in managing, maintaining and optimizing database systems. However, it is hard and tedious for DBAs to manage a large number of databases 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 that D-Bot can effectively analyze the root causes of unseen anomalies and significantly outperforms traditional methods and vanilla models like GPT-4.