Automatic Database Configuration Debugging using Retrieval-Augmented Language Models
Summary: Andromeda uses retrieval-augmented LLMs to diagnose DBMS misconfigurations and propose fixes. A RAG pipeline sources domain-specific context from historical questions, manuals, and telemetry via a heterogeneous retriever and telemetry analysis, beating baselines on real data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Sibei Chen (Renmin University of China)
- 2. Ju Fan (Renmin University of China)
- 3. Bin Wu (Alibaba)
- 4. Nan Tang (Hong Kong University of Science and Technology)
- 5. Chao Deng (Renmin University of China)
- 6. Pengyi Wang (Renmin University of China)
- 7. Ye Li (Alibaba)
- 8. Jian Tan (Alibaba)
- 9. Feifei Li (Alibaba)
- 10. Jingren Zhou (Alibaba)
- 11. Xiaoyong Du (Renmin University of China)
BibTeX Citation
@inproceedings{chen_sigmod25,
title = {{Automatic Database Configuration Debugging using Retrieval-Augmented Language Models}},
author = {Chen, Sibei and Fan, Ju and Wu, Bin and Tang, Nan and Deng, Chao and Wang, Pengyi and Li, Ye and Tan, Jian and Li, Feifei and Zhou, Jingren and Du, Xiaoyong},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709663},
url = {https://dl.acm.org/doi/10.1145/3709663},
year = {2025}
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