Andromeda: Debugging Database Performance Issues with Retrieval-Augmented Large Language Models
Summary: Andromeda uses retrieval-augmented LLMs to debug DBMS performance with context-aware guidance. Evidence from historical queries, manuals, telemetry, and execution logs is retrieved to adapt an open-source LLM for domain-specific debugging, shown via a web app. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Pengyi Wang
- 2. Sibei Chen
- 3. Ju Fan
- 4. Bin Wu
- 5. Nan Tang
- 6. Jian Tan
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,407 | DB-BERT: A Database Tuning Tool that "Reads the Manual" | 2022 | SIGMOD | 0.00012146739 |
| 1,956 | D-Bot: Database Diagnosis System using Large Language Models | 2024 | VLDB | 9.960627e-05 |
| 4,238 | Panda: Performance Debugging for Databases using LLM Agents | 2024 | CIDR | 6.331901e-05 |
| 6,765 | Automatic Database Configuration Debugging using Retrieval-Augmented Language Models | 2025 | SIGMOD | 4.9325583e-05 |
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