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Why Database Manuals Are Not Enough: Efficient and Reliable Configuration Tuning for DBMSs via Code-Driven LLM Agents
Summary: SysInsight mines DBMS source code—not manuals or expensive black-box data—to extract knob semantics and knob-controlled execution paths via static analysis + LLM reasoning. It turns these into verifiable tuning rules and uses diagnosis-guided online search, converging 7.11× faster and improving performance 19.9%.
(summarized by gpt-5.4-mini on Apr 12 2026)
- Paper ID
- 14284
- Venue
- VLDB
- Year
- 2026
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,247 | 28.79%
- DOI
-
10.14778/3797919.3797940
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
Showing 24 of 24 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank |
Cited Paper |
Year |
Venue |
Pagerank |
| 13 |
Mining Association Rules between Sets of Items in Large Databases |
1993 |
SIGMOD |
0.0010863639 |
| 183 |
Automatic Database Management System Tuning Through Large-scale Machine Learning |
2017 |
SIGMOD |
0.00036859633 |
| 339 |
OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases |
2014 |
VLDB |
0.00026895683 |
| 423 |
Tuning Database Configuration Parameters with iTuned |
2009 |
VLDB |
0.00023628474 |
| 510 |
An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning |
2019 |
SIGMOD |
0.00021420477 |
| 661 |
Adaptive Self-Tuning Memory in DB2 |
2006 |
VLDB |
0.00018488168 |
| 662 |
Database Tuning Advisor for Microsoft SQL Server 2005 |
2004 |
VLDB |
0.00018478597 |
| 704 |
Query-based Workload Forecasting for Self-Driving Database Management Systems |
2018 |
SIGMOD |
0.00017785557 |
| 779 |
QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning |
2019 |
VLDB |
0.00016719473 |
| 841 |
Self-tuning Database Technology and Information Services: from Wishful Thinking to Viable Engineering |
2002 |
VLDB |
0.00015987128 |
| 1,404 |
DB-BERT: A Database Tuning Tool that "Reads the Manual" |
2022 |
SIGMOD |
0.00012179714 |
| 1,816 |
An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems |
2021 |
VLDB |
0.00010438512 |
| 3,105 |
GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization |
2024 |
VLDB |
7.5567226e-05 |
| 3,533 |
Database Tuning: principles, experiments, and troubleshooting techniques |
2002 |
SIGMOD |
6.9972756e-05 |
| 3,655 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
6.8723042e-05 |
| 3,995 |
ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases |
2021 |
SIGMOD |
6.5475871e-05 |
| 4,180 |
LlamaTune: Sample-Efficient DBMS Configuration Tuning |
2022 |
VLDB |
6.3725334e-05 |
| 4,235 |
CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions |
2021 |
VLDB |
6.3297728e-05 |
| 4,377 |
HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements |
2022 |
SIGMOD |
6.2331947e-05 |
| 4,730 |
UDO: Universal Database Optimization using Reinforcement Learning |
2021 |
VLDB |
5.9604983e-05 |
| 4,799 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
5.9082876e-05 |
| 5,834 |
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning |
2024 |
VLDB |
5.3082111e-05 |
| 6,494 |
Foundations of Automated Database Tuning |
2006 |
VLDB |
5.0334983e-05 |
| 7,834 |
Database Tuning |
1992 |
VLDB |
4.6361103e-05 |
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