Demonstrating DB-BERT: A Database Tuning Tool that "Reads" the Manual
Summary: DB-BERT mines tuning hints from manuals and text, then RL-guided iterative tuning. The demo tunes Postgres/MySQL on TPC-C/TPC-H, traces configurations to passages, and lets users beat DB-BERT with their own settings; code at itrummer.github.io/dbbert. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Immanuel Trummer (Cornell University)
BibTeX Citation
@inproceedings{trummer_sigmod22,
title = {{Demonstrating DB-BERT: A Database Tuning Tool that "Reads" the Manual}},
author = {Trummer, Immanuel},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3520171},
url = {https://dl.acm.org/doi/10.1145/3514221.3520171},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,257 | MLOS in Action: Bridging the Gap Between Experimentation and Auto-Tuning in the Cloud | 2024 | VLDB | 5.2972217e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 8 of 8 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 234 | Self-Driving Database Management Systems | 2017 | CIDR | 0.00023810722 |
| 334 | An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning | 2019 | SIGMOD | 0.00020875082 |
| 498 | QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning | 2019 | VLDB | 0.00017440583 |
| 1,337 | DB-BERT: A Database Tuning Tool that "Reads the Manual" | 2022 | SIGMOD | 0.00011117488 |
| 1,344 | An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems | 2021 | VLDB | 0.00011094717 |
| 3,400 | A Demonstration of the OtterTune Automatic Database Management System Tuning Service | 2018 | VLDB | 7.4433294e-05 |
| 3,926 | UDO: Universal Database Optimization using Reinforcement Learning | 2021 | VLDB | 7.0128068e-05 |
| 8,127 | The Case for NLP-Enhanced Database Tuning: Towards Tuning Tools that “Read the Manual” | 2021 | VLDB | 5.4826853e-05 |
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