Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems
Summary: Boot accelerates behavior-model training for self-driving DBMSs via macro/micro acceleration: approximate runtime telemetry, altered query semantics, and skipped repetitive executions. In PostgreSQL, it cuts data-collection time up to 268× with modest accuracy loss. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Wan Shen Lim (Carnegie Mellon University)
- 2. Lin Ma (University of Michigan)
- 3. William Zhang (Carnegie Mellon University)
- 4. Matthew Butrovich (Carnegie Mellon University)
- 5. Samuel Arch (Carnegie Mellon University)
- 6. Andrew Pavlo (Carnegie Mellon University)
BibTeX Citation
@article{lim_vldb24,
title = {{Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems}},
author = {Lim, Wan Shen and Ma, Lin and Zhang, William and Butrovich, Matthew and Arch, Samuel and Pavlo, Andrew},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {11},
pages = {3680--3693},
doi = {10.14778/3681954.3682030},
url = {https://doi.org/10.14778/3681954.3682030},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,546 | Learned Offline Query Planning via Bayesian Optimization | 2025 | SIGMOD | 5.4966669e-05 |
| 9,635 | Low Rank Learning for Offline Query Optimization | 2025 | SIGMOD | 5.1453041e-05 |
| 9,725 | APQO: An Adaptive Framework for Parametric Query Optimization | 2026 | SIGMOD | 5.1325223e-05 |
| 10,296 | This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! | 2026 | SIGMOD | 5.0407989e-05 |
| 10,340 | SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression | 2025 | VLDB | 5.0257853e-05 |
| 11,148 | Automated Database Tuning vs. Human-Based Tuning in a Simulated Stressful Work Environment: A Demonstration of the Database Gym | 2025 | SIGMOD | 4.9769913e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 48 of 48 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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