Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation

Summary: Comprehensive evaluation of DB configuration tuning methods across modules, showing hyper-parameter optimization can boost tuning performance. Proposes an efficient surrogate-based unified benchmark to minimize evaluation cost and identify best algorithms per module. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12682
Venue: VLDB
Year: 2022
Pagerank: 6.8723042e-05
Overall Rank: 3,655 | 74.60%
DOI: 10.14778/3538598.3538604

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 31 of 31 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,105	GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization	2024	VLDB	7.5567226e-05
4,180	LlamaTune: Sample-Efficient DBMS Configuration Tuning	2022	VLDB	6.3725334e-05
4,870	DBPA: A Benchmark for Transactional Database Performance Anomalies	2023	SIGMOD	5.8583776e-05
5,654	AutoSteer: Learned Query Optimization for Any SQL Database	2023	VLDB	5.3882121e-05
5,834	An Efficient Transfer Learning Based Configuration Adviser for Database Tuning	2024	VLDB	5.3082111e-05
6,376	A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning	2023	SIGMOD	5.0861082e-05
6,489	Towards General and Efficient Online Tuning for Spark	2023	VLDB	5.0373773e-05
6,564	Automatic Database Configuration Debugging using Retrieval-Augmented Language Models	2025	SIGMOD	5.0037892e-05
6,883	PilotScope: Steering Databases with Machine Learning Drivers	2024	VLDB	4.8918682e-05
7,676	E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model	2025	VLDB	4.6770108e-05
8,003	The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions	2024	VLDB	4.6049527e-05
8,660	Learned Offline Query Planning via Bayesian Optimization	2025	SIGMOD	4.4680058e-05
9,196	Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale	2022	VLDB	4.3723457e-05
9,643	Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment	2025	SIGMOD	4.3067693e-05
9,732	ContTune: Continuous Tuning by Conservative Bayesian Optimization for Distributed Stream Data Processing Systems	2023	VLDB	4.2901665e-05
9,955	SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression	2025	VLDB	4.2332427e-05
10,047	AgentTune: An Agent-Based Large Language Model Framework for Database Knob Tuning	2026	SIGMOD	4.1905499e-05
10,093	MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration	2026	SIGMOD	4.1905499e-05
10,164	ESTune: Bayesian Uncertainty-Guided Early Stopping for Database Configuration Tuning	2026	SIGMOD	4.1905499e-05
10,217	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	4.1905499e-05
10,247	Why Database Manuals Are Not Enough: Efficient and Reliable Configuration Tuning for DBMSs via Code-Driven LLM Agents	2026	VLDB	4.1905499e-05
10,259	Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink	2026	VLDB	4.1905499e-05
10,271	OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning	2026	VLDB	4.1905499e-05
10,278	LakeHelm: Zero-Shot Lakehouse Advisor for Joint Engine-Format Selection and Configuration	2026	VLDB	4.1905499e-05
10,313	DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning	2026	VLDB	4.1905499e-05
10,340	Libra: One-Shot Parameter Sensitivity Estimation for Transfer Learning in Database Performance Prediction	2026	VLDB	4.1905499e-05
10,382	Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions	2025	SIGMOD	4.1905499e-05
10,403	Shapley Value Estimation Based on Differential Matrix	2025	SIGMOD	4.1905499e-05
10,569	A Systematic Study on Early Stopping Metrics in HPO and the Implications of Uncertainty	2025	VLDB	4.1905499e-05
10,641	AQETuner: Reliable Query-level Configuration Tuning for Analytical Query Engines	2025	VLDB	4.1905499e-05
10,853	AXE: A Task Decomposition Approach to Learned LSM Tuning	2025	VLDB	4.1905499e-05

Outgoing Citations (Sorted by Pagerank)

Showing 21 of 21 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
71	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059446482
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
1,816	An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems	2021	VLDB	0.00010438512
1,892	Black or White? How to Develop an AutoTuner for Memory-based Analytics	2020	SIGMOD	0.00010176219
2,845	VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition	2021	VLDB	8.0301674e-05
3,533	Database Tuning: principles, experiments, and troubleshooting techniques	2002	SIGMOD	6.9972756e-05
3,692	iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases	2019	VLDB	6.8328808e-05
3,995	ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases	2021	SIGMOD	6.5475871e-05
4,216	Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation	2021	VLDB	6.3448176e-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
4,799	Towards Dynamic and Safe Configuration Tuning for Cloud Databases	2022	SIGMOD	5.9082876e-05
6,494	Foundations of Automated Database Tuning	2006	VLDB	5.0334983e-05
7,834	Database Tuning	1992	VLDB	4.6361103e-05

Semantically Similar Papers

Overall Rank	Paper	Year	Venue	Pagerank
1,816	An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems	2021	VLDB	0.00010438512
4,180	LlamaTune: Sample-Efficient DBMS Configuration Tuning	2022	VLDB	6.3725334e-05
3,105	GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization	2024	VLDB	7.5567226e-05
10,217	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	4.1905499e-05
183	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036859633
4,799	Towards Dynamic and Safe Configuration Tuning for Cloud Databases	2022	SIGMOD	5.9082876e-05
6,494	Foundations of Automated Database Tuning	2006	VLDB	5.0334983e-05
5,834	An Efficient Transfer Learning Based Configuration Adviser for Database Tuning	2024	VLDB	5.3082111e-05
423	Tuning Database Configuration Parameters with iTuned	2009	VLDB	0.00023628474
3,276	Configuration-Parametric Query Optimization for Physical Design Tuning	2008	SIGMOD	7.2880761e-05