HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements

Summary: HUNTER is an online, hybrid tuner for cloud DBs addressing personalized constraints and workload heterogeneity. It uses GA-warmed samples to bootstrap deep RL, with PCA, Random Forest, and a Fast Exploration Strategy to prune the search space, plus clone-based parallel testing for fast, safe online tuning; up to 2.8x (1 clone) and 22.8x (20 clones) speedups. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h3e719852380feec1
Venue: SIGMOD
Year: 2022
Pagerank: 7.2603973e-05
Overall Rank: 3,487 | 76.57%
DOI: 10.1145/3514221.3517882

Incoming Non-self Citations Over Time

Authors

1. Baoqing Cai (Huazhong University of Science and Technology)
2. Yu Liu (Huazhong University of Science and Technology)
3. Ce Zhang (ETH Zurich)
4. Guangyu Zhang (Huazhong University of Science and Technology)
5. Ke Zhou (Huazhong University of Science and Technology)
6. Li Liu (Huazhong University of Science and Technology)
7. Chunhua Li (Huazhong University of Science and Technology)
8. Bin Cheng (Tencent)
9. Jie Yang (Tencent)
10. Jiashu Xing (Tencent)

BibTeX Citation

@inproceedings{cai_sigmod22,
        title = {{HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements}},
        author = {Cai, Baoqing and Liu, Yu and Zhang, Ce and Zhang, Guangyu and Zhou, Ke and Liu, Li and Li, Chunhua and Cheng, Bin and Yang, Jie and Xing, Jiashu},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517882},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517882},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 19 of 19 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,227	GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization	2024	VLDB	8.8007923e-05
5,284	An Efficient Transfer Learning Based Configuration Adviser for Database Tuning	2024	VLDB	6.1963031e-05
6,453	Towards General and Efficient Online Tuning for Spark	2023	VLDB	5.7790502e-05
6,732	A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning	2023	SIGMOD	5.6921776e-05
7,021	Rethinking Learned Cost Models: Why Start from Scratch?	2023	SIGMOD	5.6168049e-05
8,210	Grep: A Graph Learning Based Database Partitioning System	2023	SIGMOD	5.3761621e-05
9,190	Twisted Twin: A Collaborative and Competitive Memory Management Approach in HTAP Systems	2025	VLDB	5.2096115e-05
9,627	Db2une: Tuning Under Pressure via Deep Learning	2024	VLDB	5.1477233e-05
9,911	AgentTune: An Agent-Based Large Language Model Framework for Database Knob Tuning	2026	SIGMOD	5.1079647e-05
10,297	Why Database Manuals Are Not Enough: Efficient and Reliable Configuration Tuning for DBMSs via Code-Driven LLM Agents	2026	VLDB	5.0407989e-05
10,328	PGTuner: An Efficient Framework for Automatic and Transferable Configuration Tuning of Proximity Graphs	2026	SIGMOD	5.0322912e-05
10,340	SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression	2025	VLDB	5.0257853e-05
10,591	MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration	2026	SIGMOD	4.9769913e-05
10,652	ESTune: Bayesian Uncertainty-Guided Early Stopping for Database Configuration Tuning	2026	SIGMOD	4.9769913e-05
10,739	Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink	2026	VLDB	4.9769913e-05
10,896	MFTune: An Efficient Multi-fidelity Framework for Spark SQL Configuration Tuning	2026	VLDB	4.9769913e-05
11,055	DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning	2026	VLDB	4.9769913e-05
11,243	A Systematic Study on Early Stopping Metrics in HPO and the Implications of Uncertainty	2025	VLDB	4.9769913e-05
11,295	AQETuner: Reliable Query-level Configuration Tuning for Analytical Query Engines	2025	VLDB	4.9769913e-05

Outgoing Citations (Sorted by Pagerank)

Showing 25 of 25 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
40	The Case for Learned Index Structures	2018	SIGMOD	0.00046363107
78	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036675568
144	Neo: A Learned Query Optimizer	2019	VLDB	0.00029090793
151	An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server	1997	VLDB	0.00028664776
314	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.00021276452
322	Tuning Database Configuration Parameters with iTuned	2009	VLDB	0.00021034201
422	ALEX: An Updatable Adaptive Learned Index	2020	SIGMOD	0.00018488849
437	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00018310278
461	Query-based Workload Forecasting for Self-Driving Database Management Systems	2018	SIGMOD	0.00017841988
508	Adaptive Self-Tuning Memory in DB2	2006	VLDB	0.00017083245
598	Automatic Performance Diagnosis and Tuning in Oracle	2005	CIDR	0.0001575517
691	Selectivity Estimation for Range Predicates using Lightweight Models	2019	VLDB	0.00014737455
753	Dostoevsky: Better Space-Time Trade-Offs for LSM-Tree Based Key-Value Stores via Adaptive Removal of Superfluous Merging	2018	SIGMOD	0.00014232329
768	FITing-Tree: A Data-aware Index Structure	2019	SIGMOD	0.00014107655
783	DBSherlock: A Performance Diagnostic Tool for Transactional Databases	2016	SIGMOD	0.0001401708
1,289	An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems	2021	VLDB	0.00011159167
1,422	The Log-Structured Merge-Bush & the Wacky Continuum	2019	SIGMOD	0.00010720711
1,432	Towards a Learning Optimizer for Shared Clouds	2019	VLDB	0.00010676754
1,516	Automatically Indexing Millions of Databases in Microsoft Azure SQL Database	2019	SIGMOD	0.00010398346
2,237	Designing Succinct Secondary Indexing Mechanism by Exploiting Column Correlations	2019	SIGMOD	8.7738349e-05
2,278	Active Learning for ML Enhanced Database Systems	2020	SIGMOD	8.7057608e-05
2,721	ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases	2021	SIGMOD	8.0931221e-05
3,032	iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases	2019	VLDB	7.7317595e-05
3,964	MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems	2021	SIGMOD	6.889374e-05
4,058	Semi-Automatic Index Tuning: Keeping DBAs in the Loop	2012	VLDB	6.8230003e-05

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1	10,591	MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration	2026	SIGMOD
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