LlamaTune: Sample-Efficient DBMS Configuration Tuning

Summary: LlamaTune improves DBMS autotuning sample efficiency through randomized-projection dimensionality reduction, special-value-aware sampling, and knob bucketization. Across workloads, DBMS versions, and BO/RL optimizers, it achieves up to 11× fewer runs and 21% higher throughput. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 12966
Venue: VLDB
Year: 2022
Pagerank: 7.4983591e-05
Overall Rank: 3,343 | 77.07%
DOI: 10.14778/3551793.3551844

Incoming Non-self Citations Over Time

Authors

1. Konstantinos Kanellis (University of Wisconsin)
2. Cong Ding (University of Wisconsin)
3. Brian Kroth (Microsoft)
4. Andreas Müller (Microsoft)
5. Carlo Curino (Microsoft)
6. Shivaram Venkataraman (University of Wisconsin)

BibTeX Citation

@article{kanellis_vldb22,
        title = {{LlamaTune: Sample-Efficient DBMS Configuration Tuning}},
        author = {Kanellis, Konstantinos and Ding, Cong and Kroth, Brian and Müller, Andreas and Curino, Carlo and Venkataraman, Shivaram},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {11},
        pages = {2953--2965},
        doi = {10.14778/3551793.3551844},
        url = {https://doi.org/10.14778/3551793.3551844},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 26 of 26 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
2,298	GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization	2024	VLDB	8.7886538e-05
5,171	An Efficient Transfer Learning Based Configuration Adviser for Database Tuning	2024	VLDB	6.3347618e-05
6,462	PilotScope: Steering Databases with Machine Learning Drivers	2024	VLDB	5.8717744e-05
6,600	A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning	2023	SIGMOD	5.8250114e-05
6,921	Rethinking Learned Cost Models: Why Start from Scratch?	2023	SIGMOD	5.7388557e-05
6,997	E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model	2025	VLDB	5.7300324e-05
7,755	Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD	2024	VLDB	5.5519655e-05
7,846	The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions	2024	VLDB	5.5331459e-05
8,163	Learned Offline Query Planning via Bayesian Optimization	2025	SIGMOD	5.4751517e-05
8,448	Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All?	2025	CIDR	5.4235725e-05
9,257	MLOS in Action: Bridging the Gap Between Experimentation and Auto-Tuning in the Cloud	2024	VLDB	5.2972217e-05
9,771	Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment	2025	SIGMOD	5.2209769e-05
10,066	Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes	2023	VLDB	5.1643809e-05
10,105	SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression	2025	VLDB	5.1435736e-05
10,340	AgentTune: An Agent-Based Large Language Model Framework for Database Knob Tuning	2026	SIGMOD	5.093636e-05
10,384	MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration	2026	SIGMOD	5.093636e-05
10,453	ESTune: Bayesian Uncertainty-Guided Early Stopping for Database Configuration Tuning	2026	SIGMOD	5.093636e-05
10,506	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	5.093636e-05
10,535	Why Database Manuals Are Not Enough: Efficient and Reliable Configuration Tuning for DBMSs via Code-Driven LLM Agents	2026	VLDB	5.093636e-05
10,547	Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink	2026	VLDB	5.093636e-05
10,559	OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning	2026	VLDB	5.093636e-05
10,565	LakeHelm: Zero-Shot Lakehouse Advisor for Joint Engine-Format Selection and Configuration	2026	VLDB	5.093636e-05
10,626	Libra: One-Shot Parameter Sensitivity Estimation for Transfer Learning in Database Performance Prediction	2026	VLDB	5.093636e-05
10,660	Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions	2025	SIGMOD	5.093636e-05
11,073	AXE: A Task Decomposition Approach to Learned LSM Tuning	2025	VLDB	5.093636e-05
11,426	Auto-Tuning with Reinforcement Learning for Permissioned Blockchain Systems	2023	VLDB	5.093636e-05

Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
86	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00035316107
226	OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases	2014	VLDB	0.00024027277
334	An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning	2019	SIGMOD	0.00020875082
347	Tuning Database Configuration Parameters with iTuned	2009	VLDB	0.00020651582
498	QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning	2019	VLDB	0.00017440583
523	Adaptive Self-Tuning Memory in DB2	2006	VLDB	0.00017133451
1,344	An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems	2021	VLDB	0.00011094717
2,740	Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation	2022	VLDB	8.1855759e-05
3,116	ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases	2021	SIGMOD	7.7390737e-05
3,346	CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions	2021	VLDB	7.4967834e-05
3,586	Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation	2021	VLDB	7.2834069e-05
3,926	UDO: Universal Database Optimization using Reinforcement Learning	2021	VLDB	7.0128068e-05

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