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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
h3707979133a3df80
Venue
VLDB
Year
2022
Pagerank
7.6865211e-05
Overall Rank
3,057 | 79.51%
DOI
10.14778/3551793.3551844
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

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 29 of 29 citing papers.

Rank Citing Paper Year Venue Pagerank
2,227 GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization 2024 VLDB 8.7924681e-05
5,304 An Efficient Transfer Learning Based Configuration Adviser for Database Tuning 2024 VLDB 6.1758143e-05
5,713 PilotScope: Steering Databases with Machine Learning Drivers 2024 VLDB 6.0090633e-05
6,158 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 5.8518237e-05
6,590 Towards Query Optimizer as a Service (QOaaS) in a Unified LakeHouse Ecosystem: Can One QO Rule Them All? 2025 CIDR 5.7233093e-05
6,752 A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning 2023 SIGMOD 5.6733557e-05
7,092 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 5.5868637e-05
7,363 Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD 2024 VLDB 5.5279826e-05
7,561 Learned Offline Query Planning via Bayesian Optimization 2025 SIGMOD 5.4784915e-05
7,997 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.3938336e-05
9,052 Rockhopper: A Robust Optimizer for Spark Configuration Tuning in Production Environment 2025 SIGMOD 5.215088e-05
9,143 Check Out the Big Brain on BRAD: Simplifying Cloud Data Processing with Learned Automated Data Meshes 2023 VLDB 5.2074327e-05
9,469 MLOS in Action: Bridging the Gap Between Experimentation and Auto-Tuning in the Cloud 2024 VLDB 5.1588001e-05
9,946 AgentTune: An Agent-Based Large Language Model Framework for Database Knob Tuning 2026 SIGMOD 5.0910746e-05
10,323 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 5.0241309e-05
10,324 Why Database Manuals Are Not Enough: Efficient and Reliable Configuration Tuning for DBMSs via Code-Driven LLM Agents 2026 VLDB 5.0241309e-05
10,366 SCompression: Enhancing Database Knob Tuning Efficiency Through Slice-Based OLTP Workload Compression 2025 VLDB 5.0091669e-05
10,653 MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration 2026 SIGMOD 4.9605343e-05
10,713 ESTune: Bayesian Uncertainty-Guided Early Stopping for Database Configuration Tuning 2026 SIGMOD 4.9605343e-05
10,800 Scarf: Self-Adaptive Tuning via Multi-Objective Reinforcement Learning for Apache Flink 2026 VLDB 4.9605343e-05
10,812 OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning 2026 VLDB 4.9605343e-05
10,818 LakeHelm: Zero-Shot Lakehouse Advisor for Joint Engine-Format Selection and Configuration 2026 VLDB 4.9605343e-05
10,957 MFTune: An Efficient Multi-fidelity Framework for Spark SQL Configuration Tuning 2026 VLDB 4.9605343e-05
10,982 Tuning the Lookahead Distance for PostgreSQL Asynchronous IO 2026 VLDB 4.9605343e-05
10,988 Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server 2026 VLDB 4.9605343e-05
11,142 Libra: One-Shot Parameter Sensitivity Estimation for Transfer Learning in Database Performance Prediction 2026 VLDB 4.9605343e-05
11,173 Centrum: Model-based Database Auto-tuning with Minimal Distributional Assumptions 2025 SIGMOD 4.9605343e-05
11,489 AXE: A Task Decomposition Approach to Learned LSM Tuning 2025 VLDB 4.9605343e-05
11,790 Auto-Tuning with Reinforcement Learning for Permissioned Blockchain Systems 2023 VLDB 4.9605343e-05
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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
74 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00037164008
236 OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases 2014 VLDB 0.00023639649
309 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.0002143249
313 Tuning Database Configuration Parameters with iTuned 2009 VLDB 0.00021288111
411 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.0001874043
496 Adaptive Self-Tuning Memory in DB2 2006 VLDB 0.00017347275
1,294 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB 0.00011129451
2,572 ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases 2021 SIGMOD 8.2581875e-05
2,779 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.0085666e-05
3,172 CGPTuner: a Contextual Gaussian Process Bandit Approach for the Automatic Tuning of IT Configurations Under Varying Workload Conditions 2021 VLDB 7.5556865e-05
3,575 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.1769215e-05
3,655 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 7.1137504e-05
Previous Page 1 / 1 Next

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# Overall Rank Paper Year Venue
1 10,324 Why Database Manuals Are Not Enough: Efficient and Reliable Configuration Tuning for DBMSs via Code-Driven LLM Agents 2026 VLDB
2 5,304 An Efficient Transfer Learning Based Configuration Adviser for Database Tuning 2024 VLDB
3 1,294 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB
4 309 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD
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9 10,323 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
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