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Automatic Database Management System Tuning Through Large-scale Machine Learning

Summary: Automated DBMS tuning via large-scale ML (OtterTune) to select impactful knobs, map unseen workloads to known ones, and recommend settings. Evaluated on three DBMSs; achieves configurations as good as or better than existing tools or human experts. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5467
Venue
SIGMOD
Year
2017
Pagerank
0.00035316107
Overall Rank
86 | 99.42%
DOI
10.1145/3035918.3064029

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{aken_sigmod17,
        title = {{Automatic Database Management System Tuning Through Large-scale Machine Learning}},
        author = {Van Aken, Dana and Pavlo, Andrew and Gordon, Geoffrey J. and Zhang, Bohan},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3064029},
        url = {https://dl.acm.org/doi/10.1145/3035918.3064029},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 50 of 147 citing papers.

Rank Citing Paper Year Venue Pagerank
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
334 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00020875082
498 QTune: A Query-Aware Database Tuning System with Deep Reinforcement Learning 2019 VLDB 0.00017440583
568 SageDB: A Learned Database System 2019 CIDR 0.0001641553
1,135 Qd-tree: Learning Data Layouts for Big Data Analytics 2020 SIGMOD 0.00012032847
1,170 QuickSel: Quick Selectivity Learning with Mixture Models 2020 SIGMOD 0.00011827259
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011361878
1,337 DB-BERT: A Database Tuning Tool that "Reads the Manual" 2022 SIGMOD 0.00011117488
1,344 An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems 2021 VLDB 0.00011094717
1,468 Towards a Learning Optimizer for Shared Clouds 2019 VLDB 0.00010686496
1,551 Updatable Learned Index with Precise Positions 2021 VLDB 0.00010381398
1,559 Cloud-Native Database Systems at Alibaba: Opportunities and Challenges 2019 VLDB 0.00010362456
1,616 The Data Calculator*: Data Structure Design and Cost Synthesis from First Principles and Learned Cost Models 2018 SIGMOD 0.00010213691
1,686 Black or White? How to Develop an AutoTuner for Memory-based Analytics 2020 SIGMOD 0.00010008686
1,942 Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn 2019 CIDR 9.4451535e-05
1,949 Diagnosing Root Causes of Intermittent Slow Queries in Cloud Databases 2020 VLDB 9.430385e-05
1,988 FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation 2021 VLDB 9.3501502e-05
2,298 GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization 2024 VLDB 8.7886538e-05
2,313 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.762627e-05
2,620 Fauce: Fast and Accurate Deep Ensembles with Uncertainty for Cardinality Estimation 2021 VLDB 8.3363963e-05
2,740 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.1855759e-05
2,866 Optimal Column Layout for Hybrid Workloads 2019 VLDB 8.0175489e-05
2,888 AI Meets Database: AI4DB and DB4AI 2021 SIGMOD 7.9941489e-05
3,035 Instance-Optimized Data Layouts for Cloud Analytics Workloads 2021 SIGMOD 7.8297746e-05
3,116 ResTune: Resource Oriented Tuning Boosted by Meta-Learning for Cloud Databases 2021 SIGMOD 7.7390737e-05
3,219 iBTune: Individualized Buffer Tuning for Large-scale Cloud Databases 2019 VLDB 7.6283153e-05
3,320 Autoscaling Tiered Cloud Storage in Anna 2019 VLDB 7.5223482e-05
3,343 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.4983591e-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
3,367 Native Store Extension for SAP HANA 2019 VLDB 7.4718829e-05
3,400 A Demonstration of the OtterTune Automatic Database Management System Tuning Service 2018 VLDB 7.4433294e-05
3,459 A Comparative Evaluation of Systems for Scalable Linear Algebra-based Analytics 2018 VLDB 7.3953716e-05
3,573 VISTA: Optimized System for Declarative Feature Transfer from Deep CNNs at Scale 2020 SIGMOD 7.2977194e-05
3,577 Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine 2022 VLDB 7.2930211e-05
3,586 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.2834069e-05
3,587 HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements 2022 SIGMOD 7.2829345e-05
3,662 openGauss: An Autonomous Database System 2021 VLDB 7.2166682e-05
3,757 Panda: Performance Debugging for Databases using LLM Agents 2024 CIDR 7.1483644e-05
3,802 Leaper: A Learned Prefetcher for Cache Invalidation in LSM-tree based Storage Engines 2020 VLDB 7.1115502e-05
3,809 Auto-WLM: Machine Learning Enhanced Workload Management in Amazon Redshift 2023 SIGMOD 7.1074195e-05
3,961 MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems 2021 SIGMOD 6.987575e-05
3,998 Deploying a Steered Query Optimizer in Production at Microsoft 2022 SIGMOD 6.9676473e-05
4,011 Towards Dynamic and Safe Configuration Tuning for Cloud Databases 2022 SIGMOD 6.959982e-05
4,368 Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process 2022 SIGMOD 6.7393882e-05
4,516 The Price of Tailoring the Index to Your Data: Poisoning Attacks on Learned Index Structures 2022 SIGMOD 6.6489359e-05
4,789 Learned Approximate Query Processing: Make it Light, Accurate and Fast 2021 CIDR 6.5072039e-05
4,993 Key-Value Storage Engines 2020 SIGMOD 6.4096682e-05
5,023 DBPA: A Benchmark for Transactional Database Performance Anomalies 2023 SIGMOD 6.3979497e-05
5,073 Database Workload Characterization with Query Plan Encoders 2022 VLDB 6.3751266e-05
5,107 Stage: Query Execution Time Prediction in Amazon Redshift 2024 SIGMOD 6.3623786e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

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