DBScholar

Back to papers

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!

Summary: Booster augments existing DBMS autotuners with LLMs and query-level historical artifacts, turning prior tuning runs into per-query config contexts that can be reused under workload drift/schema transfer. Beam-search composition of query suggestions yields much faster re-optimization, up to 74% better configs than retraining/continuing from scratch. (summarized by gpt-5-mini on Apr 11 2026)

Paper ID
7720
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,506 | 27.92%
DOI
10.1145/3786704

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@inproceedings{zhang_sigmod26,
        title = {{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!}},
        author = {Zhang, William and Lim, Wan Shen and Pavlo, Andrew},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3786704},
        url = {https://dl.acm.org/doi/10.1145/3786704},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 50 of 69 cited papers.

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

Rank Cited Paper Year Venue Pagerank
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
86 Automatic Database Management System Tuning Through Large-scale Machine Learning 2017 SIGMOD 0.00035316107
234 Self-Driving Database Management Systems 2017 CIDR 0.00023810722
334 An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning 2019 SIGMOD 0.00020875082
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
387 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00019442332
461 Query-based Workload Forecasting for Self-Driving Database Management Systems 2018 SIGMOD 0.00018068441
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
563 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001650812
768 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.00014173242
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011361878
1,481 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010644613
1,832 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 9.6607418e-05
1,920 D-Bot: Database Diagnosis System using Large Language Models 2024 VLDB 9.4846185e-05
1,981 ReAcTable: Enhancing ReAct for Table Question Answering 2024 VLDB 9.3579557e-05
2,313 Active Learning for ML Enhanced Database Systems 2020 SIGMOD 8.762627e-05
2,355 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7022189e-05
2,408 Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet 2024 VLDB 8.6154404e-05
2,534 ELPIS: Graph-Based Similarity Search for Scalable Data Science 2023 VLDB 8.4561875e-05
2,553 LLM-R^2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiency 2025 VLDB 8.4283807e-05
2,740 Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation 2022 VLDB 8.1855759e-05
2,844 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.0608767e-05
3,035 Instance-Optimized Data Layouts for Cloud Analytics Workloads 2021 SIGMOD 7.8297746e-05
3,338 Robust Query Driven Cardinality Estimation under Changing Workloads 2023 VLDB 7.5068221e-05
3,343 LlamaTune: Sample-Efficient DBMS Configuration Tuning 2022 VLDB 7.4983591e-05
3,516 LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans 2023 VLDB 7.3524442e-05
3,586 Make Your Database System Dream of Electric Sheep: Towards Self-Driving Operation 2021 VLDB 7.2834069e-05
3,787 Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL 2024 VLDB 7.1249098e-05
3,926 UDO: Universal Database Optimization using Reinforcement Learning 2021 VLDB 7.0128068e-05
3,961 MB2: Decomposed Behavior Modeling for Self-Driving Database Management Systems 2021 SIGMOD 6.987575e-05
4,101 Automated Generation of Materialized Views in Oracle 2020 VLDB 6.9009734e-05
4,398 Real-time Workload Pattern Analysis for Large-scale Cloud Databases 2023 VLDB 6.7248611e-05
4,434 LEON: A New Framework for ML-Aided Query Optimization 2023 VLDB 6.7079088e-05
4,612 Warper: Efficiently Adapting Learned Cardinality Estimators to Data and Workload Drifts 2022 SIGMOD 6.6072026e-05
4,643 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.5907466e-05
4,751 The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data 2023 SIGMOD 6.5241784e-05
4,814 METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection 2024 VLDB 6.4955135e-05
4,929 AutoSteer: Learned Query Optimization for Any SQL Database 2023 VLDB 6.4423294e-05
5,010 Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server 2023 VLDB 6.4023732e-05
5,091 Budget-aware Index Tuning with Reinforcement Learning 2022 SIGMOD 6.3669569e-05
5,171 An Efficient Transfer Learning Based Configuration Adviser for Database Tuning 2024 VLDB 6.3347618e-05
5,856 Automatic Database Configuration Debugging using Retrieval-Augmented Language Models 2025 SIGMOD 6.0644656e-05
6,015 Dear User-Defined Functions, Inlining isn't working out so great for us. Let's try batching to make our relationship work. Sincerely, SQL 2024 CIDR 6.008272e-05
6,088 How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks 2025 SIGMOD 5.9813965e-05
6,177 Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud 2022 VLDB 5.9496208e-05
6,271 Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective 2024 VLDB 5.9326197e-05
6,543 Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation 2023 SIGMOD 5.8461929e-05
6,600 A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning 2023 SIGMOD 5.8250114e-05
6,997 E2ETune: End-to-End Knob Tuning via Fine-tuned Generative Language Model 2025 VLDB 5.7300324e-05
Previous Page 1 / 2 Next

Semantically Similar Papers