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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

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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}
}

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Showing 19 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
7,193 dbET: Execution Time Distribution-based Plan Selection 2023 SIGMOD 5.6770249e-05
7,394 DBMind: A Self-Driving Platform in openGauss 2021 VLDB 5.6259065e-05
7,580 Sibyl: Forecasting Time-Evolving Query Workloads 2024 SIGMOD 5.5925285e-05
7,720 PDX: A Data Layout for Vector Similarity Search 2025 SIGMOD 5.559007e-05
7,750 DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning 2022 VLDB 5.5523652e-05
7,825 RCRank: Multimodal Ranking of Root Causes of Slow Queries in Cloud Database Systems 2025 VLDB 5.5367884e-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
8,040 PerfGuard: Deploying ML-for-Systems without Performance Regressions, Almost! 2021 VLDB 5.5018396e-05
8,042 Grep: A Graph Learning Based Database Partitioning System 2023 SIGMOD 5.5015896e-05
8,163 Learned Offline Query Planning via Bayesian Optimization 2025 SIGMOD 5.4751517e-05
8,234 The Case for Learned In-Memory Joins 2023 VLDB 5.460955e-05
8,982 Automatic Index Selection for Large-Scale Datalog Computation 2019 VLDB 5.3397715e-05
8,984 Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems 2024 VLDB 5.3395569e-05
9,136 Sphinteract: Resolving Ambiguities in NL2SQL Through User Interaction 2025 VLDB 5.3166292e-05
9,437 GIDCL: A Graph-Enhanced Interpretable Data Cleaning Framework with Large Language Models 2024 SIGMOD 5.2687567e-05
9,441 Db2une: Tuning Under Pressure via Deep Learning 2024 VLDB 5.2678428e-05
9,536 Database Gyms 2023 CIDR 5.2529727e-05
10,108 An Elephant Under The Microscope: Analyzing The Interaction Of Optimizer Components In PostgreSQL 2025 SIGMOD 5.1347137e-05
10,109 QueryArtisan: Generating Data Manipulation Codes for Ad-hoc Analysis in Data Lakes 2025 VLDB 5.1347137e-05
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