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Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis]

Summary: Analyze structural plan-pair changes induced by recommended indexes and show most significant QPRs stem from a small set of recurring regression patterns. Propose a pattern-based QPR detector that outperforms ML alternatives across benchmarks and real workloads. (summarized by gpt-5-mini on Feb 11 2026)

Paper ID
7625
Venue
SIGMOD
Year
2026
Pagerank
5.093636e-05
Overall Rank
10,413 | 28.56%
DOI
10.1145/3769839

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Authors

BibTeX Citation

@inproceedings{wu_sigmod26,
        title = {{Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments \& Analysis]}},
        author = {Wu, Wentao and Dutt, Anshuman and Xu, Gaoxiang and Narasayya, Vivek and Chaudhuri, Surajit},
        series = {{SIGMOD} '26},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3769839},
        url = {https://dl.acm.org/doi/10.1145/3769839},
        year = {2026}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 34 of 34 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
89 On the Propagation of Errors in the Size of Join Results 1991 SIGMOD 0.00035031529
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
156 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028636811
387 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00019442332
465 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001803934
563 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.0001650812
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00015014887
768 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.00014173242
1,061 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012369764
1,122 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.0001209124
1,256 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011457194
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
1,548 Automatically Indexing Millions of Databases in Microsoft Azure SQL Database 2019 SIGMOD 0.00010392475
1,832 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 9.6607418e-05
1,904 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5040429e-05
2,355 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7022189e-05
2,822 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 8.0898536e-05
2,844 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 8.0608767e-05
3,482 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 7.3751635e-05
4,643 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.5907466e-05
5,010 Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server 2023 VLDB 6.4023732e-05
5,073 Database Workload Characterization with Query Plan Encoders 2022 VLDB 6.3751266e-05
5,091 Budget-aware Index Tuning with Reinforcement Learning 2022 SIGMOD 6.3669569e-05
5,281 Leveraging Re-costing for Online Optimization of Parameterized Queries with Guarantees 2017 SIGMOD 6.2842378e-05
5,537 Uncertainty Aware Query Execution Time Prediction 2014 VLDB 6.1820087e-05
5,558 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.1749098e-05
5,869 ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning 2022 SIGMOD 6.0610922e-05
7,655 Plan Stitch: Harnessing the Best of Many Plans 2018 VLDB 5.5741093e-05
7,750 DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning 2022 VLDB 5.5523652e-05
9,615 Wii: Dynamic Budget Reallocation In Index Tuning 2024 SIGMOD 5.2436464e-05
10,082 Wred: Workload Reduction for Scalable Index Tuning 2024 SIGMOD 5.1587525e-05
10,815 Esc: An Early-Stopping Checker for Budget-aware Index Tuning 2025 VLDB 5.093636e-05
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