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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
h7409ae897de8bd2a
Venue
SIGMOD
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,604 | 28.71%
DOI
10.1145/3769839

Incoming Non-self Citations Over Time

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

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}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
10,918 Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server 2026 VLDB 4.9793485e-05
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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
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061066921
91 On the Propagation of Errors in the Size of Join Results 1991 SIGMOD 0.0003475226
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
151 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028672526
378 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00019549382
461 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017829982
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016403151
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.0001481781
751 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.0001425375
982 Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation 2022 VLDB 0.00012714044
1,064 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012202282
1,257 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011310561
1,279 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011226878
1,397 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010789242
1,515 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 0.00010417728
1,516 Automatically Indexing Millions of Databases in Microsoft Azure SQL Database 2019 SIGMOD 0.00010402594
1,815 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5791737e-05
2,250 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7533306e-05
2,834 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9560627e-05
2,885 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 7.9094988e-05
3,519 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 7.2389387e-05
4,711 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.4573842e-05
4,968 Budget-aware Index Tuning with Reinforcement Learning 2022 SIGMOD 6.3348803e-05
5,022 Analyzing the Impact of Cardinality Estimation on Execution Plans in Microsoft SQL Server 2023 VLDB 6.3100988e-05
5,078 Leveraging Re-costing for Online Optimization of Parameterized Queries with Guarantees 2017 SIGMOD 6.2857912e-05
5,194 Database Workload Characterization with Query Plan Encoders 2022 VLDB 6.2353557e-05
5,630 ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning 2022 SIGMOD 6.0582762e-05
5,656 Uncertainty Aware Query Execution Time Prediction 2014 VLDB 6.0488629e-05
5,674 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.0430252e-05
7,072 Plan Stitch: Harnessing the Best of Many Plans 2018 VLDB 5.6074688e-05
7,900 DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning 2022 VLDB 5.4303143e-05
9,790 Wii: Dynamic Budget Reallocation In Index Tuning 2024 SIGMOD 5.1260323e-05
10,297 Wred: Workload Reduction for Scalable Index Tuning 2024 SIGMOD 5.0430432e-05
11,224 Esc: An Early-Stopping Checker for Budget-aware Index Tuning 2025 VLDB 4.9793485e-05
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