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Esc: An Early-Stopping Checker for Budget-aware Index Tuning

Summary: Introduces early stopping for budget-aware index tuning: terminate when projected remaining-budget gains fall within a user-specified quality-loss threshold. Esc cheaply estimates this point, substantially reducing what-if optimizer calls with negligible quality or runtime overhead. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
h1ffd249acc09ebb2
Venue
VLDB
Year
2025
Pagerank
4.9769913e-05
Overall Rank
11,232 | 24.51%
DOI
10.14778/3718057.3718059
PDF
Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

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

Authors

BibTeX Citation

@article{wang_vldb25,
        title = {{Esc: An Early-Stopping Checker for Budget-aware Index Tuning}},
        author = {Wang, Xiaoying and Wu, Wentao and Narasayya, Vivek and Chaudhuri, Surajit},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {5},
        pages = {1278--1290},
        doi = {10.14778/3718057.3718059},
        url = {https://doi.org/10.14778/3718057.3718059},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
10,615 Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] 2026 SIGMOD 4.9769913e-05
10,692 RIB: Robust Learning-based Index Benefit Estimation 2026 SIGMOD 4.9769913e-05
10,921 Tuning the Lookahead Distance for PostgreSQL Asynchronous IO 2026 VLDB 4.9769913e-05
10,927 Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server 2026 VLDB 4.9769913e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 30 of 30 cited papers.

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

Rank Cited Paper Year Venue Pagerank
88 Automated Selection of Materialized Views and Indexes for SQL Databases 2000 VLDB 0.00035340164
144 Neo: A Learned Query Optimizer 2019 VLDB 0.00029090793
151 An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server 1997 VLDB 0.00028664776
378 AutoAdmin "What-if" Index Analysis Utility 1998 SIGMOD 0.00019541534
462 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017836105
560 Plan-Structured Deep Neural Network Models for Query Performance Prediction 2019 VLDB 0.00016408613
682 Robust Estimation of Resource Consumption for SQL Queries using Statistical Techniques 2012 VLDB 0.00014814858
751 Automatic Physical Database Tuning: A Relaxation-based Approach 2005 SIGMOD 0.00014251362
1,258 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011308863
1,280 AI Meets AI: Leveraging Query Executions to Improve Index Recommendations 2019 SIGMOD 0.00011224914
1,396 Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms 2020 VLDB 0.00010788714
1,815 CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads 2011 VLDB 9.5756946e-05
2,025 Efficient Use of the Query Optimizer for Automated Physical Design 2007 VLDB 9.1650298e-05
2,248 QueryFormer: A Tree Transformer Model for Query Plan Representation 2022 VLDB 8.7567205e-05
2,833 Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings 2020 SIGMOD 7.9539771e-05
2,879 Zero-Shot Cost Models for Out-of-the-box Learned Cost Prediction 2022 VLDB 7.9126862e-05
3,519 Towards Predicting Query Execution Time for Concurrent and Dynamic Database Workloads 2013 VLDB 7.2361015e-05
4,609 Index Interactions in Physical Design Tuning: Modeling, Analysis, and Applications 2009 VLDB 6.5007715e-05
4,713 Learned Index Benefits: Machine Learning Based Index Performance Estimation 2022 VLDB 6.4543291e-05
4,970 Budget-aware Index Tuning with Reinforcement Learning 2022 SIGMOD 6.3319052e-05
5,188 Database Workload Characterization with Query Plan Encoders 2022 VLDB 6.2346845e-05
5,632 ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning 2022 SIGMOD 6.0554368e-05
5,658 Uncertainty Aware Query Execution Time Prediction 2014 VLDB 6.0460211e-05
5,675 HMAB: Self-Driving Hierarchy of Bandits for Integrated Physical Database Design Tuning 2023 VLDB 6.0401656e-05
6,107 Breaking It Down: An In-depth Study of Index Advisors 2024 VLDB 5.8833461e-05
7,074 Plan Stitch: Harnessing the Best of Many Plans 2018 VLDB 5.604902e-05
7,219 Refactoring Index Tuning Process with Benefit Estimation 2024 VLDB 5.5808392e-05
7,904 DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning 2022 VLDB 5.4277674e-05
9,796 Wii: Dynamic Budget Reallocation In Index Tuning 2024 SIGMOD 5.1236285e-05
9,950 Wred: Workload Reduction for Scalable Index Tuning 2024 SIGMOD 5.102891e-05
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