DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning

Summary: DISTILL enables index tuning via pattern-based pruning of spurious, rule-based indexes to cut optimizer calls. It learns cost models via workload similarity across configs to estimate costs for many candidates, enabling up to 12x faster tuning with high quality. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h4212c5a19de93caa
Venue: VLDB
Year: 2022
Pagerank: 5.4277674e-05
Overall Rank: 7,904 | 46.88%
DOI: 10.14778/3547305.3547309
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

1. Tarique Siddiqui (Microsoft)
2. Wentao Wu (Microsoft)
3. Vivek Narasayya (Microsoft)
4. Surajit Chaudhuri (Microsoft)

BibTeX Citation

@article{siddiqui_vldb22,
        title = {{DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning}},
        author = {Siddiqui, Tarique and Wu, Wentao and Narasayya, Vivek and Chaudhuri, Surajit},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {10},
        pages = {2019--2031},
        doi = {10.14778/3547305.3547309},
        url = {https://doi.org/10.14778/3547305.3547309},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 12 of 12 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,107	Breaking It Down: An In-depth Study of Index Advisors	2024	VLDB	5.8833461e-05
7,219	Refactoring Index Tuning Process with Benefit Estimation	2024	VLDB	5.5808392e-05
7,981	The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions	2024	VLDB	5.4117272e-05
9,134	Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems	2024	VLDB	5.2223611e-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
10,296	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!	2026	SIGMOD	5.0407989e-05
10,543	Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads	2026	SIGMOD	4.9769913e-05
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
10,927	Evaluating the Practical Effectiveness of LLM-Driven Index Tuning on Microsoft SQL Server	2026	VLDB	4.9769913e-05
11,232	Esc: An Early-Stopping Checker for Budget-aware Index Tuning	2025	VLDB	4.9769913e-05

Outgoing Citations (Sorted by Pagerank)

Showing 19 of 19 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
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
492	Database Tuning Advisor for Microsoft SQL Server 2005	2004	VLDB	0.00017406029
691	Selectivity Estimation for Range Predicates using Lightweight Models	2019	VLDB	0.00014737455
751	Automatic Physical Database Tuning: A Relaxation-based Approach	2005	SIGMOD	0.00014251362
995	Analyzing Plan Diagrams of Database Query Optimizers	2005	VLDB	0.00012629969
1,244	Compressing SQL Workloads	2002	SIGMOD	0.00011369155
1,280	AI Meets AI: Leveraging Query Executions to Improve Index Recommendations	2019	SIGMOD	0.00011224914
1,298	Parametric Query Optimization for Linear and Piecewise Linear Cost Functions	2002	VLDB	0.00011122244
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
1,516	Automatically Indexing Millions of Databases in Microsoft Azure SQL Database	2019	SIGMOD	0.00010398346
1,788	Plan Selection based on Query Clustering	2002	VLDB	9.6275306e-05
2,025	Efficient Use of the Query Optimizer for Automated Physical Design	2007	VLDB	9.1650298e-05
2,374	To Tune or not to Tune? A Lightweight Physical Design Alerter	2006	VLDB	8.5570131e-05
2,833	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	7.9539771e-05
3,208	Efficiently Approximating Selectivity Functions using Low Overhead Regression Models	2020	VLDB	7.5355264e-05
3,809	Comprehensive and Efficient Workload Compression	2021	VLDB	7.0064875e-05
4,970	Budget-aware Index Tuning with Reinforcement Learning	2022	SIGMOD	6.3319052e-05
5,632	ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning	2022	SIGMOD	6.0554368e-05

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1	12,593	Efficient Indexing for Diverse Query Results	2013	VLDB
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