Back to papers
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
- 12700
- Venue
- VLDB
- Year
- 2022
- Pagerank
- 4.5954398e-05
- Overall Rank
- 8,043 | 44.11%
- DOI
-
10.14778/3547305.3547309
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 11 of 11 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 6,753 |
Breaking It Down: An In-depth Study of Index Advisors |
2024 |
VLDB |
4.9345582e-05 |
| 7,332 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
4.7553758e-05 |
| 8,003 |
The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions |
2024 |
VLDB |
4.6049527e-05 |
| 9,012 |
Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management Systems |
2024 |
VLDB |
4.4059413e-05 |
| 9,930 |
Wred: Workload Reduction for Scalable Index Tuning |
2024 |
SIGMOD |
4.2469394e-05 |
| 9,931 |
Wii: Dynamic Budget Reallocation In Index Tuning |
2024 |
SIGMOD |
4.2469394e-05 |
| 10,032 |
Rainbow: Risk-aware Index Benefit Estimation Facing Out Of Distribution Workloads |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,125 |
Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,205 |
RIB: Robust Learning-based Index Benefit Estimation |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,217 |
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 |
4.1905499e-05 |
| 10,552 |
Esc: An Early-Stopping Checker for Budget-aware Index Tuning |
2025 |
VLDB |
4.1905499e-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 |
| 237 |
An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server |
1997 |
VLDB |
0.00031727601 |
| 517 |
AutoAdmin "What-if" Index Analysis Utility |
1998 |
SIGMOD |
0.00021193179 |
| 662 |
Database Tuning Advisor for Microsoft SQL Server 2005 |
2004 |
VLDB |
0.00018478597 |
| 1,018 |
Automatic Physical Database Tuning: A Relaxation-based Approach |
2005 |
SIGMOD |
0.00014626746 |
| 1,064 |
Analyzing Plan Diagrams of Database Query Optimizers |
2005 |
VLDB |
0.00014348262 |
| 1,239 |
Selectivity Estimation for Range Predicates using Lightweight Models |
2019 |
VLDB |
0.00013091459 |
| 1,443 |
Compressing SQL Workloads |
2002 |
SIGMOD |
0.00011944621 |
| 1,644 |
Parametric Query Optimization for Linear and Piecewise Linear Cost Functions |
2002 |
VLDB |
0.0001102889 |
| 1,856 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00010319105 |
| 1,961 |
Plan Selection based on Query Clustering |
2002 |
VLDB |
9.9464221e-05 |
| 2,022 |
Magic mirror in my hand, which is the best in the land? An Experimental Evaluation of Index Selection Algorithms |
2020 |
VLDB |
9.7623022e-05 |
| 2,050 |
Automatically Indexing Millions of Databases in Microsoft Azure SQL Database |
2019 |
SIGMOD |
9.6883066e-05 |
| 2,479 |
Efficient Use of the Query Optimizer for Automated Physical Design |
2007 |
VLDB |
8.6836615e-05 |
| 2,788 |
To Tune or not to Tune? A Lightweight Physical Design Alerter |
2006 |
VLDB |
8.121027e-05 |
| 3,623 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
6.9017341e-05 |
| 3,955 |
Efficiently Approximating Selectivity Functions using Low Overhead Regression Models |
2020 |
VLDB |
6.5895015e-05 |
| 4,469 |
Comprehensive and Efficient Workload Compression |
2021 |
VLDB |
6.1535623e-05 |
| 5,673 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
5.3789277e-05 |
| 6,364 |
ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning |
2022 |
SIGMOD |
5.0895007e-05 |
Semantically Similar Papers
| Overall Rank |
Paper |
Year |
Venue |
Pagerank |
| 10,125 |
Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis] |
2026 |
SIGMOD |
4.1905499e-05 |
| 12,107 |
Efficient Indexing for Diverse Query Results |
2013 |
VLDB |
4.1905499e-05 |
| 11,103 |
Looking Deeply into the Magic Mirror: An Interactive Analysis of Database Index Selection Approaches |
2024 |
VLDB |
4.1905499e-05 |
| 7,332 |
Refactoring Index Tuning Process with Benefit Estimation |
2024 |
VLDB |
4.7553758e-05 |
| 10,754 |
Benchmarking Adaptive Multidimensional Indices |
2025 |
VLDB |
4.1905499e-05 |
| 5,327 |
DILI: A Distribution-Driven Learned Index |
2023 |
VLDB |
5.5660777e-05 |
| 237 |
An Efficient, Cost-Driven Index Selection Tool for Microsoft SQL Server |
1997 |
VLDB |
0.00031727601 |
| 1,856 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations |
2019 |
SIGMOD |
0.00010319105 |
| 5,673 |
Budget-aware Index Tuning with Reinforcement Learning |
2022 |
SIGMOD |
5.3789277e-05 |
| 5,343 |
Learned Index Benefits: Machine Learning Based Index Performance Estimation |
2022 |
VLDB |
5.5582234e-05 |