Wii: Dynamic Budget Reallocation In Index Tuning
Xiaoying Wang, Wentao Wu, Chi Wang, Vivek R. Narasayya, Surajit Chaudhuri
Abstract
Index tuning aims to find the optimal index configuration for an input workload. It is often a time-consuming and resource-intensive process, largely attributed to the huge amount of "what-if" calls made to the query optimizer during configuration enumeration. Therefore, in practice it is desirable to set a budget constraint that limits the number of what-if calls allowed. This yields a new problem of budget allocation, namely, deciding on which query-configuration pairs (QCP's) to issue what-if calls. Unfortunately, optimal budget allocation is NP-hard, and budget allocation decisions made by existing solutions can be inferior. In particular, many of the what-if calls allocated by using existing solutions are devoted to QCP's whose what-if costs can be approximated by using cost derivation, a well-known technique that is computationally much more efficient and has been adopted by commercial index tuning software. This results in considerable waste of the budget, as these what-if calls are unnecessary. In this paper, we propose "Wii, " a lightweight mechanism that aims to avoid such spurious what-if calls. It can be seamlessly integrated with existing configuration enumeration algorithms. Experimental evaluation on top of both standard industrial benchmarks and real workloads demonstrates that Wii can eliminate significant number of spurious what-if calls. Moreover, by reallocating the saved budget to QCP's where cost derivation is less accurate, existing algorithms can be significantly improved in terms of the final configuration found.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext c4d40cd8-93bc-4b66-a3df-9a560c7bbcfbCited by top-tier papers4
- Hit the Gym: Accelerating Query Execution to Efficiently Bootstrap Behavior Models for Self-Driving Database Management SystemsWan Shen Lim, Lin Ma, William Zhang, Matthew Butrovich et al.VLDB 2024 · 10 citations
- Esc: An Early-Stopping Checker for Budget-aware Index TuningXiaoying Wang, Wentao Wu, Vivek R. Narasayya, Surajit ChaudhuriVLDB 2025 · 4 citations
- UTune: Towards Uncertainty-Aware Online Index TuningChenning Wu, Sifan Chen, Wentao Wu, Yinan Jing et al.ICDE 2026
- Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis]Wentao Wu, Anshuman Dutt, Gaoxiang Xu, Vivek R. Narasayya et al.SIGMOD 2026
Builds on13
- An End-to-End Learning-based Cost EstimatorJi Sun, Guoliang LiVLDB 2020 · 251 citations
- QueryFormer: A Tree Transformer Model for Query Plan RepresentationYue Zhao, Gao Cong, Jiachen Shi, Chunyan MiaoVLDB 2022 · 117 citations
- Zero-Shot Cost Models for Out-of-the-box Learned Cost PredictionBenjamin Hilprecht, Carsten BinnigVLDB 2022 · 90 citations
- Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our FindingsTarique Siddiqui, Alekh Jindal, Shi Qiao, Hiren Patel et al.SIGMOD 2020 · 80 citations
- Learned Index Benefits: Machine Learning Based Index Performance EstimationJiachen Shi, Gao Cong, Xiaoli LiVLDB 2022 · 42 citations
Related papers
- Budget-aware Index Tuning with Reinforcement LearningWentao Wu, Chi Wang, Tarique Siddiqui, Junxiong Wang et al.SIGMOD 2022 · 33 citations
- Refactoring Index Tuning Process with Benefit EstimationTao Yu, Zhaonian Zou, Weihua Sun, Yu YanVLDB 2024 · 13 citations
- DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index TuningTarique Siddiqui, Wentao Wu, Vivek R. Narasayya, Surajit ChaudhuriVLDB 2022 · 36 citations
- Wred: Workload Reduction for Scalable Index TuningMatteo Brucato, Tarique Siddiqui, Wentao Wu, Vivek R. Narasayya et al.SIGMOD 2024 · 7 citations
- Guiding Index Tuning Exploration with Potential EstimationKecheng Luo, Ruiyang Ma, Peng Cai, Aoying Zhou et al.ICDE 2025 · 1 citation
