Wii: Dynamic Budget Reallocation In Index Tuning
Xiaoying Wang, Wentao Wu, Chi Wang, Vivek R. Narasayya, Surajit Chaudhuri
摘要
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.
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引用它的顶会 Paper4
- 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 等VLDB 2024 · 被引用 10 次
- Esc: An Early-Stopping Checker for Budget-aware Index TuningXiaoying Wang, Wentao Wu, Vivek R. Narasayya, Surajit ChaudhuriVLDB 2025 · 被引用 4 次
- UTune: Towards Uncertainty-Aware Online Index TuningChenning Wu, Sifan Chen, Wentao Wu, Yinan Jing 等ICDE 2026
- Understanding and Detecting Query Performance Regression in Practical Index Tuning: [Experiments & Analysis]Wentao Wu, Anshuman Dutt, Gaoxiang Xu, Vivek R. Narasayya 等SIGMOD 2026
它引用的顶会 Paper13
- An End-to-End Learning-based Cost EstimatorJi Sun, Guoliang LiVLDB 2020 · 被引用 251 次
- QueryFormer: A Tree Transformer Model for Query Plan RepresentationYue Zhao, Gao Cong, Jiachen Shi, Chunyan MiaoVLDB 2022 · 被引用 117 次
- Zero-Shot Cost Models for Out-of-the-box Learned Cost PredictionBenjamin Hilprecht, Carsten BinnigVLDB 2022 · 被引用 90 次
- Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our FindingsTarique Siddiqui, Alekh Jindal, Shi Qiao, Hiren Patel 等SIGMOD 2020 · 被引用 80 次
- Learned Index Benefits: Machine Learning Based Index Performance EstimationJiachen Shi, Gao Cong, Xiaoli LiVLDB 2022 · 被引用 42 次
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