Esc: An Early-Stopping Checker for Budget-aware Index Tuning
Xiaoying Wang, Wentao Wu, Vivek R. Narasayya, Surajit Chaudhuri
摘要
Index tuning is a time-consuming process. One major performance bottleneck in existing index tuning systems is the large amount of "what-if" query optimizer calls that estimate the cost of a given pair of query and index configuration without materializing the indexes. There has been recent work on budget-aware index tuning that limits the amount of what-if calls allowed in index tuning. Existing budget-aware index tuning algorithms, however, typically make fast progress early on in terms of the best configuration found but slow down when more and more what-if calls are allocated. This observation of "diminishing return" on index quality leads us to introduce early stopping for budget-aware index tuning, where user specifies a threshold on the tolerable loss of index quality and we stop index tuning if the projected loss with the remaining budget is below the threshold. We further propose Esc, a low-overhead early-stopping checker that realizes this new functionality. Experimental evaluation on top of both industrial benchmarks and real customer workloads demonstrates that Esc can significantly reduce the number of what-if calls made during budget-aware index tuning while incurring little or zero improvement loss and little extra computational overhead compared to the overall index tuning time.
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引用它的顶会 Paper2
- 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
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- 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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