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VLDB2025顶会

Esc: An Early-Stopping Checker for Budget-aware Index Tuning

Xiaoying Wang, Wentao Wu, Vivek R. Narasayya, Surajit Chaudhuri

2025年份
4被引次数
2顶会引用

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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