English(EN) When Is One Query per Template Enough? Adapting Learned Cost Models to New Hardware

数据库查询成本模型以更少的测量数据适应新硬件

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了一种方法，能够以最少的额外测量数据，将用于数据库查询运行时的学习成本模型适应到新硬件上。通过分析不同环境下的查询模板及其运行时间变化，他们发现简单的速度因子是不够的。一种同时考虑串行和并行计划以及模板特定偏移量的校正方法，显著降低了误差，使性能接近在新硬件上从头开始训练的模型。这种方法在模型高效适应方面显示出潜力，尽管在复杂查询集上的性能可能会有所不同。 AI

影响提高了数据库系统中机器学习模型适应的效率，可能降低硬件特定调优成本。

排序理由学术论文，详细介绍了一种适应机器学习模型的新颖方法。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv cs.LG TIER_1 English(EN) · Krish Patel · 2026-11-05 05:00

一个模板一次查询何时足够？将学习到的成本模型适配到新硬件

arXiv:2611.05892v1 Announce Type: cross Abstract: A learned cost model that predicts query runtimes is trained on measurements from one machine. When the database moves to other hardware, its plans stay the same but their runtimes change, each by its own factor. We ask how to ada…

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一个模板一次查询何时足够？将学习到的成本模型适配到新硬件

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