[论文解读] Robust Plan Evaluation based on Approximate Probabilistic Machine Learning

Amin Kamali, Verena Kantere+2 more|arXiv (Cornell University)|Jan 26, 2024

Data Management and Algorithms被引用 5

一句话总结

本文提出 Roq，一种以鲁棒性为导向的查询优化器，使用近似的概率性机器学习来评估和选择查询计划，相较于现有方法在鲁棒性方面有所提升。

ABSTRACT

Query optimizers in RDBMSs search for execution plans expected to be optimal for given queries. They use parameter estimates, often inaccurate, and make assumptions that may not hold in practice. Consequently, they may select plans that are suboptimal at runtime if estimates and assumptions are not valid. Therefore, they do not sufficiently support robust query optimization. Using ML to improve data systems has shown promising results for query optimization. Inspired by this, we propose Robust Query Optimizer (Roq), a holistic framework based on a risk-aware learning approach. Roq includes a novel formalization of the notion of robustness in the context of query optimization and a principled approach for its quantification and measurement based on approximate probabilistic ML. It also includes novel strategies and algorithms for query plan evaluation and selection. Roq includes a novel learned cost model that is designed to predict the cost of query execution and the associated risks and performs query optimization accordingly. We demonstrate that Roq provides significant improvements in robust query optimization compared with the state-of-the-art.

研究动机与目标

• 在查询优化中形式化鲁棒性。
• 为计划评估开发一个风险感知的学习框架。
• 设计一个学习成本模型，预测执行成本及相关风险。
• 提出用于鲁棒计划评估与选择的策略。
• 证明相对于现有鲁棒优化方法的改进。

提出的方法

• 在查询优化的语境中定义鲁棒性。
• 介绍 Roq，一个以概率性机器学习为基础的整体框架。
• 开发一个学习成本模型，估计计划成本和风险。
• 创建用于评估和选择鲁棒计划的策略和算法。
• 使用近似概率技术量化鲁棒性。
• 将 Roq 与最先进的基线进行评估。

实验结果

研究问题

• RQ1 如何在查询优化中形式化并衡量鲁棒性？
• RQ2 近似概率性机器学习方法是否能提升鲁棒性下的计划评估与选择？
• RQ3 包含风险的学习成本模型对查询优化结果有何影响？
• RQ4 相对于现有的鲁棒优化方法，Roq 的表现如何？
• RQ5 在实践中哪些策略最能促进鲁棒计划评估与选择？

主要发现

• Roq 在鲁棒查询优化方面相较于最先进的方法提供了显著改进。
• 开发了新颖的鲁棒性形式化和风险感知测量框架。
• 一个学习成本模型预测查询执行成本及相关风险。
• 新的策略和算法使鲁棒计划评估与选择成为可能。
• 实证结果支持 Roq 在鲁棒优化场景中的有效性。