Proactive re-optimization
Proactive re-optimization
复制标题
主动重新优化
DOI：
10.1145/1066157.1066171
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
D. DeWitt
中科院分区：
文献类型：
--
作者：
S. Babu;P. Bizarro;D. DeWitt
传统查询优化器依靠估计统计数据的准确性来选择良好的执行计划。这种设计通常会导致复杂查询的次优计划，因为在存在偏斜和相关的数据分布的情况下，中间子表达的估计估计错误呈指数增长。重新优化是应对此类错误的有前途的技术。当前的重视者首先使用传统优化器来选择计划，然后对执行过程中计划中检测到的估计错误和导致的次级优势做出反应。这种方法的有效性是有限的，因为传统优化者选择不知道影响重新优化的问题。我们使用主动重新优化解决了这一问题，这是一种结合三种技术的新方法：i）统计估计值的不确定性以这些估计的界限框的形式计算，ii）ii）这些边界框用于选择强大的计划，这些计划是可靠的实际值与其估计值的偏差以及iii）在查询执行过程中快速有效地收集了统计的准确测量。我们使用名为Rio的原型主动重视器对这些技术进行了广泛的评估。在我们的实验中，里约优于当前的重新选择剂，高达三倍。
Traditional query optimizers rely on the accuracy of estimated statistics to choose good execution plans. This design often leads to suboptimal plan choices for complex queries, since errors in estimates for intermediate subexpressions grow exponentially in the presence of skewed and correlated data distributions. Reoptimization is a promising technique to cope with such mistakes. Current re-optimizers first use a traditional optimizer to pick a plan, and then react to estimation errors and resulting suboptimalities detected in the plan during execution. The effectiveness of this approach is limited because traditional optimizers choose plans unaware of issues affecting reoptimization. We address this problem using proactive reoptimization, a new approach that incorporates three techniques: i) the uncertainty in estimates of statistics is computed in the form of bounding boxes around these estimates, ii) these bounding boxes are used to pick plans that are robust to deviations of actual values from their estimates, and iii) accurate measurements of statistics are collected quickly and efficiently during query execution. We present an extensive evaluation of these techniques using a prototype proactive re-optimizer named Rio. In our experiments Rio outperforms current re-optimizers by up to a factor of three.