Paper Reading: How good are query optimizers, really? “How Good Are Query Optimizers, Really?” (Leis 等, 2015, p. 204) 「 查询优化器到底有多好? 大概就是通过比较查询优化器的有无时,查询执行的性能,来得到查询优化器对性能提升的程度 」 Abstract “ABSTRACT” (Leis 等, 2015, p. 204) 「 ...
首次使用真实的数据集和真实的查询对Join Order问题进行了端到端研究,注:以往很多测试都是使用了随机生成的query text 通过量化基数估计、成本模型和plan枚举算法对查询性能的影响,为查询优化器的完整设计提供了指导方向。论文提出,许多灾难性的plan是很容易避免的 背景和方法 论文使用了真实的数据集和PostgreSQL进行实验...
Neu- mann. How good are query optimizers, really? In VLDB Conf., 2016.V. Leis, A. Gubichev, A. Mirchev, P. Boncz, A. Kemper, and T. Neumann. How good are query optimizers, really? In VLDB, 2016.V. Leis, A. Gubichev, A. Mirchev, P. Boncz, A. Kemper, and T. Neumann. ...
We further show that while estimates are essential for finding a good join order, query performance is unsatisfactory if the query engine relies too heavily on these estimates. Using another set of experiments that measure the impact of the cost model, we find that it has much less influence ...
【论文翻译】How Good Are Query Optimizers, Really? 技术标签:查询优化论文数据库 How Good Are Query Optimizers, Really? 摘要 查找良好的连接顺序对查询性能至关重要,本文分析了在真实数据集上基数估计往往会产生较大误差,而虽然估计对寻找良好的链接顺序至关重要,但是过度依赖这些估计,最终的结果也无法让人满意...
Are Heuristics Good Enough? 本文的结论: 定量的分析出穷举式的优化器比启发式的优化器要好; 随着join的表越多,估计的误差会越大,从而导致不好的计划; 相比于cost model,基数估计对计划选取的影响更大; 同时本文的观点和sigmod blog上starburst作者在2014年发表的一篇博文观点基本一致:http://wp.sigmod.org/?p...
How Good Are Query Optimizers, Really? 目录 动机 问题 贡献 背景 相关工作 动机 连接顺序对查询至关重要 基数估计产生误差,过度依赖基数估计导致查询性能不太好 成本模型对查询性能影响 问题 基数估计有多好,在什么时候才会估计不好,进而导致查询变慢? 准确的成本模型对查询优化有多重要? 枚举计划的空间有多大?
数据库大神Thomas Neumann教授提出了cardinality estimator(基数估计)组件最查询性能的影响是非常大的,影响程度要高于cost model(代价模型)、 plan enumeration(plan的枚举技术),解读见[VLDB 2015] How Good Are Query Optimizers, Really。在查询优化中,cardinality estimator是依赖直方图完成预估工作的。目前,主流数据库常...
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How Good Are Query Optimizers, Really? (2015) - Leis, Viktor, et al. Improving Join Reorderability With Compensation Operators (2018) - Wang, TaiNing, and Chee-Yong Chan. Adaptive Optimization Of Very Large Join Queries (2018) - Neumann, Thomas, and Bernhard Radke.Cost...