We present a new family of join algorithms, called ripple joins, for online processing of multi-table aggregation queries in a relational database management system (DBMS). Such queries arise naturally in interactive exploratory decision-support applications.
Traditional offline join algorithms are designed to minimize the time to completion of the query. In contrast, ripple joins are designed to minimize the time until an acceptably precise estimate of the query result is available, as measured by the length of a confidence interval. Ripple joins are adaptive, adjusting their behavior during processing in accordance with the statistical properties of the data. Ripple joins also permit the user to dynamically trade off the two key performance factors of on-line aggregation: the time between successive updates of the running aggregate, and the amount by which the confidence-interval length decreases at each update. We show how ripple joins can be implemented in an existing DBMS using iterators, and we give an overview of the methods used to compute confidence intervals and to adaptively optimize the ripple join "aspect-ratio" parameters. In experiments with an initial implementation of our algorithms in the POSTGRES DBMS, the time required to produce reasonably precise online estimates was up to two orders of magnitude smaller than the time required for the best offline join algorithms to produce exact answers.
我们提出了一个新的联接算法家族,称为“涟漪联接”,用于在关系数据库管理系统(DBMS)中在线处理多表聚合查询。这样的查询自然会出现在交互式探索性决策支持应用程序中。 P>
传统的脱机联接算法旨在最大程度地减少完成查询的时间。相比之下,涟漪连接的设计目的是最大程度地减少时间,直到可用可信区间的长度衡量的查询结果的可接受的精确估计为止。波纹连接是自适应的,可以根据数据的统计属性在处理过程中调整其行为。波纹联接还允许用户动态地权衡在线聚合的两个关键性能因素:正在运行的聚合的连续更新之间的时间,以及每次更新时置信区间长度减少的数量。我们展示了如何在使用迭代器的现有DBMS中实现涟漪连接,并概述了用于计算置信区间和自适应优化涟漪连接“纵横比”参数的方法。在POSTGRES DBMS中初步实现我们的算法的实验中,产生合理精确的在线估计所需的时间比最佳的脱机联接算法产生精确答案所需的时间小两个数量级。 P>
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