当前位置: X-MOL 学术VLDB J. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
General dynamic Yannakakis: conjunctive queries with theta joins under updates
The VLDB Journal ( IF 4.2 ) Pub Date : 2019-11-19 , DOI: 10.1007/s00778-019-00590-9
Muhammad Idris , Martín Ugarte , Stijn Vansummeren , Hannes Voigt , Wolfgang Lehner

The ability to efficiently analyze changing data is a key requirement of many real-time analytics applications. In prior work, we have proposed general dynamic Yannakakis (GDyn), a general framework for dynamically processing acyclic conjunctive queries with \(\theta \)-joins in the presence of data updates. Whereas traditional approaches face a trade-off between materialization of subresults (to avoid inefficient recomputation) and recomputation of subresults (to avoid the potentially large space overhead of materialization), GDyn is able to avoid this trade-off. It intelligently maintains a succinct data structure that supports efficient maintenance under updates and from which the full query result can quickly be enumerated. In this paper, we consolidate and extend the development of GDyn. First, we give full formal proof of GDyn ’s correctness and complexity. Second, we present a novel algorithm for computing GDyn query plans. Finally, we instantiate GDyn to the case where all \(\theta \)-joins are inequalities and present extended experimental comparison against state-of-the-art engines. Our approach performs consistently better than the competitor systems with multiple orders of magnitude improvements in both time and memory consumption.

中文翻译:

通用动态Yannakakis:带有theta的联合查询正在更新中

有效分析变化的数据的能力是许多实时分析应用程序的关键要求。在先前的工作中,我们提出了通用动态Yannakakis(GDyn),这是一种用于在存在数据更新的情况下使用\(\ theta \)动态处理非循环联合查询的通用框架。传统方法在子结果的实现(以避免无效的重新计算)和子结果的重新计算(以避免实现的潜在的巨大空间开销)之间进行权衡,而GDyn能够避免这种折衷。它智能地维护了简洁的数据结构,该结构支持在更新下进行高效维护,并可以从中快速枚举完整的查询结果。在本文中,我们巩固并扩展了GDyn的开发。首先,我们给出GDyn的正确性和复杂性的完整正式证明。其次,我们提出了一种用于计算GDyn查询计划的新颖算法。最后,我们将GDyn实例化为所有\(\ theta \)-join是不等式,并且与最先进的引擎进行了扩展的实验比较。我们的方法始终比竞争对手的系统性能更好,同时在时间和内存消耗方面都提高了多个数量级。
更新日期:2019-11-19
down
wechat
bug