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引用次数: 0

摘要

数据库系统传统上是基于磁盘的,这激发了对外部存储器(EM)算法的广泛研究。然而,随着ram变得越来越大,越来越便宜,现代分布式数据系统越来越多地采用基于主存的无共享架构,例如Spark和Flink。这些系统可以通过BSP模型抽象出来(有MPC模型和MapReduce模型的变体),并且对于设计BSP算法来处理大量数据已经有了强烈的兴趣。随着硬盘逐渐淡出人们的视野,EM算法现在可能显得不那么重要了。然而,我们观察到,许多最近开发的BSP模型下的连接算法与EM模型中的对应算法具有高度的相似性。在这次演讲中,我将介绍EM和BSP模型中连接算法的一些最新成果,检查它们之间的关系,并讨论将EM算法转换为BSP的一般理论框架。
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Join Algorithms: From External Memory to the BSP
Database systems have been traditionally disk-based, which had motivated the extensive study on external memory (EM) algorithms. However, as RAMs continue to get larger and cheaper, modern distributed data systems are increasingly adopting a main memory based, shared-nothing architecture, exemplified by systems like Spark and Flink. These systems can be abstracted by the BSP model (with variants like the MPC model and the MapReduce model), and there has been a strong revived interest in designing BSP algorithms for handling large amounts of data. With hard disks starting to fade away from the picture, EM algorithms may now seem less relevant. However, we observe that many of the recently developed join algorithms under the BSP model have a high degree of resemblance with their counterparts in the EM model. In this talk, I will present some recent results on join algorithms in the EM and BSP model, examine their relationships, and discuss a general theoretical framework for converting EM algorithms to the BSP.
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