MPP平台上商业OLTP应用程序面临的问题

N. Gunther
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引用次数: 3

摘要

作者研究了在商用关系数据库系统(RDBMS)上执行在线事务处理(OLTP)工作负载的大规模并行处理(MPP)架构的可扩展性。大多数MPP硬件架构要么不共享,要么共享内存。在不久的将来,可用的可伸缩性将主要受到两个主要因素的影响:商用硬件性能和RDBMS体系结构的性能。作者为运行这种工作负载的mpp提供了一个简单的扩展模型。他引入了超串行性的新概念,以解释潜在的MPP性能损失。超串行的效果是在扩展曲线中诱导一个过早的最大值,超过这个最大值,添加更多的处理器就会适得其反。这会导致执行OLTP工作负载的mpp的可伸缩性受到意想不到的限制。对一些现有的交易处理平台进行了规模化预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Issues facing commercial OLTP applications on MPP platforms
The author examines the scalability of massively parallel processing (MPP) architectures executing online transaction processing (OLTP) workloads on commercially available relational database systems (RDBMS). Most MPP hardware architectures are either shared nothing or shared memory. In the near future, the available scalability will be impacted primarily by two major influences: commodity hardware performance and the performance of the RDBMS architecture. The author presents a simple scaling model for MPPs running such workloads. He introduces the new concept of super-seriality to account for potential MPP performance penalties. Super-seriality has the effect of inducing a premature maximum in the scaling curve beyond which it is counter-productive to add further processors. This results in unexpected limitations in the scalability of MPPs executing OLTP workloads. Scaleup predictions are presented for some existing transaction processing platforms.<>
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