使用DM-WriteCache和持久内存加速数据库工作负载

Rajesh Tadakamadla, Mikulás Patocka, Toshimitsu Kani, Scott J. Norton
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引用次数: 2

摘要

今天的企业需要能够更快地访问关键和经常使用的数据的系统。数字化导致了这种业务数据的快速爆炸,从而增加了数据库的占用空间。内存中计算是满足此类大型数据库的性能需求的一种可能的解决方案,但是数据增长的速度远远超过可以容纳数据的内存量。计算机行业正在努力保持在加速性能、防止数据丢失和最小化停机时间的技术前沿。以内存为中心的架构的发展正在推动更新的内存技术的发展,如持久内存(又名存储类内存或非易失性内存[1]),作为这些迫切需求的答案。在本文中,我们介绍了存储类内存(或持久内存)作为回写缓存的用例,以加速提交敏感的在线事务处理(OLTP)数据库工作负载。我们提供持久性内存的概述,持久性内存是一种新技术,它提供了当前一代高性能解决方案的低延迟存储选项,是字节寻址的。我们还介绍了Linux内核的新特性“DM-WriteCache”,这是一种回写缓存,计算行业几十年来一直在研究减少在持久内存解决方案之上实现的性能差距的方法。最后,我们提供了来自测试的数据,这些数据演示了采用这种技术如何使现有OLTP应用程序能够扩展其性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Accelerating Database Workloads with DM-WriteCache and Persistent Memory
Businesses today need systems that provide faster access to critical and frequently used data. Digitization has led to a rapid explosion of this business data, and thereby an increase in the database footprint. In-memory computing is one possible solution to meet the performance needs of such large databases, but the rate of data growth far exceeds the amount of memory that can hold the data. The computer industry is striving to remain on the cutting edge of technologies that accelerate performance, guard against data loss, and minimize downtime. The evolution towards a memory-centric architecture is driving development of newer memory technologies such as Persistent Memory (aka Storage Class Memory or Non-Volatile Memory [1]), as an answer to these pressing needs. In this paper, we present the use cases of storage class memory (or persistent memory) as a write-back cache to accelerate commit-sensitive online transaction processing (OLTP) database workloads. We provide an overview of Persistent Memory, a new technology that offers current generation of high-performance solutions a low latency-storage option that is byte-addressable. We also introduce the Linux kernel's new feature "DM-WriteCache", a write-back cache decades the computing industry has been researching ways to reduce the performance gap implemented on top of persistent memory solutions. And finally we present data from our tests that demonstrate how this technology adoption can enable existing OLTP applications to scale their performance.
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