HBase优化案例分析：Facebook Messages系统问题与解决方案
HDFS设计的初衷是为了存储大文件（例如日志文件），面向批处理、顺序I/O的。然而架设在HDFS之上的HBase设计的初衷却是为了解决海量数据的随机读写的请求。把这两种设计初衷截然相反的组件怎么揉在一起的呢？这种分层的结构设计主要是为了使架构更清晰，HBase
HDFS设计的初衷是为了存储大文件（例如日志文件），面向批处理、顺序I/O的。然而架设在HDFS之上的HBase设计的初衷却是为了解决海量数据的随机读写的请求。把这两种设计初衷截然相反的组件怎么揉在一起的呢？这种分层的结构设计主要是为了使架构更清晰，HBase层和HDFS层各司其职；但是却带来了潜在的性能下降。在很多业务场景中大家使用HBase抱怨最多的两个问题就是：Java GC相关的问题和随机读写性能的问题。Facebook Messages（以下简称FM系统）系统可以说是HBase在online storage场景下的第一个案例（《Apache Hadoop Goes Realtime at Facebook》, SIGMOD 2011），最近他们在存储领域顶级会议FAST2014上发表了一篇论文《Analysis of HDFS Under HBase: A Facebook Messages Case Study》分析了他们在使用HBase中遇到的一些问题和解决方案，使用HBase做online storage的同学们可以参考下。
该论文首先讲了Facebook的分析方法包括tracing/analysis/simulation，FM系统的架构和文件与数据构成等，接下来开始分析FM系统在性能方面的一些问题，并提出了解决方案。
http://www.infoq.com/cn/articles/hbase-casestudy-facebook-messages
原文地址：HBase优化案例分析：Facebook Messages系统问题与解决方案, 感谢原作者分享。
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