Hadoop/Yarn/MapReduce内存分配(配置)方案

虚拟化&大数据 靠谱运维 1178℃ 0评论

以horntonworks给出推荐配置为蓝本,给出一种常见的Hadoop集群上各组件的内存分配方案。方案最右侧一栏是一个8G VM的分配方案,方案预留1-2G的内存给操作系统,分配4G给Yarn/MapReduce,当然也包括了HIVE,剩余的2-3G是在需要使用HBase时预留给HBase的。

Configuration File Configuration Setting Value Calculation        8G VM (4G For MR)
yarn-site.xml yarn.nodemanager.resource.memory-mb = containers * RAM-per-container 4096
yarn-site.xml yarn.scheduler.minimum-allocation-mb = RAM-per-container 1024
yarn-site.xml yarn.scheduler.maximum-allocation-mb = containers * RAM-per-container 4096
mapred-site.xml mapreduce.map.memory.mb = RAM-per-container 1024
mapred-site.xml mapreduce.reduce.memory.mb = 2 * RAM-per-container 2048
mapred-site.xml mapreduce.map.java.opts = 0.8 * RAM-per-container 819
mapred-site.xml mapreduce.reduce.java.opts = 0.8 * 2 * RAM-per-container 1638
yarn-site.xml (check) yarn.app.mapreduce.am.resource.mb = 2 * RAM-per-container 2048
yarn-site.xml (check) yarn.app.mapreduce.am.command-opts = 0.8 * 2 * RAM-per-container 1638
tez-site.xml tez.am.resource.memory.mb = RAM-per-container 1024
tez-site.xml tez.am.java.opts = 0.8 * RAM-per-container 819
tez-site.xml hive.tez.container.size = RAM-per-container 1024
tez-site.xml hive.tez.java.opts = 0.8 * RAM-per-container 819

 

转载请注明:靠谱运维 » Hadoop/Yarn/MapReduce内存分配(配置)方案

喜欢 (1)or分享 (0)
发表我的评论
取消评论

表情