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 |
