分享一下我老师大神的人工智能教程！零基础，通俗易懂！http://blog.csdn.net/jiangjunshow
也欢迎大家转载本篇文章。分享知识，造福人民，实现我们中华民族伟大复兴！
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 |
给我老师的人工智能教程打call！http://blog.csdn.net/jiangjunshow
