如何设置hadoop mapreduce / yarn中的VCORES？

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问题描述

以下是我的配置：

yarn.nodemanager.resource.memory-mb code> / mapreduce。[map | reduce] .memory.mb

如果真的需要

yarn.nodemanager.resource.cpu-vcores / mapreduce。[map | reduce] .cpu.vcores

然后你需要使用不同的资源计算器。转到您的 capacity-scheduler.xml 配置并将 DefaultResourceCalculator 更改为 DominantResourceCalculator 。

除了使用vCores进行容器分配外，您还想使用vCores真正限制每个节点的CPU使用情况？您需要更改更多配置才能使用 LinuxContainerExecutor 而不是 DefaultContainerExecutor ，因为它可以管理用于限制CPU资源的linux cgroup。按照，如果您想了解更多信息。

The following are my configuration :

**mapred-site.xml**
map-mb : 4096 opts:-Xmx3072m
reduce-mb : 8192 opts:-Xmx6144m

**yarn-site.xml**
resource memory-mb : 40GB
min allocation-mb : 1GB

the Vcores in hadoop cluster displayed 8GB but i dont know how the computation or where to configure it.

hope someone could help me.

解决方案

Short Answer

It most probably doesn't matter, if you are just running hadoop out of the box on your single-node-cluster or even a small personal distributed cluster. You just need to worry about memory.

Long Answer

vCores are used for larger clusters in order to limit CPU for different users or applications. If you are using YARN for yourself there is no real reason to limit your container CPU. That is why vCores are not even taken into consideration by default in Hadoop !

Try setting your available nodemanager vcores to 1. It doesn't matter ! Your number of containers will still be 2 or 4 .. or whatever the value of :

yarn.nodemanager.resource.memory-mb / mapreduce.[map|reduce].memory.mb

If really do want the number of containers to take vCores into consideration and be limited by :

yarn.nodemanager.resource.cpu-vcores / mapreduce.[map|reduce].cpu.vcores

then you need to use a different a different Resource Calculator. Go to your capacity-scheduler.xml config and change DefaultResourceCalculator to DominantResourceCalculator.

In addition to using vCores for container allocation, you want to use vCores to really limit CPU usage of each node ? You need to change even more configurations to use the LinuxContainerExecutor instead of the DefaultContainerExecutor, because it can manage linux cgroups which are used to limit CPU resources. Follow this page if you want more info on this.

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