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6.What should you do?
Each node in your Hadoop cluster, running YARN, has 64GB memory and 24 cores. Your yarn.site.xml has the following configuration:<property><name>yarn.nodemanager.resource.memory-mb</name><value>32768</value></property><property><name>yarn.nodemanager.resource.cpu-vcores</name><value>12</value></property>You want YARN to launch no more than 16 containers per node. What should you do?A.
Modify yarn-site.xml with the following property: <name>yarn.scheduler.minimum-allocation-mb</name> <value>2048</value>B.Modify yarn-sites.xml with the following property: <name>yarn.scheduler.minimum-allocation-mb</name> <value>4096</value>C.Modify yarn-site.xml with the following property: <name>yarn.nodemanager.resource.cpu-vccores</name>D.No action is needed: YARN’s dynamic resource allocation automatically optimizes the node memory and cores问题:
Hadoop集群的每台节点内存64G,CPU 24核。当前一个节点可分配的物理内存总量是32768M(32G),可分配的虚拟cpu总个数是12。 那么yarn在每个节点上,发起不超过16个容器,我们应该怎样配置？分析:A
yarn.scheduler.minimum-allocation-mb：最小可申请内存量，默认是1024,我们设置2048M,见如下计算公式
Maximum memory YARN can utilize on the node
————————————————————————————————————————————— = minimum memory per container Number of containers 配置文件 配置项名称 配置项值yarn-site.xml yarn.nodemanager.resource.memory-mb = Containers个数* 每个Container内存yarn-site.xml yarn.scheduler.minimum-allocation-mb = 每个Container内存yarn-site.xml yarn.scheduler.maximum-allocation-mb = Containers个数* 每个Container内存mapred-site.xml mapreduce.map.memory.mb = 每个Container内存mapred-site.xml mapreduce.reduce.memory.mb = 2 * 每个Container内存mapred-site.xml mapreduce.map.java.opts = 0.8 * 每个Container内存mapred-site.xml mapreduce.reduce.java.opts = 0.8 * 2 * 每个Container内存yarn-site.xml (check) yarn.app.mapreduce.am.resource.mb = 2 * 每个Container内存yarn-site.xml (check) yarn.app.mapreduce.am.command-opts = 0.8 * 2 * 每个Container内存
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