Hadoop：Yarn生产环境核心参数配置案例

忘是亡心i 2024-03-17 20:43 38阅读 0赞

1）需求：从1G数据中，统计每个单词出现次数。服务器3台，每台配置4G内存，4核CPU，4线程。

2）需求分析：  
1G / 128m = 8个MapTask；1个ReduceTask；1个mrAppMaster  
平均每个节点运行10个 / 3台 ≈ 3个任务（4 3 3）

3）修改yarn-site.xml配置参数如下：

<property>
    	<description>The class to use as the resource scheduler.</description>
    	<name>yarn.resourcemanager.scheduler.class</name>
    	<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler</value>
    </property>
    
    
    <property>
    	<description>Number of threads to handle scheduler interface.</description>
    	<name>yarn.resourcemanager.scheduler.client.thread-count</name>
    	<value>8</value>
    </property>
    
    
    
    <property>
    	<description>Flag to determine if logical processors(such as
    	hyperthreads) should be counted as cores. Only applicable on Linux
    	when yarn.nodemanager.resource.cpu-vcores is set to -1 and
    	yarn.nodemanager.resource.detect-hardware-capabilities is true.
    	</description>
    	<name>yarn.nodemanager.resource.count-logical-processors-as-cores</name>
    	<value>false</value>
    </property>
    
    
    <property>
    	<description>Enable auto-detection of node capabilities such as
    	memory and CPU.
    	</description>
    	<name>yarn.nodemanager.resource.detect-hardware-capabilities</name>
    	<value>false</value>
    </property>
    
    
    
    <property>
    	<description>Multiplier to determine how to convert phyiscal cores to vcores. This value is used if 
    yarn.nodemanager.resource.cpu-vcores is set to -1(which implies auto-calculate vcores) and
    yarn.nodemanager.resource.detect-hardware-capabilities is set to true. The	number of vcores will be calculated as	number of CPUs * multiplier.
    	</description>
    	<name>yarn.nodemanager.resource.pcores-vcores-multiplier</name>
    	<value>1.0</value>
    </property>
    
    
    <property>
    	<description>Amount of physical memory, in MB, that can be allocated 
    	for containers. If set to -1 and
    	yarn.nodemanager.resource.detect-hardware-capabilities is true, it is
    	automatically calculated(in case of Windows and Linux).
    	In other cases, the default is 8192MB.
    	</description>
    	<name>yarn.nodemanager.resource.memory-mb</name>
    	<value>4096</value>
    </property>
    
    
    <property>
    	<description>Number of vcores that can be allocated
    	for containers. This is used by the RM scheduler when allocating
    	resources for containers. This is not used to limit the number of
    	CPUs used by YARN containers. If it is set to -1 and
    	yarn.nodemanager.resource.detect-hardware-capabilities is true, it is
    	automatically determined from the hardware in case of Windows and Linux.
    	In other cases, number of vcores is 8 by default.</description>
    	<name>yarn.nodemanager.resource.cpu-vcores</name>
    	<value>4</value>
    </property>
    
    
    <property>
    	<description>The minimum allocation for every container request at the RM	in MBs. Memory requests lower than this will be set to the value of this	property. Additionally, a node manager that is configured to have less memory	than this value will be shut down by the resource manager.
    	</description>
    	<name>yarn.scheduler.minimum-allocation-mb</name>
    	<value>1024</value>
    </property>
    
    
    <property>
    	<description>The maximum allocation for every container request at the RM	in MBs. Memory requests higher than this will throw an	InvalidResourceRequestException.
    	</description>
    	<name>yarn.scheduler.maximum-allocation-mb</name>
    	<value>2048</value>
    </property>
    
    
    <property>
    	<description>The minimum allocation for every container request at the RM	in terms of virtual CPU cores. Requests lower than this will be set to the	value of this property. Additionally, a node manager that is configured to	have fewer virtual cores than this value will be shut down by the resource	manager.
    	</description>
    	<name>yarn.scheduler.minimum-allocation-vcores</name>
    	<value>1</value>
    </property>
    
    
    <property>
    	<description>The maximum allocation for every container request at the RM	in terms of virtual CPU cores. Requests higher than this will throw an
    	InvalidResourceRequestException.</description>
    	<name>yarn.scheduler.maximum-allocation-vcores</name>
    	<value>2</value>
    </property>
    
    
    <property>
    	<description>Whether virtual memory limits will be enforced for
    	containers.</description>
    	<name>yarn.nodemanager.vmem-check-enabled</name>
    	<value>false</value>
    </property>
    
    
    <property>
    	<description>Ratio between virtual memory to physical memory when	setting memory limits for containers. Container allocations are	expressed in terms of physical memory, and virtual memory usage	is allowed to exceed this allocation by this ratio.
    	</description>
    	<name>yarn.nodemanager.vmem-pmem-ratio</name>
    	<value>2.1</value>
    </property>

关闭虚拟内存检查原因  
![在这里插入图片描述][caa7d2ab88bb4a52a98a9dce0fc8e7bf.png_pic_center]  
4）分发配置  
注意：如果集群的硬件资源不一致，要每个NodeManager单独配置。  
5）重启集群  
6）执行WordCount程序

hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount /input /output

7）观察Yarn任务执行页面  
http://hadoop103:8088/cluster/apps

[caa7d2ab88bb4a52a98a9dce0fc8e7bf.png_pic_center]: https://image.dandelioncloud.cn/pgy_files/images/2024/03/14/de6cf09fe056402a8eea9d4729ab3783.png