Yarn 生产环境核心参数配置案例

Yarn 生产环境核心参数配置案例

调整下列参数之前要拍摄Linux快照(就是保留之前的状态)，否则后续的案例，还需要重写集群

右键-拍摄快照

右键-恢复到快照

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。

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个任务，3个任务，3个任务）

修改yarn-site.xml配置

<!-- ResourceManager 处理调度器请求的线程数量，默认50，如果提交的任务数大于50可以增加该值，但是不能超过3台*4线程=12线程，去除其他应用程序(不可能全部分配给这个任务)时机不能超过8 -->

<property>

<description>Number of threads to handle scheduler

interface.</description>

<name>yarn.resourcemanager.scheduler.client.thread-count</name>

<value>8</value>

</property>

<!-- 是否让 yarn 自动检测硬件进行配置，默认是 false，如果该节点有很多其他应用程序，建议手动配置。如果该节点没有其他应用程序，可以采用自动 -->

<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>

<!-- 是否将虚拟核数当作 CPU 核数，默认是 false，采用物理 CPU 核数 -->

<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-ascores</name>

<value>false</value>

</property>

<!-- 虚拟核数和物理核数乘数，默认是 1.0 -->

<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>

<!-- NodeManager 使用内存数，默认 8G，修改为 4G 内存 -->

<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>

<!-- nodemanager 的 CPU 核数， 不按照硬件环境自动设定时默认是 8 个，修改为 4 个 -->

<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>

<!-- 容器最小内存，默认 1G -->

<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>

<!-- 容器最大内存，默认 8G，修改为 2G -->

<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>

<!-- 容器最小 CPU 核数，默认 1 个 -->

<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>

<!-- 容器最大 CPU 核数，默认 4 个， 修改为 2 个 -->

<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>

<!-- 虚拟内存检查，默认打开，修改为关闭 -->

<property>

<description>Whether virtual memory limits will be enforced for containers.</description>

<name>yarn.nodemanager.vmem-check-enabled</name>

<value>false</value>

</property>

<!-- 虚拟内存和物理内存设置比例,默认 2.1 -->

<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>

为什么关掉虚拟内存检查?

Yarn 生产环境核心参数配置案例

如图Linux系统为Java进程预留了5G，但是Java8不会去使用那部分，导致大量内存浪费。

所以在CentOS7和Java8之间不用开启。

[ranan@hadoop102 hadoop]$ pwd

/opt/module/hadoop-3.1.3/etc/hadoop

//贴入之前的代码

[ranan@hadoop102 hadoop]$ vim yarn-site.xml

分发

三台服务器硬件配置一样，可以直接分发

[ranan@hadoop102 hadoop]$ xsync yarn-site.xml

Yarn 生产环境核心参数配置案例

xsync编写记录

重启集群

//重启集群

[ranan@hadoop102 hadoop]$ myhadoop.sh start

myhadoop.sh

#!/bin/bash 

# $#获取输入的参数个数 小于1

if [ $# -lt 1 ]

then

    echo "No Args Input..."

    exit ;

fi

# $1表示第一个参数

case $1 in

"start")

        echo " =================== 启动 hadoop 集群 ===================" 

        echo " --------------- 启动 hdfs ---------------"

        # ssh无密访问其他服务器

        ssh hadoop102 "/opt/module/hadoop-3.1.3/sbin/start-dfs.sh"

        echo " --------------- 启动 yarn ---------------"

        ssh hadoop103 "/opt/module/hadoop-3.1.3/sbin/start-yarn.sh"

        echo " --------------- 启动 historyserver ---------------"

        ssh hadoop102 "/opt/module/hadoop-3.1.3/bin/mapred --daemon start historyserver"

;;

"stop")

        echo " =================== 关闭 hadoop 集群 ===================" 

        echo " --------------- 关闭 historyserver ---------------"

        ssh hadoop102 "/opt/module/hadoop-3.1.3/bin/mapred --daemon stop historyserver"

        echo " --------------- 关闭 yarn ---------------"

        ssh hadoop103 "/opt/module/hadoop-3.1.3/sbin/stop-yarn.sh"

        echo " --------------- 关闭 hdfs ---------------"

        ssh hadoop102 "/opt/module/hadoop-3.1.3/sbin/stop-dfs.sh"

;;

*)

    echo "Input Args Error..."

;;

esac

执行WordCount程序

http://hadoop103:8088/cluster 查看是否配置成功与运行时状态

Yarn 生产环境核心参数配置案例

[ranan@hadoop102 hadoop-3.1.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar  wordcount /input /output

名称	网址
NameNode	hadoop102:9870
Yarn查看任务运行情况	hadoop103:8088
历史服务器	hadoop102:10020

秒客网

Yarn 生产环境核心参数配置案例

Yarn 生产环境核心参数配置案例

需求

修改yarn-site.xml配置

分发

重启集群

执行WordCount程序

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