yarn环境设置 yarn参数设置

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我心依旧 2023-08-24 12:06:28

文章标签 yarn环境设置 yarn hadoop hdfs ci 文章分类 Yarn 大数据

文章目录

Yarn核心参数配置
1、yarn-site.xml配置
2、容量调度器多队列提交案例

测试

Yarn核心参数配置

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。

分析

1G / 128m = 8个MapTask；1个ReduceTask；1个mrAppMaster

平均每个节点运行10 / 3$\approx$3

1、yarn-site.xml配置

<!-- 选择调度器，默认容量 -->
<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>

<!-- 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（去除其他应用程序实际不能超过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-as-cores</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>

yarn.nodemanager.vmem-pmem-ratio: 物理内存与虚拟内存的比率，每用1M物理内存，默认使用2.1M虚拟内存，（建议调大）；

或是将 yarn.nodemanager.vmem-check-enabled 虚拟内存的检查false掉

否则会报如下错误：

container [pid=26086,containerID=container_1482373104195_0001_02_000001] is running beyond virtual memory limits. Current usage: 161.4 MB of 200 MB physical memory used; 879.8 MB of 420.0 MB virtual memory used. Killing container.

在物理内存不够用的情况下，如果占用了大量虚拟内存(类似于Swap分区)并且超过了一定阈值，那么就认为当前集群的性能比较差，直接让你的终端报个错提醒你。

2、容量调度器多队列提交案例

需求：default队列占总内存的40%，最大资源容量占总资源60%，hive队列占总内存的60%，最大资源容量占总资源80%。

capacity-scheduler.xml配置

修改如下配置

<!-- 指定多队列，增加hive队列 -->
<property>
    <name>yarn.scheduler.capacity.root.queues</name>
    <value>default,hive</value>
    <description>
      The queues at the this level (root is the root queue).
    </description>
</property>

<!-- 降低default队列资源额定容量为40%，默认100% -->
<property>
    <name>yarn.scheduler.capacity.root.default.capacity</name>
    <value>40</value>
</property>

<!-- 降低default队列资源最大容量为60%，默认100% -->
<property>
    <name>yarn.scheduler.capacity.root.default.maximum-capacity</name>
    <value>60</value>
</property>

为新加队列添加必要属性

<!-- 指定hive队列的资源额定容量 -->
<property>
    <name>yarn.scheduler.capacity.root.hive.capacity</name>
    <value>60</value>
</property>

<!-- 用户最多可以使用队列多少资源，1表示 -->
<property>
    <name>yarn.scheduler.capacity.root.hive.user-limit-factor</name>
    <value>1</value>
</property>

<!-- 指定hive队列的资源最大容量 -->
<property>
    <name>yarn.scheduler.capacity.root.hive.maximum-capacity</name>
    <value>80</value>
</property>

<!-- 启动hive队列 -->
<property>
    <name>yarn.scheduler.capacity.root.hive.state</name>
    <value>RUNNING</value>
</property>

<!-- 哪些用户有权向队列提交作业 -->
<property>
    <name>yarn.scheduler.capacity.root.hive.acl_submit_applications</name>
    <value>*</value>
</property>

<!-- 哪些用户有权操作队列，管理员权限（查看/杀死） -->
<property>
    <name>yarn.scheduler.capacity.root.hive.acl_administer_queue</name>
    <value>*</value>
</property>

<!-- 哪些用户有权配置提交任务优先级 -->
<property>
    <name>yarn.scheduler.capacity.root.hive.acl_application_max_priority</name>
    <value>*</value>
</property>

<!-- 任务的超时时间设置：yarn application -appId appId -updateLifetime Timeout
参考资料：https://blog.cloudera.com/enforcing-application-lifetime-slas-yarn/ -->

<!-- 如果application指定了超时时间，则提交到该队列的application能够指定的最大超时时间不能超过该值。 
-->
<property>
    <name>yarn.scheduler.capacity.root.hive.maximum-application-lifetime</name>
    <value>-1</value>
</property>

<!-- 如果application没指定超时时间，则用default-application-lifetime作为默认值 -->
<property>
    <name>yarn.scheduler.capacity.root.hive.default-application-lifetime</name>
    <value>-1</value>
</property>

测试

分发脚本，重启集群
重启Yarn或者执行yarn rmadmin -refreshQueues刷新队列，就可以看到两条队列
向hive队列提交任务

hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount -D mapreduce.job.queuename=hive /input /output
注: -D表示运行时改变参数值

打jar包的方式

public class WcDrvier {

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {

        Configuration conf = new Configuration();

        conf.set("mapreduce.job.queuename","hive");

        //1. 获取一个Job实例
        Job job = Job.getInstance(conf);

        。。。 。。。

        //6. 提交Job
        boolean b = job.waitForCompletion(true);
        System.exit(b ? 0 : 1);
    }
}

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