大数据Hadoop-Yarn学习之旅第二篇

Mark_Zoe
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「这是我参与11月更文挑战的第17天，活动详情查看：2021最后一次更文挑战」。

一、Yarn 常用命令

Yarn状态的查询，除了可以在hadoop103:8088页面查看外，还可以通过命令操作。常见的命令操作如下所示：

需求：执行WordCount案例，并用Yarn命令查看任务运行情况。

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

1、yarn application 查看任务

1.1、列出所有 Application

[moe@hadoop102 hadoop-3.1.3]$ yarn application -list
2021-11-16 22:15:26,809 INFO client.RMProxy: Connecting to ResourceManager at hadoop103/192.168.85.103:8032
Total number of applications (application-types: [], states: [SUBMITTED, ACCEPTED, RUNNING] and tags: []):1
                Application-Id	    Application-Name	    Application-Type	      User	     Queue	             State	       Final-State	       Progress	                       Tracking-URL
application_1637071605761_0002	          word count	           MAPREDUCE	       moe	   default	           RUNNING	         UNDEFINED	            50%	             http://hadoop103:42721

1.2、根据 Application 状态过滤

yarn application -list -appStates （所有状态：ALL、NEW、NEW_SAVING、SUBMITTED、ACCEPTED、RUNNING、FINISHED、FAILED、KILLED）

[moe@hadoop102 hadoop-3.1.3]$ yarn application -list -appStates FINISHED
2021-11-16 22:18:09,194 INFO client.RMProxy: Connecting to ResourceManager at hadoop103/192.168.85.103:8032
Total number of applications (application-types: [], states: [FINISHED] and tags: []):2
                Application-Id	    Application-Name	    Application-Type	      User	     Queue	             State	       Final-State	       Progress	                       Tracking-URL
application_1637071605761_0001	          word count	           MAPREDUCE	       moe	   default	          FINISHED	         SUCCEEDED	           100%	http://hadoop102:19888/jobhistory/job/job_1637071605761_0001
application_1637071605761_0002	          word count	           MAPREDUCE	       moe	   default	          FINISHED	         SUCCEEDED	           100%	http://hadoop102:19888/jobhistory/job/job_1637071605761_0002

1.3、Kill 掉 Application

[moe@hadoop102 hadoop-3.1.3]$ yarn application -kill application_1637071605761_0002
2021-11-16 22:19:49,567 INFO client.RMProxy: Connecting to ResourceManager at hadoop103/192.168.85.103:8032
Application application_1637071605761_0002 has already finished

2、yarn logs 查看日志

2.1、查询 Application 日志

yarn logs -applicationId <ApplicationId>

[moe@hadoop102 hadoop-3.1.3]$ yarn logs -applicationId application_1637071605761_0002

2.2、查询 Container 日志

yarn logs -applicationId <ApplicationId> -containerId <ContainerId>

[moe@hadoop102 hadoop-3.1.3]$ yarn logs -applicationId application_1637071605761_0002 -containerId container_1637071605761_0002_01_000003

3、yarn applicationattempt 查看尝试运行的任务

3.1、列出所有 Application 尝试的列表

yarn applicationattempt -list <ApplicationId>

[moe@hadoop102 hadoop-3.1.3]$ yarn applicationattempt -list application_1637071605761_0002
2021-11-16 22:29:59,269 INFO client.RMProxy: Connecting to ResourceManager at hadoop103/192.168.85.103:8032
Total number of application attempts :1
         ApplicationAttempt-Id	               State	                    AM-Container-Id	                       Tracking-URL
appattempt_1637071605761_0002_000001	            FINISHED	container_1637071605761_0002_01_000001	http://hadoop103:8088/proxy/application_1637071605761_0002/

3.2、打印 ApplicationAttempt 状态

yarn applicationattempt -status <ApplicationAttemptId>

[moe@hadoop102 hadoop-3.1.3]$ yarn applicationattempt -status appattempt_1637071605761_0002_000001
2021-11-16 22:35:08,505 INFO client.RMProxy: Connecting to ResourceManager at hadoop103/192.168.85.103:8032
Application Attempt Report : 
	ApplicationAttempt-Id : appattempt_1637071605761_0002_000001
	State : FINISHED
	AMContainer : container_1637071605761_0002_01_000001
	Tracking-URL : http://hadoop103:8088/proxy/application_1637071605761_0002/
	RPC Port : 36294
	AM Host : hadoop103
	Diagnostics :

4、yarn container 查看容器

4.1、列出所有 Container

yarn container -list <ApplicationAttemptId>

[moe@hadoop102 hadoop-3.1.3]$ yarn container -list appattempt_1637071605761_0002_000001
2021-11-16 22:38:05,848 INFO client.RMProxy: Connecting to ResourceManager at hadoop103/192.168.85.103:8032
Total number of containers :0
                  Container-Id	          Start Time	         Finish Time	               State	                Host	   Node Http Address	                            LOG-URL

4.2、打印 Container 状态

yarn container -status <ContainerId>

[moe@hadoop102 hadoop-3.1.3]$ yarn container -status container_1637071605761_0002_01_000001
2021-11-16 22:39:22,407 INFO client.RMProxy: Connecting to ResourceManager at hadoop103/192.168.85.103:8032
Container with id 'container_1637071605761_0002_01_000001' doesn't exist in RM or Timeline Server.

注：只有在任务跑的途中才能看到container的状态

5、yarn node 查看节点状态

列出所有节点：yarn node -list -all

[moe@hadoop102 hadoop-3.1.3]$ yarn node -list -all
2021-11-16 22:40:33,889 INFO client.RMProxy: Connecting to ResourceManager at hadoop103/192.168.85.103:8032
Total Nodes:3
         Node-Id	     Node-State	Node-Http-Address	Number-of-Running-Containers
 hadoop104:33986	        RUNNING	   hadoop104:8042	                           0
 hadoop102:41945	        RUNNING	   hadoop102:8042	                           0
 hadoop103:34927	        RUNNING	   hadoop103:8042	                           0

6、yarn rmadmin 更新配置

加载队列配置：yarn rmadmin -refreshQueues

[moe@hadoop102 hadoop-3.1.3]$ yarn rmadmin -refreshQueues
2021-11-16 22:41:56,852 INFO client.RMProxy: Connecting to ResourceManager at hadoop103/192.168.85.103:8033

7、yarn queue 查看队列

打印队列信息：yarn queue -status <QueueName>

[moe@hadoop102 hadoop-3.1.3]$ yarn queue -status default
2021-11-16 22:42:42,329 INFO client.RMProxy: Connecting to ResourceManager at hadoop103/192.168.85.103:8032
Queue Information : 
Queue Name : default
	State : RUNNING
	Capacity : 100.0%
	Current Capacity : .0%
	Maximum Capacity : 100.0%
	Default Node Label expression : <DEFAULT_PARTITION>
	Accessible Node Labels : *
	Preemption : disabled
	Intra-queue Preemption : disabled

二、Yarn 生产环境核心参数

image.png

三、Yarn 案例实操

1、Yarn 生产环境核心参数配置案例

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：

<!-- 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>

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

[moe@hadoop103 hadoop-3.1.3]$ sbin/stop-yarn.sh
[moe@hadoop103 hadoop-3.1.3]$ sbin/start-yarn.sh

6）执行WordCount程序

[moe@hadoop102 hadoop-3.1.3]$ 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

2、容量调度器多队列提交案例

1）在生产环境怎么创建队列？

（1）调度器默认就1个default队列，不能满足生产要求。

    （2）按照框架：hive /spark/ flink 每个框架的任务放入指定的队列（企业用的不是特别多）

（3）按照业务模块：登录注册、购物车、下单、业务部门1、业务部门2

2）创建多队列的好处？

（1）因为担心员工不小心，写递归死循环代码，把所有资源全部耗尽。

（2）实现任务的降级使用，特殊时期保证重要的任务队列资源充足。11.11  6.18

业务部门1（重要）=》业务部门2（比较重要）=》下单（一般）=》购物车（一般）=》登录注册（次要）

2.1、需求

需求1：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%。

需求2：配置队列优先级

2.2、配置多队列的容量调度器

1）在capacity-scheduler.xml中配置如下：

（1）修改如下配置

```xml
<!-- 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>
（2）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>
```

2）分发配置文件

3）重启Yarn或者执行yarn rmadmin -refreshQueues刷新队列，就可以看到两条队列：

image.png

2.3、向Hive队列提交任务

1）hadoop jar的方式

[moe@hadoop102 hadoop-3.1.3]$ 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

2）打jar包的方式

默认的任务提交都是提交到default队列的。如果希望向其他队列提交任务，需要在Driver中声明：

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);
    }
}

这样，这个任务在集群提交时，就会提交到hive队列：

image.png

2.4、任务优先级

容量调度器，支持任务优先级的配置，在资源紧张时，优先级高的任务将优先获取资源。默认情况，Yarn将所有任务的优先级限制为0，若想使用任务的优先级功能，须开放该限制。

1）修改yarn-site.xml文件，增加以下参数

<property>
    <name>yarn.cluster.max-application-priority</name>
    <value>5</value>
</property>

2）分发配置，并重启Yarn

[moe@hadoop102 hadoop]$ xsync yarn-site.xml
[moe@hadoop103 hadoop-3.1.3]$ sbin/stop-yarn.sh
[moe@hadoop103 hadoop-3.1.3]$ sbin/start-yarn.sh

3）模拟资源紧张环境，可连续提交以下任务，直到新提交的任务申请不到资源为止。

[moe@hadoop102 hadoop-3.1.3]$ hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi 5 2000000

image.png

4）再次重新提交优先级高的任务

[moe@hadoop102 hadoop-3.1.3]$ hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi  -D mapreduce.job.priority=5 5 2000000

image.png

5）也可以通过以下命令修改正在执行的任务的优先级。

yarn application -appID <ApplicationID> -updatePriority 优先级

[moe@hadoop102 hadoop-3.1.3]$ yarn application -appID application_1611133087930_0009 -updatePriority 5

3、公平调度器案例

3.1、需求

创建两个队列，分别是test和moe（以用户所属组命名）。期望实现以下效果：若用户提交任务时指定队列，则任务提交到指定队列运行；若未指定队列，test用户提交的任务到root.group.test队列运行，atguigu提交的任务到root.group.moe队列运行（注：group为用户所属组）。

公平调度器的配置涉及到两个文件，一个是yarn-site.xml，另一个是公平调度器队列分配文件fair-scheduler.xml（文件名可自定义）。

（1）配置文件参考资料：

hadoop.apache.org/docs/r3.1.3…

（2）任务队列放置规则参考资料：

blog.cloudera.com/untangling-…

3.2、配置多队列的公平调度器

1）修改yarn-site.xml文件，加入以下参数

<property>
    <name>yarn.resourcemanager.scheduler.class</name>
    <value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>
    <description>配置使用公平调度器</description>
</property>

<property>
    <name>yarn.scheduler.fair.allocation.file</name>
    <value>/opt/module/hadoop-3.1.3/etc/hadoop/fair-scheduler.xml</value>
    <description>指明公平调度器队列分配配置文件</description>
</property>

<property>
    <name>yarn.scheduler.fair.preemption</name>
    <value>false</value>
    <description>禁止队列间资源抢占</description>
</property>

2）配置fair-scheduler.xml

<?xml version="1.0"?>
<allocations>
  <!-- 单个队列中Application Master占用资源的最大比例,取值0-1 ，企业一般配置0.1 -->
  <queueMaxAMShareDefault>0.5</queueMaxAMShareDefault>
  <!-- 单个队列最大资源的默认值 test moe default -->
  <queueMaxResourcesDefault>4096mb,4vcores</queueMaxResourcesDefault>

  <!-- 增加一个队列test -->
  <queue name="test">
    <!-- 队列最小资源 -->
    <minResources>2048mb,2vcores</minResources>
    <!-- 队列最大资源 -->
    <maxResources>4096mb,4vcores</maxResources>
    <!-- 队列中最多同时运行的应用数，默认50，根据线程数配置 -->
    <maxRunningApps>4</maxRunningApps>
    <!-- 队列中Application Master占用资源的最大比例 -->
    <maxAMShare>0.5</maxAMShare>
    <!-- 该队列资源权重,默认值为1.0 -->
    <weight>1.0</weight>
    <!-- 队列内部的资源分配策略 -->
    <schedulingPolicy>fair</schedulingPolicy>
  </queue>
  <!-- 增加一个队列moe -->
  <queue name="moe" type="parent">
    <!-- 队列最小资源 -->
    <minResources>2048mb,2vcores</minResources>
    <!-- 队列最大资源 -->
    <maxResources>4096mb,4vcores</maxResources>
    <!-- 队列中最多同时运行的应用数，默认50，根据线程数配置 -->
    <maxRunningApps>4</maxRunningApps>
    <!-- 队列中Application Master占用资源的最大比例 -->
    <maxAMShare>0.5</maxAMShare>
    <!-- 该队列资源权重,默认值为1.0 -->
    <weight>1.0</weight>
    <!-- 队列内部的资源分配策略 -->
    <schedulingPolicy>fair</schedulingPolicy>
  </queue>

  <!-- 任务队列分配策略,可配置多层规则,从第一个规则开始匹配,直到匹配成功 -->
  <queuePlacementPolicy>
    <!-- 提交任务时指定队列,如未指定提交队列,则继续匹配下一个规则; false表示：如果指定队列不存在,不允许自动创建-->
    <rule name="specified" create="false"/>
    <!-- 提交到root.group.username队列,若root.group不存在,不允许自动创建；若root.group.user不存在,允许自动创建 -->
    <rule name="nestedUserQueue" create="true">
        <rule name="primaryGroup" create="false"/>
    </rule>
    <!-- 最后一个规则必须为reject或者default。Reject表示拒绝创建提交失败，default表示把任务提交到default队列 -->
    <rule name="reject" />
  </queuePlacementPolicy>
</allocations>

3）分发配置并重启Yarn

[moe@hadoop102 hadoop]$ xsync yarn-site.xml
[moe@hadoop102 hadoop]$ xsync fair-scheduler.xml

[moe@hadoop103 hadoop-3.1.3]$ sbin/stop-yarn.sh
[moe@hadoop103 hadoop-3.1.3]$ sbin/start-yarn.sh

四、友情链接

大数据Hadoop-Yarn学习之旅第一篇

大数据Hadoop-MapReduce学习之旅第六篇

大数据Hadoop-MapReduce学习之旅第五篇

大数据Hadoop-MapReduce学习之旅第四篇

大数据Hadoop-MapReduce学习之旅第三篇

大数据Hadoop-MapReduce学习之旅第二篇

大数据Hadoop-MapReduce学习之旅第一篇

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友情链接：