Yarn-容量调度器、公平调度器和Tool接口案例 （From 尚硅谷）
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Yarn-容量调度器、公平调度器和Tool接口案例
1. Yarn生产环境核心参数配置案例
（1）需求：从1G数据中，统计每个单词出现的次数。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
（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.classname>
<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacitySchedulervalue>
property>
<property>
<description>Number of threads to handle scheduler interface.description>
<name>yarn.resourcemanager.scheduler.client.thread-countname>
<value>8value>
property>
<property>
<description>Enable auto-detection of node capabilities such as
memory and CPU.
description>
<name>yarn.nodemanager.resource.detect-hardware-capabilitiesname>
<value>falsevalue>
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-coresname>
<value>falsevalue>
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-multipliername>
<value>1.0value>
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-mbname>
<value>4096value>
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-vcoresname>
<value>4value>
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-mbname>
<value>1024value>
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-mbname>
<value>2048value>
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-vcoresname>
<value>1value>
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-vcoresname>
<value>2value>
property>
<property>
<description>Whether virtual memory limits will be enforced for
containers.description>
<name>yarn.nodemanager.vmem-check-enabledname>
<value>falsevalue>
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-rationame>
<value>2.1value>
property>
（4）分发配置
注意：如果集群的硬件资源不一致，要为每个NodeManager单独配置
（5）重启集群
[atguigu@hadoop102 hadoop-3.1.3]$ sbin/stop-yarn.sh
[atguigu@hadoop102 hadoop-3.1.3]$ sbin/start-yarn.sh
（6）执行WordCount程序
[atguigu@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. 容器调度器多队列提交案例
-
在生产环境怎么创建队列？
（1） 调度器默认就1个default队列，不能满足生产需求。
（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、业务部分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。
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队列占总资源的40%，最大资源容量占总资源60%，hive队列占总资源的60%，最大资源容量占总资源80%。
需求2：配置队列优先级。
2.2 配置对队列的容量调度器
-
在capacity-scheduler.xml中配置如下：
（1）修改如下配置
<property>
<name>yarn.scheduler.capacity.root.queuesname>
<value>default,hivevalue>
<description>
The queues at the this level (root is the root queue).
description>
property>
<property>
<name>yarn.scheduler.capacity.root.default.capacityname>
<value>40value>
property>
<property>
<name>yarn.scheduler.capacity.root.default.maximum-capacityname>
<value>60value>
property>
（2）为新加队列添加必要属性
<property>
<name>yarn.scheduler.capacity.root.hive.capacityname>
<value>60value>
property>
<property>
<name>yarn.scheduler.capacity.root.hive.user-limit-factorname>
<value>1value>
property>
<property>
<name>yarn.scheduler.capacity.root.hive.maximum-capacityname>
<value>80value>
property>
<property>
<name>yarn.scheduler.capacity.root.hive.statename>
<value>RUNNINGvalue>
property>
<property>
<name>yarn.scheduler.capacity.root.hive.acl_submit_applicationsname>
<value>*value>
property>
<property>
<name>yarn.scheduler.capacity.root.hive.acl_administer_queuename>
<value>*value>
property>
<property>
<name>yarn.scheduler.capacity.root.hive.acl_application_max_priorityname>
<value>*value>
property>
<property>
<name>yarn.scheduler.capacity.root.hive.maximum-application-lifetimename>
<value>-1value>
property>
<property>
<name>yarn.scheduler.capacity.root.hive.default-application-lifetimename>
<value>-1value>
property>
- 分发配置文件
[atguigu@hadoop102 hadoop-3.1.3]$ xsync capacity-scheduler.xml
- 重启Yarn或者执行yarn.rmadmin -refreshQueues刷新队列，就可以看到两条队列：
[atguigu@hadoop102 hadoop-3.1.3]$ yarn rmadmin -refreshQueues
2.3 向hive队列提交任务
- hadoop jar的方式
[atguigu@hadoop102 hadoop-3.1.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount -D mapreduce.job.queuename=hive /input /output2
注：-D表示运行时改变参数
-
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 WcDriver{
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);
...
}
}
这样，这个任务在集群提交时，就会提交到hive队列：
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，需开放该限制。
- 修改yarn-site.xml文件，增加以下参数
<property>
<name>yarn.cluster.max-application-priorityname>
<value>5value>
property>
- 分发配置，并重启Yarn
[atguigu@hadoop102 hadoop]$ xsync yarn-site.xml
[atguigu@hadoop102 hadoop]$ sbin/stop-yarn.sh
[atguigu@hadoop102 hadoop]$ sbin/start-yarn.sh
- 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）
[atguigu@hadoop102 hadoop-3.1.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi 5 2000000 //ApplicationId_0001
[atguigu@hadoop103 hadoop-3.1.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi 5 2000000 //ApplicationId_0002
- 再次重新提交优先级高的任务
[atguigu@hadoop104 hadoop-3.1.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi -Dmapreduce.job.priority=5 5 2000000 //ApplicationId_0003
可以看出，任务0001完成后，任务0003先开始执行，然后再执行任务0002。
-
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-updatePriority 优先级
[atguigu@hadoop102 hadoop-3.1.3]$ yarn application -appID application_1611133087930_0003 -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atguigu（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.atguigu队列运行（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）。
- 配置文件参考
https://hadoop.apache.org/docs/r3.1.3/hadoop-yarn/hadoop-yarn-site/FairScheduler.html
- 队列任务防止规则参考资料：
https://blog.cloudera.com/untangling-apache-hadoop-yarn-part-4-fair-scheduler-queue-basics/
3.2 配置多队列的公平调度器
- 修改yarn-site.xml文件，加入以下参数
<property>
<name>yarn.resourcemanager.scheduler.classname>
<value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairSchedulervalue>
<description>配置使用公平调度器description>
property>
<property>
<name>yarn.scheduler.fair.allocation.filename>
<value>/opt/module/hadoop-3.1.3/etc/hadoop/fair-scheduler.xmlvalue>
<description>指明公平调度器队列分配配置文件description>
property>
<property>
<name>yarn.scheduler.fair.preemptionname>
<value>falsevalue>
<description>禁止队列间资源抢占description>
property>
- 配置fair-scheduler.xml
<allocations>
<queueMaxAMShareDefault>0.5queueMaxAMShareDefault>
<queueMaxResourcesDefault>4096mb,4vcoresqueueMaxResourcesDefault>
<queue name="test">
<minResources>2048mb,2vcoresminResources>
<maxResources>4096mb,4vcoresmaxResources>
<maxRunningApps>4maxRunningApps>
<maxAMShare>0.5maxAMShare>
<weight>1.0weight>
<schedulingPolicy>fairschedulingPolicy>
queue>
<queue name="atguigu" type="parent">
<minResources>2048mb,2vcoresminResources>
<maxResources>4096mb,4vcoresmaxResources>
<maxRunningApps>4maxRunningApps>
<maxAMShare>0.5maxAMShare>
<weight>1.0weight>
<schedulingPolicy>fairschedulingPolicy>
queue>
<queuePlacementPolicy>
<rule name="specified" create="false"/>
<rule name="nestedUserQueue" create="true">
<rule name="primaryGroup" create="false"/>
rule>
<rule name="reject" />
queuePlacementPolicy>
allocations>
- 分发配置并重启Yarn
[atguigu@hadoop102 hadoop]$ xsync yarn-site.xml fair-scheduler.xml
[atguigu@hadoop102 hadoop]$ sbin/stop-yarn.sh
[atguigu@hadoop102 hadoop]$ sbin/start-yarn.sh
3.3 测试提交任务
- 提交任务时指定队列，按照配置规则，任务会到指定的root.test队列
[atguigu@hadoop102 hadoop]$ hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi -Dmapreduce.job.queuenamne=root.test 1 1
- 提交任务时不指定队列，按照配置规则，任务会到root.atguigu.atguigu队列
[atguigu@hadoop102 hadoop]$ hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi 1 1
4. Yarn的Tool接口案例
回顾：
[atguigu@hadoop102 hadoop-3.1.3]$ hadoop jar wc.jar com.atguigu.mapreduce.wordcount2.WordCountDriver /input /output1
[atguigu@hadoop102 hadoop-3.1.3]$ hadoop jar wc.jar com.atguigu.mapreduce.wordcount2.WordCountDriver -Dmapreduce.job.queuename=root.test /input /output1
希望可以动态传参，结果报错，误认为是一个输入参数。
（1）需求：自己写的程序也可以动态修改参数。编写Yarn的Tool接口。
（2）具体步骤：
- 新建Maven项目YarnDemo，pom如下：
<project xmlns="http://maven.apache.org/POM/4.0.0"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
<modelVersion>4.0.0modelVersion>
<groupId>com.atguigugroupId>
<artifactId>YarnDemoartifactId>
<version>1.0-SNAPSHOTversion>
<properties>
<maven.compiler.source>8maven.compiler.source>
<maven.compiler.target>8maven.compiler.target>
properties>
<dependencies>
<dependency>
<groupId>org.apache.hadoopgroupId>
<artifactId>hadoop-clientartifactId>
<version>3.1.3version>
dependency>
dependencies>
project>
- 新建com.atguigu.yarn包名
- 创建类WordCount并实现Tool接口：
- WordCount类
package com.atguigu.yarn;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import java.io.IOException;
public class WordCount implements Tool{
private Configuration conf;
//核心驱动（conf需要传入）
@Override
public int run(String[] string) thros Exception{
Job job = Job.getInstance();
job.setJarByClass(WordCountDriver.class);
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.setInputPaths(job,new Path(strings[0]));
FileOutputFormat.setOutputPath(job,new Path(strings[1]));
return job.waitForCompletion(true)?0:1;
}
@Override
public void setConf(Configuration configuration){
this.conf = conf;
}
@Override
public Configuration getConf(){
return conf;
}
//mapper
public static class WordCountMapper extends Mapper<LongWritable,Text,Text,IntWritable>{
private Text outK = new Text();
private IntWritable outV = new IntWritable(1);
@Override
protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {
//1. 获取一行
String line = value.toString();
//2. 切割
String[] words = line.split(" ");
//3. 循环遍历写出
for (String word:words){
outK.set(word);
context.write(outK,outV);
}
}
}
//reducer
public static class WordCountReducer extends Reducer<Text,IntWritable,Text,IntWritable>{
private IntWritable outV = new IntWritable();
@Override
protected void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException {
int sum=0;
for (IntWritable value:values){
sum += value.get();
}
outV.set(sum);
context.write(key,outV);
}
}
}
- WordCountDriver类
package com.atguigu.yarn;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import java.util.Arrays;
public class WordCountDriver {
private static Tool tool;
public static void main(String[] args) throws Exception {
//创建配置
Configuration conf = new Configuration();
switch(args[0]){
case "wordcount":
tool = new WordCount();
break;
default:
throw new RuntimeException("no such tool " + args[0]);
}
//执行程序
int run = ToolRunner.run(conf,tool,Arrays.copyOfRange(args,1,args.length));
System.exit(run);
}
}
（3）在HDFS上准备输入文件，假设为/input目录，向集群提交该Jar包
[atguigu@hadoop102 hadoop-3.1.3]$ yarn jar YarnDemo.jar com.atguigu.yarn.WordCountDriver wordcount /input /output
注意此时提交的3个参数，第一个用于生成特定的Tool，第二个和第三个为输入输出目录。此时如果我们希望加入设置参数，可以在wordcount后面添加参数，例如：
[atguigu@hadoop102 hadoop-3.1.3]$ yarn jar YarnDemo.jar com.atguigu.yarn.WordCountDriver wordcount -Dmapreduce.job.queuename=root.test /input /output1
若传入非法tool接口wordcount2，则
