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。
3、容量调度器多队列提交案例 3.1、案例需求The class to use as the resource scheduler. yarn.resourcemanager.scheduler.class org.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler Number of threads to handle scheduler interface. yarn.resourcemanager.scheduler.client.thread-count 8 Enable auto-detection of node capabilities such as memory and CPU. yarn.nodemanager.resource.detect-hardware-capabilities false 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. yarn.nodemanager.resource.count-logical-processors-as-cores false 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. yarn.nodemanager.resource.pcores-vcores-multiplier 1.0 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. yarn.nodemanager.resource.memory-mb 4096 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. yarn.nodemanager.resource.cpu-vcores 4 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. yarn.scheduler.minimum-allocation-mb 1024 The maximum allocation for every container request at the RM in MBs. Memory requests higher than this will throw an InvalidResourceRequestException. yarn.scheduler.maximum-allocation-mb 2048 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. yarn.scheduler.minimum-allocation-vcores 1 The maximum allocation for every container request at the RM in terms of virtual CPU cores. Requests higher than this will throw an InvalidResourceRequestException. yarn.scheduler.maximum-allocation-vcores 2 Whether virtual memory limits will be enforced for containers. yarn.nodemanager.vmem-check-enabled false 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. yarn.nodemanager.vmem-pmem-ratio 2.1
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
在capacity-scheduler.xml中配置如下：
（1）修改如下配置
yarn.scheduler.capacity.root.queues default,hive The queues at the this level (root is the root queue). yarn.scheduler.capacity.root.default.capacity 40 yarn.scheduler.capacity.root.default.maximum-capacity 60
（2）为新加队列添加必要属性：
yarn.scheduler.capacity.root.hive.capacity 60 yarn.scheduler.capacity.root.hive.user-limit-factor 1 yarn.scheduler.capacity.root.hive.maximum-capacity 80 yarn.scheduler.capacity.root.hive.state RUNNING yarn.scheduler.capacity.root.hive.acl_submit_applications * yarn.scheduler.capacity.root.hive.acl_administer_queue * yarn.scheduler.capacity.root.hive.acl_application_max_priority * yarn.scheduler.capacity.root.hive.maximum-application-lifetime -1 yarn.scheduler.capacity.root.hive.default-application-lifetime -1
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：
yarn rmadmin -refreshQueues3.3 向Hive队列提交任务 3.3.1、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 /output
-D表示运行时改变参数值
3.3.2、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：
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。
3.4.1、修改yarn-site.xml文件，增加以下参数3.4.2、分发配置，并重启Yarnyarn.cluster.max-application-priority 5
[song@hadoop102 hadoop]$ xsync yarn-site.xml [song@hadoop103 hadoop-3.1.3]$ sbin/stop-yarn.sh [song@hadoop103 hadoop-3.1.3]$ sbin/start-yarn.sh3.4.3、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。
[song@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 20000003.4.4、再次重新提交优先级高的任务
[song@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 20000003.4.5、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 -updatePriority 优先级
[song@hadoop102 hadoop-3.1.3]$ yarn application -appID application_1611133087930_0009 -updatePriority 54、公平调度器案例 4.1、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（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：
https://hadoop.apache.org/docs/r3.1.3/hadoop-yarn/hadoop-yarn-site/FairScheduler.html
（2）任务队列放置规则参考资料：
https://blog.cloudera.com/untangling-apache-hadoop-yarn-part-4-fair-scheduler-queue-basics/
4.2.2、配置fair-scheduler.xmlyarn.resourcemanager.scheduler.class org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler 配置使用公平调度器 yarn.scheduler.fair.allocation.file /opt/module/hadoop-3.1.3/etc/hadoop/fair-scheduler.xml 指明公平调度器队列分配配置文件 yarn.scheduler.fair.preemption false 禁止队列间资源抢占
4.2.3、分发配置并重启Yarn0.5 4096mb,4vcores 2048mb,2vcores 4096mb,4vcores 4 0.5 1.0 fair 2048mb,2vcores 4096mb,4vcores 4 0.5 1.0 fair
[atguigu@hadoop102 hadoop]$ xsync yarn-site.xml [atguigu@hadoop102 hadoop]$ xsync fair-scheduler.xml [atguigu@hadoop103 hadoop-3.1.3]$ sbin/stop-yarn.sh [atguigu@hadoop103 hadoop-3.1.3]$ sbin/start-yarn.sh4.3、 测试提交任务 4.3.1、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-3.1.3]$ hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi -Dmapreduce.job.queuename=root.test 1 14.3.2、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-3.1.3]$ hadoop jar /opt/module/hadoop-3.1.3/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar pi 1 15、Yarn的Tool接口案例
0）打包集群部署回顾：
原先的执行方式：
[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
期望可以动态传参，结果报错，误认为是第一个输入参数。
5.1、需求：自己写的程序也可以动态修改参数。编写Yarn的Tool接口。 5.2、具体步骤： 5.2.1、新建Maven项目YarnDemo，pom如下5.2.2、新建com.atguigu.yarn报名 5.2.3、创建类WordCount并实现Tool接口：4.0.0 com.atguigu.hadoop yarn_tool_test 1.0-SNAPSHOT org.apache.hadoop hadoop-client 3.1.3
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;
@Override
public int run(String[] args) throws Exception {
Job job = Job.getInstance(conf);
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(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
return job.waitForCompletion(true) ? 0 : 1;
}
@Override
public void setConf(Configuration conf) {
this.conf = conf;
}
@Override
public Configuration getConf() {
return conf;
}
public static class WordCountMapper extends Mapper {
private Text outK = new Text();
private IntWritable outV = new IntWritable(1);
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
String[] words = line.split(" ");
for (String word : words) {
outK.set(word);
context.write(outK, outV);
}
}
}
public static class WordCountReducer extends Reducer {
private IntWritable outV = new IntWritable();
@Override
protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable value : values) {
sum += value.get();
}
outV.set(sum);
context.write(key, outV);
}
}
}
5.2.4、新建WordCountDriver
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 {
// 1. 创建配置文件
Configuration conf = new Configuration();
// 2. 判断是否有tool接口
switch (args[0]){
case "wordcount":
tool = new WordCount();
break;
default:
throw new RuntimeException(" No such tool: "+ args[0] );
}
// 3. 用Tool执行程序
// Arrays.copyOfRange 将老数组的元素放到新数组里面
int run = ToolRunner.run(conf, tool, Arrays.copyOfRange(args, 1, args.length));
System.exit(run);
}
}
5.2.5、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
