一、 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：
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
#4.关闭虚拟内存检查原因
#5.分发配置。
ps：如果集群的硬件资源不一致，要每个NodeManager单独配置
#6.重启集群
[delopy@hadoop102 ~]$ stop-yarn.sh
[delopy@hadoop102 ~]$ start-yarn.sh
#7,执行WordCount程序
[delopy@hadoop102 ~]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.1.jar wordcount /input /output
#8.观察Yarn任务执行页面
http://hadoop103:8088/cluster/apps
二、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）
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.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
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：
[delopy@hadoop102 ~]$ yarn rmadmin -refreshQueues
3.向Hive队列提交任务
#1.hadoop jar的方式
[delopy@hadoop102 ~]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.1.jar wordcount -D mapreduce.job.queuename=hive /input /output
ps: -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：
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
yarn.cluster.max-application-priority
5
#2.分发配置，并重启Yarn
[delopy@hadoop102 ~]$ xsync yarn-site.xml
[delopy@hadoop103 ~]$ stop-yarn.sh
[delopy@hadoop103 ~]$ 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。
[delopy@hadoop102 ~]$ hadoop jar /opt/module/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.1.jar pi 5 2000000
#4.再次重新提交优先级高的任务
[delopy@hadoop102 ~]$ hadoop jar /opt/module/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.1.jar pi -D mapreduce.job.priority=5 5 2000000
#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
[delopy@hadoop102 ~]$ yarn application -appID application_1611133087930_0009 -updatePriority 5
三、公平调度器案例
1.需求
创建两个队列，分别是test和delopy（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，delopy提交的任务到root.group.delopy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/r3.3.1/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/
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
yarn.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-3.3.1/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
#2.配置fair-scheduler.xml
<?xml version="1.0"?>
0.5
4096mb,4vcores
2048mb,2vcores
4096mb,4vcores
4
0.5
1.0
fair
2048mb,2vcores
4096mb,4vcores
4
0.5
1.0
fair
#3.分发配置并重启Yarn
[delopy@hadoop102 ~]$ xsync yarn-site.xml
[delopy@hadoop102 ~]$ xsync fair-scheduler.xml
[delopy@hadoop103 ~]$ stop-yarn.sh
[delopy@hadoop103 ~]$ start-yarn.sh
3.测试提交任务
#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
[delopy@hadoop102 ~]$ hadoop jar /opt/module/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.1.jar pi -Dmapreduce.job.queuename=root.test 1 1
#2.提交任务时不指定队列，按照配置规则，任务会到root.delopy.delopy队列
[delopy@hadoop102 ~]$ hadoop jar /opt/module/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.1.jar pi 1 1
#1.回顾：
[delopy@hadoop102 ~]$ hadoop jar wc.jar com.delopy.mapreduce.wordcount2.WordCountDriver /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。
[delopy@hadoop102 ~]$ hadoop jar wc.jar com.delopy.mapreduce.wordcount2.WordCountDriver -Dmapreduce.job.queuename=root.test /input /output1
#2.需求：自己写的程序也可以动态修改参数。编写Yarn的Tool接口。
#3.具体步骤：
1）新建Maven项目YarnDemo，pom如下：
<?xml version="1.0" encoding="UTF-8"?>
4.0.0
com.delopy.hadoop
yarn_tool_test
1.0-SNAPSHOT
org.apache.hadoop
hadoop-client
3.1.3
2）新建com.delopy.yarn报名
3）创建类WordCount并实现Tool接口：
package com.delopy.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);
}
}
}
4）新建WordCountDriver
package com.delopy.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);
}
}
#4.在HDFS上准备输入文件，假设为/input目录，向集群提交该Jar包
[delopy@hadoop102 ~]$ yarn jar YarnDemo.jar com.delopy.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：
[delopy@hadoop102 ~]$ yarn jar YarnDemo.jar com.delopy.yarn.WordCountDriver wordcount -Dmapreduce.job.queuename=root.test /input /output1