Hadoop (十八) --------- Yarn 案例实操
目录
- 一、Yarn 生产环境核心参数配置案例
- 二、容量调度器多队列提交案例
- 三、公平调度器案例
- 四、Yarn 的 Tool 接口案例
一、Yarn 生产环境核心参数配置案例
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。
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
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>
关闭虚拟内存检查
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
重启集群：
[fancyry@hadoop102 hadoop-3.1.3]$ sbin/stop-yarn.sh
[fancyry@hadoop103 hadoop-3.1.3]$ sbin/start-yarn.sh
执行 WordCount 程序
[fancyry@hadoop102 hadoop-3.1.3]$ hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-3.1.3.jar wordcount /input /output
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
在生产环境怎么创建队列？
A、调度器默认就1个 default 队列，不能满足生产要求。
B、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)
C、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
创建多队列的好处？
A、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。
B、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，比如 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）
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
配置多队列的容量调度器
A、在capacity-scheduler.xml中配置如下：
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>
B、分发配置文件
C、重启 Yarn 或者执行yarn rmadmin -refreshQueues刷新队列，就可以看到两条队列：
[fancyry@hadoop102 hadoop-3.1.3]$ yarn rmadmin -refreshQueues
向Hive队列提交任务
A、hadoop jar 的方式
[fancyry@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
B、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
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。
A、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>
B、分发配置，并重启Yarn
[fancyry@hadoop102 hadoop]$ xsync yarn-site.xml
[fancyry@hadoop103 hadoop-3.1.3]$ sbin/stop-yarn.sh
[fancyry@hadoop103 hadoop-3.1.3]$ sbin/start-yarn.sh
C、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。
[fancyry@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
D、再次重新提交优先级高的任务
[fancyry@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
E、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
[fancyry@hadoop102 hadoop-3.1.3]$ yarn application -appID application_1611133087930_0009 -updatePriority 5
三、公平调度器案例
需求：
创建两个队列，分别是 test 和fancyry（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，fancyry提交的任务到 root.group.fancyry 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)。
A、配置文件参考资料：
https://hadoop.apache.org/docs/r3.1.3/hadoop-yarn/hadoop-yarn-site/FairScheduler.html
B、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/
配置多队列的公平调度器
A、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>
B、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="fancyry" 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>
C、分发配置并重启Yarn
[fancyry@hadoop102 hadoop]$ xsync yarn-site.xml
[fancyry@hadoop102 hadoop]$ xsync fair-scheduler.xml
[fancyry@hadoop103 hadoop-3.1.3]$ sbin/stop-yarn.sh
[fancyry@hadoop103 hadoop-3.1.3]$ sbin/start-yarn.sh
测试提交任务
A、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
[fancyry@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 1
B、提交任务时不指定队列，按照配置规则，任务会到root.fancyry.fancyry队列
[fancyry@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 1
四、Yarn 的 Tool 接口案例
回顾：
[fancyry@hadoop102 hadoop-3.1.3]$ hadoop jar wc.jar com.fancy.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。
[fancyry@hadoop102 hadoop-3.1.3]$ hadoop jar wc.jar com.fancy.mapreduce.wordcount2.WordCountDriver -Dmapreduce.job.queuename=root.test /input /output1
需求： 自己写的程序也可以动态修改参数。编写 Yarn 的 Tool 接口。
具体步骤：
A、新建Maven项目YarnDemo，pom如下：
<?xml version="1.0" encoding="UTF-8"?>
<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>4.0.0</modelVersion>
<groupId>com.atguigu.hadoop</groupId>
<artifactId>yarn_tool_test</artifactId>
<version>1.0-SNAPSHOT</version>
<dependencies>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>3.1.3</version>
</dependency>
</dependencies>
</project>
B、新建com.fancy.yarn报名
C、创建类 WordCount 并实现 Tool 接口
package com.fancy.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<LongWritable, Text, Text, IntWritable> {
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<Text, IntWritable, Text, IntWritable> {
private IntWritable outV = new IntWritable();
@Override
protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable value : values) {
sum += value.get();
}
outV.set(sum);
context.write(key, outV);
}
}
}
D、新建WordCountDriver
package com.fancyry.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);
}
}
C、在 HDFS 上准备输入文件，假设为 /input 目录，向集群提交该 Jar 包
[fancyry@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：
[fancyry@hadoop102 hadoop-3.1.3]$ yarn jar YarnDemo.jar com.fancy.yarn.WordCountDriver wordcount -Dmapreduce.job.queuename=root.test /input /output1