YARN On Mapreduce搭建与wordCount案例实现-Toy模板网

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1.前言

YARN的基本思想是将资源管理RM，和作业调度、监控功能拆分成单独的守护进程。这个思想中拥有一个全局的资源管理器以及每个应用的MASTER,AM。每一个应用都是单个作业或者一个DAG作业。
架构图：
YARN On Mapreduce搭建与wordCount案例实现

1.YARN 集群搭建

1.1 ResourceManager High Availability 架构图

YARN On Mapreduce搭建与wordCount案例实现

1.2 配置文件

mapred-site.xml

 cp mapred-site.xml.template mapred-site.xml
 vi mapred-site.xml

  <property>
	<name>mapreduce.framework.name</name>
	<value>yarn</value>
</property>

yarn-site.xml

<property>
			<name>yarn.nodemanager.aux-services</name>
			<value>mapreduce_shuffle</value>
		    </property>

		 <property>
		   <name>yarn.resourcemanager.ha.enabled</name>
		   <value>true</value>
		 </property>
		 <property>
		   <name>yarn.resourcemanager.zk-address</name>
		   <value>node02:2181,node03:2181,node04:2181</value>
		 </property>

		 <property>
		   <name>yarn.resourcemanager.cluster-id</name>
		   <value>mashibing</value>
		 </property>

		 <property>
		   <name>yarn.resourcemanager.ha.rm-ids</name>
		   <value>rm1,rm2</value>
		 </property>
		 <property>
		   <name>yarn.resourcemanager.hostname.rm1</name>
		   <value>node03</value>
		 </property>
		 <property>
		   <name>yarn.resourcemanager.hostname.rm2</name>
		   <value>node04</value>
		 </property>

配置节点分发到其他节点。

1.3 启动服务和rm

启动yarn

start-yarn.sh

启动rm资源管理

yarn-daemon.sh start resourcemanager

访问页面查看集群状态

http://node03:8088
http://node04:8088

YARN On Mapreduce搭建与wordCount案例实现文章来源地址https://www.toymoban.com/news/detail-423043.html

2.运行官方的WC案例

/opt/bigdata/hadoop-2.6.5/share/hadoop/mapreduce/hadoop-mapreduce-examples-2.6.5.jar

2.1 运行jar

准备一个data.txt文件

hello  word
elite hello
hello  word
hadoop yarn
hive hbase 
hive haoop

# 创建目录
hdfs dfs -mkdir -p   /data/wc/input
#上传文件
hdfs dfs -D dfs.blocksize=1048576  -put data.txt  /data/wc/input
# 运行
hadoop jar  hadoop-mapreduce-examples-2.6.5.jar   wordcount   /data/wc/input   /data/wc/output

日志

hadoop jar  hadoop-mapreduce-examples-2.6.5.jar   wordcount   /data/wc/input   /data/wc/output
23/04/23 06:50:41 INFO input.FileInputFormat: Total input paths to process : 1
23/04/23 06:50:41 INFO mapreduce.JobSubmitter: number of splits:1
23/04/23 06:50:41 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1682257192920_0002
23/04/23 06:50:41 INFO impl.YarnClientImpl: Submitted application application_1682257192920_0002
23/04/23 06:50:41 INFO mapreduce.Job: The url to track the job: http://node03:8088/proxy/application_1682257192920_0002/
23/04/23 06:50:41 INFO mapreduce.Job: Running job: job_1682257192920_0002
23/04/23 06:50:53 INFO mapreduce.Job: Job job_1682257192920_0002 running in uber mode : false
23/04/23 06:50:53 INFO mapreduce.Job:  map 0% reduce 0%
23/04/23 06:51:02 INFO mapreduce.Job:  map 100% reduce 0%
23/04/23 06:51:10 INFO mapreduce.Job:  map 100% reduce 100%
23/04/23 06:51:11 INFO mapreduce.Job: Job job_1682257192920_0002 completed successfully
23/04/23 06:51:11 INFO mapreduce.Job: Counters: 49

查看输出

# 查看输出
[root@node01 mapreduce]# hdfs dfs -ls /data/wc/output
Found 2 items
-rw-r--r--   2 root supergroup          0 2023-04-23 06:51 /data/wc/output/_SUCCESS
-rw-r--r--   2 root supergroup         62 2023-04-23 06:51 /data/wc/output/part-r-00000
[root@node01 mapreduce]# hdfs dfs -cat /data/wc/output/part-r-00000
elite	1
hadoop	1
haoop	1
hbase	1
hello	3
hive	2
word	2
yarn	1