0、使用官方例子，将MapReduce提交到YARN上运行

1、进入文件夹

[hadoop@hadoop000 mapreduce]$ pwd
/home/hadoop/app/hadoop-2.6.0-cdh5.15.1/share/hadoop/mapreduce
[hadoop@hadoop000 mapreduce]$ ls
hadoop-mapreduce-client-app-2.6.0-cdh5.15.1.jar
hadoop-mapreduce-client-common-2.6.0-cdh5.15.1.jar
hadoop-mapreduce-client-core-2.6.0-cdh5.15.1.jar
hadoop-mapreduce-client-hs-2.6.0-cdh5.15.1.jar
hadoop-mapreduce-client-hs-plugins-2.6.0-cdh5.15.1.jar
hadoop-mapreduce-client-jobclient-2.6.0-cdh5.15.1.jar
hadoop-mapreduce-client-jobclient-2.6.0-cdh5.15.1-tests.jar
hadoop-mapreduce-client-nativetask-2.6.0-cdh5.15.1.jar
hadoop-mapreduce-client-shuffle-2.6.0-cdh5.15.1.jar
hadoop-mapreduce-examples-2.6.0-cdh5.15.1.jar

2、运行jar包里的类

（1）提示：计算pi值

pi: A map/reduce program that estimates Pi using a quasi-Monte Carlo method.

（2）提示：使用方法

Usage: org.apache.hadoop.examples.QuasiMonteCarlo <nMaps> <nSamples>

（3）运行运行jar包里的类

[hadoop@hadoop000 mapreduce]$ hadoop jar hadoop-mapreduce-examples-2.6.0-cdh5.15.1.jar pi 2 3

（4）刷新浏览器界面All Applications

3、wordcount

（1）找到wordcount

[hadoop@hadoop000 mapreduce]$ hadoop jar hadoop-mapreduce-examples-2.6.0-cdh5.15.1.jar

（2）使用vi重新创建文件

[hadoop@hadoop000 data]$ pwd
/home/hadoop/data
[hadoop@hadoop000 data]$ vi data.txt

hello   world   welcome
Hello   hello   welcome

（3）在hadoop中多层创建input文件，并传入文件

[hadoop@hadoop000 data]$ hadoop fs -mkdir -p /wc/input

[hadoop@hadoop000 data]$ hadoop fs -put data.txt /wc/input

（4）执行

[hadoop@hadoop000 mapreduce]$ hadoop jar hadoop-mapreduce-examples-2.6.0-cdh5.15.1.jar wordcount /wc/input/data.txt /wc/output

（5）查看结果

没有区分大小写，也不能自动删除重复性文档。

[hadoop@hadoop000 mapreduce]$ hadoop fs -text /wc/output/part-r-00000