Wordcount on YARN 一个MapReduce示例

Hadoop YARN版本：2.2.0

关于hadoop yarn的环境搭建可以参考这篇博文：Hadoop 2.0安装以及不停集群加datanode

hadoop hdfs yarn伪分布式运行，有如下进程

1320 DataNode
1665 ResourceManager
1771 NodeManager
1195 NameNode
1487 SecondaryNameNode

写一个mapreduce示例，在yarn上跑，wordcount数单词示例


代码在github上：https://github.com/huahuiyang/yarn-demo

步骤一

我们要处理的输入如下，每行包含一个或多个单词，空格分开。可以用hadoop fs -put ... 把本地文件放到hdfs上去，方便mapreduce程序读取

hadoop yarn
mapreduce
hello redis
java hadoop
hello world
here we go

wordcount程序希望完成数单词任务，输出格式是 <单词 出现次数>

步骤二

新建一个工程，工程结构如下，这个是个maven管理的工程



源代码如下：

pom.xml文件

<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>hadoop-yarn</groupId>
<artifactId>hadoop-demo</artifactId>
<version>0.0.1-SNAPSHOT</version>

<dependencies>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.1.1-beta</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.1.1-beta</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-common</artifactId>
<version>2.1.1-beta</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-jobclient</artifactId>
<version>2.1.1-beta</version>
</dependency>
</dependencies>
</project>

package com.yhh.mapreduce.wordcount;
import java.io.IOException;

import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;

public class WordCountMapper extends MapReduceBase implements Mapper<LongWritable, Text, Text,IntWritable>  {

@Override
public void map(LongWritable key, Text value,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {

String line = value.toString();
if(line != null) {
String[] words = line.split(" ");
for(String word:words) {
output.collect(new Text(word), new IntWritable(1));
}
}

}

}

package com.yhh.mapreduce.wordcount;

import java.io.IOException;
import java.util.Iterator;

import org.apache.hadoop.io.*;
import org.apache.hadoop.mapred.*;

public class WordCountReducer extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable>{

@Override
public void reduce(Text key, Iterator<IntWritable> values,
OutputCollector<Text, IntWritable> output, Reporter reporter)
throws IOException {
int count = 0;
while(values.hasNext()) {
values.next();
count++;
}
output.collect(key, new IntWritable(count));
}

}

package com.yhh.mapreduce.wordcount;

import java.io.IOException;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;

public class WordCount {
public static void main(String[] args) throws IOException {
if(args.length != 2) {
System.err.println("Error!");
System.exit(1);
}

JobConf conf = new JobConf(WordCount.class);
conf.setJobName("word count mapreduce demo");

conf.setMapperClass(WordCountMapper.class);
conf.setReducerClass(WordCountReducer.class);
conf.setOutputKeyClass(Text.class);
conf.setOutputValueClass(IntWritable.class);

FileInputFormat.addInputPath(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));

JobClient.runJob(conf);

}

}

步骤三

打包发布成jar，右击java工程，选择Export...，然后选择jar file生成目录，这边发布成wordcount.jar，然后上传到hadoop集群

[root@hadoop-namenodenew ~]# ll wordcount.jar
-rw-r--r--. 1 root root 4401 6月   1 22:05 wordcount.jar

运行mapreduce任务。命令如下

hadoop jar ~/wordcount.jar com.yhh.mapreduce.wordcount.WordCount data.txt /wordcount/result

可以用hadoop job -list看任务运行情况，运行成功大概会有如下输出

14/06/01 22:06:25 INFO mapreduce.Job: The url to track the job: http://hadoop-namenodenew:8088/proxy/application_1401631066126_0003/ 14/06/01 22:06:25 INFO mapreduce.Job: Running job: job_1401631066126_0003
14/06/01 22:06:33 INFO mapreduce.Job: Job job_1401631066126_0003 running in uber mode : false
14/06/01 22:06:33 INFO mapreduce.Job:  map 0% reduce 0%
14/06/01 22:06:40 INFO mapreduce.Job:  map 50% reduce 0%
14/06/01 22:06:41 INFO mapreduce.Job:  map 100% reduce 0%
14/06/01 22:06:47 INFO mapreduce.Job:  map 100% reduce 100%
14/06/01 22:06:48 INFO mapreduce.Job: Job job_1401631066126_0003 completed successfully
14/06/01 22:06:49 INFO mapreduce.Job: Counters: 43

然后mapreduce输出的任务结果如下，单词按照字典序排序

hadoop fs -cat /wordcount/result/part-00000

go    1
hadoop    2
hello    2
here    1
java    1
mapreduce    1
redis    1
we    1
world    1
yarn    1