hadoop 实现简单的wordcount实例

前置条件：

在hadoop官网下载某个版本的zip文件，这里下载的版本是2.7.3，将其解压刀你的电脑的某个目录中，这里为：D:\dev\hadoop-2.7.3

下载地址：http://apache.fayea.com/hadoop/common/hadoop-2.7.3/



src的是文件源码，有需要的可以下载下来研究~

配置环境变量：

HADOOP_HOME D:\dev\hadoop-2.7.3

1.使用idea新建一个maven项目

2.修改maven项目中pom文件，加入如下依赖

<dependency>
<groupId>junit</groupId>
<artifactId>junit</artifactId>
<version>3.8.1</version>
<scope>test</scope>
</dependency>

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.7.3</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-hdfs</artifactId>
<version>2.7.3</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.7.3</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-jobclient</artifactId>
<version>2.7.3</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-common</artifactId>
<version>2.7.3</version>
</dependency>

<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.7.3</version>
</dependency>

3.在java文件新建一个包 com.hadoop.wordcount 名字可以自定义

在包内新建一个类 wordCount

内容如下：

package com.hadoop.wordcount;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 java.io.IOException;
import java.util.StringTokenizer;

/**
* WordCount
*
* @author: wychen
* @time: 2017/3/20 20:25
*/
public class WordCount {

static class MyMapper extends Mapper<Object, Text, Text, IntWritable> {

private final static IntWritable one = new IntWritable(1);
private Text word = new Text();

@Override
protected void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException {
//分割字符串
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
//排除字母少于5个的
String tmp = itr.nextToken();
if (tmp.length() < 5) {
continue;
}
word.set(tmp);
context.write(word, one);
}
}

}

static class MyReduce extends Reducer<Text, IntWritable, Text, IntWritable> {
private IntWritable result = new IntWritable();
private Text keyEx = new Text();

@Override
protected void reduce(Text key, Iterable<IntWritable> values,
Reducer<Text, IntWritable, Text, IntWritable>.Context context)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
//将map的结果方法，乘以2
sum += val.get() + 1;

}
result.set(sum);

keyEx.set("输出：" + key.toString());
context.write(keyEx, result);
}
}

public static void main(String[] args) throws Exception {

//配置信息
Configuration conf = new Configuration();

//job名称
Job job = Job.getInstance(conf, "mywordcount");

job.setJarByClass(WordCount.class);
job.setMapperClass(MyMapper.class);

job.setReducerClass(MyReduce.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);

//输入 输出path
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));

//结束
System.exit(job.waitForCompletion(true) ? 0 : 1);
}

}

4.resources 文件中新建日志配置文件 log4j.properties

log4j.rootLogger=DEBUG, stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%c{1} - %m%n
log4j.logger.java.sql.PreparedStatement=DEBUG

接下来就可以直接WordCount类中运行main函数了

首先配置运行前的参数



接下来直接在WordCount类中右击鼠标，点击运行即可，可在控制台查看运行过程中输出的结果，以及你填写的文件输出路径的文件中结果

笔者的输出结果如下图所示：



好了，一个简单的MapReduce下的单词统计实例就完成了~