(1)WordCount.java:
package bigdata.mr;
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 java.io.IOException;
/**
* Created by xiaoguanyu on 2017/12/26.
*/
public class WordCountApp {
static class WordCountMapper extends Mapper<LongWritable,Text,Text,IntWritable>{
@Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
//拿到一行数据,将输入的序列化数据转换成字符串
String line = value.toString();
//将一行数据按照分隔符拆分
String[] words = line.split("\t");
//遍历单词数据,输出单词<k,1>
for(String word:words){
//需要序列化写出
context.write(new Text(word),new IntWritable(1));
}
}
}
static class WordCountReducer extends Reducer<Text,IntWritable,Text,IntWritable>{
//reduce方法是针对输入的一组数据,一个key和它的所有value组成一组(k:v1,v2,v3)
@Override
protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
//定义一个计数器
int count = 0;
//遍历一组数据,将key出现次数累加到count
for(IntWritable value : values){
count += value.get();
}
context.write(key,new IntWritable(count));
}
}
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
String jobName = args[0];
String inputPath = args[1];
String outputPath = args[2];
Configuration conf = new Configuration();
Job job = Job.getInstance(conf);
//设置作业名称
job.setJobName(jobName);
//设置主类
job.setJarByClass(WordCountApp.class);
//设置作业中使用的Mapper和Reducer类
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
//设置Mapper阶段的输出key类型和value类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
//设置reducer阶段的输出key类型和value类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
//设置job的输入路径和输出路径
FileInputFormat.setInputPaths(job,new Path(inputPath));
FileOutputFormat.setOutputPath(job,new Path(outputPath));
System.exit(job.waitForCompletion(true)?0:1);
}
}
(2)pom.xml
<?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>cn.chinahadoop</groupId>
<artifactId>MapReducePro</artifactId>
<version>1.0-SNAPSHOT</version>
<dependencies>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-common</artifactId>
<version>2.7.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-core</artifactId>
<version>2.7.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-common</artifactId>
<version>2.7.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-mapreduce-client-jobclient</artifactId>
<version>2.7.0</version>
</dependency>
</dependencies>
<build>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>2.3.2</version>
<executions>
<execution>
<id>default-compile</id>
<phase>compile</phase>
<goals>
<goal>compile</goal>
</goals>
<configuration>
<encoding>UTF-8</encoding>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
</project>