本机开发mapreduce，然后打包上传到hadoop服务器

一、入门wordcount

1、新建maven项目，pom.xml内容：

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">

4.0.0

com.luo

wordcount

1.0-SNAPSHOT

jar

wordcount

http://maven.apche.org

UTF-8

commons-beanutils

commons-beanutils

1.9.3

org.apache.hadoop

hadoop-common

2.7.7

org.apache.hadoop

hadoop-hdfs

2.7.7

org.apache.hadoop

hadoop-mapreduce-client-common

2.7.7

org.apache.hadoop

hadoop-mapreduce-client-core

2.7.7

junit

junit

3.8.1

然后创建源码目录：src/main/java

2、编写mapper程序：src/main/java/WordCountMapper

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class WordCountMapper extends Mapper {

@Override

protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {

//得到输入的每一行数据

String line = value.toString();

//通过空格分隔

String[] words = line.split(" ");

//循环遍历输出

for(String word:words){

context.write(new Text(word),new IntWritable(1));

}

}

}

mapreduce程序，因为需要传输数据，所以数据必须是可序列化的，Hadoop定义了自己的可序列化类型，例如Long类型在hadoop中的可序列化为LongWritable，String对应Text，int对应IntWritable。

mapreduce框架每读到一行数据，就会调用一次这里定义的map方法，map方法接收一个key-value对，继承的Mapper分别是输入key类型，输入value类型，输出key类型，输出value类型。输入的key默认是mapreduce读到一行文本的起始偏移量，value默认是一行的数据内容。

输出的key-value是用户自己定义的。

3、编写reduce程序：src/main/java/WordCountReducer

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

public class WordCountReducer extends Reducer {

@Override

protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {

Integer count = 0;

for (IntWritable value:values){

count+=value.get();

}

context.write(key, new IntWritable(count));

}

}

reduce方法继承的Reducer，分别为输入key类型，输入value类型，输出key类型，输出value类型，但是reduce方法接收的value是一个可迭代的数据集，因为reduce任务读取到的map任务处理结果例如是(good,1)(good,1)(good,1)当传给reduce方法时，会经过一个排序和合并的过程，合并完会变成，key：good，value：(1，1，1)

4、编写主程序：src/main/java/WordCountMapReduce

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.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

public class WordCountMapReduce {

public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {

//创建配置对象

Configuration conf = new Configuration();

//创建job

Job job = Job.getInstance(conf, "wordcount");

//设置运行的job类

job.setJarByClass(WordCountMapReduce.class);

//设置mapper类

job.setMapperClass(WordCountMapper.class);

//设置reduce类

job.setReducerClass(WordCountReducer.class);

//设置map输出的key，value

job.setMapOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

//设置reduce输出的key，value

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

//设置输入输出的路径

FileInputFormat.setInputPaths(job, new Path(args[0]));

FileOutputFormat.setOutputPath(job, new Path(args[1]));

//提交job

boolean b = job.waitForCompletion(true);

if(!b){

System.out.println("wordcount task fail!");

}

}

}

然后打包上传运行一下，成功~