MapReduce入门经典案例（Windows环境下）

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

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

import java.io.IOException;

public class WordCountDriver {

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

//向yarn申请一个job任务用于执行mapreduce程序

Job job = Job.getInstance(new Configuration());

//设置入口类

job.setJarByClass(WordCountDriver.class);

//设置mapper类

job.setMapperClass(WordCountMapper.class);

//设置reduce类

job.setReducerClass(WordCountReducer.class);

//设置Mapper类的输出

job.setMapOutputKeyClass(Text.class);

job.setMapOutputValueClass(IntWritable.class);

//设置reduce类的输出

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

//设置要处理的文件

FileInputFormat.addInputPath(job,new Path(“hdfs://192.168.21.128:9000/txt/words.txt”));

//设置输出路径

FileOutputFormat.setOutputPath(job,new Path(“hdfs://192.168.21.128:9000/result/wordcount”));

//启动

job.waitForCompletion(true);

}

}

运行时，选中main函数，右键

[](()二、最大值

====================================================================

package cn.top;

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 CharTopMapper extends Mapper<LongWritable,Text,Text,IntWritable> {

@Override

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

//拆分这一行中的每一个字符

String datas = value.toString();

String[] split = datas.split(" ");

int x = Integer.parseInt(split[1]);

//将每一个字符进行遍历

context.write(new Text(split[0]),new IntWritable(x));

}

}

package cn.top;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

public class CharTopReducer extends Reducer<Text,IntWritable,Text,IntWritable> {

@Override

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

int sum=0;

int sum1=0;

for(IntWritable val:values){

sum = Integer.parseInt(String.valueOf(val));

if(sum1 < sum){

sum1=sum;

}

}

context.write(key,new IntWritable(sum1));

}

}

package cn.top;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

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

import java.io.IOException;

public class CharTop {

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

//向yarn申请一个job任务用于执行mapreduce程序

Job job = Job.getInstance(new Configuration());

//设置入口类

job.setJarByClass(CharTop.class);

//设置mapper类

job.setMapperClass(CharTopMapper.class);

//设置reduce类

job.setReducerClass(CharTopReducer.class);

//设置Mapper类的输出

job.setMapOutputKeyClass(Text.class);

job.setMapOutputValueClass(IntWritable.class);

//设置reduce类的输出

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(IntWritable.class);

//设置要处理的文件

FileInputFormat.addInputPath(job,new Path(“hdfs://192.168.21.128:9000/txt/score2.txt”));

//设置输出路径

FileOutputFormat.setOutputPath(job,new Path(“hdfs://192.168.21.128:9000/result/charcount”));

//启动

job.waitForCompletion(true);

}

}

[](()三、去重

===================================================================

package cn.quchong;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class quchongMapper extends Mapper<LongWritable, Text,Text, NullWritable> {

@Override

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

String datas = value.toString();

context.write(new Text(datas),NullWritable.get());

}

}

package cn.quchong;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

public class quchongReducer extends Reducer<Text, IntWritable,Text,NullWritable> {

@Override

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

context.write(new Text(key), NullWritable.get());

}

}

package cn.quchong;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

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

import java.io.IOException;

public class quchongDriver {

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

//向yarn申请一个job任务用于执行mapreduce程序

Job job = Job.getInstance(new Configuration());

//设置入口类

job.setJarByClass(quchongDriver.class);

//设置mapper类

job.setMapperClass(quchongMapper.class);

//设置reduce类

job.setReducerClass(quchongReducer.class);

//设置Mapper类的输出

job.setMapOutputKeyClass(Text.class);

job.setMapOutputValueClass(NullWritable.class);

//设置reduce类的输出

job.setOutputKeyClass(Text.class);

job.setOutputValueClass(NullWritable.class);

//设置要处理的文件

FileInputFormat.addInputPath(job,new Path(“hdfs://192.168.21.129:9000/txt/ip.txt”));

//设置输出路径

FileOutputFormat.setOutputPath(job,new Path(“hdfs://192.168.21.129:9000/result/quchong”));

//启动

job.waitForCompletion(true);

}

}
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