1、MapReduce定义

将这些数据切分成三块，然后分别计算处理这些数据（Map），
处理完毕之后发送到一台机器上进行合并（merge），
再计算合并之后的数据，归纳（reduce）并输出。

Java 中包含3个函数：
map分割数据集
reduce处理数据
job对象来运行MapReduce作业，

2、MapReduce统计两个文本文件中，每个单词出现的次数

首先我们在当前目录下创建两个文件：

创建file01输入内容：
Hello World Bye World
创建file02输入内容：
Hello Hadoop Goodbye Hadoop
将文件上传到HDFS的/usr/input/目录下：
不要忘了启动DFS：
start-dfs.sh

public class WordCount {  
//Mapper类  
/*因为文件默认带有行数，LongWritable是用来接受文件中的行数，  
第一个Text是用来接受文件中的内容，  
第二个Text是用来输出给Reduce类的key,  
IntWritable是用来输出给Reduce类的value*/  
 public static class TokenizerMapper   
       extends Mapper<LongWritable, Text, Text, IntWritable>{  
    private final static IntWritable one = new IntWritable(1);  
    private Text word = new Text();  
    public void map(LongWritable key, Text value, Context context  
                    ) throws IOException, InterruptedException {  
      StringTokenizer itr = new StringTokenizer(value.toString());  
      while (itr.hasMoreTokens()) {  
        word.set(itr.nextToken());  
        context.write(word, one);  
      }  
    }  
  }  
  public static class IntSumReducer   
       extends Reducer<Text,IntWritable,Text,IntWritable> {  
    private IntWritable result = new IntWritable();  
    public void reduce(Text key, Iterable<IntWritable> values,   
                       Context context  
                       ) throws IOException, InterruptedException {  
      int sum = 0;  
      for (IntWritable val : values) {  
        sum += val.get();  
      }  
      result.set(sum);  
      context.write(key, result);  
    }  
  }  
  public static void main(String[] args) throws Exception {  
    //创建配置对象  
    Configuration conf = new Configuration();  
    //创建job对象  
    Job job = new Job(conf, "word count");  
    //设置运行job的类  
    job.setJarByClass(WordCount.class);  
    //设置Mapper的类  
    job.setMapperClass(TokenizerMapper.class);  
    //设置Reduce的类  
    job.setReducerClass(IntSumReducer.class);  
    //设置输出的key value格式  
    job.setOutputKeyClass(Text.class);  
    job.setOutputValueClass(IntWritable.class);  
    //设置输入路径  
    String inputfile = "/usr/input";  
    //设置输出路径  
    String outputFile = "/usr/output";  
    //执行输入  
    FileInputFormat.addInputPath(job, new Path(inputfile));  
    //执行输出  
    FileOutputFormat.setOutputPath(job, new Path(outputFile));  
    //是否运行成功，true输出0，false输出1  
    System.exit(job.waitForCompletion(true) ? 0 : 1);  
  }  
}

hadoop的MapReduce与hdfs中一定要先启动start-dfs.sh

3、用MapReduce计算班级每个学生的最好成绩

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

public class WordCount {  
    /********** Begin **********/  
    public static class TokenizerMapper extends Mapper<LongWritable, Text, Text,      IntWritable> {  
        private final static IntWritable one = new IntWritable(1);  
        private Text word = new Text();  
        private int maxValue = 0;  
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {  
            StringTokenizer itr = new StringTokenizer(value.toString(),"\n");  
            while (itr.hasMoreTokens()) {  
                String[] str = itr.nextToken().split(" ");  
                String name = str[0];  
                one.set(Integer.parseInt(str[1]));  
                word.set(name);  
                context.write(word,one);  
            }  
            //context.write(word,one);  
        }  
    }
    public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> {  
        private IntWritable result = new IntWritable();
        public void reduce(Text key, Iterable<IntWritable> values, Context context)  
                throws IOException, InterruptedException {  
            **int maxAge = 0;  
            int age = 0;  
            for (IntWritable intWritable : values) {  
                maxAge = Math.max(maxAge, intWritable.get());  
            }  
            result.set(maxAge);**  
            context.write(key, result);  
        }  
    }
    public static void main(String[] args) throws Exception {  
        Configuration conf = new Configuration();  
        Job job = new Job(conf, "word count");  
        job.setJarByClass(WordCount.class);  
        job.setMapperClass(TokenizerMapper.class);  
        job.setCombinerClass(IntSumReducer.class);  
        job.setReducerClass(IntSumReducer.class);  
        job.setOutputKeyClass(Text.class);  
        job.setOutputValueClass(IntWritable.class);  
        String inputfile = "/user/test/input";  
        String outputFile = "/user/test/output/";  
        FileInputFormat.addInputPath(job, new Path(inputfile));  
        FileOutputFormat.setOutputPath(job, new Path(outputFile));  
        job.waitForCompletion(true);  
    /********** End **********/  
    }  
}

4、 MapReduce 文件内容合并去重

import java.io.IOException;
import java.util.*;  
import org.apache.hadoop.conf.Configuration;  
import org.apache.hadoop.fs.Path;  
import org.apache.hadoop.io.*;  
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 org.apache.hadoop.util.GenericOptionsParser;  
public class Merge {  
    /**  
     * @param args  
     * 对A,B两个文件进行合并，并剔除其中重复的内容，得到一个新的输出文件C  
     */  
    //在这重载map函数，直接将输入中的value复制到输出数据的key上 注意在map方法中要抛出异常：throws IOException,InterruptedException  
    /********** Begin **********/  
    public static class Map extends Mapper<LongWritable, Text, Text, Text >  
    {  
        protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, Text>.Context context)  
                throws IOException, InterruptedException {  
            String str = value.toString();  
            String[] data = str.split(" ");  
            Text t1= new Text(data[0]);  
            Text t2 = new Text(data[1]);  
            context.write(t1,t2);  
        }  
    }   
    /********** End **********/  
    //在这重载reduce函数，直接将输入中的key复制到输出数据的key上  注意在reduce方法上要抛出异常：throws IOException,InterruptedException  
    /********** Begin **********/  
    public static class Reduce  extends Reducer<Text, Text, Text, Text>  
    {  
        protected void reduce(Text key, Iterable<Text> values, Reducer<Text, Text, Text, Text>.Context context)  
                throws IOException, InterruptedException {  
            List<String> list = new ArrayList<>();  
            for (Text text : values) {  
                String str = text.toString();  
                if(!list.contains(str)){  
                    list.add(str);  
                }  
            }  
            Collections.sort(list);  
            for (String text : list) {  
                context.write(key, new Text(text));  
            }  
        }  
    /********** End **********/  
    }  
    public static void main(String[] args) throws Exception{  
        Configuration conf = new Configuration();  
         Job job = new Job(conf, "word count");  
        job.setJarByClass(Merge.class);  
        job.setMapperClass(Map.class);  
        job.setCombinerClass(Reduce.class);  
        job.setReducerClass(Reduce.class);  
        job.setOutputKeyClass(Text.class);  
        job.setOutputValueClass(Text.class);  
        String inputPath = "/user/tmp/input/";  //在这里设置输入路径  
        String outputPath = "/user/tmp/output/";  //在这里设置输出路径  
        FileInputFormat.addInputPath(job, new Path(inputPath));  
        FileOutputFormat.setOutputPath(job, new Path(outputPath));  
        System.exit(job.waitForCompletion(true) ? 0 : 1);  
    }  
}