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1、导入Hadoop的核心依赖包

1. \share\hadoop\common
2. \share\hadoop\common\lib
3. \share\hadoop\hdfs
4. \share\hadoop\hdfs\lib
5. \share\hadoop\mapreduce
6. \share\hadoop\mapreduce\lib

2、Map类

package MapReduce;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Mapper;import org.apache.hadoop.util.StringUtils;import java.io.IOException;// 四个泛型，前两个是输入数据的类型，后两个是输出数据的类型public class Map extends Mapper
   
     {
       @Override    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
           // 将数据转换为String类型        String line = value.toString();        // 分割字符串        String[] words =  StringUtils.split(line,' ');        // 遍历这个数组        for (String word:words){
               context.write(new Text(word),new LongWritable(1));        }    }}
   

3、Red类

package MapReduce;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;public class Red extends Reducer
   
    {
       @Override    protected void reduce(Text key, Iterable
    
      values, Context context) throws IOException, InterruptedException {
           long count = 0;        for (LongWritable value:values){
               count+=value.get();        }        context.write(key,new LongWritable(count));    }}
    
   

4、Run类

package MapReduce;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;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 Run {
       public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
           Configuration conf =new Configuration();        Job job = Job.getInstance(conf);        // 设置job所用的那些类在那个jar包        job.setJarByClass(Run.class);        //设置使用的Map和Reduced的类        job.setMapperClass(Map.class);        job.setReducerClass(Red.class);        // 设置Reduce输出的数据类型        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(LongWritable.class);        //设置map输出数据的类型        job.setMapOutputKeyClass(Text.class);        job.setMapOutputValueClass(LongWritable.class);        // 设置原始数据存放路径        FileInputFormat.setInputPaths(job,new Path("/input/"));        //设置处理结果输出路径        FileOutputFormat.setOutputPath(job,new Path("/output/"));        // 将job提交给集群运行        job.waitForCompletion(true);    }}

5、打包为Jar包

6、运行命令

创建input目录

hadoop fs -mkdir /input

提交文件

hadoop fs -put 文件路径 /input

运行

hadoop jar MapReduce.jar MapReduce.Run

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