1.在D盘下创建ceshi.txt文件

2.创建Maven文件

 3.在src文件下的java下创建包mapreduce

4.在mapreduce包下创建wordcountMapper类

5.编写wordcountMapper类

 

package mapreduce;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

/*
  四个泛型解释：
   KEYIN:k1的类型
   VALUEIN:V1的类型

   KEYOUT:K2的类型
   VALUEOUT:V2的类型
 */
public class wordcountMapper extends Mapper<LongWritable, Text,Text,LongWritable> {

    //map方法就是将k1和v1转为k2和v2
    /*
       参数：key：k1   行偏移量
            value：v1  每一行的文本数据
            context:标识上下文对象
     */
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        Text text=new Text();
        LongWritable longWritable = new LongWritable();
        //1：将一行的文本数据进行拆分
        String[] split=value.toString().split(",");
        //2:遍历数组，组装k2 和 v2
        for (String word : split) {
            //3:将k2和v2写入上下文
            text.set(word);
            longWritable.set(1);
            context.write(text,longWritable);
        }

    }
}

 6.在mapreduce下创建wordcountReduce类

 7.编写wordcountReduce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;

/*
  四个泛型解释：
   KEYIN:k2的类型
   VALUEIN:V2的类型

   KEYOUT:K3的类型
   VALUEOUT:V3的类型
 */
public class wordcountReduce extends Reducer<Text, LongWritable,Text,LongWritable> {
    //reduce方法的作用是将新的k2和v2转换为k3和v3，将k3和v3写入上下文中
    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
        long count=0;
        //1:遍历集合，将集合中的数字相加，得到v3
        for (LongWritable value : values) {
            count+=value.get();
        }
        //2:将k3和v3写入上下文中
        context.write(key,new LongWritable(count));
    }
}

8.在mapreduce包下创建jobMain主类

 9.编写jobMain主类

package mapreduce;


import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class jobMain extends Configured implements Tool {
    //该方法用于指定一个job任务
    public int run(String[] args) throws Exception {
        //1:创建一个job任务对象
        Job job = Job.getInstance(super.getConf(), "wordcount");
        //2：配置job任务对象
        //第一步：指定文件的读取方式和读取路径
        job.setInputFormatClass(TextInputFormat.class);//指定输入的类
        TextInputFormat.addInputPath(job,new Path("D:\\ceshi.txt"));//指定读取路径
        //第二步指定Map阶段的处理方式和数据类型
        job.setMapperClass(wordcountMapper.class);
        job.setMapOutputKeyClass(Text.class);//设置map阶段k2的类型
        job.setMapOutputValueClass(LongWritable.class);//设置map阶段v2的类型
        //第三，四，五，六采用默认的方式
        //第七步指定reduce阶段的处理方式和数据类型
        job.setReducerClass(wordcountReduce.class);
        //设置k3的类型
        job.setOutputKeyClass(Text.class);
        //设置v3的类型
        job.setOutputValueClass(LongWritable.class);
        //第八步，设置输出类型
        job.setOutputFormatClass(TextOutputFormat.class);
        //设置输出的路径
        TextOutputFormat.setOutputPath(job,new Path("D:\\mapreduc\\ou1"));
        boolean bl = job.waitForCompletion(true);
        return bl ? 0:1;
    }

    public static void main(String[] args) throws Exception {
        Configuration configuration = new Configuration();
        //启动job任务
        int run = ToolRunner.run(configuration, new jobMain(), args);
        System.exit(run);
    }
}