MapReduce的wordcount代码

一.在IDEA下编写java程序实现hadoop的wordcount操作。

Mapper代码：

package MapReduce_wc;

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;
import java.util.Locale;

public class MRMapper extends Mapper<LongWritable,Text,Text,IntWritable> {

    IntWritable ONE = new IntWritable(1);
    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        System.out.println("-----------setup-----------");
    }

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        System.out.println("-----------map-------------------");

        //获取内容并转为小写
        final String line = value.toString().toLowerCase(Locale.ROOT);

        //按照分隔符进行拆分
        final String[] splits = line.split(" ");

        //输出
        for (String word:splits){
            context.write(new Text(word),ONE);
        }
    }

    @Override
    protected void cleanup(Context context) throws IOException, InterruptedException {
        System.out.println("--------------cleanup-------------------");
    }
}

2.Reduce代码：

package MapReduce_wc;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

/**
 * 功能：从Map端将数据传过来进行对数据的统计
 **/

public class MRReducer extends Reducer<Text,IntWritable,Text,IntWritable> {
    @Override
    protected void setup(Context context) throws IOException, InterruptedException {
        System.out.println("------------------setup--------------------");
    }

    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        System.out.println("--------------------reduce----------");

        //初始count
        int count = 0;

        //对于key进行聚合操作
        for (IntWritable value : values){
            count+=value.get();      //IntWritable转Int，用get（）方法
        }
        //输出
        context.write(key,new IntWritable(count));

    }

    @Override
    protected void cleanup(Context context) throws IOException, InterruptedException {
        System.out.println("------------cleanup-------------------");
    }
}

编写测试类（Driver）：

package MapReduce_wc;


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;

public class MRDriver {
    public static void main(String[] args) throws Exception {
        String input = "data/wc.txt";
        String output = "out_mr_wc";

        final Configuration configuration = new Configuration();

        //获取job对象
        final Job job = Job.getInstance(configuration);

        //设置class
        job.setJarByClass(MRDriver.class);

        //设置Mapper和Reduce
        job.setMapperClass(MRMapper.class);
        job.setReducerClass(MRReducer.class);

        //设置Mapper段的数字类型，对应的就是MRMapper的第三四个参数的类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        //设置Reduce段的数字类型，对应的就是MapReduce的第三四个参数的类型

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        //设置输入输出的路径
        FileInputFormat.setInputPaths(job,new Path(input));
        FileOutputFormat.setOutputPath(job,new Path(output));

        //提交job
        final boolean result = job.waitForCompletion(true);
        System.exit(result ? 0 :1 );
    }
}

框架：

运行结果：