一、需求说明

1.输入文件图示

在这里插入图片描述

2.需求

统计数据文件中每个字母出现的次数，以字母-次数的形式输出，例如（a 14）。

二、代码实现

1.书写思路

在map阶段读取每一行以空格分隔的数据，以（字母，1）的形式输出到reduce阶段，在输出之前会底层会实现分区功能，把字母相同的结果分到一个区间，传递到reduce阶段，在reduce阶段统计每一个分区的字母出现次数。最终输出结果到指定文件夹。

2.代码

Mapper：

package com.worldcount.zqd;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

public class WCMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
    
    
    Text k = new Text();
    LongWritable l = new LongWritable();
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
    
    
        String line = value.toString(); //读取一行数据
        String[] words = StringUtils.split(line, " ");  //利用工具类对读取的数据的空格进行切割，返回数组
        for (String word:words) {
    
    
            k.set(word);    // 获取word
            l.set(1);       // 默认为1
            context.write(k, l);    //写入键值对
        }

    }
}

Reducer:

package com.worldcount.zqd;

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;

public class WCreducer extends Reducer<Text, LongWritable, Text, LongWritable> {
    
    

    LongWritable l = new LongWritable();

    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
    
    
        long count = 0;     //计数变量
        // 增强for拿到分区的所有value值， 进行累加
        for (LongWritable value : values) {
    
    
            count += value.get();
        }
        l.set(count);
        context.write(key, l);
    }
}

主程序：

package com.worldcount.zqd;

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;


public class WCrunner {
    
    
    public static void main(String[] args) throws Exception {
    
    
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf);
        job.setJarByClass(WCrunner.class);
        job.setMapperClass(WCMapper.class);
        job.setReducerClass(WCreducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        FileInputFormat.setInputPaths(job, new Path("C:\\Users\\Lenovo\\Desktop\\hadoop_mr\\wc_input"));
        FileOutputFormat.setOutputPath(job, new Path("C:\\Users\\Lenovo\\Desktop\\hadoop_mr\\wc_out"));
        job.waitForCompletion(true);
    }
}

结果如下图：
在这里插入图片描述

Mapreduce之wordcount词频统计