KeyValueTextInputFormat 案例

一、需求分析

1、文件

hadoop is ok
hadoop not ok
java is fun
php is ok
php is pretty
python is all

2、需求

统计输入文件中每一行的第一个单词相同的行数

3、分析

每一行第一个单词的数量，只能用KeyValueTextInputFormat

key value 的分割符为空格

二、代码

前提条件：创建maven项目，导入依赖，配置log文件

1、Mapper

package com.kv;

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

import java.io.IOException;

public class KVMapper extends Mapper<Text, Text, Text, IntWritable> {
    /*
    分析： 
        切割符，切割后，第一个是 key，后面的是 value 都是Text类型
     */
    IntWritable v = new IntWritable(1);
    @Override
    protected void map(Text key, Text value, Context context) throws IOException, InterruptedException {
        // 写入
        context.write(key, v);
    }
}

2、Reducer

package com.kv;

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

import java.io.IOException;

public class KVReducer extends Reducer<Text, IntWritable,Text,IntWritable> {
    IntWritable v = new IntWritable();
    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable value : values) {
            sum += value.get();
        }
        v.set(sum);
        context.write(key, v);
    }
}

3、Driver

package com.kv;


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.input.KeyValueLineRecordReader;
import org.apache.hadoop.mapreduce.lib.input.KeyValueTextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

public class KVDriver {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        args = new String[]{"E:\a\input", "E:\a\output"};
        // 1. 获取job
        Configuration conf = new Configuration();
        // a、设置切割符
        conf.set(KeyValueLineRecordReader.KEY_VALUE_SEPERATOR, " ");
        Job job = Job.getInstance(conf);
        // 2. 设置 jar
        job.setJarByClass(KVDriver.class);
        // 3. 关联 mapper 和 reducer 类
        job.setMapperClass(KVMapper.class);
        job.setReducerClass(KVReducer.class);
        // 4. 设置 mapper 输出 的 k v 类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        // 5. 设置 输出结果的 k v
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        // b、设置输入格式
        job.setInputFormatClass(KeyValueTextInputFormat.class);
        // 6. 设置 输入 输出 路径
        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        // 7. 提交job
        boolean wait = job.waitForCompletion(true);
        System.exit(wait? 0: 1);
    }
}

注意：

设置切割符

conf.set(KeyValueLineRecordReader.KEY_VALUE_SEPERATOR, " ");

设置InputFormat的的格式

job.setInputFormatClass(KeyValueTextInputFormat.class);