《Hadoop权威指南 第4版》 - 第二章：关于MapReduce -一种可用于Hadoop数据处理的编程模型

1. 实现一个简单的MapReduce

• map函数进行准备数据,reduce函数进行处理

引入pom依赖

    
        
        
            org.apache.hadoop
            hadoop-mapreduce-client-core
            2.7.1
        

        
        
            org.apache.hadoop
            hadoop-common
            2.7.1
        
    

input.txt 简化后的数据源

1950 0
1950 22
1950 -11
1949 111
1949 78
1898 2222

MaxTemperatureMapper.java 输入处理

package test.chapter02;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;

import java.io.IOException;

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

/**
 * @author baize
 * @description 查找最高气温的mapper类;
 * map函数进行准备数据,reduce函数进行处理;
 * Mapper四个参数对应: 输入键/输入值 / 输出键/输出值
 * @Date 2019/12/31 19:27
 */
public class MaxTemperatureMapper extends Mapper {

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        // 简单处理
        String line = value.toString();
        String year = line.substring(0, 4);
        int airTemperature = Integer.parseInt(line.substring(5, line.length()));
        // 写入到返回的键值对中
        context.write(new Text(year), new IntWritable(airTemperature));
    }
}

MaxTemperatureReducer.java 输出处理

package test.chapter02;

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

import java.io.IOException;

/**
 * @author baize
 * @description 使用Reducer处理得到最高温度
 * @Date 2020/1/1 16:40
 */
public class MaxTemperatureReducer extends Reducer {
    @Override
    protected void reduce(Text key, Iterable values, Context context) throws IOException, InterruptedException {
        // Reducer中处理键值对, 将一对多转为一对一,仅保留最大的温度值
        int maxValue = Integer.MIN_VALUE;
        for (IntWritable x : values) {
            maxValue = Integer.max(x.get(), maxValue);
        }
        context.write(key, new IntWritable(maxValue));
    }
}

Main.java MapReduce作业处理

package test.chapter02;

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;

import java.io.IOException;

/**
 * @author baize
 * @description 负责运行MapReduce作业
 * @Date 2019/12/31 19:48
 */
public class Main {
    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        if (args == null || args.length != 2) {
            System.out.println("Error! 计算最大问题异常!");
            System.exit(-1);
        }

        // 1.Job指定整个作业规范, 设置Jar包名字提交给Hadoop集群来运行作业
        Job job = new Job();
        job.setJarByClass(Main.class);
        job.setJobName("主测试");

        // 2.构造Job对象之后,指定输入输出数据的路径
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[2]));

        // 3.指定map/reduce的Class类型
        job.setMapperClass(MaxTemperatureMapper.class);
        job.setReducerClass(MaxTemperatureReducer.class);

        // 4.控制输入输出数据的类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        // 5.waitForCompletion 提交作业等待执行完成
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

TODO

• 使用maven 命令打成jar包
• 放到hadoop上执行,查看输出