尚硅谷大数据技术之Hadoop（MapReduce）（新）第3章 MapReduce框架原理

package com.atguigu.mapreduce.order;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.NullWritable;

import org.apache.hadoop.mapreduce.Job;

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;

import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class OrderDriver {

public static void main(String[] args) throws Exception, IOException {

// 输入输出路径需要根据自己电脑上实际的输入输出路径设置

args  = new String[]{"e:/input/inputorder" , "e:/output1"};

// 1 获取配置信息

Configuration conf = new Configuration();

Job job = Job.getInstance(conf);

// 2 设置jar包加载路径

job.setJarByClass(OrderDriver.class);

// 3 加载map/reduce类

job.setMapperClass(OrderMapper.class);

job.setReducerClass(OrderReducer.class);

// 4 设置map输出数据key和value类型

job.setMapOutputKeyClass(OrderBean.class);

job.setMapOutputValueClass(NullWritable.class);

// 5 设置最终输出数据的key和value类型

job.setOutputKeyClass(OrderBean.class);

job.setOutputValueClass(NullWritable.class);

// 6 设置输入数据和输出数据路径

FileInputFormat.setInputPaths(job, new Path(args[0]));

FileOutputFormat.setOutputPath(job, new Path(args[1]));

// 8 设置reduce端的分组

job.setGroupingComparatorClass(OrderGroupingComparator.class);

// 7 提交

boolean result = job.waitForCompletion(true);

System.exit(result ? 0 : 1);

}

}