map reduce程序示例

map reduce程序示例

package test2;

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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

/**
 样例数据中包含了年份和温度，提出年份里温度最大的
 (0, 0067011990999991950051507+0000+),
 (33, 0043011990999991950051512+0022+),
 (66, 0043011990999991950051518-0011+),
 (99, 0043012650999991949032412+0111+),
 (132, 0043012650999991949032418+0078+),
 (165, 0067011990999991937051507+0001+),
 (198, 0043011990999991937051512-0002+),
 (231, 0043011990999991945051518+0001+),
 (264, 0043012650999991945032412+0002+),
 (297, 0043012650999991945032418+0078+),
 * */
public class mytest {

static String INPUT_PATH="input/t1_num.txt";   //待统计的文件路径
static String OUTPUT_PATH="output/t1_num";    //统计结果存放的路径

static class MyMapper extends Mapper <Object,Object,Text,IntWritable> {     //定义继承mapper类
    protected void map(Object key, Object value, Context context) throws IOException, InterruptedException{    //定义map方法

    String[] arr=value.toString().split("\\),");      //文件中的单词是以“),”分割的，并将每一行定义为一个数组
    for(int i=0;i<arr.length;i++){      //遍历循环每一行，统计单词出现的数量
        String line = arr[i].toString();
        String year = line.substring(line.length()-16, line.length()-12);
        String airTemperature = line.substring(line.length()-6, line.length()-1);
        context.write(new Text(year),new IntWritable(Integer.valueOf(airTemperature)));
    }
        /**
         map过程中，通过对字符串的解析，得到年-温度的key-value对作为输出
         (1950, 0)
         (1950, 22)
         (1950, -11)
         (1949, 111)
         (1949, 78)
         (1937, 1)
         (1937, -2)
         (1945, 1)
         (1945, 2)
         (1945, 78)
         */
 }
}

static class MyReduce extends Reducer<Text,IntWritable,Text,IntWritable>{     //定义继承reducer类
    protected void reduce(Text key,Iterable<IntWritable> values,Context context) throws IOException,InterruptedException{      //定义reduce方法
         int max = 0;
         for(IntWritable c:values){     //统计同一个单词的数量
             if(c.get()>max){
                 max = c.get();//获取value值
             }
         }
        IntWritable outValue=new IntWritable(max);//挨个输出
        context.write(key,outValue);
     }
    /**
     在reduce过程，将map过程中的输出，按照相同的key(年份)将value放到同一个列表中作为reduce的输入
     (1950, [0, 22, –11])
     (1949, [111, 78])
     (1937, [1, -2])
     (1945, [1, 2, 78])

     在reduce过程中，在列表中选择出最大的温度，将年-max温度的key-value作为输出：
     (1950, 22)
     (1949, 111)
     (1937, 1)
     (1945, 78)
     */

}

 public static void main(String[] args) throws Exception{    //main函数
     System.setProperty("hadoop.home.dir", "D:\\hadoop-2.7.6");//这一行一定要
     Path outputpath=new Path(OUTPUT_PATH);    //输出路径
     Configuration conf=new Configuration();
     Job job=Job.getInstance(conf);     //定义一个job，启动任务
     FileInputFormat.setInputPaths(job, INPUT_PATH);
     FileOutputFormat.setOutputPath(job,outputpath);

     job.setMapperClass(MyMapper.class);
     job.setReducerClass(MyReduce.class);
     job.setOutputKeyClass(Text.class);
     job.setOutputValueClass(IntWritable.class);
     job.waitForCompletion(true);
    }

}