每日总结

MAPREDUCE并行程序开发

 

package com.atguigu.hdfs;

import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
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;

public class yujishuan {
    static class TempMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
        @Override
        public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
            // 打印样本: Before Mapper: 0, 2000010115
            System.out.print("Before Mapper: " + key + ", " + value);
            String line = value.toString();
            String year = line.substring(0, 4);
            int temperature = Integer.parseInt(line.substring(8));
            context.write(new Text(year), new IntWritable(temperature));
            // 打印样本: After Mapper:2000, 15
            System.out.println("======" + "After Mapper:" + new Text(year) + ", " + new IntWritable(temperature));
        }
    }

 
            /**
      * 四个泛型类型分别代表：
      * KeyIn        Reducer的输入数据的Key，这里是每行文字中的“年份”
      * ValueIn      Reducer的输入数据的Value，这里是每行文字中的“气温”
      * KeyOut       Reducer的输出数据的Key，这里是不重复的“年份”
      * ValueOut     Reducer的输出数据的Value，这里是这一年中的“最高气温”**/
        static class TempReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
         @Override
         public void reduce(Text key, Iterable<IntWritable> values,
                 Context context) throws IOException, InterruptedException {
             int maxValue = Integer.MIN_VALUE;
             StringBuffer sb = new StringBuffer();
             //取values的最大值
             for (IntWritable value : values) {
                 maxValue = Math.max(maxValue, value.get());
                 sb.append(value).append(", ");
             }
             // 打印样本： Before Reduce: 2000, 15, 23, 99, 12, 22,
             System.out.print("Before Reduce: " + key + ", " + sb.toString());
             context.write(key, new IntWritable(maxValue));
             // 打印样本： After Reduce: 2000, 99
             System.out.println(
                     "======" +
                     "After Reduce: " + key + ", " + maxValue);
         }
     }
  
     public static void main(String[] args) throws Exception {
         //输入路径
         String dst = "hdfs://hadoop102:8020/mymapreduce2/in/input.txt";
         //输出路径，必须是不存在的，空文件加也不行。
         String dstOut = "hdfs://hadoop102:8020/mymapreduce2/output100";
         Configuration hadoopConfig = new Configuration();
          
         hadoopConfig.set("fs.hdfs.impl", org.apache.hadoop.hdfs.DistributedFileSystem.class.getName()
         );
         hadoopConfig.set("fs.file.impl", org.apache.hadoop.fs.LocalFileSystem.class.getName()
         );
         Job job = new Job(hadoopConfig);
          
         //如果需要打成jar运行，需要下面这句
         //job.setJarByClass(NewMaxTemperature.class);
  
         //job执行作业时输入和输出文件的路径
         FileInputFormat.addInputPath(job, new Path(dst));
         FileOutputFormat.setOutputPath(job, new Path(dstOut));
  
         //指定自定义的Mapper和Reducer作为两个阶段的任务处理类
         job.setMapperClass(TempMapper.class);
         job.setReducerClass(TempReducer.class);
          
         //设置最后输出结果的Key和Value的类型
         job.setOutputKeyClass(Text.class);
         job.setOutputValueClass(IntWritable.class);       
         //执行job，直到完成
         job.waitForCompletion(true);
         System.out.println("Finished");
     }
}