Hadoop实战第一篇

前言：
 　　都说现在是草根为尊的时代，近年来hadoop及spark技术在国内越来越流行。而且渐渐现成为企业的新宠。在DT时代全面来临之前，能提早接触大数据的技术必然能先人一步。本文作为Hadoop系列的第一篇，将HDFS和MapRed两个技术核心用2个实例简单实现一些，希望能供hadoop入门的朋友些许参考。

--HDFS

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;

public class  HDFStest {
    final static String P_IN="hdfs://hadoop0:9000/data";
    final static String P_F1="hdfs://hadoop0:9000/a.txt";
    
    
    public static void main(String[] args) throws IOException {
        
        FileSystem fileSystem = FileSystem.get(new Configuration());
        System.out.println("make diretory:");
        fileSystem.mkdirs(new Path(P_IN));
        System.out.println("judgy if exist 'File':");
        System.out.println(fileSystem.exists(new Path(P_F1)));

    }

}

--MapReduce

实现文本单词出现次数的统计：

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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 WC {
    
    static String INPUT="hdfs://hadoop0:9000/hello";
    static String OUTPUT="hdfs://hadoop0:9000/output";
    
    public static void main(String[] args) throws Exception{
        
        
        Job job = new Job(new Configuration(),WC.class.getSimpleName());
        job.setMapperClass(MyMapper.class);
        job.setReducerClass(MyReducer.class);
        job.setJarByClass(WC.class);
        //输出结果格式
        job.setMapOutputKeyClass(Text.class);;
        job.setMapOutputValueClass(LongWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        //路径设置
        FileInputFormat.setInputPaths(job, INPUT);
        FileOutputFormat.setOutputPath(job, new Path(OUTPUT));
        //waitfor
        job.waitForCompletion(true);
        
    }
    
    static class MyMapper extends Mapper<LongWritable, Text,Text,LongWritable >{
        
        @Override
        protected void map(LongWritable key, Text value,
                Mapper<LongWritable, Text, Text, LongWritable>.Context context)
                throws IOException, InterruptedException {

            String[] words = value.toString().split(" ");
            for(String word:words){
                context.write(new Text(word), new LongWritable(1));
            }
        }
    }
    static class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable>{
        
        @Override
        protected void reduce(Text arg0, Iterable<LongWritable> arg1,Context context)
                throws IOException, InterruptedException {

            Long sum=0L;
            for(LongWritable c:arg1){
                sum += c.get();
            }
            context.write(arg0,new LongWritable(sum));
        }
    }
}

以上代码相对简单，map读取到一行“Text”之后通过字符串切分函数split()得到各个单词，每个单词出现一次计数为1：

Reduce操作，实际就是一个集合元素累计的操作：