主要對於Hadoop的MapReduce:

MapReduce:

Map: 解析分發資料

Reduce： 執行計算邏輯

例如：DB--sql語句根據地址來從access運算資料（採用分而治之，並非採用單一的節點進行運算，多節點參與運算）

select count(*) from access log group by url;

例如：

A B C D

url1 url1 url1 url1

url2 url2 url2 url2

url3 url3 url3 url3

url4 url4 url4 url4

當進行運算時（多節點分而治之）： count1節點來計算url1

count2節點來計算url2

count3節點來計算url3

count4節點來計算url4

具體程式碼實現上：

public class WordCount{

//分發資料
public static class TokenizerMapper 
extends Mapper<Object,Text,Text,IntWritable>{

 
private final static IntWritable one = new IntWritable(1);
private Text word=new Text();
//核心的Map 解析分析資料
public void map(Object key,Text value,Context context) throws IOExcption,InterruptedException{
//對資料進行切分和資料化
StringTokenizer itr= new StringTokenizer(value.toString());
while(itr.hasMoreTakens()){
word.set（itr.nextToken());
 
//將歸納組隊 相同的Key分成一組（這裡的key為word） value為統計的某個屬性
context.write(word,one);
       }    
    }
}
public static IntSumReduce extends Reduce<Text,IntWritable,Text,IntWritable>{
private IntWritable result=new IntWritable();
public void reduce(Text key,Iterable<IntWritable> values,Context context) 
throws IOException,IntrruptException{
        int sum=0;
        for(IntWritable val:values){
           sum+=val.get();
     }
         result.set(sum);
         context.write(key,result);   
  }
}


public static void main(String[] args){
       Configuration config=new Configuration();
       Job job=Job.getInstance(config,"word count");
       job.setJarByClass(WordCount.class)
           .setMapperClass(Tokenizer.class)
           .setCombinerClass(IntSumReduce.class)
           .setReducerClass(IntSumReduce.class) 
           .setOutputKeyClass(Text.class)
           .setOutputValueClass(IntWritable.class);
     FileInputFormat.addInputPath(job,new Path(args[0]));
     FileOutputFormat.addOutputPath(job,new Path(args[1]));
           System.out.println(job.waitForCompletion(true)?0:1);
   }
 }