hadoop programming 完全公式

		輸入 key		輸入 value		輸出 Key		輸出 Value
Mapper	<	A	,	B	,	C	,	D	>
map	(	A	,	B	,	OutputCollector < C , D >	,	Reporter reporter	)
output	.	collect	(	c	,	d	)
Reducer	<	C	,	D	,	E	,	F	>
reduce	(	C	,	D	,	OutputCollector < E , F >	,	Reporter reporter	)
output	.	collect	(	e	,	f	)

• A, B, C, D ,E, F 分別代表可以用的類別；c, d, e, f 代表由C,D,E,F所產生的物件
• 有了這張表，我們規劃要寫M/R程式的時候:
  • 先把Map的輸入<key,value> 應該屬於哪種類別的，則A,B定好
  • Map的輸出<key,value>定好，則 C,D也ok了
  • 接下來想最終輸出的<key,value>該為何類別，則 E,F 決定好
  • 分別填入 ABCDEF之後，整個程式的架構就出來了，接下來就看你的程式如何實做

14.        public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> {
15.        private final static IntWritable one = new IntWritable(1);
16.        private Text word = new Text();
17.   
18.        public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
19.          String line = value.toString();
20.          StringTokenizer tokenizer = new StringTokenizer(line);
21.          while (tokenizer.hasMoreTokens()) {
22.            word.set(tokenizer.nextToken());
23.            output.collect(word, one);
24.          }
25.        }
26.      }
27.   
28.      public static class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> {
29.        public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
30.          int sum = 0;
31.          while (values.hasNext()) {
32.            sum += values.next().get();
33.          }
34.          output.collect(key, new IntWritable(sum));
35.        }
36.      }