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

public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();

public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
  String line = value.toString();
  StringTokenizer tokenizer = new StringTokenizer(line);
  while (tokenizer.hasMoreTokens()) {
    word.set(tokenizer.nextToken());
    output.collect(word, one);
  }
}

public static class Reduce extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> {
  public void reduce(Text key, Iterator<IntWritable> values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
    int sum = 0;
    while (values.hasNext()) {
      sum += values.next().get();
    }
    output.collect(key, new IntWritable(sum));
  }
}