= hadoop programming 0715 =
 * 整理出hadoop programming 的完全公式

 ||       ||   || 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                           || ) ||   || 



{{{
public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> {
public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {

while (tokenizer.hasMoreTokens()) {
		private final static IntWritable one = new IntWritable(1);
		private Text word = new Text();
		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));

}}}