= 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));

}}}