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MapReduce编程之WordCount，mapreducewordcount
//mapreduce程序
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCount {
/**
* TokenizerMapper 继续自 Mapper<LongWritable, Text, Text, IntWritable>
*
* [一个文件就一个map,两个文件就会有两个map]
* map[这里读入输入文件内容 以" \t\n\r\f" 进行分割，然后设置 word ==> one 的key/value对]
*
* @param Object Input key Type:
* @param Text Input value Type:
* @param Text Output key Type:
* @param IntWritable Output value Type:
*
* Writable的主要特点是它使得Hadoop框架知道对一个Writable类型的对象怎样进行serialize以及deserialize.
* WritableComparable在Writable的基础上增加了compareT接口，使得Hadoop框架知道怎样对WritableComparable类型的对象进行排序。
*
* @ author liuqingjie
*
*/
public static class TokenizerMapper
extends 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, Context context) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
/**
* IntSumReducer 继承自 Reducer<Text,IntWritable,Text,IntWritable>
*
* [不管几个Map,都只有一个Reduce,这是一个汇总]
* reduce[循环所有的map值,把word ==> one 的key/value对进行汇总]
*
* 这里的key为Mapper设置的word[每一个key/value都会有一次reduce]
*
* 当循环结束后，最后的确context就是最后的结果.
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