map端的设置:
package wordcount;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class MyMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
public void map(Object 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);
}
}
}
map端主要是将输入进来的数值转换成(key,1)的形式
reduce端的设置:
package wordcount;
import java.util.Iterator;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class MyReducer extends Reducer<Text, IntWritable,Text,IntWritable>{
private IntWritable result = new IntWritable();
public void reduce(Text key,Iterable<IntWritable> values,Context context) {
int sum = 0;
for (IntWritable val: values) {
sum += val.get();
}
Iterator<IntWritable> it = values.iterator();
while(it.hasNext()) {
IntWritable n = it.next();
System.out.println(n);
System.out.println(key);
}
result.set(sum);
context.write(key, result);
}
}
在用MapReduce来计算Wordcount中,reduce端才是真正按照相同的key进行设置将value的值相加的。在这期间使用的是迭代器进项转换的。