dependencia de Maven
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.8.1</version>
</dependency>
WordcountMapper
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
/**
* KEYIN :是map task读取到的数据的key的类型,是一行的起始偏移量Long
* VALUEIN:是map task读取到的数据的value的类型,是一行的内容String
*
* KEYOUT:是用户的自定义map方法要返回的结果kv数据的key的类型,在wordcount逻辑中,我们需要返回的是单词String
* VALUEOUT:是用户的自定义map方法要返回的结果kv数据的value的类型,在wordcount逻辑中,我们需要返回的是整数Integer
*
*
* 但是,在mapreduce中,map产生的数据需要传输给reduce,需要进行序列化和反序列化,而jdk中的原生序列化机制产生的数据量比较冗余,就会导致数据在mapreduce运行过程中传输效率低下
* 所以,hadoop专门设计了自己的序列化机制,那么,mapreduce中传输的数据类型就必须实现hadoop自己的序列化接口
*
* hadoop为jdk中的常用基本类型Long String Integer Float等数据类型封住了自己的实现了hadoop序列化接口的类型:LongWritable,Text,IntWritable,FloatWritable
*
*
*
*
* @author ThinkPad
*
*/
public class WordcountMapper extends Mapper<LongWritable, Text, Text, IntWritable>{
@Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// 切单词
String line = value.toString();
String[] words = line.split(" ");
for(String word:words){
context.write(new Text(word), new IntWritable(1));
}
}
}
WordcountReducer
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WordcountReducer extends Reducer<Text, IntWritable, Text, IntWritable>{
@Override
protected void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException {
int count = 0;
Iterator<IntWritable> iterator = values.iterator();
while(iterator.hasNext()){
IntWritable value = iterator.next();
count += value.get();
}
context.write(key, new IntWritable(count));
}
}