编写WordCount程序数据如下：
hello beijing
hello shanghai
hello chongqing
hello tianjin
hello guangzhou
hello shenzhen
...
1、WCMapper：
package com.hadoop.testHadoop;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
//4个泛型中，前两个是指定mapper输入数据的类型，KEYIN是输入的key的类型，VALUEIN是输入的value的类型
//map 和 reduce 的数据输入输出都是以 key-value对的形式封装的
//默认情况下，框架传递给我们的mapper的输入数据中，key是要处理的文本中一行的起始偏移量，value为这一行的内容
//LongWritable Text 是hadoop为了序列化 定义的数据类型
public class WCMapper extends Mapper<LongWritable,Text,Text,LongWritable>{
//mapreduce框架每读一行数据就调用一次该方法
@Override
protected void map(LongWritable key, Text value,Context context) throws IOException, InterruptedException {
String line=value.toString();
String [] words = line.split(" ");
for(String word:words){
context.write(new Text(word), new LongWritable(1));
}
}
}
2、WCReducer：
package com.hadoop.testHadoop;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WCReducer extends Reducer<Text, LongWritable, Text, LongWritable>{
//框架在map处理完成之后，将所有kv对缓存起来，进行分组，然后传递一个组<key,valus{}>，调用一次reduce方法
//<hello,{1,1,1,1,1,1.....}>
@Override
protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws java.io.IOException ,InterruptedException {
long count=0;
for(LongWritable value:values){
count+=value.get();
}
context.write(key, new LongWritable(count));
}
}
3、WCRunner:
package com.hadoop.testHadoop;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WCRunner {
public static void main(String[] args) throws Exception {
Configuration conf=new Configuration();
Job job = Job.getInstance(conf);
//设置整个job所用的那些类在哪个jar包
job.setJarByClass(WCRunner.class);
job.setMapperClass(WCMapper.class);
job.setReducerClass(WCReducer.class);
//map输出数据kv类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(LongWritable.class);
//reduce输出数据kv类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(LongWritable.class);
//执行输入数据的路径
FileInputFormat.setInputPaths(job, new Path("/wordcount/inpput"));
//执行输出数据的路径
FileOutputFormat.setOutputPath(job, new Path("/wordcount/outputmy"));
//将job提交给集群运行
job.waitForCompletion(true);
}
}
本文转自lzf0530377451CTO博客，原文链接：http://blog.51cto.com/8757576/1839294，如需转载请自行联系原作者
