如题WordCount！
一、普通版本
1、TokenizerMapper.java
package hadooptest2;
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;
//必须继承org.apache.hadoop.mapreduce.Mapper类，实现map函数
//Mapper类的四个泛型代表map函数输入键值对的键的类，值的类；map函数输出键值对的键的类，值的类
public class TokenizerMapper extends Mapper<LongWritable,Text,Text,IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
/**
* 将键值作为参数传给map函数
* @param key 键，代表行号
* @param value 代表该行的内容
* @throws IOException
*/
public void map(LongWritable key,Text value,Context context)throws IOException, InterruptedException
{
StringTokenizer itr = new StringTokenizer(value.toString());
while(itr.hasMoreTokens())
{
//使用StringTokenizer类的nextToken()方法，将每行文本拆分的单个单词
word.set(itr.nextToken());
//Context类的Write(key,value) 将其作为中间结果输出
context.write(word, one);
}
}
}
2、IntSumReducer.java
package hadooptest2;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
/**
* Reduce接受mapper输出的中间结果并执行reduce函数
* Reducer类的四个泛型代表reduce函数输入键值对的键的类，值的类；map函数输出键值对的键的类，值的类
*/
public class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable>
{
private IntWritable result = new IntWritable();
/**
*
* reduce中实现形同key值（同一单词）的计数，并将最后结果输出
* @param key 键，代表单词
* @param values reduce函数接收到的参数形如<key,List<value>>
* 这是因为map函数将key值相同的所有value都发送给reduce函数
* 也就是特定单词的value都在这个List内
* @throws IOException
*/
public void reduce(Text key,Iterable<IntWritable>values,Context context) throws IOException,InterruptedException
{
int sum = 0;
for(IntWritable val:values)
{
//只需要对List求和即可
sum += val.get();
}
result.set(sum);
context.write(key,result);
}
}
3、WordCount.java
package hadooptest2;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCount {
public static void main(String[] args)throws IOException,ClassNotFoundException,InterruptedException
{
//Configuation包含了对Hadoop的配置
//也可以在代码中用该对象设置作业级别的配置
Configuration conf = new Configuration();
if(args.length !=2)
{
System.err.println("Usage:wordcount <in> <out>");
System.exit(2);
}
Job job = new Job(conf,"word count");
job.setJarByClass(WordCount.class);
//指定Mapper类
job.setMapperClass(TokenizerMapper.class);
//指定Reduce类
job.setReducerClass(IntSumReducer.class);
//指定reduce函数输出key的类
job.setOutputKeyClass(Text.class);
//指定reduce函数输出value的类
job.setOutputValueClass(IntWritable.class);
//输入路径
FileInputFormat.addInputPath(job,new Path(args[0]));
//输出路径
FileOutputFormat.setOutputPath(job,new Path(args[1]));
//wartForCompletion函数向Hadoop函数提交任务
System.exit(job.waitForCompletion(true)?0:1);
}
}
二、提前聚合版
方法：设置Combiner函数，对map函数输出结构进行早期聚合以减少传输的数据量
Tip:
- Conbine过程发生在map和reduce函数之间，将中间结果进行了一次合并
- Hadoop不保证combiner是否被执行，可能会执行，可能不会执行，可能执行多次
- Combiner并不是所有场景都适应，随意使用可能导致结果错误。适合Combiner场景有最大值、最小值、求和等
