hadoop参考代码

package com.lb.hadoop;


import java.io.IOException;
import java.io.UnsupportedEncodingException;
import java.util.Iterator;
import java.util.Set;


import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;


/**
 * 
 * 描述：  山东培训测试
 * 
 * @author Wangbin
 */
public class SDCount {
/**
* MapReduceBase类:实现了Mapper和Reducer接口的基类（其中的方法只是实现接口，而未作任何事情） Mapper接口：
* WritableComparable接口：实现WritableComparable的类可以相互比较。所有被用作key的类应该实现此接口。
* Reporter 则可用于报告整个应用的运行进度，本例中未使用。
* 
* @author Wangbin
*/
public static class Map extends MapReduceBase implements
Mapper<LongWritable, Text, Text, LongWritable> {
/**
* LongWritable, IntWritable, Text 均是 Hadoop 中实现的用于封装 Java
* 数据类型的类，这些类实现了WritableComparable接口，
* 都能够被串行化从而便于在分布式环境中进行数据交换，你可以将它们分别视为long,int,String 的替代品。
*/
private final static LongWritable one = new LongWritable(1);


/**
* Mapper接口中的map方法： void map(K1 key, V1 value, OutputCollector<K2,V2>
* output, Reporter reporter) 映射一个单个的输入k/v对到一个中间的k/v对
* 输出对不需要和输入对是相同的类型，输入对可以映射到0个或多个输出对。
* OutputCollector接口：收集Mapper和Reducer输出的<k,v>对。
* OutputCollector接口的collect(k, v)方法:增加一个(k,v)对到output
*/
public void map(LongWritable key, Text value,
OutputCollector<Text, LongWritable> output, Reporter reporter)
throws IOException {

String line = transformTextToUTF8(value, "GBK").toString();
Text outKey = new Text();
LongWritable outValue = new LongWritable();


String[] arrLine = line.split(";");
outKey.set(arrLine[1]);
 
outValue.set(1);
 
output.collect(outKey, outValue);


}


}


// public static class Combiner extends MapReduceBase implements
// Reducer<Text, LongWritable, Text, LongWritable> {
// public void reduce(Text key, Iterator<LongWritable> values,
// OutputCollector<Text, LongWritable> output, Reporter reporter)
// throws IOException {
// long data = 0;
// long k = 0;
//
// while (values.hasNext()) {
// // String str=values.next().toString();
// k = values.next().get();
// data += k;
// }
// output.collect(key, new LongWritable(data));
// }
// }


public static class Reduce extends MapReduceBase implements
Reducer<Text, LongWritable, Text, LongWritable> {
public void reduce(Text key, Iterator<LongWritable> values,
OutputCollector<Text, LongWritable> output, Reporter reporter)
throws IOException {
long data = 0;
long k = 0;


  while (values.hasNext()) { 
  k = values.next().get();
  data += k;
  }
output.collect(key, new LongWritable(data));
}
}


public static void main(String[] args) throws Exception {
/**
* JobConf：map/reduce的job配置类，向hadoop框架描述map-reduce执行的工作
* 构造方法：JobConf()、JobConf(Class exampleClass)、JobConf(Configuration
* conf)等
*/
// String str="";
System.setProperty("hadoop.home.dir", "D:\\MyEclipse\\hadoop-2.6.4\\");
long starTime = System.currentTimeMillis();
JobConf conf = new JobConf(SDCount.class);
conf.setJobName("-SD-Count-*-\nInputPath:" + args[0]
+ "\nOutputPath:" + args[1]); // 设置一个用户定义的job名称
conf.setOutputKeyClass(Text.class); // 为job的输出数据设置Key类
conf.setOutputValueClass(LongWritable.class); // 为job输出设置value类
conf.setMapperClass(Map.class); // 为job设置Mapper类
conf.setCombinerClass(Reduce.class); // 为job设置归约Combiner类
conf.setReducerClass(Reduce.class); // 为job设置Reduce类
conf.setInputFormat(TextInputFormat.class); // 为map-reduce任务设置InputFormat实现类
conf.setOutputFormat(TextOutputFormat.class); // 为map-reduce任务设置OutputFormat实现类
/**
* InputFormat描述map-reduce中对job的输入定义 setInputPaths():为map-reduce
* job设置路径数组作为输入列表 setInputPath()：为map-reduce job设置路径数组作为输出列表
*/


FileInputFormat.setInputPaths(conf, new Path(args[0]));
FileOutputFormat.setOutputPath(conf, new Path(args[1]));
JobClient.runJob(conf); // 运行一个job
long endTime = System.currentTimeMillis();
long Time = endTime - starTime;
System.out.println("本次执行 共开销 " + Time + " 毫秒");
}


public static Text transformTextToUTF8(Text text, String encoding) {
String value = null;
try {
value = new String(text.getBytes(), 0, text.getLength(), encoding);
} catch (UnsupportedEncodingException e) {
e.printStackTrace();
}
return new Text(value);
}
}