mapreduce的一个模版

import java.io.IOException;
import java.text.DateFormat;
import java.text.SimpleDateFormat;
import java.util.Date;import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;/**
 * map就是把key先分出来。系统会自己主动把同样key的value放到一个iterator里面，reduce就是去处理key和已经归并好的iterator
 */
public class Template extends Configured implements Tool {/**
 * 计数器
 * 用于计数各种异常数据
 */
enum Counter
{
LINESKIP,//出错的行
}/**
 * MAP任务
 */
public static class Map extends Mapper<LongWritable, Text, Text, Text> //输入的key（详细是什么由job.setInputFormatClass决定）,输入的value,输出的key,输出的value
{
public void map ( LongWritable key, Text value, Context context ) throws IOException, InterruptedException
{
String line = value.toString();//读取源数据try
{
//数据处理
String [] lineSplit = line.split(" ");
String anum = lineSplit[0];
String bnum = lineSplit[1];context.write( new Text(bnum), new Text(anum) );//输出
}
catch ( java.lang.ArrayIndexOutOfBoundsException e )
{
context.getCounter(Counter.LINESKIP).increment(1);//出错令计数器+1
return;
}
}
}/**
 * REDUCE任务
 */
public static class Reduce extends Reducer<Text, Text, Text, Text>
{
public void reduce ( Text key, Iterable<Text> values, Context context ) throws IOException, InterruptedException
{
String valueString;
String out = "";for ( Text value : values )
{
valueString = value.toString();
out += valueString + "|";
}context.write( key, new Text(out) );
}
}@Override
public int run(String[] args) throws Exception
{
Configuration conf = getConf();Job job = new Job(conf, "Test_2");//任务名
job.setJarByClass(Test_2.class);//指定ClassFileInputFormat.addInputPath( job, new Path(args[0]) );//输入路径
FileOutputFormat.setOutputPath( job, new Path(args[1]) );//输出路径job.setMapperClass( Map.class );//调用上面Map类作为Map任务代码
job.setReducerClass ( Reduce.class );//调用上面Reduce类作为Reduce任务代码,没有这行就调用默认的reduce
job.setOutputFormatClass( TextOutputFormat.class );
job.setOutputKeyClass( Text.class );//指定输出的KEY的格式
job.setOutputValueClass( Text.class );//指定输出的VALUE的格式job.waitForCompletion(true);//输出任务完毕情况
System.out.println( "任务名称：" + job.getJobName() );
System.out.println( "任务成功：" + ( job.isSuccessful()?"是":"否" ) );
System.out.println( "输入行数：" + job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_INPUT_RECORDS").getValue() );
System.out.println( "输出行数：" + job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_OUTPUT_RECORDS").getValue() );
System.out.println( "跳过的行：" + job.getCounters().findCounter(Counter.LINESKIP).getValue() );return job.isSuccessful() ? 0 : 1;
}/**
 * 设置系统说明
 * 设置MapReduce任务
 */
public static void main(String[] args) throws Exception
{//推断參数个数是否正确
//假设无參数执行则显示以作程序说明
if ( args.length != 2 )
{
System.err.println("");
System.err.println("Usage: Test_2 < input path > < output path > ");
System.err.println("Example: hadoop jar ~/Test_2.jar hdfs://localhost:9000/home/james/Test_2 hdfs://localhost:9000/home/james/output");
System.err.println("Counter:");
System.err.println("\t"+"LINESKIP"+"\t"+"Lines which are too short");
System.exit(-1);
}//记录開始时间
DateFormat formatter = new SimpleDateFormat( "yyyy-MM-dd HH:mm:ss" );
Date start = new Date();//执行任务
int res = ToolRunner.run(new Configuration(), new Test_2(), args);//输出任务耗时
Date end = new Date();
float time =  (float) (( end.getTime() - start.getTime() ) / 60000.0) ;
System.out.println( "任务開始：" + formatter.format(start) );
System.out.println( "任务结束：" + formatter.format(end) );
System.out.println( "任务耗时：" + String.valueOf( time ) + " 分钟" );         System.exit(res);
}
}