如何使用Hadoop的ChainMapper和ChainReducer

Hadoop的MR作业支持链式处理,类似在一个生产牛奶的流水线上,每一个阶段都有特定的任务要处理,比如提供牛奶盒,装入牛奶,封盒,打印出厂日期,等等,通过这样进一步的分工,从而提高了生产效率,那么在我们的Hadoop的MapReduce中也是如此,支持链式的处理方式,这些Mapper像Linux管道一样,前一个Mapper的输出结果直接重定向到下一个Mapper的输入,形成一个流水线,而这一点与Lucene和Solr中的Filter机制是非常类似的,Hadoop项目源自Lucene,自然也借鉴了一些Lucene中的处理方式。

举个例子,比如处理文本中的一些禁用词,或者敏感词,等等,Hadoop里的链式操作,支持的形式类似正则Map+ Rrduce Map*,代表的意思是全局只能有一个唯一的Reduce,但是在Reduce的前后是可以存在无限多个Mapper来进行一些预处理或者善后工作的。

下面来看下的散仙今天的测试例子,先看下我们的数据,以及需求。

数据如下:


手机 5000
电脑 2000
衣服 300
鞋子 1200
裙子 434
手套 12
图书 12510
小商品 5
小商品 3
订餐 2

需求是:

/**	
 * 需求:
 * 在第一个Mapper里面过滤大于10000万的数据
 * 第二个Mapper里面过滤掉大于100-10000的数据
 * Reduce里面进行分类汇总并输出
 * Reduce后的Mapper里过滤掉商品名长度大于3的数据
 */

预计处理完的结果是:
手套	12
订餐	2


散仙的hadoop版本是1.2的,在1.2的版本里,hadoop支持新的API,但是链式的ChainMapper类和ChainReduce类却不支持新 的,新的在hadoop2.x里面可以使用,差别不大,散仙今天给出的是旧的API的,需要注意一下。
代码如下:


package com.qin.test.hadoop.chain;

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

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
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.lib.ChainMapper;
import org.apache.hadoop.mapred.lib.ChainReducer;
 

 

import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

import com.qin.reducejoin.NewReduceJoin2;
 

/**
 * 
 * 测试Hadoop里面的
 * ChainMapper和ReduceMapper的使用
 * 
 * @author qindongliang
 * @date 2014年5月7日
 * 
 * 大数据交流群:  376932160
 * 
 * 
 * 
 * 
 * ***/
public class HaoopChain {
	
/**	
 * 需求:
 * 在第一个Mapper里面过滤大于10000万的数据
 * 第二个Mapper里面过滤掉大于100-10000的数据
 * Reduce里面进行分类汇总并输出
 * Reduce后的Mapper里过滤掉商品名长度大于3的数据
 */
	
	
	
	
	/**
	 * 
	 * 过滤掉大于10000万的数据
	 * 
	 * */
	private static class AMapper01 extends MapReduceBase implements  Mapper<LongWritable, Text, Text, Text>{
		
		
	 @Override
	public void map(LongWritable key, Text value, OutputCollector<Text, Text> output, Reporter reporter)
			throws IOException {
			String text=value.toString();
			String texts[]=text.split(" ");
			
		System.out.println("AMapper01里面的数据: "+text);
	    if(texts[1]!=null&&texts[1].length()>0){
		int count=Integer.parseInt(texts[1]);	
		if(count>10000){
			System.out.println("AMapper01过滤掉大于10000数据:  "+value.toString());
			return;
		}else{
			output.collect(new Text(texts[0]), new Text(texts[1]));
			
		}
			
	    }
	}
	}
	

	/**
	 * 
	 * 过滤掉大于100-10000的数据
	 * 
	 * */
	private static class AMapper02 extends MapReduceBase implements  Mapper<Text, Text, Text, Text>{
		
	 @Override
	public void map(Text key, Text value,
			OutputCollector<Text, Text> output, Reporter reporter)
			throws IOException {
		 
		 int count=Integer.parseInt(value.toString());	
			if(count>=100&&count<=10000){
				System.out.println("AMapper02过滤掉的小于10000大于100的数据: "+key+"    "+value);
				return;
			} else{
				
				output.collect(key, value);
			}
		
	}
	} 
	
	
	/**
	 * Reuduce里面对同种商品的
	 * 数量相加数据即可
	 * 
	 * **/
	private static class AReducer03 extends MapReduceBase implements Reducer<Text, Text, Text, Text>{
	 
		@Override
		public void reduce(Text key, Iterator<Text> values,
				OutputCollector<Text, Text> output, Reporter reporter)
				throws IOException {
			int sum=0;
			 System.out.println("进到Reduce里了");
			
			while(values.hasNext()){
				
				Text t=values.next();
				sum+=Integer.parseInt(t.toString());
				
			}
			
			//旧API的集合,不支持foreach迭代
//			for(Text t:values){
//				sum+=Integer.parseInt(t.toString());
//			}
			
			output.collect(key, new Text(sum+""));
			
		}
		
	}
	
	
	/***
	 * 
	 * Reduce之后的Mapper过滤
	 * 过滤掉长度大于3的商品名
	 * 
	 * **/
	
	private static class AMapper04 extends MapReduceBase implements Mapper<Text, Text, Text, Text>{
	 
		@Override
		public void map(Text key, Text value,
				OutputCollector<Text, Text> output, Reporter reporter)
				throws IOException {
			 
			
			int len=key.toString().trim().length();
			
			if(len>=3){
				System.out.println("Reduce后的Mapper过滤掉长度大于3的商品名: "+ key.toString()+"   "+value.toString());
				return ;
			}else{
				output.collect(key, value);
			}
			
		}
		
		
	}
	
	

	 /***
	  * 驱动主类
	  * **/
	public static void main(String[] args) throws Exception{
		 //Job job=new Job(conf,"myjoin");
		 JobConf conf=new JobConf(HaoopChain.class); 
		   conf.set("mapred.job.tracker","192.168.75.130:9001");
		   conf.setJobName("t7");
		    conf.setJar("tt.jar");
		  conf.setJarByClass(HaoopChain.class);
		   
		//  Job job=new Job(conf, "2222222");
		// job.setJarByClass(HaoopChain.class);
		 System.out.println("模式:  "+conf.get("mapred.job.tracker"));;
		 
		// job.setMapOutputKeyClass(Text.class);
		// job.setMapOutputValueClass(Text.class);
		 
		 
		  //Map1的过滤
		 JobConf mapA01=new JobConf(false);
		 ChainMapper.addMapper(conf, AMapper01.class, LongWritable.class, Text.class, Text.class, Text.class, false, mapA01);
		 
		 //Map2的过滤
		 JobConf mapA02=new JobConf(false);
		 ChainMapper.addMapper(conf, AMapper02.class, Text.class, Text.class, Text.class, Text.class, false, mapA02);
		 
		 
		 //设置Reduce
		 JobConf recduceFinallyConf=new JobConf(false);
		 ChainReducer.setReducer(conf, AReducer03.class, Text.class, Text.class, Text.class, Text.class, false, recduceFinallyConf);
		
		 
		//Reduce过后的Mapper过滤
		 JobConf  reduceA01=new  JobConf(false);
		 ChainReducer.addMapper(conf, AMapper04.class, Text.class, Text.class, Text.class, Text.class, true, reduceA01);
		
		
		 conf.setOutputKeyClass(Text.class);
		 conf.setOutputValueClass(Text.class);
 
		 conf.setInputFormat(org.apache.hadoop.mapred.TextInputFormat.class);
		 conf.setOutputFormat(org.apache.hadoop.mapred.TextOutputFormat.class);
	 
		 
		 FileSystem fs=FileSystem.get(conf);
//		 
		 Path op=new Path("hdfs://192.168.75.130:9000/root/outputchain");		 
		 if(fs.exists(op)){
			 fs.delete(op, true);
			 System.out.println("存在此输出路径,已删除!!!");
		 }
//		 
//		 
		  
		 org.apache.hadoop.mapred.FileInputFormat.setInputPaths(conf, new Path("hdfs://192.168.75.130:9000/root/inputchain"));
		 org.apache.hadoop.mapred.FileOutputFormat.setOutputPath(conf, op);
//	   
	  //System.exit(conf.waitForCompletion(true)?0:1);
		JobClient.runJob(conf);
		
		
	}
	
	
	
	

}





运行日志如下:

模式:  192.168.75.130:9001
存在此输出路径,已删除!!!
WARN - JobClient.copyAndConfigureFiles(746) | Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
WARN - NativeCodeLoader.<clinit>(52) | Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
WARN - LoadSnappy.<clinit>(46) | Snappy native library not loaded
INFO - FileInputFormat.listStatus(199) | Total input paths to process : 1
INFO - JobClient.monitorAndPrintJob(1380) | Running job: job_201405072054_0009
INFO - JobClient.monitorAndPrintJob(1393) |  map 0% reduce 0%
INFO - JobClient.monitorAndPrintJob(1393) |  map 50% reduce 0%
INFO - JobClient.monitorAndPrintJob(1393) |  map 100% reduce 0%
INFO - JobClient.monitorAndPrintJob(1393) |  map 100% reduce 33%
INFO - JobClient.monitorAndPrintJob(1393) |  map 100% reduce 100%
INFO - JobClient.monitorAndPrintJob(1448) | Job complete: job_201405072054_0009
INFO - Counters.log(585) | Counters: 30
INFO - Counters.log(587) |   Job Counters 
INFO - Counters.log(589) |     Launched reduce tasks=1
INFO - Counters.log(589) |     SLOTS_MILLIS_MAPS=11357
INFO - Counters.log(589) |     Total time spent by all reduces waiting after reserving slots (ms)=0
INFO - Counters.log(589) |     Total time spent by all maps waiting after reserving slots (ms)=0
INFO - Counters.log(589) |     Launched map tasks=2
INFO - Counters.log(589) |     Data-local map tasks=2
INFO - Counters.log(589) |     SLOTS_MILLIS_REDUCES=9972
INFO - Counters.log(587) |   File Input Format Counters 
INFO - Counters.log(589) |     Bytes Read=183
INFO - Counters.log(587) |   File Output Format Counters 
INFO - Counters.log(589) |     Bytes Written=19
INFO - Counters.log(587) |   FileSystemCounters
INFO - Counters.log(589) |     FILE_BYTES_READ=57
INFO - Counters.log(589) |     HDFS_BYTES_READ=391
INFO - Counters.log(589) |     FILE_BYTES_WRITTEN=174859
INFO - Counters.log(589) |     HDFS_BYTES_WRITTEN=19
INFO - Counters.log(587) |   Map-Reduce Framework
INFO - Counters.log(589) |     Map output materialized bytes=63
INFO - Counters.log(589) |     Map input records=10
INFO - Counters.log(589) |     Reduce shuffle bytes=63
INFO - Counters.log(589) |     Spilled Records=8
INFO - Counters.log(589) |     Map output bytes=43
INFO - Counters.log(589) |     Total committed heap usage (bytes)=336338944
INFO - Counters.log(589) |     CPU time spent (ms)=1940
INFO - Counters.log(589) |     Map input bytes=122
INFO - Counters.log(589) |     SPLIT_RAW_BYTES=208
INFO - Counters.log(589) |     Combine input records=0
INFO - Counters.log(589) |     Reduce input records=4
INFO - Counters.log(589) |     Reduce input groups=3
INFO - Counters.log(589) |     Combine output records=0
INFO - Counters.log(589) |     Physical memory (bytes) snapshot=460980224
INFO - Counters.log(589) |     Reduce output records=2
INFO - Counters.log(589) |     Virtual memory (bytes) snapshot=2184105984
INFO - Counters.log(589) |     Map output records=4




产生的数据如下:



如何使用Hadoop的ChainMapper和ChainReducer



总结,测试过程中,发现如果Reduce后面,还有Mapper执行,那么注意一定要,在ChainReducer里面先set一个全局唯一的Reducer,然后再add一个Mapper,否则,在运行的时候,会报空指针异常,这一点需要特别注意!






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