Hadoop的ChainMapper和ChainReducer实战

浏览数：16 / 时间：2015年06月11日

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