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Hadoop MapReduce链式实践--ChainReducer

版本:CDH5.0.0,HDFS:2.3.0,Mapreduce:2.3.0,Yarn:2.3.0。

场景描述:求一组数据中按照不同类别的最大值,比如,如下的数据:

data1:

A,10
A,11
A,12
A,13
B,21
B,31
B,41
B,51
data2:
A,20
A,21
A,22
A,23
B,201
B,301
B,401
B,501
最后输出为:

A,23
B,501
假如这样的逻辑的mapreduce数据流如下:

假设C组数据比较多,同时假设集群有2个节点,那么这个任务分配2个reducer,且C组数据平均分布到两个reducer中,(这样做是为了效率考虑,如果只有一个reducer,那么当一个节点在运行reducer的时候另外一个节点会处于空闲状态)那么如果在reducer之后,还可以再次做一个reducer,那么不就可以整合数据到一个文件了么,同时还可以再次比较C组数据中,以得到真正比较大的数据。

首先说下,不用上面假设的方式进行操作,那么一般的操作方法。一般有两种方法:其一,直接读出HDFS数据,然后进行整合;其二,新建另外一个Job来进行整合。这两种方法,如果就效率来说的话,可能第一种效率会高点。

考虑到前面提出的mapreduce数据流,以前曾对ChainReducer有点印象,好像可以做这个,所以就拿ChainReducer来试,同时为了学多点知识,也是用了多个Mapper(即使用ChainMapper)。

主程序代码如下:

package chain;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.apache.hadoop.mapred.lib.ChainMapper;
import org.apache.hadoop.mapred.lib.ChainReducer;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class ChainDriver2 extends Configured implements Tool{

	/**
	 * ChainReducer 实战
	 * 验证多个reducer的整合
	 * 逻辑:寻找最大值
	 * @param args
	 */
	
	private String input=null;
	private String output=null;
	private String delimiter=null;
	private int reducer=1;
	public static void main(String[] args) throws Exception {
		ToolRunner.run(new Configuration(), new ChainDriver2(),args);
	}
	
	@Override
	public int run(String[] arg0) throws Exception {
		configureArgs(arg0);
		checkArgs();
		Configuration conf = getConf();
		conf.set("delimiter", delimiter);
		JobConf  job= new JobConf(conf,ChainDriver2.class);
		
		ChainMapper.addMapper(job, MaxMapper.class, LongWritable.class,
				Text.class, Text.class, IntWritable.class, true, new JobConf(false)) ;
		
		ChainMapper.addMapper(job, MergeMaxMapper.class, Text.class,
				IntWritable.class, Text.class, IntWritable.class, true, new JobConf(false));
		
		ChainReducer.setReducer(job, MaxReducer.class, Text.class, IntWritable.class,
				Text.class, IntWritable.class, true, new JobConf(false));
		ChainReducer.addMapper(job, MergeMaxMapper.class, Text.class,
				IntWritable.class, Text.class, IntWritable.class, false, new JobConf(false));
		job.setJarByClass(ChainDriver2.class);
		job.setJobName("ChainReducer test job");
		
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        
       /* job.setMapperClass(MaxMapper.class);
        job.setReducerClass(MaxReducer.class);*/
        job.setInputFormat(TextInputFormat.class);;
        job.setOutputFormat(TextOutputFormat.class);
        job.setNumReduceTasks(reducer);
        
        FileInputFormat.addInputPath(job, new Path(input));
        FileOutputFormat.setOutputPath(job, new Path(output));
        
        JobClient.runJob(job);
		return 0;
	}
	
	
	/**
	 * check the args 
	 */
	private void checkArgs() {
		if(input==null||"".equals(input)){
			System.out.println("no input...");
			printUsage();
			System.exit(-1);
		}
		if(output==null||"".equals(output)){
			System.out.println("no output...");
			printUsage();
			System.exit(-1);
		}
		if(delimiter==null||"".equals(delimiter)){
			System.out.println("no delimiter...");
			printUsage();
			System.exit(-1);
		}
		if(reducer==0){
			System.out.println("no reducer...");
			printUsage();
			System.exit(-1);
		}
	}

	/**
	 * configuration the args
	 * @param args
	 */
	private void configureArgs(String[] args) {
    	for(int i=0;i<args.length;i++){
    		if("-i".equals(args[i])){
    			input=args[++i];
    		}
    		if("-o".equals(args[i])){
    			output=args[++i];
    		}
    		
    		if("-delimiter".equals(args[i])){
    			delimiter=args[++i];
    		}
    		if("-reducer".equals(args[i])){
    			try {
    				reducer=Integer.parseInt(args[++i]);
				} catch (Exception e) {
					reducer=0;
				}
    		}
    	}
	}
	public static void printUsage(){
    	System.err.println("Usage:");
    	System.err.println("-i input \t cell data path.");
    	System.err.println("-o output \t output data path.");
    	System.err.println("-delimiter  data delimiter , default is blanket  .");
    	System.err.println("-reducer  reducer number , default is 1  .");
    }
	
}

MaxMapper:

package chain;

import java.io.IOException;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
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.Reporter;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class MaxMapper extends MapReduceBase implements Mapper<LongWritable ,Text,Text,IntWritable>{
	private Logger log = LoggerFactory.getLogger(MaxMapper.class);
	private String delimiter=null;
	@Override
	public void configure(JobConf conf){
		delimiter=conf.get("delimiter");
		log.info("delimiter:"+delimiter);
		log.info("This is the begin of MaxMapper");
	}
	
	@Override
	public void map(LongWritable key, Text value,
			OutputCollector<Text, IntWritable> out, Reporter reporter)
			throws IOException {
		// TODO Auto-generated method stub
		String[] values= value.toString().split(delimiter);
		log.info(values[0]+"-->"+values[1]);
		out.collect(new Text(values[0]), new IntWritable(Integer.parseInt(values[1])));
		
	}
	public void close(){
		log.info("This is the end of MaxMapper");
	}
}

MaxReducer:

package chain;

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

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public   class MaxReducer extends MapReduceBase implements Reducer<Text,IntWritable,Text,IntWritable>{
	private Logger log = LoggerFactory.getLogger(MaxReducer.class);
	@Override
	public void configure(JobConf conf){
		log.info("This is the begin of the MaxReducer");
	}
	@Override
	public void reduce(Text key, Iterator<IntWritable> values,
			OutputCollector<Text, IntWritable> out, Reporter reporter)
			throws IOException {
		// TODO Auto-generated method stub
		int max=-1;
		while(values.hasNext()){
			int value=http://www.mamicode.com/values.next().get();>
MergeMaxMapper:

package chain;

import java.io.IOException;
//import java.util.ArrayList;
//import java.util.HashMap;
//import java.util.Map;

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
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.Reporter;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

public class MergeMaxMapper extends MapReduceBase implements Mapper<Text ,IntWritable,Text,IntWritable>{
	private Logger log = LoggerFactory.getLogger(MergeMaxMapper.class);
//	private Map<Text,ArrayList<IntWritable>> outMap= new HashMap<Text,ArrayList<IntWritable>>();
	@Override
	public void configure(JobConf conf){
		log.info("This is the begin of MergeMaxMapper");
	}
	
	@Override
	public void map(Text key, IntWritable value,
			OutputCollector<Text, IntWritable> out, Reporter reporter)
			throws IOException {
		log.info(key.toString()+"_MergeMaxMapper"+"-->"+value.get());
		out.collect(new Text(key.toString()+"_MergeMaxMapper"), value);
		
	}
	
	@Override
	public void close(){
		log.info("this is the end of MergeMaxMapper");
	}
}

编程思路如下:原始测试数据data1、data2首先经过MaxMapper(由于两个文件,所以生成了2个map),然后经过MergeMaxMapper,到MaxReducer,最后再次经过MergeMaxMapper。

在程序中添加了输出数据的log,可以通过log来查看各个map和reduce的数据流程。

mapper端的log(其中的一个mapper):

2014-05-14 17:23:51,307 INFO [main] chain.MaxMapper: delimiter:,
2014-05-14 17:23:51,307 INFO [main] chain.MaxMapper: This is the begin of MaxMapper
2014-05-14 17:23:51,454 INFO [main] chain.MergeMaxMapper: This is the begin of MergeMaxMapper
2014-05-14 17:23:51,471 INFO [main] chain.MaxMapper: A-->20
2014-05-14 17:23:51,476 INFO [main] chain.MergeMaxMapper: A_MergeMaxMapper-->20
2014-05-14 17:23:51,476 INFO [main] chain.MaxMapper: A-->21
2014-05-14 17:23:51,477 INFO [main] chain.MergeMaxMapper: A_MergeMaxMapper-->21
2014-05-14 17:23:51,477 INFO [main] chain.MaxMapper: A-->22
2014-05-14 17:23:51,477 INFO [main] chain.MergeMaxMapper: A_MergeMaxMapper-->22
2014-05-14 17:23:51,477 INFO [main] chain.MaxMapper: A-->23
2014-05-14 17:23:51,477 INFO [main] chain.MergeMaxMapper: A_MergeMaxMapper-->23
2014-05-14 17:23:51,477 INFO [main] chain.MaxMapper: B-->201
2014-05-14 17:23:51,477 INFO [main] chain.MergeMaxMapper: B_MergeMaxMapper-->201
2014-05-14 17:23:51,477 INFO [main] chain.MaxMapper: B-->301
2014-05-14 17:23:51,477 INFO [main] chain.MergeMaxMapper: B_MergeMaxMapper-->301
2014-05-14 17:23:51,478 INFO [main] chain.MaxMapper: B-->401
2014-05-14 17:23:51,478 INFO [main] chain.MergeMaxMapper: B_MergeMaxMapper-->401
2014-05-14 17:23:51,478 INFO [main] chain.MaxMapper: B-->501
2014-05-14 17:23:51,478 INFO [main] chain.MergeMaxMapper: B_MergeMaxMapper-->501
2014-05-14 17:23:51,481 INFO [main] chain.MaxMapper: This is the end of MaxMapper
2014-05-14 17:23:51,481 INFO [main] chain.MergeMaxMapper: this is the end of MergeMaxMapper

通过上面log,可以看出,通过ChainMapper添加mapper的方式的mapper的处理顺序为:首先初始化第一个mapper(即调用configure方法);接着初始第二个mapper(调用configure方法);然后开始map函数,map函数针对一条记录,首先采用mapper1进行处理,然后使用mapper2进行处理;最后是关闭阶段,关闭的顺序同样是首先关闭mapper1(调用close方法),然后关闭mapper2。

reducer端的log(其中一个reducer)

2014-05-14 17:24:10,171 INFO [main] chain.MergeMaxMapper: This is the begin of MergeMaxMapper
2014-05-14 17:24:10,311 INFO [main] chain.MaxReducer: This is the begin of the MaxReducer
2014-05-14 17:24:10,671 INFO [main] chain.MaxReducer: B_MergeMaxMapper-->501
2014-05-14 17:24:10,672 INFO [main] chain.MergeMaxMapper: B_MergeMaxMapper_MergeMaxMapper-->501
2014-05-14 17:24:10,673 INFO [main] chain.MergeMaxMapper: this is the end of MergeMaxMapper
2014-05-14 17:24:10,673 INFO [main] chain.MaxReducer: This is the end of the MaxReducer

通过上面的log可以看出,通过ChainReducer添加mapper的方式,其数据处理顺序为:首先初始化Reducer之后的Mapper,接着初始化Reducer(看configure函数即可知道);然后处理reducer,reducer的输出接着交给mapper处理;最后先关闭Mapper,接着关闭reducer。

同时,注意到,reducer后面的mapper也是两个的,即有多少个reducer,就有多少个mapper。

通过实验得到上面的ChainReducer的数据处理流程,且ChainReducer没有addReducer的方法,也即是不能添加reducer了,那么最开始提出的mapreduce数据流程就不能采用这种方式实现了。


最后,前面提出的mapreduce数据流程应该是错的,在reducer out里面C组数据不会被拆分为两个reducer,相同的key只会向同一个reducer传输。这里同样做了个试验,通过对接近90M的数据(只有一个分组A)执行上面的程序,可以看到有2个mapper,2个reducer(此数值为设置值),但是在其中一个reducer中并没有A分组的任何数据,在另外一个reducer中才有数据。其实,不用试验也是可以的,以前看的书上一般都会说相同的key进入同一个reducer中。不过,如果是这样的话,那么这样的数据效率应该不高。


返回最开始提出的场景,最开始提出的问题,如果相同的key只会进入一个reducer中,那么最后的2个数据文件(2个reducer生成2个数据文件)其实里面不会有key冲突的数据,所以在进行后面的操作的时候可以直接读多个文件即可,就像是读一个文件一样。

会产生这样的认知错误,应该是对mapreduce 原理不清楚导致。


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