Hadoop的Map侧join -

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Hadoop的Map侧join

博客分类：

hadoop/spark/Hbase/Hive/pig/Zookeeper/HAWQ/cloudera/Impala/Oozie

hadoop

写了关于Hadoop的Map侧join
和Reduce的join，今天我们就来在看另外一种比较中立的Join。

SemiJoin，一般称为半链接，其原理是在Map侧过滤掉了一些不需要join的数据，从而大大减少了reduce的shffule时间，因为我们知道，如果仅仅使用Reduce侧连接，那么如果一份数据中，存在大量的无效数据，而这些数据，在join中，并不需要，但是因为没有做过预处理，所以这些数据，直到真正的执行reduce函数时，才被定义为无效数据，而这时候，前面已经执行过shuffle和merge和sort，所以这部分无效的数据，就浪费了大量的网络IO和磁盘IO，所以在整体来讲，这是一种降低性能的表现，如果存在的无效数据越多，那么这种趋势，就越明显。

之所以会出现半连接，这其实也是reduce侧连接的一个变种，只不过我们在Map侧，过滤掉了一些无效的数据，所以减少了reduce过程的shuffle时间，所以能获取一个性能的提升。

具体的原理也是利用DistributedCache将小表的的分发到各个节点上，在Map过程的setup函数里，读取缓存里面的文件，只将小表的链接键存储在hashset里，在map函数执行时，对每一条数据，进行判断，如果这条数据的链接键为空或者在hashset里面不存在，那么则认为这条数据，是无效的数据，所以这条数据，并不会被partition分区后写入磁盘，参与reduce阶段的shuffle和sort，所以在一定程序上，提升了join性能。需要注意的是如果
小表的key依然非常巨大，可能会导致我们的程序出现OOM的情况，那么这时候我们就需要考虑其他的链接方式了。

测试数据如下：
模拟小表数据：
1,三劫散仙,13575468248
2,凤舞九天,18965235874
3,忙忙碌碌,15986854789
4,少林寺方丈,15698745862

模拟大表数据：
3,A,99,2013-03-05
1,B,89,2013-02-05
2,C,69,2013-03-09
3,D,56,2013-06-07
5,E,100,2013-09-09
6,H,200,2014-01-10

代码如下：

Java代码

package com.semijoin;
import java.io.BufferedReader;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.FileReader;
import java.io.IOException;
import java.net.URI;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.filecache.DistributedCache;
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.io.WritableComparable;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
/***
*
* Hadoop1.2的版本
*
* hadoop的半链接
*
* SemiJoin实现
*
* @author qindongliang
*
* 大数据交流群：376932160
* 搜索技术交流群：324714439
*
*
*
* **/
public class Semjoin {
/**
*
*
* 自定义一个输出实体
*
* **/
private static class CombineEntity implements WritableComparable<CombineEntity>{
private Text joinKey;//连接key
private Text flag;//文件来源标志
private Text secondPart;//除了键外的其他部分的数据
public CombineEntity() {
// TODO Auto-generated constructor stub
this.joinKey=new Text();
this.flag=new Text();
this.secondPart=new Text();
}
public Text getJoinKey() {
return joinKey;
}
public void setJoinKey(Text joinKey) {
this.joinKey = joinKey;
}
public Text getFlag() {
return flag;
}
public void setFlag(Text flag) {
this.flag = flag;
}
public Text getSecondPart() {
return secondPart;
}
public void setSecondPart(Text secondPart) {
this.secondPart = secondPart;
}
@Override
public void readFields(DataInput in) throws IOException {
this.joinKey.readFields(in);
this.flag.readFields(in);
this.secondPart.readFields(in);
}
@Override
public void write(DataOutput out) throws IOException {
this.joinKey.write(out);
this.flag.write(out);
this.secondPart.write(out);
}
@Override
public int compareTo(CombineEntity o) {
// TODO Auto-generated method stub
return this.joinKey.compareTo(o.joinKey);
}
}
private static class JMapper extends Mapper<LongWritable, Text, Text, CombineEntity>{
private CombineEntity combine=new CombineEntity();
private Text flag=new Text();
private Text joinKey=new Text();
private Text secondPart=new Text();
/**
* 存储小表的key
*
*
* */
private HashSet<String> joinKeySet=new HashSet<String>();
@Override
protected void setup(Context context)throws IOException, InterruptedException {
//读取文件流
BufferedReader br=null;
String temp;
// 获取DistributedCached里面的共享文件
Path path[]=DistributedCache.getLocalCacheFiles(context.getConfiguration());
for(Path p:path){
if(p.getName().endsWith("a.txt")){
br=new BufferedReader(new FileReader(p.toString()));
//List<String> list=Files.readAllLines(Paths.get(p.getName()), Charset.forName("UTF-8"));
while((temp=br.readLine())!=null){
String ss[]=temp.split(",");
//map.put(ss[0], ss[1]+"\t"+ss[2]);//放入hash表中
joinKeySet.add(ss[0]);//加入小表的key
}
}
}
}
@Override
protected void map(LongWritable key, Text value,Context context)
throws IOException, InterruptedException {
//获得文件输入路径
String pathName = ((FileSplit) context.getInputSplit()).getPath().toString();
if(pathName.endsWith("a.txt")){
String valueItems[]=value.toString().split(",");
/**
* 在这里过滤必须要的连接字符
*
* */
if(joinKeySet.contains(valueItems[0])){
//设置标志位
flag.set("0");
//设置链接键
joinKey.set(valueItems[0]);
//设置第二部分
secondPart.set(valueItems[1]+"\t"+valueItems[2]);
//封装实体
combine.setFlag(flag);//标志位
combine.setJoinKey(joinKey);//链接键
combine.setSecondPart(secondPart);//其他部分
//写出
context.write(combine.getJoinKey(), combine);
}else{
System.out.println("a.txt里");
System.out.println("在小表中无此记录，执行过滤掉！");
for(String v:valueItems){
System.out.print(v+" ");
}
return ;
}
}else if(pathName.endsWith("b.txt")){
String valueItems[]=value.toString().split(",");
/**
*
* 判断是否在集合中
*
* */
if(joinKeySet.contains(valueItems[0])){
//设置标志位
flag.set("1");
//设置链接键
joinKey.set(valueItems[0]);
//设置第二部分注意不同的文件的列数不一样
secondPart.set(valueItems[1]+"\t"+valueItems[2]+"\t"+valueItems[3]);
//封装实体
combine.setFlag(flag);//标志位
combine.setJoinKey(joinKey);//链接键
combine.setSecondPart(secondPart);//其他部分
//写出
context.write(combine.getJoinKey(), combine);
}else{
//执行过滤 ......
System.out.println("b.txt里");
System.out.println("在小表中无此记录，执行过滤掉！");
for(String v:valueItems){
System.out.print(v+" ");
}
return ;
}
}
}
}
private static class JReduce extends Reducer<Text, CombineEntity, Text, Text>{
//存储一个分组中左表信息
private List<Text> leftTable=new ArrayList<Text>();
//存储一个分组中右表信息
private List<Text> rightTable=new ArrayList<Text>();
private Text secondPart=null;
private Text output=new Text();
//一个分组调用一次
@Override
protected void reduce(Text key, Iterable<CombineEntity> values,Context context)
throws IOException, InterruptedException {
leftTable.clear();//清空分组数据
rightTable.clear();//清空分组数据
/**
* 将不同文件的数据，分别放在不同的集合
* 中，注意数据量过大时，会出现
* OOM的异常
*
* **/
for(CombineEntity ce:values){
this.secondPart=new Text(ce.getSecondPart().toString());
//左表
if(ce.getFlag().toString().trim().equals("0")){
leftTable.add(secondPart);
}else if(ce.getFlag().toString().trim().equals("1")){
rightTable.add(secondPart);
}
}
//=====================
for(Text left:leftTable){
for(Text right:rightTable){
output.set(left+"\t"+right);//连接左右数据
context.write(key, output);//输出
}
}
}
}
public static void main(String[] args)throws Exception {
//Job job=new Job(conf,"myjoin");
JobConf conf=new JobConf(Semjoin.class);
conf.set("mapred.job.tracker","192.168.75.130:9001");
conf.setJar("tt.jar");
//小表共享
String bpath="hdfs://192.168.75.130:9000/root/dist/a.txt";
//添加到共享cache里
DistributedCache.addCacheFile(new URI(bpath), conf);
Job job=new Job(conf, "aaaaa");
job.setJarByClass(Semjoin.class);
System.out.println("模式： "+conf.get("mapred.job.tracker"));;
//设置Map和Reduce自定义类
job.setMapperClass(JMapper.class);
job.setReducerClass(JReduce.class);
//设置Map端输出
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(CombineEntity.class);
//设置Reduce端的输出
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileSystem fs=FileSystem.get(conf);
Path op=new Path("hdfs://192.168.75.130:9000/root/outputjoindbnew4");
if(fs.exists(op)){
fs.delete(op, true);
System.out.println("存在此输出路径，已删除！！！");
}
FileInputFormat.setInputPaths(job, new Path("hdfs://192.168.75.130:9000/root/inputjoindb"));
FileOutputFormat.setOutputPath(job, op);
System.exit(job.waitForCompletion(true)?0:1);
}
}

package com.semijoin;

import java.io.BufferedReader;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.FileReader;
import java.io.IOException;
import java.net.URI;
import java.util.ArrayList;
import java.util.HashSet;
import java.util.List;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.filecache.DistributedCache;
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.io.WritableComparable;

import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

/***
 * 
 * Hadoop1.2的版本
 * 
 * hadoop的半链接
 * 
 * SemiJoin实现
 * 
 * @author qindongliang
 * 
 *    大数据交流群：376932160
 *  搜索技术交流群：324714439
 * 
 * 
 * 
 * **/
public class Semjoin {
	
	
	
	/**
	 * 
	 * 
	 * 自定义一个输出实体
	 * 
	 * **/
	private static class CombineEntity implements WritableComparable<CombineEntity>{

		
		private Text joinKey;//连接key
		private Text flag;//文件来源标志
		private Text secondPart;//除了键外的其他部分的数据
		
		
		public CombineEntity() {
			// TODO Auto-generated constructor stub
			this.joinKey=new Text();
			this.flag=new Text();
			this.secondPart=new Text();
		}
		
		public Text getJoinKey() {
			return joinKey;
		}

		public void setJoinKey(Text joinKey) {
			this.joinKey = joinKey;
		}

		public Text getFlag() {
			return flag;
		}

		public void setFlag(Text flag) {
			this.flag = flag;
		}

		public Text getSecondPart() {
			return secondPart;
		}

		public void setSecondPart(Text secondPart) {
			this.secondPart = secondPart;
		}

		@Override
		public void readFields(DataInput in) throws IOException {
			this.joinKey.readFields(in);
			this.flag.readFields(in);
			this.secondPart.readFields(in);
			
		}

		@Override
		public void write(DataOutput out) throws IOException {
			this.joinKey.write(out);
			this.flag.write(out);
			this.secondPart.write(out);
			
		}

		@Override
		public int compareTo(CombineEntity o) {
			// TODO Auto-generated method stub
			return this.joinKey.compareTo(o.joinKey);
		}
		
		
		
	}
	
	
	
	
	private static class JMapper extends Mapper<LongWritable, Text, Text, CombineEntity>{
		
		private CombineEntity combine=new CombineEntity();
		private Text flag=new Text();
		private  Text joinKey=new Text();
		private Text secondPart=new Text();
		/**
		 * 存储小表的key
		 * 
		 * 
		 * */
		private HashSet<String> joinKeySet=new HashSet<String>();
		
		
		@Override
		protected void setup(Context context)throws IOException, InterruptedException {
		 
			//读取文件流
			BufferedReader br=null;
			String temp;
			// 获取DistributedCached里面 的共享文件
			Path path[]=DistributedCache.getLocalCacheFiles(context.getConfiguration());
			
			for(Path p:path){
				
				if(p.getName().endsWith("a.txt")){
					br=new BufferedReader(new FileReader(p.toString()));
					//List<String> list=Files.readAllLines(Paths.get(p.getName()), Charset.forName("UTF-8"));
					
					while((temp=br.readLine())!=null){
						String ss[]=temp.split(",");
						//map.put(ss[0], ss[1]+"\t"+ss[2]);//放入hash表中
						joinKeySet.add(ss[0]);//加入小表的key
					}
				}
			}
			
			
		}
		
		
		
		@Override
		protected void map(LongWritable key, Text value,Context context)
				throws IOException, InterruptedException {
			
		
			   //获得文件输入路径
            String pathName = ((FileSplit) context.getInputSplit()).getPath().toString();
		
            if(pathName.endsWith("a.txt")){
            	
            	String  valueItems[]=value.toString().split(",");
            	
            	
            	/**
            	 * 在这里过滤必须要的连接字符
            	 * 
            	 * */
            	if(joinKeySet.contains(valueItems[0])){
            		//设置标志位
                	flag.set("0");   
                	//设置链接键
                	joinKey.set(valueItems[0]);          
                	//设置第二部分
                	secondPart.set(valueItems[1]+"\t"+valueItems[2]);
                	
                	//封装实体
                	combine.setFlag(flag);//标志位
                	combine.setJoinKey(joinKey);//链接键
                	combine.setSecondPart(secondPart);//其他部分
                	
                	 //写出
                	context.write(combine.getJoinKey(), combine);	
            	}else{
            		System.out.println("a.txt里");
            		System.out.println("在小表中无此记录，执行过滤掉！");
            		for(String v:valueItems){
            			System.out.print(v+"   ");
            		}
            		
            		return ;
            		
            	}
            	
            	
            	
            }else if(pathName.endsWith("b.txt")){
            	String  valueItems[]=value.toString().split(",");
            	
            	/**
            	 * 
            	 * 判断是否在集合中
            	 * 
            	 * */
            	if(joinKeySet.contains(valueItems[0])){
            		

                	//设置标志位
                	flag.set("1");   
                	
                	//设置链接键
                	joinKey.set(valueItems[0]);
          
                	//设置第二部分注意不同的文件的列数不一样
                	secondPart.set(valueItems[1]+"\t"+valueItems[2]+"\t"+valueItems[3]);
                	
                	//封装实体
                	combine.setFlag(flag);//标志位
                	combine.setJoinKey(joinKey);//链接键
                	combine.setSecondPart(secondPart);//其他部分
                	
                	 //写出
                	context.write(combine.getJoinKey(), combine);
            		
            		
            	}else{            		
            		//执行过滤 ......
            		System.out.println("b.txt里");
            		System.out.println("在小表中无此记录，执行过滤掉！");
            		for(String v:valueItems){
            			System.out.print(v+"   ");
            		}
            		
            		return ;
            		
            		
            	}
            	
            
            	
            	
            }
            
            
			
			
			 
		 
			
		}
		
	}
	
	
	private static class JReduce extends Reducer<Text, CombineEntity, Text, Text>{
		
		
		//存储一个分组中左表信息
		private List<Text> leftTable=new ArrayList<Text>();
		//存储一个分组中右表信息
		private List<Text> rightTable=new ArrayList<Text>();
		
		private Text secondPart=null;
		
		private Text output=new Text();
		
		
		 
		//一个分组调用一次
		@Override
		protected void reduce(Text key, Iterable<CombineEntity> values,Context context)
				throws IOException, InterruptedException {
			 leftTable.clear();//清空分组数据
			 rightTable.clear();//清空分组数据
			 
			 
			 /**
			  * 将不同文件的数据，分别放在不同的集合
			  * 中，注意数据量过大时，会出现
			  * OOM的异常
			  * 
			  * **/
			 
			 for(CombineEntity ce:values){
				 
				 this.secondPart=new Text(ce.getSecondPart().toString());
				 
				 
				 //左表
				 
				 if(ce.getFlag().toString().trim().equals("0")){
					 leftTable.add(secondPart);
					 
				 }else if(ce.getFlag().toString().trim().equals("1")){
					 
					 rightTable.add(secondPart);
					 
				 }
				 
				 
				 
				 
			 }
			 
			 //=====================
			 for(Text left:leftTable){
				 
				 for(Text right:rightTable){
					 
					 output.set(left+"\t"+right);//连接左右数据
					 context.write(key, output);//输出
				 }
				 
			 }
			 
			 
			 
			
		}
		
	}
	
	
	
	
	
	
	
	
	public static void main(String[] args)throws Exception {
		
		 
	
	
		 //Job job=new Job(conf,"myjoin");
		 JobConf conf=new JobConf(Semjoin.class); 
		   conf.set("mapred.job.tracker","192.168.75.130:9001");
		    conf.setJar("tt.jar");
		  
		  
			//小表共享
			String bpath="hdfs://192.168.75.130:9000/root/dist/a.txt";
			//添加到共享cache里
		DistributedCache.addCacheFile(new URI(bpath), conf);
		
		  Job job=new Job(conf, "aaaaa");
		 job.setJarByClass(Semjoin.class);
		 System.out.println("模式：  "+conf.get("mapred.job.tracker"));;
		 
		 
		 //设置Map和Reduce自定义类
		 job.setMapperClass(JMapper.class);
		 job.setReducerClass(JReduce.class);
		 
		 //设置Map端输出
		 job.setMapOutputKeyClass(Text.class);
		 job.setMapOutputValueClass(CombineEntity.class);
		 
		 //设置Reduce端的输出
		 job.setOutputKeyClass(Text.class);
		 job.setOutputValueClass(Text.class);
		 
	
		 job.setInputFormatClass(TextInputFormat.class);
		 job.setOutputFormatClass(TextOutputFormat.class);
		 
	 
		 FileSystem fs=FileSystem.get(conf);
		 
		 Path op=new Path("hdfs://192.168.75.130:9000/root/outputjoindbnew4");
		 
		 if(fs.exists(op)){
			 fs.delete(op, true);
			 System.out.println("存在此输出路径，已删除！！！");
		 }
		 
		 
	  FileInputFormat.setInputPaths(job, new Path("hdfs://192.168.75.130:9000/root/inputjoindb"));
	  FileOutputFormat.setOutputPath(job, op);
	   
	  System.exit(job.waitForCompletion(true)?0:1);
	  
	  
		 
		 
		 
		 
		 
		 
		 
				
				 
		
		
		
	}
	
	
	

}

运行日志如下：

Java代码

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.
INFO - FileInputFormat.listStatus(237) | Total input paths to process : 2
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 - JobClient.monitorAndPrintJob(1380) | Running job: job_201404260312_0002
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_201404260312_0002
INFO - Counters.log(585) | Counters: 29
INFO - Counters.log(587) | Job Counters
INFO - Counters.log(589) | Launched reduce tasks=1
INFO - Counters.log(589) | SLOTS_MILLIS_MAPS=12445
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=9801
INFO - Counters.log(587) | File Output Format Counters
INFO - Counters.log(589) | Bytes Written=172
INFO - Counters.log(587) | FileSystemCounters
INFO - Counters.log(589) | FILE_BYTES_READ=237
INFO - Counters.log(589) | HDFS_BYTES_READ=455
INFO - Counters.log(589) | FILE_BYTES_WRITTEN=169503
INFO - Counters.log(589) | HDFS_BYTES_WRITTEN=172
INFO - Counters.log(587) | File Input Format Counters
INFO - Counters.log(589) | Bytes Read=227
INFO - Counters.log(587) | Map-Reduce Framework
INFO - Counters.log(589) | Map output materialized bytes=243
INFO - Counters.log(589) | Map input records=10
INFO - Counters.log(589) | Reduce shuffle bytes=243
INFO - Counters.log(589) | Spilled Records=16
INFO - Counters.log(589) | Map output bytes=215
INFO - Counters.log(589) | Total committed heap usage (bytes)=336338944
INFO - Counters.log(589) | CPU time spent (ms)=1770
INFO - Counters.log(589) | Combine input records=0
INFO - Counters.log(589) | SPLIT_RAW_BYTES=228
INFO - Counters.log(589) | Reduce input records=8
INFO - Counters.log(589) | Reduce input groups=4
INFO - Counters.log(589) | Combine output records=0
INFO - Counters.log(589) | Physical memory (bytes) snapshot=442564608
INFO - Counters.log(589) | Reduce output records=4
INFO - Counters.log(589) | Virtual memory (bytes) snapshot=2184306688
INFO - Counters.log(589) | Map output records=8

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.
INFO - FileInputFormat.listStatus(237) | Total input paths to process : 2
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 - JobClient.monitorAndPrintJob(1380) | Running job: job_201404260312_0002
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_201404260312_0002
INFO - Counters.log(585) | Counters: 29
INFO - Counters.log(587) |   Job Counters 
INFO - Counters.log(589) |     Launched reduce tasks=1
INFO - Counters.log(589) |     SLOTS_MILLIS_MAPS=12445
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=9801
INFO - Counters.log(587) |   File Output Format Counters 
INFO - Counters.log(589) |     Bytes Written=172
INFO - Counters.log(587) |   FileSystemCounters
INFO - Counters.log(589) |     FILE_BYTES_READ=237
INFO - Counters.log(589) |     HDFS_BYTES_READ=455
INFO - Counters.log(589) |     FILE_BYTES_WRITTEN=169503
INFO - Counters.log(589) |     HDFS_BYTES_WRITTEN=172
INFO - Counters.log(587) |   File Input Format Counters 
INFO - Counters.log(589) |     Bytes Read=227
INFO - Counters.log(587) |   Map-Reduce Framework
INFO - Counters.log(589) |     Map output materialized bytes=243
INFO - Counters.log(589) |     Map input records=10
INFO - Counters.log(589) |     Reduce shuffle bytes=243
INFO - Counters.log(589) |     Spilled Records=16
INFO - Counters.log(589) |     Map output bytes=215
INFO - Counters.log(589) |     Total committed heap usage (bytes)=336338944
INFO - Counters.log(589) |     CPU time spent (ms)=1770
INFO - Counters.log(589) |     Combine input records=0
INFO - Counters.log(589) |     SPLIT_RAW_BYTES=228
INFO - Counters.log(589) |     Reduce input records=8
INFO - Counters.log(589) |     Reduce input groups=4
INFO - Counters.log(589) |     Combine output records=0
INFO - Counters.log(589) |     Physical memory (bytes) snapshot=442564608
INFO - Counters.log(589) |     Reduce output records=4
INFO - Counters.log(589) |     Virtual memory (bytes) snapshot=2184306688
INFO - Counters.log(589) |     Map output records=8

在map侧过滤的数据，在50030中查看的截图如下：

运行结果如下所示：

Java代码

1 三劫散仙 13575468248 B 89 2013-02-05
2 凤舞九天 18965235874 C 69 2013-03-09
3 忙忙碌碌 15986854789 A 99 2013-03-05
3 忙忙碌碌 15986854789 D 56 2013-06-07

1	三劫散仙	13575468248	B	89	2013-02-05
2	凤舞九天	18965235874	C	69	2013-03-09
3	忙忙碌碌	15986854789	A	99	2013-03-05
3	忙忙碌碌	15986854789	D	56	2013-06-07

至此，这个半链接就完成了，结果正确，在hadoop的几种join方式里，只有在Map侧的链接比较高效，但也需要根据具体的实际情况，进行选择。

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在Reudce端进行连接是MapReduce框架进行表 ... | 在Map侧高效完成的join

2016-06-16 14:57
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