Hadoop MR 之(六) Join操作 (MapJoin & ReduceJon & 缓存)

深碍√TFBOYSˉ_ 2022-02-23 03:26 261阅读 0赞

### 前言 ###

在前面的几章内, 我们主要讲解了如何进行简单的MR操作, 以及如何开发自定义组件.(`Combiner`/`InputFormat`等).

在本章中, 我们主要讲解MR中的一个非常经典的问题: `如何实现Join操作?`

本文相关代码, 可在我的Github项目 [https://github.com/SeanYanxml/bigdata/][https_github.com_SeanYanxml_bigdata] 目录下可以找到. PS: (如果觉得项目不错, 可以给我一个Star.)

--------------------

### 知识准备 ###

##### Mysql Join操作 #####

*  Table A 订单编号表

<table> 
 <thead> 
  <tr> 
   <th>订单编号</th> 
   <th>手机种类编号</th> 
   <th>时间</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>1</td> 
   <td>1</td> 
   <td>2018-09-09</td> 
  </tr> 
  <tr> 
   <td>2</td> 
   <td>2</td> 
   <td>2018-09-10</td> 
  </tr> 
  <tr> 
   <td>3</td> 
   <td>1</td> 
   <td>2018-09-11</td> 
  </tr> 
 </tbody> 
</table>

*  Table B 手机类别表

<table> 
 <thead> 
  <tr> 
   <th>手机种类编号</th> 
   <th>手机类型名称</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <td>1</td> 
   <td>XiaoMi</td> 
  </tr> 
  <tr> 
   <td>2</td> 
   <td>HuaWei</td> 
  </tr> 
 </tbody> 
</table>

左外链接的SQL应该这样写

select orderId,phoneTypeNumber,phoneTypeName,orderTime from TableA a left join on TableB b on a.phoneTypeNumber = b.phoneTypeNumber;

##### 业务场景 #####

我们今天就要使用`Map/Reduce`来完成上面的业务操作问题. 使用左外链接来获取全表数据.

使用数据: 主要包括 OrderData 与 PhoneData.

# orderData
    1 1 
    2 2
    3 1
    4 1
    5 3
    6 1
    
    # PhoneData
    1 xiaomi
    2 huawei
    3 iphone

在写下方的程序之前, 同理. 我们将其放入Hadoop的HDFS上:

hadoop fs -mkdir -p /join/phone/input
    hadoop fs -put orderdata /join/phone/input/
    hadoop fs -put phonedata /join/phone/input/

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly95YW54bWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70]

--------------------

### 设计&编码 ###

在实现`Join`操作的时候,常见的场景是`MapJoin`与`ReduceJoin`. 两者的使用场景各不相同. 下面我们讲解下两种做法的使用场景、具体实现以及优劣点.

##### ReduceJoin #####

一开始, 最容易想到的就是`ReduceJoin`. 将类别对象&一组对象放入一个Mapper内进行操作. 这个地方使用的技巧是设置相同的Key, 这样一个类别的一组对象就会以`Iterator`的形式传入一个集合内进行处理了.

我们定义三个类:

*  OrderInfoBean 订单类
 *  PhoneTypeBean 手机类别类
 *  HadoopTransferBean 转换类

其中`HadoopTransferBean`是用于将订单类与类别类进行封装, 用于传输所用. 本来想使用`Writeable`接口进行实现的, 但是`Hadoop`貌似不支持这样做.

几个类别的基本编码如下所示:

// OrderInfoBean.java
    /**
     * 存放处理后的join结果Bean.
     * 
     * */
    public class OrderInfoBean implements Writable{
    	private int orderId;
    	private int phonetype;
    	private String phoneName;
    	public OrderInfoBean(){}
    	public OrderInfoBean(int orderId, int phonetype, String phoneName){
    		this.orderId = orderId;
    		this.phonetype = phonetype;
    		this.phoneName = phoneName;
    	}
    	
    	public int getOrderId() {
    		return orderId;
    	}
    
    	public void setOrderId(int orderId) {
    		this.orderId = orderId;
    	}
    
    	public int getPhonetype() {
    		return phonetype;
    	}
    
    	public void setPhonetype(int phonetype) {
    		this.phonetype = phonetype;
    	}
    
    	public String getPhoneName() {
    		return phoneName;
    	}
    
    	public void setPhoneName(String phoneName) {
    		this.phoneName = phoneName;
    	}
    
    	// write的顺序要与read的顺序对应.
    	@Override
    	public void write(DataOutput out) throws IOException {
    		out.writeInt(orderId);
    		out.writeInt(phonetype);
    		out.writeUTF(phoneName);
    	}
    	
    	@Override
    	public void readFields(DataInput in) throws IOException {
    		// TODO Auto-generated method stub
    		this.orderId = in.readInt();
    		this.phonetype = in.readInt();
    		this.phoneName = in.readUTF();
    	}
    	
    	@Override
    	public String toString() {
    		//123-1-xiaomi
    		return orderId+" "+phonetype+" "+phoneName;
    	}
    }
    
    // PhoneTypeBean.java
    public class PhoneTypeBean implements Writable{
    	private int phonetype;
    	private String phoneName;
    	
    	public PhoneTypeBean(){}
    	public PhoneTypeBean(int phonetype, String phoneName){
    		this.phonetype = phonetype;
    		this.phoneName = phoneName;
    	}
    	
    	public int getPhonetype() {
    		return phonetype;
    	}
    
    	public void setPhonetype(int phonetype) {
    		this.phonetype = phonetype;
    	}
    
    	public String getPhoneName() {
    		return phoneName;
    	}
    
    	public void setPhoneName(String phoneName) {
    		this.phoneName = phoneName;
    	}
    
    	// write的顺序要与read的顺序对应.
    	@Override
    	public void write(DataOutput out) throws IOException {
    		out.writeInt(phonetype);
    		out.writeUTF(phoneName);
    	}
    	
    	@Override
    	public void readFields(DataInput in) throws IOException {
    		// TODO Auto-generated method stub
    		this.phonetype = in.readInt();
    		this.phoneName = in.readUTF();
    	}
    	
    	@Override
    	public String toString() {
    		//123-1-xiaomi
    		return phonetype+" "+phoneName;
    	}
    }
    
    // 封装类
    public class HadoopTransferBean implements Writable{
    	private OrderInfoBean infoBean;
    	private PhoneTypeBean typeBean;
    	
    	public HadoopTransferBean(){}
    	public HadoopTransferBean(OrderInfoBean infoBean){
    		this.infoBean = infoBean;
    	}
    	
    	public HadoopTransferBean(PhoneTypeBean tyepeBean){
    		this.typeBean = tyepeBean;
    	}
    	
    	public OrderInfoBean getInfoBean() {
    		return infoBean;
    	}
    	public void setInfoBean(OrderInfoBean infoBean) {
    		this.infoBean = infoBean;
    	}
    	public PhoneTypeBean getTypeBean() {
    		return typeBean;
    	}
    	public void setTypeBean(PhoneTypeBean typeBean) {
    		this.typeBean = typeBean;
    	}
    	@Override
    	public void write(DataOutput out) throws IOException {
    		out.writeUTF(JSON.toJSONString(infoBean));
    		out.writeUTF(JSON.toJSONString(typeBean));
    //		out.writeUTF();
    		//		out.write(ByteUtils.toByteArray(infoBean));
    //		out.write(ByteUtils.toByteArray(typeBean));
    	}
    
    	@Override
    	public void readFields(DataInput in) throws IOException {
    		this.infoBean = JSON.parseObject(in.readUTF(), OrderInfoBean.class);
    		this.typeBean = JSON.parseObject(in.readUTF(), PhoneTypeBean.class);
    //		this.infoBean = ByteUtils.toObject(in.readByte());
    //		this.infoBean = ByteUtils.toObject(in.readByte());
    	}
    }

好了, 有了上面三个封装类之后, 我们可以编写具体的逻辑.

*  Mapper端: 我们将带处理文件放入一个统一的目录, 通过文件的头部名称辨别数据类型;
 *  Mapper端: 将需要外链接的类作为key放入返回的context `key`中.  
    基本编码如所示:

static class PhoneOrderInfoMapper extends Mapper<LongWritable, Text, IntWritable, HadoopTransferBean>{
    		@Override
    		protected void map(LongWritable key, Text value,Mapper<LongWritable, Text, IntWritable, HadoopTransferBean>.Context context)
    				throws IOException, InterruptedException {
    			HadoopTransferBean bean = null;
    			int outkey = 0;
    			String line = value.toString();
    			FileSplit inputSplit = (FileSplit) context.getInputSplit();
    			String name = inputSplit.getPath().getName();
    			// 通过文件名称判断是哪种类型的数据
    			if(name.startsWith("order")){
    				String []fields = line.split(" ");
    				// orderid - phonetype
    				// 注意这边是将fields[1]作为key输出出去 目的在于能将其与类型一同分到一个结点上去.
    				outkey = Integer.parseInt(fields[1]);
    				OrderInfoBean orderBean = new OrderInfoBean(Integer.parseInt(fields[0]),Integer.parseInt(fields[1]),null);
    				bean = new HadoopTransferBean(orderBean); 
    			}else{
    				// 手机种类类型的数据处理.
    				// phonetype - phoneName
    				String []fields = line.split(" ");
    				outkey = Integer.parseInt(fields[0]);
    				PhoneTypeBean phoneTypeBean = new PhoneTypeBean(Integer.parseInt(fields[0]), fields[1]);
    				bean = new HadoopTransferBean(phoneTypeBean); 
    			}
    			context.write(new IntWritable(outkey), bean);
    		}
    	}

上方需要特别注意的是:`outkey = Integer.parseInt(fields[1]);`和`outkey = Integer.parseInt(fields[0]);`. 通过指定相同的手机类型编号, 将其分配到一起.(基本的数据类型见`业务场景`部分.)

*  Reducer端: 取出封装为`HadoopTransferBean`类型数据, 并辨别其到底是`订单表数据`还是`手机类别表数据`. (注: 订单表数据应当是多个, 手机类别表数据应当为1个. 这是业务的逻辑决定的.)
 *  Reducer端: 将数据拆分后, 重新组装丰富`订单表`内的`手机类别名称`字段.随后,将其写入输出.

其相关的编码如下所示:

static class PhoneOrderInfoReducer extends Reducer<IntWritable, HadoopTransferBean, OrderInfoBean, NullWritable>{
    		@Override
    		// 产品唯一, 订单
    		protected void reduce(IntWritable key, Iterable<HadoopTransferBean> values,Reducer<IntWritable, HadoopTransferBean, OrderInfoBean, NullWritable>.Context context)throws IOException, InterruptedException {
    			OrderInfoBean resultBean = new OrderInfoBean();
    			
    			PhoneTypeBean typeBean = new PhoneTypeBean();
    			List<OrderInfoBean> list = new ArrayList<>();
    
    			// 疑问? 为什么另一种的订单类型为空?
    			for(HadoopTransferBean bean:values){
    				// 传入的数据类型为OrderInfoBean类型的
    				if(bean.getInfoBean() != null){
    					OrderInfoBean orderBean = new OrderInfoBean();
    					try {
    						BeanUtils.copyProperties(orderBean, bean.getInfoBean());
    					} catch (IllegalAccessException e) {
    						e.printStackTrace();
    					} catch (InvocationTargetException e) {
    						e.printStackTrace();
    					}
    					list.add(orderBean);
    				}else if(bean.getTypeBean() != null){
    					try {
    						BeanUtils.copyProperties(typeBean, bean.getTypeBean());
    					} catch (IllegalAccessException e) {
    						e.printStackTrace();
    					} catch (InvocationTargetException e) {
    						e.printStackTrace();
    					}
    				}
    			}
    			
    			// 拼接两类数据
    			for(OrderInfoBean orderBean:list){
    				orderBean.setPhoneName(typeBean.getPhoneName());
    				context.write(orderBean, NullWritable.get());
    			}
    		}
    	}

*  Driver类的编写(指定并生成Job)

public class PhoneOrderInfoDriver {
    public static void main(String []args) throws IOException, ClassNotFoundException, InterruptedException{
    		Configuration conf =  new Configuration();
    //		conf.set("mapreduce.framework.name", "yarn");
    		conf.set("mapreduce.framework.name", "local");
    		conf.set("yarn.resourcemanager.hostname", "localhost");
    		conf.set("fs.defaultFS", "hdfs://localhost:9000/");
    		
    		Job job =  Job.getInstance(conf);
    		
    //		job.setJar("/");
    //		job.setJar("/Users/Sean/Documents/Gitrep/bigdata/hadoop/target/hadoop-0.0.1-SNAPSHOT.jar");
    		// 指定本程序jar包所在地址
    		job.setJarByClass(PhoneOrderInfoDriver.class);
    		
    		//指定本业务job需要使用的mapper业务类
    		job.setMapperClass(PhoneOrderInfoMapper.class);
    		job.setReducerClass(PhoneOrderInfoReducer.class);
    		
    		// 指定mapper输出数据的kv类型
    		job.setMapOutputKeyClass(IntWritable.class);
    		job.setMapOutputValueClass(HadoopTransferBean.class);
    		
    		// 指定最终输出数据的kv类型
    		job.setOutputKeyClass(OrderInfoBean.class);
    		job.setOutputKeyClass(NullWritable.class);
    		
    		// 设置匹配器
    //		job.setPartitionerClass(ProvincePartitioner.class);
    //		job.setInputFormatClass(cls);
    		// 当数目小于分片数目时候 会报错. 多余时,则有的分片内为空.
    //		job.setNumReduceTasks(5);
    		
    //		// 指定job的输入文件所在目录
    //		FileInputFormat.setInputPaths(job, new Path(args[0]));
    //		// 指定job的输出结果
    //        FileOutputFormat.setOutputPath(job,new Path(args[1]));
    		
    		// 指定job的输入文件所在目录
    		FileInputFormat.setInputPaths(job, new Path("/join/phone/input"));
    		// 指定job的输出结果
    		FileOutputFormat.setOutputPath(job, new Path("/join/phone/output"));
    		
    		Path outPath = new Path("/join/phone/output");
    		FileSystem fs = FileSystem.get(conf);
    		if(fs.exists(outPath)){
    			fs.delete(outPath,true);
    		}
    		// 将job中配置的相关参数, 以及job所用的java类所在的jar包,提交给yarn执行
    //		job.submit();
    		
    		// 
    		boolean flag = job.waitForCompletion(true);
    		System.exit(flag?0:1);
    	}
    }

运行结果(注意这里的输出,是根据你写入类的`toString()`方法决定的,并且可以自定义`OutputFormat`类.这在前面的章节内我们已经说过了.):

localhost:Desktop Sean$ hadoop fs -cat /join/phone/output/part-m-00001
    Picked up JAVA_TOOL_OPTIONS: -Dfile.encoding=UTF-8
    19/04/05 16:48:26 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
    1 1  xiaomi
    2 2 huawei
    3 1 xiaomi
    4 1 xiaomi
    5 3 iphone
    6 1 xiaomi

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly95YW54bWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 1]

##### MapJoin #####

在上文, 我们注意讲述了如何通过`ReduceJoin`进行完成传统的`Join`操作. 但是这样会导致一个问题. 那就是,数据资源分配不均匀的问题.

在下方的图片中, 有3个`MapperTask`和2个`ReducerTask`. 其中`pid0001`的放入`Reducer1`内, `pid00002`的放入`Reducer2`内. 我们发现`Reducer1`的负载为10, `Reducer2`的负载为2.  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly95YW54bWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 2]  
明显的负载不均衡, 这也就是常说的数据倾斜问题. 解决数据倾斜的办法非常简单. 使数据变得均匀分配. (Ps: 貌似像废话,什么都没说. 汗) 对于这个问题的解决办法主要有2:

*  将数据重新切分, 不使用以`key`为主要分配标准的切法. 但是又想要保持之前的业务逻辑有点不太可能, 需要寻找一种可靠的切法. 又要将订单表与匹配的手机类别放在一起会复杂化逻辑.(不是不可以, 比如一块内处理多少的数据. 并且将其切成两份. 比如:`<订单块1><订单块2><类别1>` 分成`<订单块1><类别1>`/ `<订单块2><类别1>`, 从原来的1个Reducer上变成分配到2个Reducer上.)
 *  使用缓存技术. 像我们的`手机类别表`的数据量不是特别多的时候,可以使用这种策略. 这种`Join`的方式, 其数据的处理主要是在`Mapper`端进行完成的, 我们将其称为`MapJoin`.

其代码主要如下所示:

public class MapJoinPhoneOrderInfoDriver {
    	// 1. setup() 2. map() 3. cleanup()
    	// 通过阅读父类Mapper的源码发现 setup方法是在maptask处理数据之前调用一次 可以用来处理一些初始化操作.
    	static class MapJoinPhoneOrderInfoMapper extends Mapper<LongWritable, Text, Text, NullWritable>{
    		// 使用一个HashMap缓存一个产品信息表
    		Map<String, String> pdInfoMap = new HashMap();
    		
    		@Override
    		protected void setup(Mapper<LongWritable, Text, Text, NullWritable>.Context context)throws IOException, InterruptedException {
    			BufferedReader br = new BufferedReader(new InputStreamReader(new FileInputStream("phonedata")));
    			String line;
    			while(StringUtils.isNotEmpty(line = br.readLine())){
    				String []fields = line.split(" ");
    				pdInfoMap.put(fields[0], fields[1]);
    			}
    		}
    		
    		@Override
    		protected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, NullWritable>.Context context) throws IOException, InterruptedException {
    			String orderLine = value.toString();
    			// 设置只读取订单类型的文件.
    			
    			String []fields = orderLine.split(" ");
    			String name = pdInfoMap.get(fields[1]);
    			
    			Text k =new Text();
    			k.set(orderLine + " "+name);
    			context.write(k, NullWritable.get());
     		}
    
    	}
    	
    	public static void main(String []args) throws IOException, ClassNotFoundException, InterruptedException, URISyntaxException{
    		Configuration conf =  new Configuration();
    //		conf.set("mapreduce.framework.name", "yarn");
    		conf.set("mapreduce.framework.name", "local");
    		conf.set("yarn.resourcemanager.hostname", "localhost");
    		conf.set("fs.defaultFS", "hdfs://localhost:9000/");
    		
    		Job job =  Job.getInstance(conf);
    		
    //		job.setJar("/");
    //		job.setJar("/Users/Sean/Documents/Gitrep/bigdata/hadoop/target/hadoop-0.0.1-SNAPSHOT.jar");
    		// 指定本程序jar包所在地址
    		job.setJarByClass(MapJoinPhoneOrderInfoDriver.class);
    		
    		//指定本业务job需要使用的mapper业务类
    		job.setMapperClass(MapJoinPhoneOrderInfoMapper.class);
    		
    		// 指定mapper输出数据的kv类型
    		job.setMapOutputKeyClass(Text.class);
    		job.setMapOutputValueClass(NullWritable.class);
    		
    		// 指定map端的join操作不需要reduce阶段, 设置reduce阶段的数目为0
    		job.setNumReduceTasks(0);
    		
    		// 指定最终输出数据的kv类型
    		job.setOutputKeyClass(Text.class);
    		job.setOutputKeyClass(NullWritable.class);
    		
    		
    		// 缓存一个文件到所有到maptask的目录classpath
    //		job.addArchiveToClassPath(archive);
    		// 缓存一个文件到task结点的工作目录.
    //		job.addCacheArchive(uri);
    		// 缓存普通文件到task的classpath
    //		job.addFileToClassPath(file);
    //		job.addCacheFile(uri);
    		// 将产品表文件 缓存到task工作结点到工作目录中.
    		job.addCacheFile(new URI("hdfs://localhost:9000/join/phone/input/phonedata"));
    		
    		// 当数目小于分片数目时候 会报错. 多余时,则有的分片内为空.
    //		job.setNumReduceTasks(5);
    		
    //		// 指定job的输入文件所在目录
    //		FileInputFormat.setInputPaths(job, new Path(args[0]));
    //		// 指定job的输出结果
    //        FileOutputFormat.setOutputPath(job,new Path(args[1]));
    		
    		// 指定job的输入文件所在目录(或者准确定位为某个单独的文件.)
    		FileInputFormat.setInputPaths(job, new Path("/join/phone/input/orderdata"));
    		// 指定job的输出结果
    		FileOutputFormat.setOutputPath(job, new Path("/join/phone/output"));
    		
    		Path outPath = new Path("/join/phone/output");
    		FileSystem fs = FileSystem.get(conf);
    		if(fs.exists(outPath)){
    			fs.delete(outPath,true);
    		}
    		// 将job中配置的相关参数, 以及job所用的java类所在的jar包,提交给yarn执行
    //		job.submit();
    		
    		// 
    		boolean flag = job.waitForCompletion(true);
    		System.exit(flag?0:1);
    	}
    }

值得注意的是:

*  Driver端: 我们将`手机类别表`放入通用的缓存空间 `job.addCacheFile(new URI("hdfs://localhost:9000/join/phone/input/phonedata"));`.
 *  Mapper端: 我们在`setup()`方法时,将`手机类别表`加载到内存中了.
 *  Reducer端: 本次, 未使用到`Reducer`. 我们通过`job.setNumReduceTasks(0);`将其设置为`0`.

##### 缓存相关 #####

在上防的Driver中, 我们通过缓存加载资源. 本小节简单的介绍下Job的几种缓存类型.

// 缓存一个文件到所有到maptask的目录classpath
    //		job.addArchiveToClassPath(archive);
    		// 缓存一个文件到task结点的工作目录.
    //		job.addCacheArchive(uri);
    		// 缓存普通文件到task的classpath
    //		job.addFileToClassPath(file);
    //		job.addCacheFile(uri);
    		// 将产品表文件 缓存到task工作结点到工作目录中.
    		job.addCacheFile(new URI("hdfs://localhost:9000/join/phone/input/phonedata"));

--------------------

### Q & A ###

> 异常1:
> 
>     Error: java.lang.ClassCastException: >com.yanxml.bigdata.hadoop.mr.join.reducejoin.HadoopTransferBean cannot be cast to com.sun.corba.se.spi.ior.Writeable
>     	at com.yanxml.bigdata.hadoop.mr.join.reducejoin.PhoneOrderInfoDriver$PhoneOrderInfoMapper.map(PhoneOrderInfoDriver.java:56)
>     	at com.yanxml.bigdata.hadoop.mr.join.reducejoin.PhoneOrderInfoDriver$PhoneOrderInfoMapper.map(PhoneOrderInfoDriver.java:1)
>     	at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:146)
>     	at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:787)
>     	at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
>     	at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:164)
>     	at java.security.AccessController.doPrivileged(Native Method)
>     	at javax.security.auth.Subject.doAs(Subject.java:422)
>     	at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1754)
>     	at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)
>     
>     Error: java.lang.ClassCastException: com.yanxml.bigdata.hadoop.mr.join.reducejoin.HadoopTransferBean cannot be cast to com.sun.corba.se.spi.ior.Writeable
>     	at com.yanxml.bigdata.hadoop.mr.join.reducejoin.PhoneOrderInfoDriver$PhoneOrderInfoMapper.map(PhoneOrderInfoDriver.java:56)
>     	at com.yanxml.bigdata.hadoop.mr.join.reducejoin.PhoneOrderInfoDriver$PhoneOrderInfoMapper.map(PhoneOrderInfoDriver.java:1)
>     	at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:146)
>     	at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:787)
>     	at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
>     	at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:164)
>     	at java.security.AccessController.doPrivileged(Native Method)
>     	at javax.security.auth.Subject.doAs(Subject.java:422)
>     	at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1754)
>     	at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)
>     
>     Container killed by the ApplicationMaster.
>     Container killed on request. Exit code is 143
>     Container exited with a non-zero exit code 143
>     
>     Error: java.lang.ClassCastException: com.yanxml.bigdata.hadoop.mr.join.reducejoin.HadoopTransferBean cannot be cast to com.sun.corba.se.spi.ior.Writeable
>     	at com.yanxml.bigdata.hadoop.mr.join.reducejoin.PhoneOrderInfoDriver$PhoneOrderInfoMapper.map(PhoneOrderInfoDriver.java:56)
>     	at com.yanxml.bigdata.hadoop.mr.join.reducejoin.PhoneOrderInfoDriver$PhoneOrderInfoMapper.map(PhoneOrderInfoDriver.java:1)
>     	at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:146)
>     	at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:787)
>     	at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
>     	at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:164)
>     	at java.security.AccessController.doPrivileged(Native Method)
>     	at javax.security.auth.Subject.doAs(Subject.java:422)
>     	at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1754)
>     	at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)
>     
>     Error: java.lang.ClassCastException: com.yanxml.bigdata.hadoop.mr.join.reducejoin.HadoopTransferBean cannot be cast to com.sun.corba.se.spi.ior.Writeable
>     	at com.yanxml.bigdata.hadoop.mr.join.reducejoin.PhoneOrderInfoDriver$PhoneOrderInfoMapper.map(PhoneOrderInfoDriver.java:56)
>     	at com.yanxml.bigdata.hadoop.mr.join.reducejoin.PhoneOrderInfoDriver$PhoneOrderInfoMapper.map(PhoneOrderInfoDriver.java:1)
>     	at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:146)
>     	at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:787)
>      at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
>     	at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:164)
>     	at java.security.AccessController.doPrivileged(Native Method)
>     	at javax.security.auth.Subject.doAs(Subject.java:422)
>     	at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1754)
>     	at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)
> 
> 解决措施: `static class PhoneOrderInfoMapper extends Mapper<LongWritable, Text, IntWritable, Writeable>` 转变为`static class PhoneOrderInfoMapper extends Mapper<LongWritable, Text, IntWritable, HadoopTransferBean>`. 个人猜测这是因为传递数据的类型不能是接口类型导致.(猜测). 用于传输的数据只能是实体类.(`Writerable`为接口类型)

--------------------

### Reference ###

\[1\]. [Markdown创建表格][Markdown]

[https_github.com_SeanYanxml_bigdata]: https://github.com/SeanYanxml/bigdata/
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