1.1.hadoop的序列化格式
序列化和反序列化就是结构化对象和字节流之间的转换，主要用在内部进程的通讯和持久化存储方面
hadoop在节点间的内部通讯使用的是RPC，RPC协议把消息翻译成二进制字节流发送到远程节点，远程节点再通过反序
列化把二进制流转成原始的信息 ![Hadoop_21_MapReduce程序实现Join功能-LMLPHP]( "Hadoop_21_MapReduce程序实现Join功能-LMLPHP")
hadoop自身的序列化存储格式实现了Writable接口的类,他只实现了前面压缩和快速。但是不容易扩展也不跨语言
我们先来看下Writable接口，Writable接口定义了两个方法：
1.将数据写入到二进制流中
2.从二进制数据流中读取数据
2.reduce端join算法实现
1.需求：
假如数据量巨大，两表的数据是以文件的形式存储在HDFS中，需要用mapreduce程序来实现以下SQL查询运算：
select a.id,a.date,b.name,b.category_id,b.price from t_order a join t_product b on a.pid = b.id
2.实现机制：
通过将关联的条件pid作为map输出的key，将两表满足join条件的数据并携带数据所来源的文件信息，发往同
一个reducetask，在reduce中进行数据的串联
3.代码实现：
package cn.bigdata.mr.rjoin;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import org.apache.hadoop.io.Writable; public class InfoBean implements Writable { private int order_id;
private String dateString;
private String p_id;
private int amount;
private String pname;
private int category_id;
private float price; // flag=0表示这个对象是封装订单表记录
// flag=1表示这个对象是封装产品信息记录
private String flag; public InfoBean() {
} public void set(int order_id, String dateString, String p_id, int amount, String pname, int category_id, float price, String flag) {
this.order_id = order_id;
this.dateString = dateString;
this.p_id = p_id;
this.amount = amount;
this.pname = pname;
this.category_id = category_id;
this.price = price;
this.flag = flag;
} public int getOrder_id() {
return order_id;
} public void setOrder_id(int order_id) {
this.order_id = order_id;
} public String getDateString() {
return dateString;
} public void setDateString(String dateString) {
this.dateString = dateString;
} public String getP_id() {
return p_id;
} public void setP_id(String p_id) {
this.p_id = p_id;
} public int getAmount() {
return amount;
} public void setAmount(int amount) {
this.amount = amount;
} public String getPname() {
return pname;
} public void setPname(String pname) {
this.pname = pname;
} public int getCategory_id() {
return category_id;
} public void setCategory_id(int category_id) {
this.category_id = category_id;
} public float getPrice() {
return price;
} public void setPrice(float price) {
this.price = price;
} public String getFlag() {
return flag;
} public void setFlag(String flag) {
this.flag = flag;
} /**
* private int order_id; private String dateString; private int p_id;
* private int amount; private String pname; private int category_id;
* private float price;
*/
@Override
public void write(DataOutput out) throws IOException {
out.writeInt(order_id);
out.writeUTF(dateString);
out.writeUTF(p_id);
out.writeInt(amount);
out.writeUTF(pname);
out.writeInt(category_id);
out.writeFloat(price);
out.writeUTF(flag);
} @Override
public void readFields(DataInput in) throws IOException {
this.order_id = in.readInt();
this.dateString = in.readUTF();
this.p_id = in.readUTF();
this.amount = in.readInt();
this.pname = in.readUTF();
this.category_id = in.readInt();
this.price = in.readFloat();
this.flag = in.readUTF(); } @Override
public String toString() {
return "order_id=" + order_id + ", dateString=" + dateString + ", p_id=" + p_id + ", amount=" + amount + ", pname=" + pname + ", category_id=" + category_id + ", price=" + price ;
}
}
package cn.bigdata.mr.rjoin;
import java.io.IOException;
import java.util.ArrayList;
import org.apache.commons.beanutils.BeanUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
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.output.FileOutputFormat; /**
* 订单表和商品表合到一起
order.txt(订单id, 日期, 商品编号, 数量)
1001 20150710 P0001 2
1002 20150710 P0001 3
1002 20150710 P0002 3
1003 20150710 P0003 3
product.txt(商品编号, 商品名字, 价格, 数量)
P0001 小米5 1001 2
P0002 锤子T1 1000 3
P0003 锤子 1002 4
*/
public class RJoin { static class RJoinMapper extends Mapper<LongWritable, Text, Text, InfoBean> {
InfoBean bean = new InfoBean();
Text k = new Text(); @Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString(); FileSplit inputSplit = (FileSplit) context.getInputSplit();
String name = inputSplit.getPath().getName();
System.out.println("kkkkkkkkkkkkkkkkkkkkkk"+name);
// 通过文件名判断是哪种数据
String pid = "";
if (name.startsWith("order")) {
String[] fields = line.split(",");
// id date pid amount
pid = fields[];
bean.set(Integer.parseInt(fields[]), fields[], pid, Integer.parseInt(fields[]), "", , , ""); } else {
String[] fields = line.split(",");
// id pname category_id price
pid = fields[];
bean.set(, "", pid, , fields[], Integer.parseInt(fields[]), Float.parseFloat(fields[]), ""); }
k.set(pid);
context.write(k, bean);
}
} static class RJoinReducer extends Reducer<Text, InfoBean, InfoBean, NullWritable> { @Override
protected void reduce(Text pid, Iterable<InfoBean> beans, Context context) throws IOException, InterruptedException {
InfoBean pdBean = new InfoBean();
ArrayList<InfoBean> orderBeans = new ArrayList<InfoBean>(); for (InfoBean bean : beans) {
if ("".equals(bean.getFlag())) { //产品的
try {
BeanUtils.copyProperties(pdBean, bean);
} catch (Exception e) {
e.printStackTrace();
}
} else {
InfoBean odbean = new InfoBean();
try {
BeanUtils.copyProperties(odbean, bean);
orderBeans.add(odbean);
} catch (Exception e) {
e.printStackTrace();
}
}
} // 拼接两类数据形成最终结果
for (InfoBean bean : orderBeans) { bean.setPname(pdBean.getPname());
bean.setCategory_id(pdBean.getCategory_id());
bean.setPrice(pdBean.getPrice()); context.write(bean, NullWritable.get());
}
}
} public static void main(String[] args) throws Exception {
Configuration conf = new Configuration(); conf.set("mapred.textoutputformat.separator", ","); Job job = Job.getInstance(conf); // 指定本程序的jar包所在的本地路径
// job.setJarByClass(RJoin.class);
// job.setJar("c:/join.jar"); job.setJarByClass(RJoin.class);
// 指定本业务job要使用的mapper/Reducer业务类
job.setMapperClass(RJoinMapper.class);
job.setReducerClass(RJoinReducer.class); // 指定mapper输出数据的kv类型
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(InfoBean.class); // 指定最终输出的数据的kv类型
job.setOutputKeyClass(InfoBean.class);
job.setOutputValueClass(NullWritable.class); // 指定job的输入原始文件所在目录
FileInputFormat.setInputPaths(job, new Path(args[]));
// 指定job的输出结果所在目录
FileOutputFormat.setOutputPath(job, new Path(args[])); // 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去运行
/* job.submit(); */
boolean res = job.waitForCompletion(true);
System.exit(res ? : );
}
}
运行结果：
order_id=1002, dateString=20150710, p_id=P0001, amount=3, pname=sss, category_id=1001, price=2.0
order_id=1001, dateString=20150710, p_id=P0001, amount=2, pname=sss, category_id=1001, price=2.0
order_id=1002, dateString=20150710, p_id=P0002, amount=3, pname=111, category_id=1000, price=3.0
order_id=1003, dateString=20150710, p_id=P0003, amount=3, pname=www, category_id=1002, price=4.0