Reduce Join案例实操
1．需求
表4-4 订单数据表t_order
| id | pid | amount |
| 1001 | 01 | 1 |
| 1002 | 02 | 2 |
| 1003 | 03 | 3 |
| 1004 | 01 | 4 |
| 1005 | 02 | 5 |
| 1006 | 03 | 6 |
表4-5 商品信息表t_product
| pid | pname |
| 01 | 小米 |
| 02 | 华为 |
| 03 | 格力 |
将商品信息表中数据根据商品pid合并到订单数据表中。
表4-6 最终数据形式
| id | pname | amount |
| 1001 | 小米 | 1 |
| 1004 | 小米 | 4 |
| 1002 | 华为 | 2 |
| 1005 | 华为 | 5 |
| 1003 | 格力 | 3 |
| 1006 | 格力 | 6 |
2．需求分析
通过将关联条件作为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，如图4-20所示。
图4-20 Reduce端表合并
3．代码实现
1）创建商品和订合并后的Bean类
| package com.atguigu.mapreduce.table; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.hadoop.io.Writable; public class TableBean implements Writable { private String order_id; // 订单id private String p_id; // 产品id private int amount; // 产品数量 private String pname; // 产品名称 private String flag; // 表的标记 public TableBean() { super(); } public TableBean(String order_id, String p_id, int amount, String pname, String flag) { super(); this.order_id = order_id; this.p_id = p_id; this.amount = amount; this.pname = pname; this.flag = flag; } public String getFlag() { return flag; } public void setFlag(String flag) { this.flag = flag; } public String getOrder_id() { return order_id; } public void setOrder_id(String order_id) { this.order_id = order_id; } 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; } @Override public void write(DataOutput out) throws IOException { out.writeUTF(order_id); out.writeUTF(p_id); out.writeInt(amount); out.writeUTF(pname); out.writeUTF(flag); } @Override public void readFields(DataInput in) throws IOException { this.order_id = in.readUTF(); this.p_id = in.readUTF(); this.amount = in.readInt(); this.pname = in.readUTF(); this.flag = in.readUTF(); } @Override public String toString() { return order_id + “\t” + pname + “\t” + amount + “\t” ; } } |
2）编写TableMapper类
| package com.atguigu.mapreduce.table; import java.io.IOException; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.lib.input.FileSplit; public class TableMapper extends Mapper<LongWritable, Text, Text, TableBean>{ String name; TableBean bean = new TableBean(); Text k = new Text(); @Override protected void setup(Context context) throws IOException, InterruptedException { // 1 获取输入文件切片 FileSplit split = (FileSplit) context.getInputSplit(); // 2 获取输入文件名称 name = split.getPath().getName(); } @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { // 1 获取输入数据 String line = value.toString(); // 2 不同文件分别处理 if (name.startsWith(“order”)) {// 订单表处理 // 2.1 切割 String[] fields = line.split(“\t”); // 2.2 封装bean对象 bean.setOrder_id(fields[0]); bean.setP_id(fields[1]); bean.setAmount(Integer.parseInt(fields[2])); bean.setPname(“”); bean.setFlag(“order”); k.set(fields[1]); }else {// 产品表处理 // 2.3 切割 String[] fields = line.split(“\t”); // 2.4 封装bean对象 bean.setP_id(fields[0]); bean.setPname(fields[1]); bean.setFlag(“pd”); bean.setAmount(0); bean.setOrder_id(“”); k.set(fields[0]); } // 3 写出 context.write(k, bean); } } |
3）编写TableReducer类
| package com.atguigu.mapreduce.table; import java.io.IOException; import java.util.ArrayList; import org.apache.commons.beanutils.BeanUtils; import org.apache.hadoop.io.NullWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class TableReducer extends Reducer<Text, TableBean, TableBean, NullWritable> { @Override protected void reduce(Text key, Iterable<TableBean> values, Context context) throws IOException, InterruptedException { // 1准备存储订单的集合 ArrayList<TableBean> orderBeans = new ArrayList<>(); // 2 准备bean对象 TableBean pdBean = new TableBean(); for (TableBean bean : values) { if (“order”.equals(bean.getFlag())) {// 订单表 // 拷贝传递过来的每条订单数据到集合中 TableBean orderBean = new TableBean(); try { BeanUtils.copyProperties(orderBean, bean); } catch (Exception e) { e.printStackTrace(); } orderBeans.add(orderBean); } else {// 产品表 try { // 拷贝传递过来的产品表到内存中 BeanUtils.copyProperties(pdBean, bean); } catch (Exception e) { e.printStackTrace(); } } } // 3 表的拼接 for(TableBean bean:orderBeans){ bean.setPname (pdBean.getPname()); // 4 数据写出去 context.write(bean, NullWritable.get()); } } } |
4）编写TableDriver类
| package com.atguigu.mapreduce.table; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.NullWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; public class TableDriver { public static void main(String[] args) throws Exception { // 0 根据自己电脑路径重新配置 args = new String[]{“e:/input/inputtable”,”e:/output1″}; // 1 获取配置信息，或者job对象实例 Configuration configuration = new Configuration(); Job job = Job.getInstance(configuration); // 2 指定本程序的jar包所在的本地路径 job.setJarByClass(TableDriver.class); // 3 指定本业务job要使用的Mapper/Reducer业务类 job.setMapperClass(TableMapper.class); job.setReducerClass(TableReducer.class); // 4 指定Mapper输出数据的kv类型 job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(TableBean.class); // 5 指定最终输出的数据的kv类型 job.setOutputKeyClass(TableBean.class); job.setOutputValueClass(NullWritable.class); // 6 指定job的输入原始文件所在目录 FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); // 7 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 boolean result = job.waitForCompletion(true); System.exit(result ? 0 : 1); } } |
4．测试
运行程序查看结果
| 1001 小米 1 1001 小米 1 1002 华为 2 1002 华为 2 1003 格力 3 1003 格力 3 |
5．总结
