Hadoop案例（七）MapReduce中多表合并

小米
华为
格力

package com.xyg.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" ;
}
}

package com.xyg.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>{
TableBean bean = new TableBean();
Text k = new Text(); @Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException { // 1 获取输入文件类型
FileSplit split = (FileSplit) context.getInputSplit();
String name = split.getPath().getName(); // 2 获取输入数据
String line = value.toString(); // 3 不同文件分别处理
if (name.startsWith("order")) {// 订单表处理
// 3.1 切割
String[] fields = line.split(","); // 3.2 封装bean对象
bean.setOrder_id(fields[]);
bean.setP_id(fields[]);
bean.setAmount(Integer.parseInt(fields[]));
bean.setPname("");
bean.setFlag(""); k.set(fields[]);
}else {// 产品表处理
// 3.3 切割
String[] fields = line.split(","); // 3.4 封装bean对象
bean.setP_id(fields[]);
bean.setPname(fields[]);
bean.setFlag("");
bean.setAmount();
bean.setOrder_id(""); k.set(fields[]);
}
// 4 写出
context.write(k, bean);
}
}

package com.xyg.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 ("".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.getPname(pdBean.getPname()); // 4 数据写出去
context.write(bean, NullWritable.get());
}
}
}

package com.xyg.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 {
// 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[]));
FileOutputFormat.setOutputPath(job, new Path(args[])); // 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 ? : );
}
}

1001 小米 1
1001 小米 1
1002 华为 2
1002 华为 2
1003 格力 3
1003 格力 3

package test;
import java.net.URI;
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 DistributedCacheDriver { public static void main(String[] args) throws Exception {
// 1 获取job信息
Configuration configuration = new Configuration();
Job job = Job.getInstance(configuration); // 2 设置加载jar包路径
job.setJarByClass(DistributedCacheDriver.class); // 3 关联map
job.setMapperClass(DistributedCacheMapper.class); // 4 设置最终输出数据类型
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(NullWritable.class); // 5 设置输入输出路径
FileInputFormat.setInputPaths(job, new Path(args[]));
FileOutputFormat.setOutputPath(job, new Path(args[])); // 6 加载缓存数据
job.addCacheFile(new URI("file:///e:/inputcache/pd.txt")); // 7 map端join的逻辑不需要reduce阶段，设置reducetask数量为0
job.setNumReduceTasks(); // 8 提交
boolean result = job.waitForCompletion(true);
System.exit(result ? : );
}
}

package test;
import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.IOException;
import java.io.InputStreamReader;
import java.util.HashMap;
import java.util.Map;
import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;public class DistributedCacheMapper extends Mapper<LongWritable, Text, Text, NullWritable>{ Map<String, String> pdMap = new HashMap<>(); @Override
protected void setup(Mapper<LongWritable, Text, Text, NullWritable>.Context context)
throws IOException, InterruptedException {
// 1 获取缓存的文件
BufferedReader reader = new BufferedReader(new InputStreamReader(new FileInputStream("pd.txt"),"UTF-8")); String line;
while(StringUtils.isNotEmpty(line = reader.readLine())){
// 2 切割
String[] fields = line.split("\t"); // 3 缓存数据到集合
pdMap.put(fields[], fields[]);
} // 4 关流
reader.close();
} Text k = new Text(); @Override
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
// 1 获取一行
String line = value.toString(); // 2 截取
String[] fields = line.split("\t"); // 3 获取产品id
String pId = fields[]; // 4 获取商品名称
String pdName = pdMap.get(pId); // 5 拼接
k.set(line + "\t"+ pdName); // 6 写出
context.write(k, NullWritable.get());
}
}