题目描述：
现在有两个文件，1为存放公司名字和城市ID，2为存放城市ID和城市名
表一：
factoryname,addressed Beijing Red Star,1 Shenzhen Thunder,3 Guangzhou Honda,2 Beijing Rising,1 Guangzhou Development Bank,2 Tencent,3 Back of Beijing,1
表2：
1,Beijing 2,Guangzhou 3,Shenzhen 4,Xian
现在要求输出公司名和城市名。例如：
Beijing Red Star Beijing
这个类似数据库里的多表连接。整体思路和单表连接差不多。还是利用reduce阶段对城市ID进行归并，我们在map阶段统一输出key=城市ID value=falg+“+”+城市名or公司名。然后通过reduce对flag的解析，分析后者是城市名还是公司名，并放到两个数组中，最后利用笛卡尔积将其输出
具体代码
public class MyMapper extends Mapper<LongWritable, Text, Text, Text> {
public void map(LongWritable ikey, Text ivalue, Context context )
throws IOException, InterruptedException {
String line=ivalue.toString();
StringTokenizer st= new StringTokenizer(line, "," );
String value0=st.nextToken();
String value1=st.nextToken();
if (value0.compareTo( "factoryname" )!=0){
if (value0.length()==1){
context.write( new Text(value0), new Text( "1" + "+" +value1));
} else {
context.write( new Text(value1), new Text( "2" + "+" +value0));
}
}
}
}
public class MyReducer extends Reducer<Text, Text, Text, Text> {
public void reduce(Text _key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
// process values
ArrayList<String> address= new ArrayList<String>();
ArrayList<String> factory= new ArrayList<String>();
for (Text val : values) {
String line=val.toString();
StringTokenizer st= new StringTokenizer(line, "+" );
int flag=Integer.parseInt(st.nextToken());
if (flag==1){
String addressname=st.nextToken();
address.add(addressname);
} else if (flag==2){
String factoryname=st.nextToken();
factory.add(factoryname);
}
}
if (address.size()!=0&&factory.size()!=0){
for ( int i=0;i<address.size();i++){
for ( int j=0;j<factory.size();j++){
context.write( new Text(address.get(i)), new Text(factory.get(j)));
}
}
}
}
}