MapReduce编程之实现多表关联

多表关联和单表关联类似，它也是通过对原始数据进行一定的处理，，从其中挖掘出关心的信息。如下

输入的是两个文件，一个代表工厂表，包含工厂名列和地址编号列；另一个代表地址表，包含地址名列和地址编号列。

要求从输入数据中找出工厂名和地址名的对应关系，输出工厂名-地址名表

样本如下：

factory:

factoryname addressedBeijing Red Star 1Shenzhen Thunder 3Guangzhou Honda 2Beijing Rising 1Guangzhou Development Bank 2Tencent 3Back of Beijing 1

address:

addressID addressname1 Beijing2 Guangzhou3 Shenzhen4 Xian

结果：

factorynameaddressnameBeijing Red StarBeijingBeijing Rising BeijingBank of BeijingBeijingGuangzhou HondaGuangzhouGuangzhou Development BankGuangzhouShenzhen ThunderShenzhenTencentShenzhen

代码如下：

import java.io.IOException;import java.util.*;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.IntWritable;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.output.FileOutputFormat;import org.apache.hadoop.util.GenericOptionsParser; public class MTjoin {public static int time = 0;/** 在map中先区分输入行属于左表还是右表，然后对两列值进行分割，* 保存连接列在key值，剩余列和左右表标志在value中，最后输出*/public static class Map extends Mapper<Object, Text, Text, Text> {// 实现map函数public void map(Object key, Text value, Context context)throws IOException, InterruptedException {String line = value.toString();// 每行文件String relationtype = new String();// 左右表标识// 输入文件首行，不处理if (line.contains("factoryname") == true|| line.contains("addressed") == true) {return;}// 输入的一行预处理文本StringTokenizer itr = new StringTokenizer(line);String mapkey = new String();String mapvalue = new String();int i = 0;while (itr.hasMoreTokens()) {// 先读取一个单词String token = itr.nextToken();// 判断该地址ID就把存到"values[0]"if (token.charAt(0) >= '0' && token.charAt(0) <= '9') {mapkey = token;if (i > 0) {relationtype = "1";} else {relationtype = "2";}continue;}// 存工厂名mapvalue += token + " ";i++;}// 输出左右表context.write(new Text(mapkey), new Text(relationtype + "+"+ mapvalue));}}/** reduce解析map输出，将value中数据按照左右表分别保存，　　 * 然后求出笛卡尔积，并输出。*/public static class Reduce extends Reducer<Text, Text, Text, Text> {// 实现reduce函数public void reduce(Text key, Iterable<Text> values, Context context)throws IOException, InterruptedException {// 输出表头if (0 == time) {context.write(new Text("factoryname"), new Text("addressname"));time++;}int factorynum = 0;String[] factory = new String[10];int addressnum = 0;String[] address = new String[10];Iterator ite = values.iterator();while (ite.hasNext()) {String record = ite.next().toString();int len = record.length();int i = 2;if (0 == len) {continue;}// 取得左右表标识char relationtype = record.charAt(0);// 左表if ('1' == relationtype) {factory[factorynum] = record.substring(i);factorynum++;}// 右表if ('2' == relationtype) {address[addressnum] = record.substring(i);addressnum++;}}// 求笛卡尔积if (0 != factorynum && 0 != addressnum) {for (int m = 0; m < factorynum; m++) {for (int n = 0; n < addressnum; n++) {// 输出结果context.write(new Text(factory[m]),new Text(address[n]));}}}}}public static void main(String[] args) throws Exception {Configuration conf = new Configuration();// 这句话很关键 //conf.set("mapred.job.tracker", "192.168.1.2:9001");//可使用args//String[] ioArgs = new String[] { "MTjoin_in", "MTjoin_out" };String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();if (otherArgs.length != 2) {System.err.println("Usage: Multiple Table Join <in> <out>");System.exit(2);}Job job = new Job(conf, "Multiple Table Join");job.setJarByClass(MTjoin.class);// 设置Map和Reduce处理类job.setMapperClass(Map.class);job.setReducerClass(Reduce.class);// 设置输出类型job.setOutputKeyClass(Text.class);job.setOutputValueClass(Text.class);// 设置输入和输出目录FileInputFormat.addInputPath(job, new Path(otherArgs[0]));FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));System.exit(job.waitForCompletion(true) ? 0 : 1);}}javac -classpath hadoop-core-1.1.2.jar:/opt/hadoop-1.1.2/lib/commons-cli-1.2.jar -d firstProject firstProject/MTJoin.javajar -cvf MTJoin.jar -C firstProject/ .好好扮演自己的角色，做自己该做的事

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