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。从当中挖掘出关心的信息。例如以下

输入的是两个文件，一个代表工厂表，包括工厂名列和地址编号列；还有一个代表地址表，包括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相应关系。输出工厂名-地址名表

样本例如以下：

factory:

<span style="font-size:14px;">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
</span>

address:

<span style="font-size:14px;">addressID addressname
1 Beijing
2 Guangzhou
3 Shenzhen
4 Xian
</span>

结果：

<span style="font-size:14px;">factoryname     addressname
Beijing Red Star        Beijing
Beijing Rising  Beijing
Bank of Beijing         Beijing
Guangzhou Honda         Guangzhou
Guangzhou Development Bank      Guangzhou
Shenzhen Thunder        Shenzhen
Tencent         Shenzhen
</span>

代码例如以下：

<span style="font-size:14px;">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，然后对两列值进行切割，
     * 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</span>

<span style="font-size:14px;">        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();
                // 推断该地址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中数据依照左右表分别保存，
　　 * 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(); // 这句话非常关键 // 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); // 设置输入和输出文件夹 FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } } </span>

<span style="font-size:14px;">javac -classpath hadoop-core-1.1.2.jar:/opt/hadoop-1.1.2/lib/commons-cli-1.2.jar -d firstProject firstProject/MTJoin.java
</span>

<span style="font-size:14px;">jar -cvf MTJoin.jar -C firstProject/ .     </span>

<span style="font-size:14px;">
</span>

删除已经存在的output

<span style="font-size:14px;">hadoop fs -rmr output
</span>

<span style="font-size:14px;">hadoop fs -mkdir input
</span>

<span style="font-size:14px;">hadoop fs -put factory input
</span>

<span style="font-size:14px;"> hadoop fs -put address input
</span>

执行

<span style="font-size:14px;">hadoop jar  MTJoin.jar MTJoin input output
</span>

查看结果

<span style="font-size:14px;"> hadoop fs -cat output/part-r-00000</span>