hadoop實例---多表關聯

多表關聯和單表關聯類似，它也是通過對原始數據進行一定的處理，從其中挖掘出關心的信息。如下

輸入的是兩個文件，一個代表工廠表，包含工廠名列和地址編號列；另一個代表地址表，包含地址名列和地址編號列。要求從輸入數據中找出工廠名和地址名的對應關系，輸出工廠名-地址名表

樣本如下：

factory:

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

address:

addressID addressname
1 Beijing
2 Guangzhou
3 Shenzhen
4 Xian

結果：

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

代碼如下：

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中先區分輸入行屬於左表還是右表，然后對兩列值進行分割，

     * 保存連接列在key值，剩余列和左右表標志在value中，最后輸出

     */

    public static class Map extends Mapper<Object, Text, Text, Text> {

 

        // 實現map函數

        public void map(Object key, Text value, Context context)

                throws IOException, InterruptedException {

            String line = value.toString();// 每行文件

            String relationtype = new String();// 左右表標識

 

            // 輸入文件首行，不處理

            if (line.contains("factoryname") == true

                    || line.contains("addressed") == true) {

                return;

            }

 

            // 輸入的一行預處理文本

            StringTokenizer itr = new StringTokenizer(line);

            String mapkey = new String();

            String mapvalue = new String();

            int i = 0;

            while (itr.hasMoreTokens()) {

                // 先讀取一個單詞

                String token = itr.nextToken();

                // 判斷該地址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;

                }

 

                // 存工廠名

                mapvalue += token + " ";

                i++;

            }

 

            // 輸出左右表

            context.write(new Text(mapkey), new Text(relationtype + "+"+ mapvalue));

        }

    }

 

    /*

     * reduce解析map輸出，將value中數據按照左右表分別保存，

　　* 然后求出笛卡爾積，並輸出。

     */

    public static class Reduce extends Reducer<Text, Text, Text, Text> {

 

        // 實現reduce函數

        public void reduce(Text key, Iterable<Text> values, Context context)

                throws IOException, InterruptedException {

 

            // 輸出表頭

            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;

                }

 

                // 取得左右表標識

                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++;

                }

            }

 

            // 求笛卡爾積

            if (0 != factorynum && 0 != addressnum) {

                for (int m = 0; m < factorynum; m++) {

                    for (int n = 0; n < addressnum; n++) {

                        // 輸出結果

                        context.write(new Text(factory[m]),

                                new Text(address[n]));

                    }

                }

            }

 

        }

    }

 

    public static void main(String[] args) throws Exception {

        Configuration conf = new Configuration();

        // 這句話很關鍵

  //      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);

 

        // 設置Map和Reduce處理類

        job.setMapperClass(Map.class);

        job.setReducerClass(Reduce.class);

 

        // 設置輸出類型

        job.setOutputKeyClass(Text.class);

        job.setOutputValueClass(Text.class);

 

        // 設置輸入和輸出目錄

        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.java

jar -cvf MTJoin.jar -C firstProject/ .     

刪除已經存在的output

hadoop fs -rmr output

hadoop fs -mkdir input

hadoop fs -put factory input

 hadoop fs -put address input

運行

hadoop jar  MTJoin.jar MTJoin input output

查看結果

 hadoop fs -cat output/part-r-00000