hadoop实例---多表关联

「爱情、让人受尽委屈。」 2022-08-05 05:26 158阅读 0赞

# [hadoop实例---多表关联][hadoop_---] #

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 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

结果：

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.java

jar -cvf MTJoin.jar -C firstProject/ .

删除已经存在的output

hadoop fs -rmr output

hadoop fs -mkdir input

hadoop fs -put factory input

hadoop fs -put address input

运行

hadoop jar  MTJoin.jar MTJoin input output

查看结果

hadoop fs -cat output/part-r-00000

[hadoop_---]: http://www.cnblogs.com/dyllove98/p/3236865.html