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:

<span style="font-size:14px;">factoryname addressedBeijing Red Star 1Shenzhen Thunder 3Guangzhou Honda 2Beijing Rising 1Guangzhou Development Bank 2Tencent 3Back of Beijing 1</span>

address:

<span style="font-size:14px;">addressID addressname1 Beijing2 Guangzhou3 Shenzhen4 Xian</span>

结果：

<span style="font-size:14px;">factoryname     addressnameBeijing Red Star        BeijingBeijing Rising  BeijingBank of Beijing         BeijingGuangzhou Honda         GuangzhouGuangzhou Development Bank      GuangzhouShenzhen Thunder        ShenzhenTencent         Shenzhen</span>

代码如下：

<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</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();                // 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);    }}</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>

运行

<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>