hadoop实例---多表关联

hadoop实例---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 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
));

}

}

}

}

}

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