MapReduce编程系列 — 6：多表关联

1、项目名称：

2、程序代码：

版本一（详细版）：

package com.mtjoin;

import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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;
    public static class Map extends Mapper<Object, Text, Text, Text>{
        public void map(Object key, Text value, Context context)throws IOException,InterruptedException{
            System.out.println("mapper........................");
            String line = value.toString();            
            if(line.contains("factoryname")==true || line.contains("addressID")== true){
                return ;
            }
            int i = 0;
            while(line.charAt(i) >= '9'|| line.charAt(i) <= '0'){
                i++;
            }

            if(line.charAt(0) >= '9'|| line.charAt(0) <= '0'){
                int j = i-1;
                while(line.charAt(j) != ' ') j--;
                System.out.println("key:"+line.substring(i)+"  value:"+line.substring(0,j));

                String values[] = {line.substring(0, j),line.substring(i)};

                context.write(new Text(values[1]), new Text("1+"+values[0]));
            }
            else {
                int j = i + 1;
                while(line.charAt(j)!=' ')  j++;
                System.out.println("key:"+line.substring(0, i+1)+"  value:"+line.substring(j));
                String values[] ={line.substring(0,i+1),line.substring(j)};
                context.write(new Text(values[0]), new Text("2+"+values[1]));
            }
        }
    }

        public static class Reduce extends Reducer<Text, Text, Text, Text>{
            public void reduce(Text key, Iterable<Text> values, Context context)throws IOException,InterruptedException{
                System.out.println("reducer........................");
                if( time == 0){
                    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();
                    char type = record.charAt(0);
                    if(type == '1'){
                        factory[factorynum] = record.substring(2);
                        factorynum++;
                    }
                    else{
                        address[addressnum] = record.substring(2);
                        addressnum++;
                    }
                }
                if(factorynum != 0 && addressnum != 0){
                    for(int m = 0 ; m < factorynum ; m++){
                        for(int n = 0; n < addressnum; n++){
                            context.write(new Text(factory[m]), new Text(address[n]));
                            System.out.println("factoryname:"+factory[m]+"  addressname:"+address[n]);
                        }
                    }
                }
            }
        }
    public static void main(String [] args)throws Exception{
        Configuration conf = new Configuration();
        String otherArgs[] = new GenericOptionsParser(conf,args).getRemainingArgs();
        if(otherArgs.length != 2){
            System.err.println("Usage:MTjoin<in><out>");
            System.exit(2);
        }
        Job job = new Job(conf,"multiple table join");
        job.setJarByClass(MTjoin.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));        
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        System.exit(job.waitForCompletion(true)? 0:1);
        }
}

版本二（简化版）：

package com.mtjoin;

import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
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;
    public static class Map extends Mapper<Object, Text, Text, Text>{
        public void map(Object key, Text value, Context context)throws IOException,InterruptedException{
            System.out.println("mapper........................");
            String line = value.toString();            
            if(line.contains("factoryname")==true || line.contains("addressID")== true){
                return ;
            }
            int len = line.length();

            if(line.charAt(0) > '9'|| line.charAt(0) < '0'){
                System.out.println("key:"+line.substring(len-1)+"  value:"+line.substring(0,len-2));

                String values[] = {line.substring(0, len-2),line.substring(len-1)};

                context.write(new Text(values[1]), new Text("1+"+values[0]));
            }
            else {
                System.out.println("key:"+line.substring(0, 1)+"  value:"+line.substring(2));
                String values[] ={line.substring(0,1),line.substring(2)};
                context.write(new Text(values[0]), new Text("2+"+values[1]));
            }
        }
    }

        public static class Reduce extends Reducer<Text, Text, Text, Text>{
            public void reduce(Text key, Iterable<Text> values, Context context)throws IOException,InterruptedException{
                System.out.println("reducer........................");
                if( time == 0){
                    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();
                    char type = record.charAt(0);
                    if(type == '1'){
                        factory[factorynum] = record.substring(2);
                        factorynum++;
                    }
                    else{
                        address[addressnum] = record.substring(2);
                        addressnum++;
                    }
                }
                if(factorynum != 0 && addressnum != 0){
                    for(int m = 0 ; m < factorynum ; m++){
                        for(int n = 0; n < addressnum; n++){
                            context.write(new Text(factory[m]), new Text(address[n]));
                            System.out.println("factoryname:"+factory[m]+"  addressname:"+address[n]);
                        }
                    }
                }
            }
        }

    public static void main(String [] args)throws Exception{
        Configuration conf = new Configuration();
        String otherArgs[] = new GenericOptionsParser(conf,args).getRemainingArgs();
        if(otherArgs.length != 2){
            System.err.println("Usage:MTjoin<in><out>");
            System.exit(2);
        }
        Job job = new Job(conf,"multiple table join");
        job.setJarByClass(MTjoin.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        FileInputFormat.addInputPath(job, new Path(otherArgs[0]));        
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
        System.exit(job.waitForCompletion(true)? 0:1);
        }
}

3、测试数据：

address：

addressID addressname
1 Beijing
2 Guangzhou
3 Shenzhen
4 Xian

factory：

factoryname addressname
Beijing Red Star 1
Shenzhen Thunder 3
Guangzhou Honda 2
Beijing Rising 1
Guangzhou Development Bank 2
Tencent 3
Bank of Beijing 1

4、运行过程：

14/09/24 09:39:55 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
14/09/24 09:39:55 WARN mapred.JobClient: No job jar file set. User classes may not be found. See JobConf(Class) or JobConf#setJar(String).
14/09/24 09:39:55 INFO input.FileInputFormat: Total input paths to process : 2
14/09/24 09:39:55 WARN snappy.LoadSnappy: Snappy native library not loaded
14/09/24 09:39:55 INFO mapred.JobClient: Running job: job_local_0001
14/09/24 09:39:55 INFO util.ProcessTree: setsid exited with exit code 0
14/09/24 09:39:55 INFO mapred.Task: Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@e095722
14/09/24 09:39:55 INFO mapred.MapTask: io.sort.mb = 100
14/09/24 09:39:55 INFO mapred.MapTask: data buffer = 79691776/99614720
14/09/24 09:39:55 INFO mapred.MapTask: record buffer = 262144/327680
mapper........................
mapper........................
key:1 value:Beijing Red Star
mapper........................
key:3 value:Shenzhen Thunder
mapper........................
key:2 value:Guangzhou Honda
mapper........................
key:1 value:Beijing Rising
mapper........................
key:2 value:Guangzhou Development Bank
mapper........................
key:3 value:Tencent
mapper........................
key:1 value:Bank of Beijing
14/09/24 09:39:55 INFO mapred.MapTask: Starting flush of map output
14/09/24 09:39:55 INFO mapred.MapTask: Finished spill 0
14/09/24 09:39:55 INFO mapred.Task: Task:attempt_local_0001_m_000000_0 is done. And is in the process of commiting
14/09/24 09:39:56 INFO mapred.JobClient: map 0% reduce 0%
14/09/24 09:39:58 INFO mapred.LocalJobRunner:
14/09/24 09:39:58 INFO mapred.Task: Task 'attempt_local_0001_m_000000_0' done.
14/09/24 09:39:58 INFO mapred.Task: Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@7dabd20
14/09/24 09:39:58 INFO mapred.MapTask: io.sort.mb = 100
14/09/24 09:39:58 INFO mapred.MapTask: data buffer = 79691776/99614720
14/09/24 09:39:58 INFO mapred.MapTask: record buffer = 262144/327680
mapper........................
mapper........................
key:1 value:Beijing
mapper........................
key:2 value:Guangzhou
mapper........................
key:3 value:Shenzhen
mapper........................
key:4 value:Xian
14/09/24 09:39:58 INFO mapred.MapTask: Starting flush of map output
14/09/24 09:39:58 INFO mapred.MapTask: Finished spill 0
14/09/24 09:39:58 INFO mapred.Task: Task:attempt_local_0001_m_000001_0 is done. And is in the process of commiting
14/09/24 09:39:59 INFO mapred.JobClient: map 100% reduce 0%
14/09/24 09:40:01 INFO mapred.LocalJobRunner:
14/09/24 09:40:01 INFO mapred.Task: Task 'attempt_local_0001_m_000001_0' done.
14/09/24 09:40:01 INFO mapred.Task: Using ResourceCalculatorPlugin : org.apache.hadoop.util.LinuxResourceCalculatorPlugin@49fa6f3c
14/09/24 09:40:01 INFO mapred.LocalJobRunner:
14/09/24 09:40:01 INFO mapred.Merger: Merging 2 sorted segments
14/09/24 09:40:01 INFO mapred.Merger: Down to the last merge-pass, with 2 segments left of total size: 218 bytes
14/09/24 09:40:01 INFO mapred.LocalJobRunner:
reducer........................
factoryname:Beijing Red Star addressname:Beijing
factoryname:Beijing Rising addressname:Beijing
factoryname:Bank of Beijing addressname:Beijing
reducer........................
factoryname:Guangzhou Honda addressname:Guangzhou
factoryname:Guangzhou Development Bank addressname:Guangzhou
reducer........................
factoryname:Shenzhen Thunder addressname:Shenzhen
factoryname:Tencent addressname:Shenzhen
reducer........................
14/09/24 09:40:01 INFO mapred.Task: Task:attempt_local_0001_r_000000_0 is done. And is in the process of commiting
14/09/24 09:40:01 INFO mapred.LocalJobRunner:
14/09/24 09:40:01 INFO mapred.Task: Task attempt_local_0001_r_000000_0 is allowed to commit now
14/09/24 09:40:01 INFO output.FileOutputCommitter: Saved output of task 'attempt_local_0001_r_000000_0' to hdfs://localhost:9000/user/hadoop/mtjoin_output02
14/09/24 09:40:04 INFO mapred.LocalJobRunner: reduce > reduce
14/09/24 09:40:04 INFO mapred.Task: Task 'attempt_local_0001_r_000000_0' done.
14/09/24 09:40:05 INFO mapred.JobClient: map 100% reduce 100%
14/09/24 09:40:05 INFO mapred.JobClient: Job complete: job_local_0001
14/09/24 09:40:05 INFO mapred.JobClient: Counters: 22
14/09/24 09:40:05 INFO mapred.JobClient:   Map-Reduce Framework
14/09/24 09:40:05 INFO mapred.JobClient:     Spilled Records=22
14/09/24 09:40:05 INFO mapred.JobClient:     Map output materialized bytes=226
14/09/24 09:40:05 INFO mapred.JobClient:     Reduce input records=11
14/09/24 09:40:05 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=0
14/09/24 09:40:05 INFO mapred.JobClient:     Map input records=13
14/09/24 09:40:05 INFO mapred.JobClient:     SPLIT_RAW_BYTES=238
14/09/24 09:40:05 INFO mapred.JobClient:     Map output bytes=192
14/09/24 09:40:05 INFO mapred.JobClient:     Reduce shuffle bytes=0
14/09/24 09:40:05 INFO mapred.JobClient:     Physical memory (bytes) snapshot=0
14/09/24 09:40:05 INFO mapred.JobClient:     Reduce input groups=4
14/09/24 09:40:05 INFO mapred.JobClient:     Combine output records=0
14/09/24 09:40:05 INFO mapred.JobClient:     Reduce output records=8
14/09/24 09:40:05 INFO mapred.JobClient:     Map output records=11
14/09/24 09:40:05 INFO mapred.JobClient:     Combine input records=0
14/09/24 09:40:05 INFO mapred.JobClient:     CPU time spent (ms)=0
14/09/24 09:40:05 INFO mapred.JobClient:     Total committed heap usage (bytes)=813170688
14/09/24 09:40:05 INFO mapred.JobClient:   File Input Format Counters
14/09/24 09:40:05 INFO mapred.JobClient:     Bytes Read=216
14/09/24 09:40:05 INFO mapred.JobClient:   FileSystemCounters
14/09/24 09:40:05 INFO mapred.JobClient:     HDFS_BYTES_READ=586
14/09/24 09:40:05 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=122093
14/09/24 09:40:05 INFO mapred.JobClient:     FILE_BYTES_READ=1658
14/09/24 09:40:05 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=202
14/09/24 09:40:05 INFO mapred.JobClient:   File Output Format Counters
14/09/24 09:40:05 INFO mapred.JobClient:     Bytes Written=202

5、运行结果：

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

posted @ 2015-12-05 11:02 我是一名老菜鸟阅读(455) 评论(0) 编辑收藏举报

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MapReduce编程系列 — 6：多表关联

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