Hadoop基于DistributedCache的复制联结

上一种方法让所有的数据在网络上重排，然后在许多情况下大部分数据又被丢弃了，如果我们在map阶段就去除不必要的数据，会更有效率。

 

当较小的数据源可以装入mapper的内存时，可以通过将较小的数据源复制到所有的mapper，并在mapper阶段进行联结，以实现效率的极大提高。

 

管理分布式缓存的类为DistributedCache，使用这个类有2个步骤:

第一步：调用静态方法DistributedCache.addCacheFile( )来设定要传播到所有结点的文件，这些文件被指定为URI对象。

第二步：在每个单独TaskTracker上的mapper会调用静态方法DistributedCache.getLocalCacheFiles( )来获取数组本地副本所在的本地文件路径。

 

Mapper接口还有另外两个抽象方法：configure( )和close( )。当我们最初实例化Mapper时，调用configure( )方法，而在mapper结束处理其分片时，调用close( )方法。MapReduceBase类为这些方法提供默认的no-op实现。

 

在这里需要重写configure( ),目的是在mapper第一次初始化时，将连接的数据加载到内存中。通过这种方式，在每次调用map( )处理一条新记录时，都可以获得这个数据。

 

当调用configure( )时，会得到一个文件路径数组，指向DistributedCache填入的本地文件副本，在本示例下数组长度为1。

使用标准的Java文件I/O读取该文件。

这里假定文件的每一行都是一条记录，键值对以逗号分隔，且键是唯一的并会用于联结。

configure( )将该文件读入名为joinData的Java散列表，它可以在mapper的整个生命周期中获得（以joinData的形式驻留在内存中）。

如果在joinData中找不到联结键，就丢弃这个记录，否则将这个联结键与joinData中的值进行匹配，并连接这些值。

 

结果直接写入HDFS，因为我们不需要任何reducer做进一步的处理。

 

待处理的数据：

Customers

1,Stephanie Leung,555-555-5555
2,Edward Kim,123-456-7890
3,Jose Madriz,281-330-8004
4,David Stork,408-555-0000 

Orders

3,A,12.95,02-Jun-2008
1,B,88.25,20-May-2008
2,C,32.00,30-Nov-2007
3,D,25.02,22-Jan-2009

 

程序如下：

package com.lcy.hadoop.advanced;

import java.io.BufferedReader;
import java.io.FileReader;
import java.io.IOException;
import java.util.Hashtable;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.KeyValueTextInputFormat;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

public class DataJoinDC2 extends Configured implements Tool {

    public static class MapClass extends MapReduceBase implements
            Mapper<Text, Text, Text, Text> {
        private Hashtable<String, String> joinData = new Hashtable<String, String>();

        @Override
        public void configure(JobConf conf) {
            try {
                Path[] cacheFiles = DistributedCache.getLocalCacheFiles(conf);
                if (cacheFiles != null && cacheFiles.length > 0) {
                    String line;
                    String[] tokens;
                    BufferedReader joinReader = new BufferedReader(
                            new FileReader(cacheFiles[0].toString()));
                    try {
                        while ((line = joinReader.readLine()) != null) {
                            tokens = line.split(",", 2);
                            joinData.put(tokens[0], tokens[1]);
                        }
                    } finally {
                        joinReader.close();
                    }
                }
            } catch (IOException e) {
                System.err.println("Exception reading DistributedCache: " + e);
            }
        }

        public void map(Text key, Text value,
                OutputCollector<Text, Text> output, Reporter reporter)
                throws IOException {
            String joinValue = joinData.get(key.toString());
            if (joinValue != null) {
                output.collect(key,
                        new Text(value.toString() + "," + joinValue));
            }
        }
    }

    @Override
    public int run(String[] args) throws Exception {
        Configuration conf = getConf();
        DistributedCache.addCacheFile(new Path(args[0]).toUri(), conf);
        JobConf job = new JobConf(conf, DataJoinDC2.class);
        Path in = new Path(args[1]);
        Path out = new Path(args[2]);
        FileInputFormat.setInputPaths(job, in);
        FileOutputFormat.setOutputPath(job, out);
        job.setJobName("DataJoin with DistributedCache");
        job.setMapperClass(MapClass.class);
        job.setNumReduceTasks(0);
        job.setInputFormat(KeyValueTextInputFormat.class);
        job.setOutputFormat(TextOutputFormat.class);
        job.set("key.value.separator.in.input.line", ",");
        JobClient.runJob(job);
        return 0;
    }

    public static void main(String[] args) throws Exception {
        int res = ToolRunner.run(new Configuration(), new DataJoinDC2(), args);
        System.exit(res);
    }

}

 

运行程序：

hadoop@lcyvino-VirtualBox:/usr/local/hadoop-1.2.1$ hadoop jar /usr/local/testJar/DataJoinDC2.jar com.lcy.hadoop.advanced.DataJoinDC2 DataJoin/Customer.txt DataJoin/Orders.txt output
15/03/23 15:48:35 INFO util.NativeCodeLoader: Loaded the native-hadoop library
15/03/23 15:48:35 WARN snappy.LoadSnappy: Snappy native library not loaded
15/03/23 15:48:35 INFO mapred.FileInputFormat: Total input paths to process : 1
15/03/23 15:48:36 INFO mapred.JobClient: Running job: job_201503231529_0004
15/03/23 15:48:37 INFO mapred.JobClient:  map 0% reduce 0%
15/03/23 15:48:49 INFO mapred.JobClient:  map 100% reduce 0%
15/03/23 15:48:51 INFO mapred.JobClient: Job complete: job_201503231529_0004
15/03/23 15:48:51 INFO mapred.JobClient: Counters: 20
15/03/23 15:48:51 INFO mapred.JobClient:   Job Counters 
15/03/23 15:48:51 INFO mapred.JobClient:     SLOTS_MILLIS_MAPS=21822
15/03/23 15:48:51 INFO mapred.JobClient:     Total time spent by all reduces waiting after reserving slots (ms)=0
15/03/23 15:48:51 INFO mapred.JobClient:     Total time spent by all maps waiting after reserving slots (ms)=0
15/03/23 15:48:51 INFO mapred.JobClient:     Launched map tasks=2
15/03/23 15:48:51 INFO mapred.JobClient:     Data-local map tasks=2
15/03/23 15:48:51 INFO mapred.JobClient:     SLOTS_MILLIS_REDUCES=0
15/03/23 15:48:51 INFO mapred.JobClient:   File Input Format Counters 
15/03/23 15:48:51 INFO mapred.JobClient:     Bytes Read=132
15/03/23 15:48:51 INFO mapred.JobClient:   File Output Format Counters 
15/03/23 15:48:51 INFO mapred.JobClient:     Bytes Written=191
15/03/23 15:48:51 INFO mapred.JobClient:   FileSystemCounters
15/03/23 15:48:51 INFO mapred.JobClient:     HDFS_BYTES_READ=344
15/03/23 15:48:51 INFO mapred.JobClient:     FILE_BYTES_WRITTEN=117866
15/03/23 15:48:51 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=191
15/03/23 15:48:51 INFO mapred.JobClient:   Map-Reduce Framework
15/03/23 15:48:51 INFO mapred.JobClient:     Map input records=4
15/03/23 15:48:51 INFO mapred.JobClient:     Physical memory (bytes) snapshot=82599936
15/03/23 15:48:51 INFO mapred.JobClient:     Spilled Records=0
15/03/23 15:48:51 INFO mapred.JobClient:     CPU time spent (ms)=380
15/03/23 15:48:51 INFO mapred.JobClient:     Total committed heap usage (bytes)=31850496
15/03/23 15:48:51 INFO mapred.JobClient:     Virtual memory (bytes) snapshot=697352192
15/03/23 15:48:51 INFO mapred.JobClient:     Map input bytes=88
15/03/23 15:48:51 INFO mapred.JobClient:     Map output records=4
15/03/23 15:48:51 INFO mapred.JobClient:     SPLIT_RAW_BYTES=212

 

运行结果：