// Setting up the cache for the application          1. Copy the requisite files to the FileSystem:          $ bin/hadoop fs -copyFromLocal lookup.dat /myapp/lookup.dat       $ bin/hadoop fs -copyFromLocal map.zip /myapp/map.zip       $ bin/hadoop fs -copyFromLocal mylib.jar /myapp/mylib.jar     $ bin/hadoop fs -copyFromLocal mytar.tar /myapp/mytar.tar     $ bin/hadoop fs -copyFromLocal mytgz.tgz /myapp/mytgz.tgz     $ bin/hadoop fs -copyFromLocal mytargz.tar.gz /myapp/mytargz.tar.gz          2. Setup the application's JobConf:          JobConf job = new JobConf();　　  //　#lookup.dat 表示给前面的这个文件取一个别名，类似sql里面的as别名一样     DistributedCache.addCacheFile(new URI("/myapp/lookup.dat#lookup.dat"),                                    job);     DistributedCache.addCacheArchive(new URI("/myapp/map.zip", job);     DistributedCache.addFileToClassPath(new Path("/myapp/mylib.jar"), job);     DistributedCache.addCacheArchive(new URI("/myapp/mytar.tar", job);     DistributedCache.addCacheArchive(new URI("/myapp/mytgz.tgz", job);     DistributedCache.addCacheArchive(new URI("/myapp/mytargz.tar.gz", job);          3. Use the cached files in the Mapper     or Reducer:          public static class MapClass extends MapReduceBase       implements Mapper
     
       {            private Path[] localArchives;       private Path[] localFiles;              public void configure(JobConf job) {         // Get the cached archives/files         localArchives = DistributedCache.getLocalCacheArchives(job);         localFiles = DistributedCache.getLocalCacheFiles(job);       }              public void map(K key, V value,                        OutputCollector
      
        output, Reporter reporter)        throws IOException {         // Use data from the cached archives/files here         // ...         // ...         output.collect(k, v);       }     }