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/**
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.hadoop.filecache;

import org.apache.hadoop.classification.InterfaceAudience;
import org.apache.hadoop.classification.InterfaceStability;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.Job;

/**
 * Distribute application-specific large, read-only files efficiently.
 * 
 * 

DistributedCache is a facility provided by the Map-Reduce * framework to cache files (text, archives, jars etc.) needed by applications. *

* *

Applications specify the files, via urls (hdfs:// or http://) to be cached * via the {@link org.apache.hadoop.mapred.JobConf}. The * DistributedCache assumes that the files specified via urls are * already present on the {@link FileSystem} at the path specified by the url * and are accessible by every machine in the cluster.

* *

The framework will copy the necessary files on to the slave node before * any tasks for the job are executed on that node. Its efficiency stems from * the fact that the files are only copied once per job and the ability to * cache archives which are un-archived on the slaves.

* *

DistributedCache can be used to distribute simple, read-only * data/text files and/or more complex types such as archives, jars etc. * Archives (zip, tar and tgz/tar.gz files) are un-archived at the slave nodes. * Jars may be optionally added to the classpath of the tasks, a rudimentary * software distribution mechanism. Files have execution permissions. * In older version of Hadoop Map/Reduce users could optionally ask for symlinks * to be created in the working directory of the child task. In the current * version symlinks are always created. If the URL does not have a fragment * the name of the file or directory will be used. If multiple files or * directories map to the same link name, the last one added, will be used. All * others will not even be downloaded.

* *

DistributedCache tracks modification timestamps of the cache * files. Clearly the cache files should not be modified by the application * or externally while the job is executing.

* *

Here is an illustrative example on how to use the * DistributedCache:

*

 *     // 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();
 *     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 {@link org.apache.hadoop.mapred.Mapper}
 *     or {@link org.apache.hadoop.mapred.Reducer}:
 *     
 *     public static class MapClass extends MapReduceBase  
 *     implements Mapper<K, V, K, V> {
 *     
 *       private Path[] localArchives;
 *       private Path[] localFiles;
 *       
 *       public void configure(JobConf job) {
 *         // Get the cached archives/files
 *         File f = new File("./map.zip/some/file/in/zip.txt");
 *       }
 *       
 *       public void map(K key, V value, 
 *                       OutputCollector<K, V> output, Reporter reporter) 
 *       throws IOException {
 *         // Use data from the cached archives/files here
 *         // ...
 *         // ...
 *         output.collect(k, v);
 *       }
 *     }
 *     
 * 

* * It is also very common to use the DistributedCache by using * {@link org.apache.hadoop.util.GenericOptionsParser}. * * This class includes methods that should be used by users * (specifically those mentioned in the example above, as well * as {@link DistributedCache#addArchiveToClassPath(Path, Configuration)}), * as well as methods intended for use by the MapReduce framework * (e.g., {@link org.apache.hadoop.mapred.JobClient}). * * @see org.apache.hadoop.mapred.JobConf * @see org.apache.hadoop.mapred.JobClient * @see org.apache.hadoop.mapreduce.Job */ @InterfaceAudience.Public @InterfaceStability.Stable public class DistributedCache extends org.apache.hadoop.mapreduce.filecache.DistributedCache { // }




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