Distribute Cached 使用
在Kettle中说到Pentaho的MapReduce要用到它，就查了一下关于它的资料，以下是从官方查到的内容，记录一下。
DistributedCache: 一些比较小的需要共享的文件或者jar包，我们先存到hdfs上，然后在MapReduce线程当中进行共享，直接用了。
// 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<K, V, K, V> {
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<K, V> output, Reporter reporter)
throws IOException {
// Use data from the cached archives/files here
// ...
// ...
output.collect(k, v);
}
}
