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From Apache Wiki <wikidi...@apache.org>
Subject [Lucene-hadoop Wiki] Update of "HadoopMapReduce" by MattKangas
Date Wed, 19 Apr 2006 16:11:52 GMT
Dear Wiki user,

You have subscribed to a wiki page or wiki category on "Lucene-hadoop Wiki" for change notification.

The following page has been changed by MattKangas:
http://wiki.apache.org/lucene-hadoop/HadoopMapReduce

------------------------------------------------------------------------------
  offsets.
  
  As key-value pairs are read from the !RecordReader they are
- passed to the configured Mapper. The user supplied Mapper does
+ passed to the configured [http://lucene.apache.org/hadoop/docs/api/org/apache/hadoop/mapred/Mapper.html
Mapper]. The user supplied Mapper does
  whatever it wants with the input pair and calls	[http://lucene.apache.org/hadoop/docs/api/org/apache/hadoop/mapred/OutputCollector.html#collect(org.apache.hadoop.io.WritableComparable,%20org.apache.hadoop.io.Writable)
OutputCollector.collect] with key-value pairs of its own choosing. The output it
  generates must use one key class and one value class, because
- the Map output will be eventually written into a !SequenceFile,
+ the Map output will be eventually written into a [http://lucene.apache.org/hadoop/docs/api/org/apache/hadoop/io/SequenceFile.html
SequenceFile],
  which has per file type information and all the records must
  have the same type (use subclassing if you want to output
  different data structures). The Map input and output key-value
@@ -39, +39 @@

  
  When Mapper output is collected it is partitioned, which means
  that it will be written to the output specified by the
- Partitioner. The default !HashPartitioner uses the key value's
+ [http://lucene.apache.org/hadoop/docs/api/org/apache/hadoop/mapred/Partitioner.html Partitioner].
The default [http://lucene.apache.org/hadoop/docs/api/org/apache/hadoop/mapred/lib/HashPartitioner.html
HashPartitioner] uses the key value's
  hashcode (which means that for even workload on the Reduce tasks
  the key class hashCode must be good).
  

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