do so by shoving the keys into memcache, and a few implementations might 
    work: 
       1. One memcache key, all mappers do a get-update-put on the value, 
       which is a list of keys 
       2. One memcache key per mapper output key, an incrementing integer. 
       Next integer to use is maintained in a special memcache key, and mappers 
       request that next key every time they want to output a value. 
 Doesn't solve 
       duplicate keys, and sharding the reducers has to get into 
 modulo-arithmetic 
       (reducer 5 of 20 takes every (20x + 5)th key), and that kinda 
 makes my head 
       hurt. 
       3. Variant of (1), but with each mapper getting its own key to which 
       to write.  Better concurrency, but doesn't handle uniquness and 
 would thus 
       require a second pass. 




t) const {
    return NULL;
  }

  /**
   * Create an application record reader.

 * @return the new RecordReader or NULL, if the Java RecordReader should be
   *    used.
   */
  virtual RecordReader* createRecordReader(MapContext& context) const {
    return NULL;
  }

  /**
   * Create an application record writer.

 * @return the new RecordWriter or NULL, if the Java RecordWriter should be
   *    used.
   */

 virtual RecordWriter* createRecordWriter(ReduceContext& context) const {
    return NULL;
  }

  virtual ~Factory() {}
};

/**

* Start the event handling loop that runs the task. This will use the given
* factory to create Mappers and Reducers and so on.
* @return true, if the task succeeded.
*/
bool runTask(const Factory& factory);

}

#endif






http://groups.google.com/group/httpmr-discuss/files


----- Forwarded message from Robert Barta <rho@devc.at> -----

Date: Sun, 6 Jul 2008 12:46:44 +0200
From: Robert Barta <rho@devc.at>
To: rho@devc.at
Reply-To: rho@devc.at
Subject: [rho@devc.at: mapreduce]

----- Forwarded message from Robert Barta <rho@devc.at> -----

Date: Fri, 4 Jul 2008 15:26:12 +0200
From: Robert Barta <rho@devc.at>
To: rho@devc.at
Reply-To: rho@devc.at
Subject: mapreduce

mapreduce
slide: http://labs.google.com/papers/mapreduce-osdi04-slides/index.html
paper: http://labs.google.com/papers/mapreduce.html

http://code.google.com/edu/parallel/mapreduce-tutorial.html
paper: http://www.cs.vu.nl/~ralf/MapReduce/paper.pdf
criticism: http://www.databasecolumn.com/2008/01/mapreduce-a-major-step-back.html


[1] "MapReduce:  Simplified Data Processing on Large Clusters," Jeff Dean and Sanjay Ghemawat, Proceedings of the 2004 OSDI Conference, 2004.

----- End forwarded message -----

http://swik.net/hadoop/Google+Blog+Search:+hadoop/MapReduce+with+multi-languages/b9msa

http://cs.baylor.edu/~speegle/5335/2007slides/MapReduceMerge.pdf

http://wiki.apache.org/pig/PigTutorial#Pig_Script_1

http://elichen.blogspot.com/2008_07_01_archive.html

http://startupmeme.com/google-google-you-slow-acquired-companies-down/

http://www.tropo.com/dave/blog/2008/07/13/infinitesortedobjectsequence-for-large-data-sets-in-python/

http://genericlanguage.blogspot.com/2008/07/mapreduce-part-2-pagerank.html

http://www.databasecolumn.com/2008/01/mapreduce-continued.html


http://tech.rufy.com/2006/08/mapreduce-for-ruby-ridiculously-easy.html

http://www.raja-gopal.com/?p=42


----- Forwarded message from Robert Barta <rho@devc.at> -----

Date: Sat, 12 Jul 2008 09:54:06 +0200
From: Robert Barta <rho@devc.at>
To: rho@devc.at
Reply-To: rho@devc.at
Subject: [rho@devc.at: RDF hadoop] mapreduce

http://www.uwtv.org/programs/displayevent.aspx?rID=3898

----- Forwarded message from Robert Barta <rho@devc.at> -----

Date: Wed, 9 Jul 2008 15:32:31 +0200
From: Robert Barta <rho@devc.at>
To: rho@devc.at
Reply-To: rho@devc.at
Subject: RDF hadoop

rd> hmm someone emptied two pages about hbase and rdf:
          http://wiki.apache.org/hadoop/HRDF?action=info
          http://wiki.apache.org/hadoop/Hbase/RDF?action=info           [14:57]
<Shepard> at least http://wiki.apache.org/incubator/HRdfStoreProposal is still
          there



%cancel

http://mark.aufflick.com/blog/2006/11/18/mapreduce-in-perl

http://www.lexemetech.com/search/label/MapReduce

http://backpan.perl.org/authors/id/I/IW/IWOODHEAD/MapReduce-0.03.readme

http://github.com/naoya/mapreduce-lite/tree/master

http://www.grotto-group.com/~gulfie/projects/cluster/pdmr.subpage.html