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courses:mapreduce-tutorial:step-5 [2012/01/28 12:50]
majlis Commands for execution were added. 		courses:mapreduce-tutorial:step-5 [2012/01/29 20:21]
straka
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 The interesting part of a Hadoop job is the //reducer// -- after all mappers produce the (key, value) pairs, for every unique key and all its values a ''reduce'' function is called. The ''reduce'' function can output (key, value) pairs, which are written to disk. The interesting part of a Hadoop job is the //reducer// -- after all mappers produce the (key, value) pairs, for every unique key and all its values a ''reduce'' function is called. The ''reduce'' function can output (key, value) pairs, which are written to disk.
  
-The ''reduce'' is similar to ''map'', but instead of one value it gets an iterator, which enumerates all values associated with the key:+The ''reduce'' is similar to ''map'', but instead of one value it gets an iterator (instance of Hadoop::Runner::ValueIterator), which enumerates all values associated with the key:
  
 <file perl> <file perl>
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   less step-5-out-sol2/part-*   less step-5-out-sol2/part-*
  
 +----
  
 +<html> 
 +<table style="width:100%"> 
 +<tr> 
 +<td style="text-align:left; width: 33%; "></html>[[step-4|Step 4]]: Counters.<html></td> 
 +<td style="text-align:center; width: 33%; "></html>[[.|Overview]]<html></td> 
 +<td style="text-align:right; width: 33%; "></html>[[step-6|Step 6]]: Running on cluster.<html></td> 
 +</tr> 
 +</table> 
 +</html>

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