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courses:mapreduce-tutorial:step-3 [2012/01/24 19:14]
straka 		courses:mapreduce-tutorial:step-3 [2012/01/24 20:09]
straka
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 The simplest MR job consists of a mapper only.  The input data is divided in several parts, every processed by an independent mapper, and the results are collected in one directory, one file per mapper. The simplest MR job consists of a mapper only.  The input data is divided in several parts, every processed by an independent mapper, and the results are collected in one directory, one file per mapper.
  
-===== Example perl mapper =====+===== Example Perl mapper =====
  
-<code perl>+<code perl mapper.pl>
 #!/usr/bin/perl #!/usr/bin/perl
  
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 The values ''input_format'', ''output_format'' and ''output_compression'' could be left out, because they are all set to their default value. The values ''input_format'', ''output_format'' and ''output_compression'' could be left out, because they are all set to their default value.
  
-Resulting script can be executed using +Resulting script can be executed locally (not distributed) using
   perl script.pl run input_directory output_directory   perl script.pl run input_directory output_directory
- 
 All files in input_directory are processes. The output_directory must not exist. All files in input_directory are processes. The output_directory must not exist.
 +
 +===== Exercise =====
 +
 +To check that your Hadoop environment works, try running a MR job on ''/home/straka/wiki/cs-text'', which outputs only articles with names beginning with a (ignoring the case).
 +

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