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MapReduce Tutorial : Basic 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

#!/usr/bin/perl
 
package Mapper;
use Moose;
with 'Hadoop::Mapper';
 
sub map {
  my ($self, $key, $value, $context) = @_;
 
  $context->write($key, $value);
}
 
package Main;
use Hadoop::Runner;
 
my $runner = Hadoop::Runner->new(
  mapper => Mapper->new(),
  input_format => 'TextInputFormat',
  output_format => 'TextOutputFormat',
  output_compression => 0);
 
$runner->run();

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 locally (not distributed) using

perl script.pl run input_directory output_directory

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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