The simplest Hadoop 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 Hadoop framework silently handles failures. If a mapper task fails, another is executed and the input of the failed attempt is discarded.

#!/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 in a single thread using

perl script.pl run input_directory output_directory

All files in input_directory are processes. The output_directory must not exist.

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 an A (ignoring the case). You can download the template step-3-exercise.pl and execute it.

wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_media/courses:mapreduce-tutorial:step-3-exercise.txt' -O 'step-3-exercise.pl'
rm -rf step-3-out-ex; perl step-3-exercise.pl run /home/straka/wiki/cs-text-medium/ step-3-out-ex
less step-3-out-ex/part-m-*

You can also download the solution step-3-solution.pl and check the correct output.

wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_media/courses:mapreduce-tutorial:step-3-solution.txt' -O 'step-3-solution.pl'
rm -rf step-3-out-sol; perl step-3-solution.pl run /home/straka/wiki/cs-text-medium/ step-3-out-sol
less step-3-out-sol/part-m-*