During the mapper or reducer task execution the following steps take place:

• Perl script is executed in the current directory, ie. in the directory where the job was executed / submitted from.
• Mapper/Reducer object is constructed.
• Method setup($self, $context) is called on this object. The $context can be already used to produce (key, value) pairs or increment counters.
• Method map or reduce is called for all input values.
• Method cleanup($self, $context) is called after all (key, value) pairs of this task are processed. Again, the $context can be used to produce (key, value) pairs or increment counters.
• Perl script finishes.

The setup and cleanup methods are very useful for initialization and cleanup of the tasks.

Please note that complex initialization should not be performed during construction of Mapper and Reducer objects, as these are constructed every time the script is executed.

Improve the step-11-wc-without-combiner.pl script by manually combining the results in the Mapper – create a hash of word occurrences, populate it during the map calls without outputting results and finally output all (key, value) pairs in the cleanup method.

wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_media/courses:mapreduce-tutorial:step-5-solution1.txt' -O 'step-11-wc-without-combiner.pl'
rm -rf step-11-out-wout; time perl step-11-wc-without-combiner.pl run /home/straka/wiki/cs-text-medium/ step-11-out-wout
less step-11-out-wout/part-*
    

Measure the improvement.

You can also download the solution step-11-wc-with-perl-hash.pl and check the correct output.

wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_media/courses:mapreduce-tutorial:step-11-solution.txt' -O 'step-11-wc-with-perl-hash.pl'
rm -rf step-11-out-with-hash; time perl step-11-wc-with-perl-hash.pl run /home/straka/wiki/cs-text-medium/ step-11-out-with-hash
less step-11-out-with-hash/part-*

As you have seen, the combiners are not very efficient when using the Perl API. This is a problem of the Perl API – reading and writing the (key, value) pairs is relatively slow and a combiner does not help – it in fact increases the number of (key, value) pairs that need to be read/written.

This is even more obvious with larger input data:

wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_media/courses:mapreduce-tutorial:step-5-solution1.txt' -O 'step-11-wc-without-combiner.pl'
rm -rf step-11-out-wout; time perl step-11-wc-without-combiner.pl run /home/straka/wiki/cs-text/ step-11-out-wout

wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_media/courses:mapreduce-tutorial:step-10-solution.txt' -O 'step-11-wc-with-combiner.pl'
rm -rf step-11-out-with-combiner; time perl step-11-wc-with-combiner.pl run /home/straka/wiki/cs-text/ step-11-out-with-combiner

wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_media/courses:mapreduce-tutorial:step-11-solution.txt' -O 'step-11-wc-with-perl-hash.pl'
rm -rf step-11-out-with-perl-hash; time perl step-11-wc-with-perl-hash.pl run /home/straka/wiki/cs-text/ step-11-out-with-perl-hash

For comparison, here are times of Java solutions:

Using the combiner is beneficial, although manually combining the word occurrences in mapper manually is still faster.