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MapReduce Tutorial : Initialization and cleanup of MR tasks, performance of combiners

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

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'
# $EDITOR step-11-exercise.pl
rm -rf step-11-out-wout; time perl step-11-wc-without-combiner.pl /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'
# $EDITOR step-11-solution.pl
rm -rf step-11-out-with-hash; time perl step-11-wc-with-perl-hash.pl /home/straka/wiki/cs-text-medium/ step-11-out-with-hash
less step-11-out-with-hash/part-*

Combiners and Perl API performance

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:

Script Time to complete on /home/straka/wiki/cs-text Commands
step-11-wc-without-combiner.pl 5mins, 4sec
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 /home/straka/wiki/cs-text/ step-11-out-wout
step-11-wc-with-combiner.pl 5mins, 33sec
wget --no-check-certificate 'https://wiki.ufal.ms.mff.cuni.cz/_media/courses:mapreduce-tutorial:step-10.txt' -O 'step-11-wc-with-combiner.pl'
rm -rf step-11-out-with-combiner; time perl step-11-wc-with-combiner.pl /home/straka/wiki/cs-text/ step-11-out-with-combiner
step-11-wc-with-perl-hash.pl 2mins, 24sec
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 /home/straka/wiki/cs-text/ step-11-out-with-perl-hash

For comparison, here are times of Java solutions:

Program Time to complete on /home/straka/wiki/cs-text Size of map output
Wordcount without combiner 2mins, 26sec 367MB
Wordcount with combiner 1min, 51sec 51MB
Wordcount with hash in mapper 1min, 14sec 51MB

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

Step 10: Combiners. Overview Step 12: Additional output from mappers and reducers.

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