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--- courses:mapreduce-tutorial:step-10 [2012/01/25 15:46]
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+++ courses:mapreduce-tutorial:step-10 [2012/01/25 19:01]
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-====== MapReduce Tutorial :  ======
+====== MapReduce Tutorial : Combiners ======
+Sometimes the reduce is a binary operation, which is associative and commutative, e.g. ''+''. In that case it is inefficient to produce all the (key, value) pairs in the mappers and send them through the network.
+Instead, reducer can be executed right after the map, on //some portion// of values belonging to the same key. Only the results are then sent through the network.
+A Hadoop job can have such locally executed reducer, called //combiner//. If a combiner is specified, the output of a mapper is processed by a combiner before sending the pairs to reducer. The combiner may be invoked 0, 1 or multiple times, usually when the data are written to disk.
+Typically, the combiner is the same as the reducer of a MR job.
+<code perl>
+package Mapper;
+use Moose;
+with 'Hadoop::Mapper';
+sub map {
+  my ($self, $key, $value, $context) = @_;
+  foreach my $word (split /\W/, $value) {
+    next if not length $word;
+    $context->write($word, 1);
+  }
+}
+package Reducer;
+use Moose;
+with 'Hadoop::Reducer';
+sub reduce {
+  my ($self, $key, $values, $context) = @_;
+  my $sum = 0;
+  while ($values->next) {
+    $sum += $values->value;
+  }
+  $context->write($key, $sum);
+}
+package Main;
+use Hadoop::Runner;
+my $runner = Hadoop::Runner->new(
+  mapper => Mapper->new(),
+  combiner => Reducer->new(),
+  reducer => Reducer->new(),
+  input_format => 'KeyValueTextInputFormat');
+$runner->run();
+</code>

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