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--- courses:mapreduce-tutorial:step-5 [2012/01/24 19:04]
straka vytvořeno
+++ courses:mapreduce-tutorial:step-5 [2012/01/24 22:30]
straka
@@ Line 1: / Line 1: @@
-====== MapReduce Tutorial : ======
+====== MapReduce Tutorial : Basic reducer ======
+The interesting part of a MR job is the reducer -- after all mappers produce the (key, value) pairs, for every unique key and all its values a ''reduce'' function is called. The ''reduce'' function can output (key, value) pairs, which are written to disk.
+The ''reduce'' is similar to ''map'', but instead of one value it gets an iterator, which can enumerate all values:
+<file perl reducer.pl>
+package Mapper;
+use Moose;
+with 'Hadoop::Mapper';
+sub map {
+  my ($self, $key, $value, $context) = @_;
+  $context->write($key, $value);
+}
+package Reducer;
+use Moose;
+with 'Hadoop::Reducer';
+sub reduce {
+  my ($self, $key, $values, $context) = @_;
+  while ($values->next) {
+    $context->write($key, $values->value);
+  }
+}
+package Main;
+use Hadoop::Runner;
+my $runner = Hadoop::Runner->new(
+  mapper => Mapper->new(),
+  reducer => Reducer->new());
+$runner->run();
+</file>
+===== Exercise 1 =====
+Run a MR job on /home/straka/wiki/cs-text-small, which counts occurences of every word in the article texts.
+===== Exercise 2 =====
+Run a MR job on /home/straka/wiki/cs-text-small, which generates an inverted index. Inverted index contains for each word all its occurrences, each occurrence is pair (article of occurrence, position of occurrence).

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