Write your first MapReduce program in 20 minutes (opens in new tab)

Here's a Perl implementation:

  use strict; use warnings;
  use List::Util  qw(reduce);
  use File::Slurp qw(read_file);

  sub word_count {
  	reduce { $a->{$b}++; $a  } {},
  	map    { split(/\W+/)    }
  	map    { lc read_file $_ } @_;
  }

If i understand correctly collect is more like map from functional programing languages and inject is a fold. One maps a collection of one type to a collection of another type, and the other reduces a collection of one type into something of another type (which could be a collection itself) in that case MapReduce does not quite translate to ruby's collect and inject. In fancy language the reduce in MapReduce is not merely a catamorphism nor is the map actually a collect. The types do not align. I learned of his from a hackernews post by grav1tas http://news.ycombinator.com/item?id=2477238. In there he links to a paper: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104....

If you look at the types implicit in his code you should see that the essence lies not with collect or inject which are incidental plumbing but the nature of the hash and in particular, the act of generating the intermediate collection for each key. What is called map is actually more a reverse reduce. The pedagogical emphasis on map and foldr actually belies the true nature and power of algorithm in parallelizing.

You can also use Enumerable#map and Enumerable#reduce, to use names that match the pattern (and #map is arguably more idiomatic than #collect).

Kind of a odd example just to use map/reduce where each works just fine:

    result = Hash.new(0)
    Dir['test?.txt'].each do |file|
      File.read(file).split(' ').each {|word|
        result[word.downcase.tr('.,\'', '')] += 1}
    end

If you really needed map/reduce here you'd probably want to write it this way:

    Dir['test?.txt'].map do |file|
      Hash.new(0).tap {|result|
        File.read(file).split(' ').each {|word|
          result[word.downcase.tr('.,\'', '')] += 1}}
    end.reduce do |result, partial|
      result.merge(partial) {|k, v1, v2| v1 + v2}
    end

BigCouch will let you build indexes in parallel.