This is a module for MongoMapper which provides an easy way to add in some simple map/reduce functions to your data. If you have time series data and you want to show some sort of counter per date or time, then this should do it.
You have a bunch of objects in your MongoDB. You need to get some basic information about them such as: Simple aggregation of documents per key, Finding an average of a value, Counting the number of documents that contain a key.
You've probably read you can do this sort of stuff with MongoDB's map/reduce functionality, maybe you already know exactly how that works or maybe you don't really have a clue. Every guide I've seen for MongoMapper recommends you execute the map/reduce calculation every single time it's accessed, and they all demand that you write your own map and reduce functions.
Here are some use cases
# Count how many times each tag is used
Model.count_by(:tag, query = {})
# Sum all the weights of the different types of wrestlers
Model.sum_of(:weight, :wrestler_type, query = {})
# Find Average Weight of the different types of wrestlers
Model.average_of(:weight, :wrestler_type, query = {})- mongoid support
- Sum by composite index
- More Unit Tests :(
gem install reduceable
# or
sudo gem install reduceable
require 'mongo_mapper'
require 'reduceable'
MongoMapper.database = 'my_database_name'
class BlogPost
include MongoMapper::Document
include Reduceable
key :article_body, String
key :categories, Array
key :time_posted, Time
key :article_length, Integer
end
# Insert some data
BlogPost.count_by(:categories).to_a.each do |x|
puts "You have posted #{x['value']} posts from catefory #{x['_id']}"
end
BlogPost.sum_of(:article_length, :categories).to_a.each do |x|
puts "You have written #{x['value']} characters in category #{x['_id']}"
end
BlogPost.average_of(:article_length, :categories).to_a.each do |x|
puts "An article in category #{x['_id']} has an average of #{x['value']} characters"
endSee example.rb
# require the example model
require './example.rb' #=> true
# setup some base data
setup #=> #<Test _id: BSON: ......
#
# Calculate how many times each tag is used
# You will use a similar map/reduce for a tag cloud
Test.count_by(:tags).to_a
#=> [{"_id"=>"alternative", "value"=>1.0}, {"_id"=>"book", "value"=>5.0}, {"_id"=>"classical", "value"=>1.0}, {"_id"=>"fantasy", "value"=>2.0}, {"_id"=>"fiction", "value"=>2.0}, {"_id"=>"music", "value"=>4.0}, {"_id"=>"non-fiction", "value"=>1.0}, {"_id"=>"pop", "value"=>1.0}, {"_id"=>"rock", "value"=>1.0}]
# Sum up the sale_amounts per tag
Test.sum_of(:sale_amount, :tags).to_a
# Find the average sale_amounts per tag
Test.average_of(:sale_amount, :tags).to_a
# Sum up the sale_amounts per tag where tags contains 'book'
Test.sum_of(:sale_amount, :tags, {:tags => 'book'}).to_a
# you can optionally pass in a mongo query that limits the initial dataset being
# fed to the map function.
# Find the average of sale_amounts per tag where tags contains 'book'
Test.average_of(:sale_amount, :tags, {:tags => 'book'}).to_a
# you can optionally pass in a mongo query that limits the initial dataset being
# fed to the map function.For such a small collection the speed benefits aren't present, but once you get to several hundred thousand record, recreating the map_reduce collection on every call really slows things down. Reduceable solves that problem.