Bonito

Data, in a can

TL;DR

Bonito is a ruby DSL for generating canned data where timing is important.

Bonito uses Timecop to simulate the flow of time as it generates data.

Installation

gem install bonito

or, if using Bundler

gem 'bonito', '~> 0.2.1'

require 'bonito'

Introduction

At the core of Bonito is the concept of a timeline. A timeline is a sort of schema that defines in what time period each of a sequence of actions can occur.

An action in Bonito is called a Moment and is considered to have a duration of 0 itself.

`Bonito` can generate data in series:

Suppose we wish to simulate an online content creator's data, where _author_s write articles and users comment on these articles.

We could use Bonito to define a serial timeline:

# First we create data structures to store out data and keep track of them in
# a `Scope` object: 
scope = Bonito::Scope.new.tap do |scope|
  scope.authors = []
  scope.articles = []
  scope.users = []
  scope.comments = []
  scope.users_and_authors = []
end

# Next we define out serial timeline:
serial = Bonito.over 1.week do
  please do |scope|  # The `please` method denotes the definition of an action
    author = scope.authors.sample
    title = Faker::Company.bs
    scope.article = Article.new(title, author)
    scope.articles << article
  end

  repeat times: rand(10), over: 5.days do
    please do |scope|
      user = scope.users.sample
      content = Faker::Lorem.sentence
      scope.comments << Comment.new(content, scope.article, user)
    end
  end
end

This timeline specifies the creation of an instance of Article followed by the creation of up to 10 Comment belonging to that Article. The created_at time on the Article will be before that of each of the Comments. The total elapsed time between the creation of the Article and the creation of the final Comment will not be more than 1.week.

`Bonito` can generate data in parallel:

Consider the timeline serial we defined above. We might realistically want to generate data that represents many authors working simultaneously on articles with users then commenting on these once they have been published.

This can be done as follows:

parallel = Bonito.over 2.weeks do
  simultaneously do
    repeat times: 5 do
      use serial
    end
  end
end

The above will create 5 Articles, each having up to 9 Comments where the moment at which Article is created is independent of any other Article or Comment. The times at which the Comments are created, meanwhile, are dependent only on the Article to which they belong.

Execution

Now we have defined the shape of the data we wish to create, it remains to actually create it.

This is achieved via a Runner object that takes a timeline and uses it to evaluate the individual actions. It distributes these actions randomly yet within the confines of the schedule defined by the timeline.

  Bonito.run parallel_window, scope: scope, starting: 8.weeks_ago

This will take the Window object parallel_window and distribute the Moments it contains according to its configuration, mapping each Moment to a point in time relative to the start time given by the starting parameter.

Scaling

A typical use case may require different data set sizes for different applications: For example, a large dataset to live in a staging environment in order to sanity check releases and a small, easy to load dataset that can be used locally while developing.

Suppose we have certain events that we wish to occur only once, (using the above example, this may be the creation of some Publication object representing the newspaper for which articles are being written) as well as events that we wish to be able to scale, such as the creation of Article objects and their associated Comments.

Using Bonito, we could define two timelines: a singleton_timeline that is run once per dataset and generates our Organisation model, as well as a scalable_timeline that results in different sizes of data according to some size parameter.

These timelines can then be combined as follows, where the size parameter n can be provided dynamically as, say, an argument provided to a Rake task.

scaled = singleton_timeline + (scalable_timeline ** n)

The scaled_timeline, when run, will run the scalable_timeline n times in parallel.

Scoping

Data can be shared amongst Moments via a Scope object. Attributes on a Scope object are available within the current serial timeline and in all child serial timelines.

Consider the following example:

Bonito.over 1.week do
  please do |scope|
    scope.foo = 'bar'
  end

  over 2.days do
    please do |scope|
      puts scope.foo # prints 'bar'
    end

    please do |scope|
      scope.foo = 'baz'
    end

    please do |scope|
      puts scope.foo # now prints 'baz'
    end
  end
  
  please do |scope|
    puts scope.foo # still prints 'bar'
  end
end

An Example

Consider the following:

# Initialise the data store, in practice a database would probably be used for 
# this. Defined this way, these variables are available globally.
 
scope = Bonito::Scope.new.tap do |scope|
  scope.publications = []
  scope.authors = []
  scope.articles = []
  scope.users = []
  scope.comments = []
  scope.users_and_authors = []
end

# We only ever want to create publication, regardless of how we scale, so we 
# create a setup timeline to handle this
singleton_timeline = Bonito.over 1.day do
  please do |scope|
    scope.publications << Publication.new
  end
end


scalable_timeline = Bonito.over 1.week do
  # Make the publication available to the current timeline.
  please do |scope|
    scope.publication = scope.publications.first
  end
  # Simultaneously create authors and users, interweaving the two.
  simultaneously do
    over 1.day do
      # Create 5 authors over the course of a day 
      repeat times: 5, over: 1.day do
        please do |scope|
          name = Faker::Name.name
          author = Author.new(name)
          scope.authors << author
          scope.users_and_authors << author
        end
      end
    end

    # Create 10 users, also over one day, waiting at least 2 hours before 
    # creating the first. 
    also over: 1.day, after: 2.hours do
      repeat times: 10, over: 1.day do
        please do |scope|
          name = Faker::Name.name
          email = Faker::Internet.safe_email(name)
          user = User.new(name, email)
          scope.users << user
          scope.users_and_authors << user
        end
      end
    end
  end
  
  # Repeat the following sequence of events 5 times over 5 days
  repeat times: 5, over: 5.days do
    # Choose one of the existing authors and create an article belonging to that
    # author. 
    please do |scope|
      author = scope.authors.sample
      title = Faker::Company.bs
      scope.article = Article.new(title, author, scope.publication)
      scope.articles << scope.article
    end
    
    # Choose one of the existing users and have them leave a comment on the 
    # article that was just created. 
    repeat times: rand(10), over: 5.hours do
      please do |scope|
        user = scope.users.sample
        content = Faker::Lorem.sentence
        scope.comments << Comment.new(content, scope.article, user)
      end
    end
  end
end

# Finally, we run the timeline to generate our data 
scale = 5
scaled_timeline = singleton_timeline + (scalable_timeline ** scale)

Bonito.run scaled_timeline, scope: scope, starting: 8.weeks_ago

bonito

Development

Runtime

Bonito

TL;DR

Installation

Introduction

`Bonito` can generate data in series:

`Bonito` can generate data in parallel:

Execution

Scaling

Scoping

An Example

bonito

Development

Runtime

Bonito

TL;DR

Installation

Introduction

Bonito can generate data in series:

Bonito can generate data in parallel:

Execution

Scaling

Scoping

An Example

`Bonito` can generate data in series:

`Bonito` can generate data in parallel: