Note: Verdict is no longer actively maintained. If you have questions, reach out to [email protected].
This library allows you to define and use experiments in your application.
- It can be used in any Ruby application, and comes with a
Railtie
to make integrating it with a Rails app easy. - It handles consistently assigning subjects to experiment groups, and storing/logging these assignments for analysis.
This library doesn't do any analysis of results. That should happen elsewhere, e.g. in a data warehouse environment.
Add this line to your application's Gemfile, and run bundle install
:
gem 'verdict'
If you're using Rails, the Railtie will handle setting the logger to Rails.logger
and the experiments directory to app/experiments
. It will also load the rake tasks for you (run bundle exec rake -T | grep experiments:
for options).
You may find the Concepts documentation a good place to familiarise yourself with Verdict's nomenclature.
The Verdict::Experiment
class is used to create an experiment, define control and experiment groups, and to qualify subjects.
You define an experiment like so:
Verdict::Experiment.define :my_experiment do
# This block should return true if the subject is qualified to participate
qualify { |subject, context| ... }
# Specify the groups and the percentages
groups do
group :test, :half
group :control, :rest
end
# Specify how assignments will be stored.
storage Verdict::Storage::MemoryStorage.new
end
Usually you'll want to place this in a file called my_experiment.rb in the /app/experiments folder.
We recommend that you subclass Verdict::Experiment
to set some default options for your app's environment, and call define
on that class instead.
At the relevant point in your application, you can check the group that a particular subject belongs to using the switch
method.
You'll need to pass along the subject (think User, Product or any other Model class) as well as any context to be used for qualifying the subject.
context = { ... } # anything you want to pass along to the qualify block.
case Verdict['my_experiment'].switch(shop, context)
when :test
# Handle test group
when :control
# Handle control group
else
# Handle unqualified subjects.
end
Verdict uses a very simple interface for storing experiment assignments. Out of the box, Verdict ships with storage providers for:
- Memory
- Redis
- Cookies
You can set up storage for your experiment by calling the storage
method with an object that responds to the following methods:
store_assignment(assignment)
retrieve_assignment(experiment, subject)
remove_assignment(experiment, subject)
retrieve_start_timestamp(experiment)
store_start_timestamp(experiment, timestamp)
Regarding the method signatures above, experiment
is the Experiment instance, subject
is the Subject instance, and assignment
is a Verdict::Assignment
instance.
The subject
instance will be identified internally by its subject_identifier
. By default it will use subject.id.to_s
as subject_identifier
, but you can change that by overriding def subject_identifier(subject)
on the experiment.
Storage providers simply store subject assignments and require quick lookups of subject identifiers. They allow for complex (high CPU) assignments, and for assignments that might not always put the same subject in the same group by storing the assignment for later use.
Storage providers are intended for operational use and should not be used for data analysis. For data analysis, you should use the logger.
When removing old experiments you might want to clean up corresponding experiment assignments, to reduce the amount of data stored and loaded. By using the logger, this data removal doesn't impact historic data or data analysis.
For more details about these methods, check out the source code for Verdict::Storage::MockStorage
Every assignment will be logged to Verdict.logger
. For rails apps, this logger will be automatically set to Rails.logger
so experiment assignments will show up in your Rails log.
You can override the logging by overriding the def log_assignment(assignment)
method on the experiment.
Logging (as opposed to storage) should be used for data analysis. The logger requires a write-only / forward-only stream to write to, e.g. a log file, Kafka, or an insert-only database table.
It's possible to run an experiment without defining any storage, though this comes with several drawbacks. Logging on the other hand is required in order to analyze the results.