Implementing an A/B/n experiment using GLEX

Introduction

Gitlab::Experiment (GLEX) is tightly coupled with the concepts provided by Feature flags in development of GitLab. Here, we refer to this layer as feature flags, and may also use the term Flipper, because we built our development and experiment feature flags atop it.

You’re strongly encouraged to read and understand the Feature flags in development of GitLab portion of the documentation before considering running experiments. Experiments add additional concepts which may seem confusing or advanced without understanding the underpinnings of how GitLab uses feature flags in development. One concept: GLEX supports experiments with multiple variants, which are sometimes referred to as A/B/n tests.

The gitlab-experiment project exists in a separate repository, so it can be shared across any GitLab property that uses Ruby. You should feel comfortable reading the documentation on that project as well if you want to dig into more advanced topics.

Glossary of terms

To ensure a shared language, you should understand these fundamental terms we use when communicating about experiments:

  • experiment: Any deviation of code paths we want to run at some times, but not others.
  • context: A consistent experience we provide in an experiment.
  • control: The default, or “original” code path.
  • candidate: Defines an experiment with only one code path.
  • variant(s): Defines an experiment with multiple code paths.

How it works

Use this decision tree diagram to understand how GLEX works. When an experiment runs, the following logic is executed to determine what variant should be provided, given how the experiment has been defined and using the provided context:

graph TD GP[General Pool/Population] --> Running? Running? -->|Yes| Cached?[Cached? / Pre-segmented?] Running? -->|No| Excluded[Control / No Tracking] Cached? -->|No| Excluded? Cached? -->|Yes| Cached[Cached Value] Excluded? -->|Yes| Excluded Excluded? -->|No| Segmented? Segmented? -->|Yes / Cached| VariantA Segmented? -->|No| Included?[Experiment Group?] Included? -->|Yes| Rollout Included? -->|No| Control Rollout -->|Cached| VariantA Rollout -->|Cached| VariantB Rollout -->|Cached| VariantC classDef included fill:#380d75,color:#ffffff,stroke:none classDef excluded fill:#fca121,stroke:none classDef cached fill:#2e2e2e,color:#ffffff,stroke:none classDef default fill:#fff,stroke:#6e49cb class VariantA,VariantB,VariantC included class Control,Excluded excluded class Cached cached

Implement an experiment

Start by generating a feature flag using the bin/feature-flag command as you normally would for a development feature flag, making sure to use experiment for the type. For the sake of documentation let’s name our feature flag (and experiment) “pill_color”.

bin/feature-flag pill_color -t experiment

After you generate the desired feature flag, you can immediately implement an experiment in code. An experiment implementation can be as simple as:

experiment(:pill_color, actor: current_user) do |e|
  e.use { 'control' }
  e.try(:red) { 'red' }
  e.try(:blue) { 'blue' }
end

When this code executes, the experiment is run, a variant is assigned, and (if within a controller or view) a window.gon.experiment.pill_color object will be available in the client layer, with details like:

  • The assigned variant.
  • The context key for client tracking events.

In addition, when an experiment runs, an event is tracked for the experiment :assignment. We cover more about events, tracking, and the client layer later.

In local development, you can make the experiment active by using the feature flag interface. You can also target specific cases by providing the relevant experiment to the call to enable the feature flag:

# Enable for everyone
Feature.enable(:pill_color)

# Get the `experiment` method -- already available in controllers, views, and mailers.
include Gitlab::Experiment::Dsl
# Enable for only the first user
Feature.enable(:pill_color, experiment(:pill_color, actor: User.first))

To roll out your experiment feature flag on an environment, run the following command using ChatOps (which is covered in more depth in the Feature flags in development of GitLab documentation). This command creates a scenario where half of everyone who encounters the experiment would be assigned the control, 25% would be assigned the red variant, and 25% would be assigned the blue variant:

/chatops run feature set pill_color 50 --actors

For an even distribution in this example, change the command to set it to 66% instead of 50.

noteTo immediately stop running an experiment, use the /chatops run feature set pill_color false command.
cautionWe strongly recommend using the --actors flag when using the ChatOps commands, as anything else may give odd behaviors due to how the caching of variant assignment is handled.

We can also implement this experiment in a HAML file with HTML wrappings:

#cta-interface
  - experiment(:pill_color, actor: current_user) do |e|
    - e.use do
      .pill-button control
    - e.try(:red) do
      .pill-button.red red
    - e.try(:blue) do
      .pill-button.blue blue

The importance of context

In our previous example experiment, our context (this is an important term) is a hash that’s set to { actor: current_user }. Context must be unique based on how you want to run your experiment, and should be understood at a lower level.

It’s expected, and recommended, that you use some of these contexts to simplify reporting:

  • { actor: current_user }: Assigns a variant and is “sticky” to each user (or “client” if current_user is nil) who enters the experiment.
  • { project: project }: Assigns a variant and is “sticky” to the project currently being viewed. If running your experiment is more useful when viewing a project, rather than when a specific user is viewing any project, consider this approach.
  • { group: group }: Similar to the project example, but applies to a wider scope of projects and users.
  • { actor: current_user, project: project }: Assigns a variant and is “sticky” to the user who is viewing the given project. This creates a different variant assignment possibility for every project that current_user views. Understand this can create a large cache size if an experiment like this in a highly trafficked part of the application.
  • { wday: Time.current.wday }: Assigns a variant based on the current day of the week. In this example, it would consistently assign one variant on Friday, and a potentially different variant on Saturday.

Context is critical to how you define and report on your experiment. It’s usually the most important aspect of how you choose to implement your experiment, so consider it carefully, and discuss it with the wider team if needed. Also, take into account that the context you choose affects our cache size.

After the above examples, we can state the general case: given a specific and consistent context, we can provide a consistent experience and track events for that experience. To dive a bit deeper into the implementation details: a context key is generated from the context that’s provided. Use this context key to:

  • Determine the assigned variant.
  • Identify events tracked against that context key.

We can think about this as the experience that we’ve rendered, which is both dictated and tracked by the context key. The context key is used to track the interaction and results of the experience we’ve rendered to that context key. These concepts are somewhat abstract and hard to understand initially, but this approach enables us to communicate about experiments as something that’s wider than just user behavior.

noteUsing actor: utilizes cookies if the current_user is nil. If you don’t need cookies though - meaning that the exposed functionality would only be visible to signed in users - { user: current_user } would be just as effective.
cautionThe caching of variant assignment is done by using this context, and so consider your impact on the cache size when defining your experiment. If you use { time: Time.current } you would be inflating the cache size every time the experiment is run. Not only that, your experiment would not be “sticky” and events wouldn’t be resolvable.

Advanced experimentation

GLEX allows for two general implementation styles:

  1. The simple experiment style described previously.
  2. A more advanced style where an experiment class can be provided.

The advanced style is handled by naming convention, and works similar to what you would expect in Rails.

To generate a custom experiment class that can override the defaults in ApplicationExperiment (our base GLEX implementation), use the rails generator:

rails generate gitlab:experiment pill_color control red blue

This generates an experiment class in app/experiments/pill_color_experiment.rb with the variants (or behaviors) we’ve provided to the generator. Here’s an example of how that class would look after migrating the previous example into it:

class PillColorExperiment < ApplicationExperiment
  def control_behavior
    'control'
  end

  def red_behavior
    'red'
  end

  def blue_behavior
    'blue'
  end
end

We can now simplify where we run our experiment to the following call, instead of providing the block we were initially providing, by explicitly calling run:

experiment(:pill_color, actor: current_user).run

The behavior methods we defined in our experiment class represent the default implementation. You can still use the block syntax to override these behavior methods however, so the following would also be valid:

experiment(:pill_color, actor: current_user) do |e|
  e.use { '<strong>control</strong>' }
end
noteWhen passing a block to the experiment method, it is implicitly invoked as if run has been called.

Segmentation rules

You can use runtime segmentation rules to, for instance, segment contexts into a specific variant. The segment method is a callback (like before_action) and so allows providing a block or method name.

In this example, any user named 'Richard' would always be assigned the red variant, and any account older than 2 weeks old would be assigned the blue variant:

class PillColorExperiment < ApplicationExperiment
  segment(variant: :red) { context.actor.first_name == 'Richard' }
  segment :old_account?, variant: :blue

  # ...behaviors

  private

  def old_account?
    context.actor.created_at < 2.weeks.ago
  end
end

When an experiment runs, the segmentation rules are executed in the order they’re defined. The first segmentation rule to produce a truthy result assigns the variant.

In our example, any user named 'Richard', regardless of account age, will always be assigned the red variant. If you want the opposite logic, flip the order.

noteKeep in mind when defining segmentation rules: after a truthy result, the remaining segmentation rules are skipped to achieve optimal performance.

Exclusion rules

Exclusion rules are similar to segmentation rules, but are intended to determine if a context should even be considered as something we should include in the experiment and track events toward. Exclusion means we don’t care about the events in relation to the given context.

These examples exclude all users named 'Richard', and any account older than 2 weeks old. Not only are they given the control behavior - which could be nothing - but no events are tracked in these cases as well.

class PillColorExperiment < ApplicationExperiment
  exclude :old_account?, ->{ context.actor.first_name == 'Richard' }

  # ...behaviors

  private

  def old_account?
    context.actor.created_at < 2.weeks.ago
  end
end

We can also do exclusion when we run the experiment. For instance, if we wanted to prevent the inclusion of non-administrators in an experiment, consider the following experiment. This type of logic enables us to do complex experiments while preventing us from passing things into our experiments, because we want to minimize passing things into our experiments:

experiment(:pill_color, actor: current_user) do |e|
  e.exclude! unless can?(current_user, :admin_project, project)
end

You may also need to check exclusion in custom tracking logic by calling should_track?:

class PillColorExperiment < ApplicationExperiment
  # ...behaviors

  def expensive_tracking_logic
    return unless should_track?

    track(:my_event, value: expensive_method_call)
  end
end

Exclusion rules aren’t the best way to determine if an experiment is active. Override the enabled? method for a high-level way of determining if an experiment should run and track. Make the enabled? check as efficient as possible because it’s the first early opt-out path an experiment can implement.

Tracking events

One of the most important aspects of experiments is gathering data and reporting on it. GLEX provides an interface that allows tracking events across an experiment. You can implement it consistently if you provide the same context between calls to your experiment. If you do not yet understand context, you should read about contexts now.

We can assume we run the experiment in one or a few places, but track events potentially in many places. The tracking call remains the same, with the arguments you would normally use when tracking events using snowplow. The easiest example of tracking an event in Ruby would be:

experiment(:pill_color, actor: current_user).track(:created)

When you run an experiment with any of these examples, an :assigned event is tracked automatically by default. All events that are tracked from an experiment have a special experiment context added to the event. This can be used - typically by the data team - to create a connection between the events on a given experiment.

If our current user hasn’t encountered the experiment yet (meaning where the experiment is run), and we track an event for them, they are assigned a variant and see that variant if they ever encountered the experiment later, when an :assignment event would be tracked at that time for them.

noteGitLab tries to be sensitive and respectful of our customers regarding tracking, so GLEX allows us to implement an experiment without ever tracking identifying IDs. It’s not always possible, though, based on experiment reporting requirements. You may be asked from time to time to track a specific record ID in experiments. The approach is largely up to the PM and engineer creating the implementation. No recommendations are provided here at this time.

Record experiment subjects

Snowplow tracking of identifiable users or groups is prohibited, but you can still determine if an experiment is successful or not. We’re allowed to record the ID of a namespace, project or user in our database. Therefore, we can tell the experiment to record their ID together with the assigned experiment variant in the experiment_subjects database table for later analysis.

For the recording to work, the experiment’s context must include a namespace, group, project, user, or actor.

To record the experiment subject when you first assign a variant, call record! in the experiment’s block:

experiment(:pill_color, actor: current_user) do |e|
  e.record!
end

Test with RSpec

This gem provides some RSpec helpers and custom matchers. These are in flux as of GitLab 13.10.

First, require the RSpec support file to mix in some of the basics:

require 'gitlab/experiment/rspec'

You still need to include matchers and other aspects, which happens automatically for files in spec/experiments, but for other files and specs you want to include it in, you can specify the :experiment type:

it "tests", :experiment do
end

Stub helpers

You can stub experiments using stub_experiments. Pass it a hash using experiment names as the keys, and the variants you want each to resolve to, as the values:

# Ensures the experiments named `:example` & `:example2` are both
# "enabled" and that each will resolve to the given variant
# (`:my_variant` & `:control` respectively).
stub_experiments(example: :my_variant, example2: :control)

experiment(:example) do |e|
  e.enabled? # => true
  e.variant.name # => 'my_variant'
end

experiment(:example2) do |e|
  e.enabled? # => true
  e.variant.name # => 'control'
end

Exclusion and segmentation matchers

You can also test the exclusion and segmentation matchers.

class ExampleExperiment < ApplicationExperiment
  exclude { context.actor.first_name == 'Richard' }
  segment(variant: :candidate) { context.actor.username == 'jejacks0n' }
end

excluded = double(username: 'rdiggitty', first_name: 'Richard')
segmented = double(username: 'jejacks0n', first_name: 'Jeremy')

# exclude matcher
expect(experiment(:example)).to exclude(actor: excluded)
expect(experiment(:example)).not_to exclude(actor: segmented)

# segment matcher
expect(experiment(:example)).to segment(actor: segmented).into(:candidate)
expect(experiment(:example)).not_to segment(actor: excluded)

Tracking matcher

Tracking events is a major aspect of experimentation. We try to provide a flexible way to ensure your tracking calls are covered.

You can do this on the instance level or at an “any instance” level:

subject = experiment(:example)

expect(subject).to track(:my_event)

subject.track(:my_event)

You can use the on_any_instance chain method to specify that it could happen on any instance of the experiment. This helps you if you’re calling experiment(:example).track downstream:

expect(experiment(:example)).to track(:my_event).on_any_instance

experiment(:example).track(:my_event)

A full example of the methods you can chain onto the track matcher:

expect(experiment(:example)).to track(:my_event, value: 1, property: '_property_')
  .on_any_instance
  .with_context(foo: :bar)
  .for(:variant_name)

experiment(:example, :variant_name, foo: :bar).track(:my_event, value: 1, property: '_property_')

Recording and assignment tracking

To test assignment tracking and the record! method, you can use or adopt the following shared example: tracks assignment and records the subject.

Experiments in the client layer

This is in flux as of GitLab 13.10, and can’t be documented just yet.

Any experiment that’s been run in the request lifecycle surfaces in window.gon.experiment, and matches this schema so you can use it when resolving some concepts around experimentation in the client layer.

Use experiments in Vue

With the gitlab-experiment component, you can define slots that match the name of the variants pushed to window.gon.experiment. For example, if we alter the pill_color experiment to just use the default variants of control and candidate like so:

def show
  experiment(:pill_color) do |e|
    e.use { } # control
    e.try { } # candidate
  end.run
end

We can make use of the named slots control and candidate in the Vue component:

<script>
import GitlabExperiment from '~/experimentation/components/gitlab_experiment.vue';

export default {
  components: { GitlabExperiment }
}
</script>

<template>
  <gitlab-experiment name="pill_color">
    <template #control>
      <button class="bg-default">Click default button</button>
    </template>

    <template #candidate>
      <button class="bg-red">Click red button</button>
    </template>
  </gitlab-experiment>
</template>

When you’re coding for an experiment with multiple variants, you can use the variant names. For example, the Vue component for the previously-defined pill_color experiment with red and blue variants would look like this:

<template>
  <gitlab-experiment name="pill_color">
    <template #control>
      <button class="bg-default">Click default button</button>
    </template>

    <template #red>
      <button class="bg-red">Click red button</button>
    </template>

    <template #blue>
      <button class="bg-blue">Click blue button</button>
    </template>
  </gitlab-experiment>
</template>
noteWhen there is no experiment data in the window.gon.experiment object for the given experiment name, the control slot will be used, if it exists.

Notes on feature flags

noteWe use the terms “enabled” and “disabled” here, even though it’s against our documentation style guide recommendations because these are the terms that the feature flag documentation uses.

You may already be familiar with the concept of feature flags in GitLab, but using feature flags in experiments is a bit different. While in general terms, a feature flag is viewed as being either on or off, this isn’t accurate for experiments.

Generally, off means that when we ask if a feature flag is enabled, it will always return false, and on means that it will always return true. An interim state, considered conditional, also exists. GLEX takes advantage of this trinary state of feature flags. To understand this conditional aspect: consider that either of these settings puts a feature flag into this state:

  • Setting a percentage_of_actors of any percent greater than 0%.
  • Enabling it for a single user or group.

Conditional means that it returns true in some situations, but not all situations.

When a feature flag is disabled (meaning the state is off), the experiment is considered inactive. You can visualize this in the decision tree diagram as reaching the first Running? node, and traversing the negative path.

When a feature flag is rolled out to a percentage_of_actors or similar (meaning the state is conditional) the experiment is considered to be running where sometimes the control is assigned, and sometimes the candidate is assigned. We don’t refer to this as being enabled, because that’s a confusing and overloaded term here. In the experiment terms, our experiment is running, and the feature flag is conditional.

When a feature flag is enabled (meaning the state is on), the candidate will always be assigned.

We should try to be consistent with our terms, and so for experiments, we have an inactive experiment until we set the feature flag to conditional. After which, our experiment is then considered running. If you choose to “enable” your feature flag, you should consider the experiment to be resolved, because everyone is assigned the candidate unless they’ve opted out of experimentation.

As of GitLab 13.10, work is being done to improve this process and how we communicate about it.