github/dat-science

---

github/dat-science.json

{
  "createdAt": "2013-02-22T20:09:23Z",
  "defaultBranch": "master",
  "description": "Replaced by https://github.com/github/scientist",
  "fullName": "github/dat-science",
  "homepage": "",
  "language": "Ruby",
  "name": "dat-science",
  "pushedAt": "2014-11-17T22:59:10Z",
  "stargazersCount": 581,
  "topics": [],
  "updatedAt": "2025-09-29T15:21:02Z",
  "url": "https://github.com/github/dat-science"
}

Science is happening elsewhere!

This repository is historical. Up-to-date bits are over in github/scientist.

A Ruby library for carefully refactoring critical paths. Science isn’t a feature flipper or an A/B testing tool, it’s a pattern that helps measure and validate large code changes without altering behavior.

How do I do science?

Let’s pretend you’re changing the way you handle permissions in a large web app. Tests can help guide your refactoring, but you really want to compare the current and new behaviors live, under load.

require "dat/science"

class MyApp::Widget
  def allows?(user)
    experiment = Dat::Science::Experiment.new "widget-permissions" do |e|
      e.control   { model.check_user(user).valid? } # old way
      e.candidate { user.can? :read, model } # new way
    end

    experiment.run
  end
end

Wrap a control block around the code’s original behavior, and wrap candidate around the new behavior. experiment.run will always return whatever the control block returns, but it does a bunch of stuff behind the scenes:

• Decides whether or not to run candidate,
• Runs candidate before control 50% of the time,
• Measures the duration of both behaviors,
• Compares the results of both behaviors,
• Swallows any exceptions raised by the candidate behavior, and
• Publishes all this information for tracking and reporting.

If you’d like a bit less verbosity, the Dat::Science#science helper instantiates an experiment and calls run:

require "dat/science"

class MyApp::Widget
  include Dat::Science

  def allows?(user)
    science "widget-permissions" do |e|
      e.control   { model.check_user(user).valid? } # old way
      e.candidate { user.can? :read, model } # new way
    end
  end
end

Making science useful

The examples above will run, but they’re not particularly helpful. The candidate block runs every time, and none of the results get published. Let’s fix that by creating an app-specific sublass of Dat::Science::Experiment. This makes it easy to add custom behavior for enabling/disabling/throttling experiments and publishing results.

require "dat/science"

module MyApp
  class Experiment < Dat::Science::Experiment
    def enabled?
      # See "Ramping up experiments" below.
    end

    def publish(name, payload)
      # See "Publishing results" below.
    end
  end
end

After creating a subclass, tell Dat::Science to instantiate it any time the science helper is called:

Dat::Science.experiment = MyApp::Experiment

Controlling comparison

By default the results of the candidate and control blocks are compared with ==. Use comparator to do something more fancy:

science "loose-comparison" do |e|
  e.control    { "vmg" }
  e.candidate  { "VMG" }
  e.comparator { |a, b| a.downcase == b.downcase }
end

Ramping up experiments

By default the candidate block of an experiment will run 100% of the time. This is often a really bad idea when testing live. Experiment#enabled? can be overridden to run all candidates, say, 10% of the time:

def enabled?
  rand(100) < 10
end

Or, even better, use a feature flag library like [Flipper][]. Delegating the decision makes it easy to define different rules for each experiment, and can help keep all your entropy concerns in one place.

[Flipper] !: https://github.com/jnunemaker/flipper

def enabled?
  MyApp.flipper[name].enabled?
end

Publishing results

By default the results of an experiment are discarded. This isn’t very useful. Experiment#publish can be overridden to publish results via any instrumentation mechanism, which makes it easy to graph durations or matches/mismatches and store results. The only two events published by an experiment are :match when the result of the control and candidate behaviors are the same, and :mismatch when they aren’t.

def publish(event, payload)
  MyApp.instrument "science.#{event}", payload
end

The published payload is a Symbol-keyed Hash:

{
  :experiment => "widget-permissions",
  :first      => :control,
  :timestamp  => <a-Time-instance>,

  :candidate => {
    :duration  => 2.5,
    :exception => nil,
    :value     => 42
  },

  :control => {
    :duration  => 25.0,
    :exception => nil,
    :value     => 24
  }
}

:experiment is the name of the experiment. :first is either :candidate or :control, depending on which block was run first during the experiment. :timestamp is the Time when the experiment started.

The :candidate and :control Hashes have the same keys:

• :duration is the execution in ms, expressed as a float.
• :exception is a reference to any raised exception or nil.
• :value is the result of the block.

Adding context

It’s often useful to add more information to your results, and Experiment#context makes it easy:

science "widget-permissions" do |e|
  e.context :user => user

  e.control   { model.check_user(user).valid? } # old way
  e.candidate { user.can? :read, model } # new way
end

context takes a Symbol-keyed Hash of additional information to publish and merges it with the default payload.

Keeping it clean

Sometimes the things you’re comparing can be huge, and there’s no good way to do science against something simpler. Use a cleaner to publish a simple version of a big nasty object graph:

science "huge-results" do |e|
  e.control   { OldAndBusted.huge_results_for query }
  e.candidate { NewHotness.huge_results_for query }
  e.cleaner   { |result| result.count }
end

The results of the control and candidate blocks will be run through the cleaner. You could get the same behavior by calling count in the blocks, but the cleaner makes it easier to keep things in sync. The original control result is still returned.

What do I do with all these results?

Once you’ve started an experiment and published some results, you’ll want to analyze the mismatches from your experiment. Check out dat-analysis where you’ll find an analysis toolkit to help you understand your experiment results.

Hacking on science

Be on a Unixy box. Make sure a modern Bundler is available. script/test runs the unit tests. All development dependencies will be installed automatically if they’re not available. Dat science happens primarily on Ruby 1.9.3 and 1.8.7, but science should be universal.

Maintainers

@jbarnette and @rick