A Ruby library for carefully refactoring critical paths. 
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 refactored behaviors under load.
require "scientist"
class MyWidget
def allows?(user)
experiment = Scientist::Default.new "widget-permissions"
experiment.use { model.check_user(user).valid? } # old way
experiment.try { user.can?(:read, model) } # new way
experiment.run
end
endWrap a use block around the code's original behavior, and wrap try around the new behavior. experiment.run will always return whatever the use block returns, but it does a bunch of stuff behind the scenes:
- It decides whether or not to run the
tryblock, - Randomizes the order in which
useandtryblocks are run, - Measures the wall time and cpu time of all behaviors in seconds,
- Compares the result of
tryto the result ofuse, - Swallow and record exceptions raised in the
tryblock when overridingraised, and - Publishes all this information.
The use block is called the control. The try block is called the candidate.
Creating an experiment is wordy, but when you include the Scientist module, the science helper will instantiate an experiment and call run for you:
require "scientist"
class MyWidget
include Scientist
def allows?(user)
science "widget-permissions" do |experiment|
experiment.use { model.check_user(user).valid? } # old way
experiment.try { user.can?(:read, model) } # new way
end # returns the control value
end
endIf you don't declare any try blocks, none of the Scientist machinery is invoked and the control value is always returned.
The examples above will run, but they're not really doing anything. The try blocks don't run yet and none of the results get published. Replace the default experiment implementation to control execution and reporting:
require "scientist/experiment"
class MyExperiment
include Scientist::Experiment
attr_accessor :name
def initialize(name)
@name = name
end
def enabled?
# see "Ramping up experiments" below
true
end
def raised(operation, error)
# see "In a Scientist callback" below
p "Operation '#{operation}' failed with error '#{error.inspect}'"
super # will re-raise
end
def publish(result)
# see "Publishing results" below
p result
end
endWhen Scientist::Experiment is included in a class, it automatically sets it as the default implementation via Scientist::Experiment.set_default. This set_default call is skipped if you include Scientist::Experiment in a module.
Now calls to the science helper will load instances of MyExperiment.
Scientist compares control and candidate values using ==. To override this behavior, use compare to define how to compare observed values instead:
class MyWidget
include Scientist
def users
science "users" do |e|
e.use { User.all } # returns User instances
e.try { UserService.list } # returns UserService::User instances
e.compare do |control, candidate|
control.map(&:login) == candidate.map(&:login)
end
end
end
endIf either the control block or candidate block raises an error, Scientist compares the two observations' classes and messages using ==. To override this behavior, use compare_errors to define how to compare observed errors instead:
class MyWidget
include Scientist
def slug_from_login(login)
science "slug_from_login" do |e|
e.use { User.slug_from_login login } # returns String instance or ArgumentError
e.try { UserService.slug_from_login login } # returns String instance or ArgumentError
compare_error_message_and_class = -> (control, candidate) do
control.class == candidate.class &&
control.message == candidate.message
end
compare_argument_errors = -> (control, candidate) do
control.class == ArgumentError &&
candidate.class == ArgumentError &&
control.message.start_with?("Input has invalid characters") &&
candidate.message.start_with?("Invalid characters in input")
end
e.compare_errors do |control, candidate|
compare_error_message_and_class.call(control, candidate) ||
compare_argument_errors.call(control, candidate)
end
end
end
endResults aren't very useful without some way to identify them. Use the context method to add to or retrieve the context for an experiment:
science "widget-permissions" do |e|
e.context :user => user
e.use { model.check_user(user).valid? }
e.try { user.can?(:read, model) }
endcontext takes a Symbol-keyed Hash of extra data. The data is available in Experiment#publish via the context method. If you're using the science helper a lot in a class, you can provide a default context:
class MyWidget
include Scientist
def allows?(user)
science "widget-permissions" do |e|
e.context :user => user
e.use { model.check_user(user).valid? }
e.try { user.can?(:read, model) }
end
end
def destroy
science "widget-destruction" do |e|
e.use { old_scary_destroy }
e.try { new_safe_destroy }
end
end
def default_scientist_context
{ :widget => self }
end
endThe widget-permissions and widget-destruction experiments will both have a :widget key in their contexts.
If an experiment requires expensive setup that should only occur when the experiment is going to be run, define it with the before_run method:
# Code under test modifies this in-place. We want to copy it for the
# candidate code, but only when needed:
value_for_original_code = big_object
value_for_new_code = nil
science "expensive-but-worthwhile" do |e|
e.before_run do
value_for_new_code = big_object.deep_copy
end
e.use { original_code(value_for_original_code) }
e.try { new_code(value_for_new_code) }
endSometimes you don't want to store the full value for later analysis. For example, an experiment may return User instances, but when researching a mismatch, all you care about is the logins. You can define how to clean these values in an experiment:
class MyWidget
include Scientist
def users
science "users" do |e|
e.use { User.all }
e.try { UserService.list }
e.clean do |value|
value.map(&:login).sort
end
end
end
endAnd this cleaned value is available in observations in the final published result:
class MyExperiment
include Scientist::Experiment
# ...
def publish(result)
result.control.value # [<User alice>, <User bob>, <User carol>]
result.control.cleaned_value # ["alice", "bob", "carol"]
end
endNote that the #clean method will discard the previous cleaner block if you call it again. If for some reason you need to access the currently configured cleaner block, Scientist::Experiment#cleaner will return the block without further ado. (This probably won't come up in normal usage, but comes in handy if you're writing, say, a custom experiment runner that provides default cleaners.)
The #clean method will not be used for comparison of the results, so in the following example it is not possible to remove the #compare method without the experiment failing:
def user_ids
science "user_ids" do
e.use { [1,2,3] }
e.try { [1,3,2] }
e.clean { |value| value.sort }
e.compare { |a, b| a.sort == b.sort }
end
endDuring the early stages of an experiment, it's possible that some of your code will always generate a mismatch for reasons you know and understand but haven't yet fixed. Instead of these known cases always showing up as mismatches in your metrics or analysis, you can tell an experiment whether or not to ignore a mismatch using the ignore method. You may include more than one block if needed:
def admin?(user)
science "widget-permissions" do |e|
e.use { model.check_user(user).admin? }
e.try { user.can?(:admin, model) }
e.ignore { user.staff? } # user is staff, always an admin in the new system
e.ignore do |control, candidate|
# new system doesn't handle unconfirmed users yet:
control && !candidate && !user.confirmed_email?
end
end
endThe ignore blocks are only called if the values don't match. Unless a compare_errors comparator is defined, two cases are considered mismatches: a) one observation raising an exception and the other not, b) observations raising exceptions with different classes or messages.
Sometimes you don't want an experiment to run. Say, disabling a new codepath for anyone who isn't staff. You can disable an experiment by setting a run_if block. If this returns false, the experiment will merely return the control value. Otherwise, it defers to the experiment's configured enabled? method.
class DashboardController
include Scientist
def dashboard_items
science "dashboard-items" do |e|
# only run this experiment for staff members
e.run_if { current_user.staff? }
# ...
end
endAs a scientist, you know it's always important to be able to turn your experiment off, lest it run amok and result in villagers with pitchforks on your doorstep. In order to control whether or not an experiment is enabled, you must include the enabled? method in your Scientist::Experiment implementation.
class MyExperiment
include Scientist::Experiment
attr_accessor :name, :percent_enabled
def initialize(name)
@name = name
@percent_enabled = 100
end
def enabled?
percent_enabled > 0 && rand(100) < percent_enabled
end
# ...
endThis code will be invoked for every method with an experiment every time, so be sensitive about its performance. For example, you can store an experiment in the database but wrap it in various levels of caching such as memcache or per-request thread-locals.
What good is science if you can't publish your results?
You must implement the publish(result) method, and can publish data however you like. For example, timing data can be sent to graphite, and mismatches can be placed in a capped collection in redis for debugging later.
The publish method is given a Scientist::Result instance with its associated Scientist::Observations:
class MyExperiment
include Scientist::Experiment
# ...
def publish(result)
# Wall time
# Store the timing for the control value,
$statsd.timing "science.#{name}.control", result.control.duration
# for the candidate (only the first, see "Breaking the rules" below,
$statsd.timing "science.#{name}.candidate", result.candidates.first.duration
# CPU time
# Store the timing for the control value,
$statsd.timing "science.cpu.#{name}.control", result.control.cpu_time
# for the candidate (only the first, see "Breaking the rules" below,
$statsd.timing "science.cpu.#{name}.candidate", result.candidates.first.cpu_time
# and counts for match/ignore/mismatch:
if result.matched?
$statsd.increment "science.#{name}.matched"
elsif result.ignored?
$statsd.increment "science.#{name}.ignored"
else
$statsd.increment "science.#{name}.mismatched"
# Finally, store mismatches in redis so they can be retrieved and examined
# later on, for debugging and research.
store_mismatch_data(result)
end
end
def store_mismatch_data(result)
payload = {
:name => name,
:context => context,
:control => observation_payload(result.control),
:candidate => observation_payload(result.candidates.first),
:execution_order => result.observations.map(&:name)
}
key = "science.#{name}.mismatch"
$redis.lpush key, payload
$redis.ltrim key, 0, 1000
end
def observation_payload(observation)
if observation.raised?
{
:exception => observation.exception.class,
:message => observation.exception.message,
:backtrace => observation.exception.backtrace
}
else
{
# see "Keeping it clean" above
:value => observation.cleaned_value
}
end
end
endWhen running your test suite, it's helpful to know that the experimental results always match. To help with testing, Scientist defines a raise_on_mismatches class attribute when you include Scientist::Experiment. Only do this in your test suite!
To raise on mismatches:
class MyExperiment
include Scientist::Experiment
# ... implementation
end
MyExperiment.raise_on_mismatches = trueScientist will raise a Scientist::Experiment::MismatchError exception if any observations don't match.
To instruct Scientist to raise a custom error instead of the default Scientist::Experiment::MismatchError:
class CustomMismatchError < Scientist::Experiment::MismatchError
def to_s
message = "There was a mismatch! Here's the diff:"
diffs = result.candidates.map do |candidate|
Diff.new(result.control, candidate)
end.join("\n")
"#{message}\n#{diffs}"
end
endscience "widget-permissions" do |e|
e.use { Report.find(id) }
e.try { ReportService.new.fetch(id) }
e.raise_with CustomMismatchError
endThis allows for pre-processing on mismatch error exception messages.
Scientist rescues and tracks all exceptions raised in a try or use block, including some where rescuing may cause unexpected behavior (like SystemExit or ScriptError). To rescue a more restrictive set of exceptions, modify the RESCUES list:
# default is [Exception]
Scientist::Observation::RESCUES.replace [StandardError]Timeout ⏲️: If you're introducing a candidate that could possibly timeout, use caution.
If an exception is raised within any of Scientist's internal helpers, like publish, compare, or clean, the raised method is called with the symbol name of the internal operation that failed and the exception that was raised. The default behavior of Scientist::Default is to simply re-raise the exception. Since this halts the experiment entirely, it's often a better idea to handle this error and continue so the experiment as a whole isn't canceled entirely:
class MyExperiment
include Scientist::Experiment
# ...
def raised(operation, error)
InternalErrorTracker.track! "science failure in #{name}: #{operation}", error
end
endThe operations that may be handled here are:
:clean- an exception is raised in acleanblock:compare- an exception is raised in acompareblock:enabled- an exception is raised in theenabled?method:ignore- an exception is raised in anignoreblock:publish- an exception is raised in thepublishmethod:run_if- an exception is raised in arun_ifblock
Because enabled? and run_if determine when a candidate runs, it's impossible to guarantee that it will run every time. For this reason, Scientist is only safe for wrapping methods that aren't changing data.
When using Scientist, we've found it most useful to modify both the existing and new systems simultaneously anywhere writes happen, and verify the results at read time with science. raise_on_mismatches has also been useful to ensure that the correct data was written during tests, and reviewing published mismatches has helped us find any situations we overlooked with our production data at runtime. When writing to and reading from two systems, it's also useful to write some data reconciliation scripts to verify and clean up production data alongside any running experiments.
Keep in mind that Scientist's try and use blocks run sequentially in random order. As such, any data upon which your code depends may change before the second block is invoked, potentially yielding a mismatch between the candidate and control return values. To calibrate your expectations with respect to false negatives arising from systemic conditions external to your proposed changes, consider starting with an experiment in which both the try and use blocks invoke the control method. Then proceed with introducing a candidate.
As your candidate behavior converges on the controls, you'll start thinking about removing an experiment and using the new behavior.
- If there are any ignore blocks, the candidate behavior is guaranteed to be different. If this is unacceptable, you'll need to remove the ignore blocks and resolve any ongoing mismatches in behavior until the observations match perfectly every time.
- When removing a read-behavior experiment, it's a good idea to keep any write-side duplication between an old and new system in place until well after the new behavior has been in production, in case you need to roll back.
Sometimes scientists just gotta do weird stuff. We understand.
Science is useful even when all you care about is the timing data or even whether or not a new code path blew up. If you have the ability to incrementally control how often an experiment runs via your enabled? method, you can use it to silently and carefully test new code paths and ignore the results altogether. You can do this by setting ignore { true }, or for greater efficiency, compare { true }.
This will still log mismatches if any exceptions are raised, but will disregard the values entirely.
It's not usually a good idea to try more than one alternative simultaneously. Behavior isn't guaranteed to be isolated and reporting + visualization get quite a bit harder. Still, it's sometimes useful.
To try more than one alternative at once, add names to some try blocks:
require "scientist"
class MyWidget
include Scientist
def allows?(user)
science "widget-permissions" do |e|
e.use { model.check_user(user).valid? } # old way
e.try("api") { user.can?(:read, model) } # new service API
e.try("raw-sql") { user.can_sql?(:read, model) } # raw query
end
end
endWhen the experiment runs, all candidate behaviors are tested and each candidate observation is compared with the control in turn.
Define the candidates with named try blocks, omit a use, and pass a candidate name to run:
experiment = MyExperiment.new("various-ways") do |e|
e.try("first-way") { ... }
e.try("second-way") { ... }
end
experiment.run("second-way")The science helper also knows this trick:
science "various-ways", run: "first-way" do |e|
e.try("first-way") { ... }
e.try("second-way") { ... }
endIf you're writing tests that depend on specific timing values, you can provide canned durations using the fabricate_durations_for_testing_purposes method, and Scientist will report these in Scientist::Observation#duration and Scientist::Observation#cpu_time instead of the actual execution times.
science "absolutely-nothing-suspicious-happening-here" do |e|
e.use { ... } # "control"
e.try { ... } # "candidate"
e.fabricate_durations_for_testing_purposes({
"control" => { "duration" => 1.0, "cpu_time" => 0.9 },
"candidate" => { "duration" => 0.5, "cpu_time" => 0.4 }
})
endfabricate_durations_for_testing_purposes takes a Hash of duration & cpu_time values, keyed by behavior names. (By default, Scientist uses "control" and "candidate", but if you override these as shown in Trying more than one thing or No control, just candidates, use matching names here.) If a name is not provided, the actual execution time will be reported instead.
We should mention these durations will be used both for the duration field and the cpu_time field.
Like Scientist::Experiment#cleaner, this probably won't come up in normal usage. It's here to make it easier to test code that extends Scientist.
If you need to use Scientist in a place where you aren't able to include the Scientist module, you can call Scientist.run:
Scientist.run "widget-permissions" do |e|
e.use { model.check_user(user).valid? }
e.try { user.can?(:read, model) }
endBe on a Unixy box. Make sure a modern Bundler is available. script/test runs the unit tests. All development dependencies are installed automatically. Scientist requires Ruby 2.3 or newer.
- RealGeeks/lab_tech is a Rails engine for using this library by controlling, storing, and analyzing experiment results with ActiveRecord.
- daylerees/scientist (PHP)
- scientistproject/scientist.net (.NET)
- joealcorn/laboratory (Python)
- rawls238/Scientist4J (Java)
- tomiaijo/scientist (C++)
- trello/scientist (node.js)
- ziyasal/scientist.js (node.js, ES6)
- TrueWill/tzientist (node.js, TypeScript)
- TrueWill/paleontologist (Deno, TypeScript)
- yeller/laboratory (Clojure)
- lancew/Scientist (Perl 5)
- lancew/ScientistP6 (Perl 6)
- MadcapJake/Test-Lab (Perl 6)
- cwbriones/scientist (Elixir)
- calavera/go-scientist (Go)
- jelmersnoeck/experiment (Go)
- spoptchev/scientist (Kotlin / Java)
- junkpiano/scientist (Swift)
- serverless scientist (AWS Lambda)
- fightmegg/scientist (TypeScript, Browser / Node.js)
- MisterSpex/misterspex-scientist (Java, no dependencies)
@jbarnette, @jesseplusplus, @rick, and @zerowidth