A Scientist-like module for Deno, implemented in TypeScript.

It permits comparing legacy and refactored code paths in production environments, verifying both functional and non-functional requirements. This is also known as the Parallel Run pattern.

This is a port of Tzientist.

import * as scientist from "https://raw.githubusercontent.com/TrueWill/paleontologist/v1.0.0/mod.ts";

const experiment = scientist.experiment({
  name: "trial1",
  control: (s: string) => "Control " + s,
  candidate: (s: string) => "not quite right " + s,
});

console.log(experiment("C"));

This uses the default options and prints:

Experiment trial1: difference found
Control C

Note that scientist.experiment is a factory; it returns a function (named experiment in the example) that matches the signature of the control and the candidate.

The control is the source of truth. It's typically the legacy code you're trying to replace. The experiment (the function returned by scientist.experiment) will always return whatever the control returns (or will throw if the control throws). You would replace the original call to control in your codebase with a call to experiment.

The candidate is the new code you're testing that's intended to replace the control eventually. The experiment runs this code and publishes the result (along with the control result). The experiment will swallow any errors thrown by the candidate.

The experiment runs both the control and the candidate, and it publishes the results to a callback function. Normally you will provide a custom publish function in the options that will report the results to some location for later analysis.

function publish(results: scientist.Results<[string], string>): void {
  if (results.candidateResult !== results.controlResult) {
    console.log(
      `Experiment ${results.experimentName}: expected "${results.controlResult}" but got "${results.candidateResult}"`,
    );
  }
}

const experiment = scientist.experiment({
  name: "trial2",
  control: (s: string) => "Control " + s,
  candidate: (s: string) => "not quite right " + s,
  options: { publish },
});

console.log(experiment("C"));

This prints:

Experiment trial2: expected "Control C" but got "not quite right C"
Control C

You will probably want to check results.candidateError and results.controlError as well.

Typically you would replace console.log in publish with a call to a logging framework, persisting to a database, sending metrics to Grafana, etc.

The results include the arguments passed to the experiment (experimentArguments).

Running experiments can be expensive. Both the control and the candidate execute. If either may be slow or if the experiment runs in a performance-sensitive context, you may want to run the experiment on a percentage of traffic. You can provide a custom enabled function in the options. If enabled returns false, the experiment will still return what the control returns but it will not call the candidate nor will it publish results. If enabled returns true, the experiment will run normally. Paleontologist passes the arguments to the experiment to the enabled function in case you want to base the sampling on them.

Note: enabled receives the same arguments as the experiment, where publish receives a Results object with experimentArguments and other properties.

function enabled(_: string): boolean {
  // Run candidate 25% of the time
  return Math.floor(Math.random() * 100 + 1) <= 25;
}

const experiment = scientist.experiment({
  name: "trial3",
  control: (s: string) => "Control " + s,
  candidate: (s: string) => "not quite right " + s,
  options: { enabled },
});

If your functions are async (returning a Promise), use experimentAsync. The resulting experiment function will return a Promise.

const experiment = scientist.experimentAsync({
  name: "async trial1",
  control: myAsyncControl,
  candidate: myAsyncCandidate,
  options: { publish },
});

const result: number = await experiment(1, 2);

The control and the candidate will be run in parallel (that is, concurrently). Options are the same as for a normal experiment.

Published results include timings for both the control and the candidate. Timings are in milliseconds (ms). Note that other queued tasks could affect asynchronous timings, at least in theory.

Q. Why would I use this module?

A. You want to refactor or replace existing code, but that code is difficult or impossible to test with automated unit or integration tests. Perhaps it's nondeterministic. It might rely on data or on user input that is only available in a production environment. It could be a combinatorial explosion of states that requires too many test cases. Typically you would use this for high-risk changes, since you'll want to run the experiment for some time in production and check the results.

Q. What if my candidate or control have side effects (such as updating a database)?

A. In general, don't use Paleontologist in those cases.

Q. My candidate and control take different parameters. How do I handle that?

A. Create a facade for one or both so that the parameters match. You don't need to use all of the parameters in both functions.

Q. How do I configure custom compare, clean, or ignore functions?

A. Paleontologist always publishes results, so you can do all of the above in your publish function. publish can also delegate to other functions.

Q. How do I configure a custom run_if function to conditionally disable an experiment?

A. Paleontologist passes the arguments to the experiment to the enabled function (if this is present in the options). If enabled returns false, the experiment will still return what the control returns but it will not call the candidate nor will it publish results.

Q. What are some guidelines for writing publish and enabled functions?

A.

• Both publish and enabled should be fast
• Both publish and enabled should not throw (they should catch any errors)
• Both publish and enabled should not mutate the results (particularly the controlResult). Paleontologist does not do any deep cloning on its own. If you want to alter the results for a custom compare, consider using something like Lodash's cloneDeep first. (lodash_require might help; I haven't tried it though.)

Q. Why doesn't Paleontologist randomize the order in which the control and the candidate are run?

A. Because those functions should not have side effects.

Q. What if the results always differ due to the data containing timestamps, GUIDs, etc.?

A. One technique is to match those with regular expressions and replace them with a placeholder before comparing.

GitHub's Scientist Ruby library is a brilliant concept.

The goals of this project:

• Simplicity
• Deno compatibility
• TypeScript support
• Easy setup
• Reasonable defaults
• Good documentation
• High test coverage

Feature parity with Scientist is not a goal.

• Deno

• No classes
• Deno formatting
• No warnings/errors
• All tests pass
• Semantic Versioning 2.0.0

• GitHub and all contributors for Scientist
• Microsoft and all contributors for TypeScript
• The Deno authors and community
• Kyle June for Mock
• Titian Cernicova-Dragomir for a key Stack Overflow answer on types
• The rest of the TypeScript community
• All of the creators, contributors, and maintainers of the open source used here
• Sam Newman for discussing the Parallel Run pattern in the book Monolith to Microservices
• Livio Brunner for the article Create your first module with Deno
• Future contributors 😺