A Python library for carefully refactoring critical paths (and a port of GitHub's Scientist).

• Installation
• Getting started
• Publishing results
• Controlling comparison
• Adding context

See GitHub's blog post — http://githubengineering.com/scientist/

Imagine you've implemented a complex caching strategy for some objects in your database and a stale cache is simply not acceptable. How could you test this and ensure parity with your previous implementation, under load, with production data? Run it in production!

Laboratory will:

• Run both the new and the old code
• Compare their results
• Record timing information about all code
• Swallow and record exceptions in the new code
• Publish all of this information

Of course, you're still unsure your candidate code works correctly, so laboratory will always return the result from the control block.

import laboratory

experiment = laboratory.Experiment()
with experiment.control() as c:
    c.record(get_objects_from_database())

with experiment.candidate() as c:
    c.record(get_objects_from_cache())

objects = experiment.conduct()

Note that the Experiment class can also be used as a decorator.

@Experiment(candidate=get_objects_from_cache)
def get_objects_from_database():
    return True

This data is useless unless we can do something with it. Laboratory makes no assumptions about how to do this — it's entirely for you to implement to suit your needs. 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 passed a Result instance, with control and candidate data is available in Result.control and Result.observations respectively.

class MyExperiment(laboratory.Experiment):
    def publish(self, result):
        statsd.timing('MyExperiment.control', result.control.duration)
        for o in result.observations:
            statsd.timing('MyExperiment.%s' % o.name, o.duration)

Not all data is created equal. By default laboratory compares using ==, but sometimes you may need to tweak this to suit your needs. It's easy enough — just subclass Experiment and implement the compare(control, observation) method.

class MyExperiment(Experiment):
    def compare(self, control, observation):
        return control.value['id'] == observation.value['id']

A lot of the time there's going to be extra context around an experiment that's useful to use in publishing or comparisons. You can set this data in a few ways.

# The first is experiment-wide context, which will be set on every observation laboratory makes.

experiment = laboratory.Experiment(name='Object Cache Experiment', context={'user': user})


# Observation-specific context can be updated before or as the experiment is running.

with experiment.control(name='Object DB Strategy', context={'using': 'db'}) as e:
    e.update_context({'uuid': uuid})

    e.get_context()
    # {
    #     'user': <User>,
    #     'uuid': 'c08d46f1-92a6-46e5-9185-82d90dcb5af1',
    #     'using': 'db',
    # }


with experiment.candidate(name='Object Cache Strategy', context={'using': 'cache'}) as e:
    e.update_context({'uuid': uuid})

    e.get_context()
    # {
    #     'user': <User>,
    #     'using': 'cache',
    # }

Context can be retrieved using the get_context method on Experiment and Observation classes.

class Experiment(laboratory.Experiment):

    def publish(self, result):
        self.get_context()
        result.control.get_context()
        result.observations[0].get_context()

Installing from pypi is recommended

$ pip install laboratory

Laboratory is actively maintained by Joe Alcorn (Github, Twitter)