Laboratory!

Laboratory是重构关键路径一个python库(是Github的Scientist一个移植).

Why?

See Github's blog post - http://githubengineering.com/scientist/

But how?

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 candidate 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.run()

Publishing results

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)

Controlling comparison

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']

Adding context

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',      # }

Installation

Installing from pypi is recommended

$ pip install laboratory

官方网站：http://www.open-open.com/lib/view/home/1455582479058