My previous blog post, Python Mocking 101: Fake It Before You Make It , discussed the basic mechanics of mocking and unit testing in Python. This post covers some higher-level software engineering principles demonstrated in my experience with Python testing over the past year and half. In particular, I want to revisit the idea of patching mock.
Welcome to a guide to the basics of mocking in Python. It was born out of my need to test some code that used a lot of network services and my experience with GoMock , which showed me how powerful mocking can be when done correctly (thanks, Tyler ). I'll begin with a philosophical discussion about mocking because good mocking requires a different.
Fugue uses Python extensively throughout the Conductor and in our support tools, due to its ease-of-use, extensive package library, and powerful language tools. One thing we've learned from building complex software for the cloud is that a language is only as good as its debugging and profiling tools. Logic errors, CPU spikes, and memory leaks.