Imitating Objects Using Mocks

Learn how to use Python’s mocking techniques for effective unit testing.

Isolated problems are easier to diagnose and solve. Figuring out why a gasoline car won’t start can be tricky because there are so many interrelated parts. If a test fails, uncovering all the interrelationships makes diagnosis of the problem difficult. We often want to isolate items by providing simplified imitations. It turns out there are two reasons to replace perfectly good code with imitation (or mock) objects:

  • The most common case is to isolate a unit under test. We want to create collaborating classes and functions so we can test one unknown component in an environment of known, trusted test fixtures.
  • Sometimes, we want to test code that requires an object that is either expensive or risky to use. Things like shared databases, filesystems, and cloud infrastructures can be very expensive to set up and tear down for testing.

In some cases, this may lead to designing an API to have a testable interface. Designing for testability often means designing a more usable interface, too. In particular, we have to expose assumptions about collaborating classes so we can inject a mock object instead of an instance of an actual application class.

For example, imagine we have some code that keeps track of flight statuses in an external key-value store (such as redis or memcache), such that we can store the timestamp and the most recent status. The implementation will require the redis client; it’s not needed to write unit tests.


Here’s some code that saves status in a redis cache server:

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