What is mock annotated dependency in Python?

In Python, a "mock annotated dependency" typically refers to the practice of using the unittest.mock library, or a similar mocking framework, to replace actual dependencies of a function or class with mock objects when writing tests. This is particularly useful when dealing with dependencies that are external (like database connections or API calls), slow, or difficult to set up for testing. Annotation, in this context, often involves using Python's type hints to define dependencies.

Let's break down the key aspects:

1. Dependency Injection: In modern software design, components often depend on other components. This is known as dependency injection. Instead of hardcoding dependencies within a class or function, they are passed in as arguments, often using type hints to specify the type of dependency.

2. Type Hints: Python type hints, such as those introduced by the typing module, allow developers to declare the expected types of variables, function parameters, and function return values. For example, def my_function(db: DatabaseConnection) indicates that my_function expects an object of type DatabaseConnection as an argument.

3. Mocking: The unittest.mock library allows you to create mock objects that simulate real objects. These mocks can be configured to return specific values, raise exceptions, or track calls. When you need to test a piece of code that uses a complex dependency, you can replace the actual dependency with a mock object, making the test more focused and efficient.

4. Combining Mocking with Type Hints: When you have type-annotated dependencies, mocking becomes more straightforward because you know exactly what type of object your code expects. You can create a mock object that adheres to the interface (methods and attributes) of the type specified in the annotation. This helps in writing tests that accurately replicate the usage patterns of your dependencies.

Example:

Consider this code snippet:

from typing import Protocol
class DatabaseConnection(Protocol):
  def query(self, sql: str) -> list:
    ...

def get_users(db: DatabaseConnection) -> list:
  return db.query("SELECT FROM users;")

Here, get_users has an annotated dependency db of type DatabaseConnection. To test get_users, we would not want to interact with an actual database, but with a mock:

import unittest
from unittest.mock import MagicMock

class TestGetUsers(unittest.TestCase):
  def test_get_users(self):
    mock_db = MagicMock(spec=DatabaseConnection)
    mock_db.query.return_value = [{"id": 1, "name": "John Doe"}]
    users = get_users(mock_db)
    self.assertEqual(users, [{"id": 1, "name": "John Doe"}])
    mock_db.query.assert_called_once_with("SELECT FROM users;")

In this test, MagicMock is used to create a mock object. The spec=DatabaseConnection ensures the mock object adheres to the DatabaseConnection protocol. The mock’s query method is set up to return a list of user data. This allows the test to focus solely on testing the logic of the get_users function without needing a real database.

In Summary:

Mock annotated dependency, in Python, is about replacing your typed dependencies with mock objects to facilitate unit testing. This ensures that you can test your code in isolation without dealing with the complexity of actual dependencies, making your tests faster, more reliable, and more focused on the code you are writing.

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