How to Create a Sticky Consistent Mock with unittest.mock in Python

Shows how to use unittest.mock.patch.object to mock a method consistently across multiple calls, returning a sticky value every time.

Easy Python 3.8+ Aug 9, 2026 A/B testing & experimentation 15 views 0 copies

unittest mock testing patch

Python code

18 lines

Python 3.8+

from unittest.mock import patch

class Database:
    def fetch(self, key):
        return f"real value for {key}"

def get_value(db, key):
    return db.fetch(key)

if __name__ == "__main__":
    db = Database()
    with patch.object(db, "fetch", return_value="sticky value") as mock_fetch:
        result1 = get_value(db, "user:1")
        result2 = get_value(db, "user:2")

    print("First call:", result1)
    print("Second call:", result2)
    print("Mock called:", mock_fetch.call_count)

Output

stdout

First call: sticky value
Second call: sticky value
Mock called: 2

How it works

The patch.object context manager temporarily replaces the fetch method on the db instance with a Mock that returns the fixed string "sticky value" whenever called. Because the mock persists for the entire with block, both calls to get_value return the same consistent result, demonstrating a sticky mock. After the block exits, the original method is restored automatically, so the mock does not leak into other tests. The call_count attribute confirms the mock was invoked twice, proving the sticky behavior across multiple calls.

Common mistakes

Forgetting that the mock is restored on exit, so calling it outside the `with` block hits the real method.
Using `patch` without `object` when you need to target an instance method, causing the mock to affect all instances.
Assuming `call_count` resets between tests without explicitly clearing the mock.

Variations

Use `patch('module.Database.fetch', return_value='sticky value')` to mock the method at class level rather than instance level.
Use `mock_fetch.side_effect = lambda key: "sticky value"` to customize behavior per call.

Real-world use cases

Simulating a consistent A/B test assignment so the same user always sees the same variant during a session.
Stubbing a database call in unit tests to return a fixed value, isolating logic from real data.
Mocking an external API dependency to verify your code handles repeated identical responses gracefully.

How to Create a Sticky Consistent Mock with unittest.mock in Python

Python code

Output

How it works

Common mistakes

Variations

Real-world use cases

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Python code

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How it works

Common mistakes

Variations

Real-world use cases

More from A/B testing & experimentation

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