Testing with external services in PyTest - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the main challenge when testing code that interacts with external services?

The main challenge is that external services can be unreliable, slow, or change unexpectedly, which can cause tests to fail even if the code is correct.

beginner

Why do we use mocking in tests involving external services?

Mocking replaces the real external service with a fake one that returns controlled responses, making tests faster, reliable, and independent of the real service.

intermediate

What pytest feature helps to replace parts of your system during tests?

Pytest supports fixtures and the 'monkeypatch' fixture to replace functions or objects temporarily during a test.

intermediate

How can you simulate different responses from an external API in pytest?

You can create a mock function that returns different data or raises exceptions, then use monkeypatch to replace the real API call with this mock during tests.

advanced

What is a good practice to ensure tests with external services remain fast and reliable?

Use mocking or stubbing to avoid real network calls, and test integration with real services separately in dedicated integration tests.

Why should tests avoid calling real external services directly?

A Because real services always return errors

B Because real services can be slow or unavailable, causing flaky tests

C Because tests must always run offline

D Because external services are illegal to use in tests

Real external services can be slow, unreliable, or change unexpectedly, which makes tests flaky and slow.

What does mocking an external service mean in testing?

A Replacing the real service with a fake one that returns controlled data

B Deleting the external service

C Running the external service on your local machine

D Ignoring the external service calls

Mocking means replacing the real service with a fake version that returns predictable responses for testing.

Which pytest feature helps you replace functions or objects during a test?

A parametrize decorator

B assert statement

C monkeypatch fixture

D skip marker

The monkeypatch fixture allows you to replace or modify objects temporarily during a test.

What is a benefit of using mocking in tests with external services?

A Tests run faster and are more reliable

B Tests become more complex

C Tests require internet connection

D Tests always fail

Mocking avoids real network calls, making tests faster and less likely to fail due to external issues.

When should you test with the real external service instead of mocking?

A Only when the service is down

B Never, mocking is always better

C Only for unit tests

D In dedicated integration tests to verify real interactions

Integration tests check real interactions with external services to ensure everything works end-to-end.

Explain why mocking external services is important in automated tests.

Think about what happens if your test depends on the internet or a service that might be down.

Describe how you would use pytest to mock an external API call in a test.

Focus on how pytest's monkeypatch can help replace parts of your code during tests.

Practice

1. What is the main reason to use mocking when testing with external services in pytest?

easy

A. To avoid calling the real external service and make tests faster

B. To increase the number of real API calls during testing

C. To make tests dependent on internet speed

D. To test the external service itself

Solution

1. Step 1: Understand the role of mocking in tests

   Mocking replaces real external calls with fake ones to avoid delays and failures.

2. Step 2: Identify the benefit of mocking external services

   Mocking makes tests faster and more reliable by not depending on real services.

3. Final Answer:

   To avoid calling the real external service and make tests faster -> Option A

4. Quick Check:

   Mocking speeds up tests by faking external calls [OK]

Hint: Mock external calls to speed tests and avoid failures [OK]

Common Mistakes:

• Thinking mocking increases real API calls
• Believing tests should depend on internet speed
• Confusing testing external service with testing your code

2. Which of the following is the correct way to mock a function get_data from module external_api using pytest's patch decorator?

easy

A. @patch('external_api.get_data')

B. @patch('get_data.external_api')

C. @patch('external_api->get_data')

D. @patch('external_api.getData')

Solution

1. Step 1: Recall correct patch syntax

   The patch decorator requires the full import path as a string: 'module.function'.

2. Step 2: Match the correct option

   @patch('external_api.get_data') uses 'external_api.get_data' which is the correct format and case-sensitive.

3. Final Answer:

   @patch('external_api.get_data') -> Option A

4. Quick Check:

   patch('module.function') syntax is correct [OK]

Hint: Use 'module.function' string in patch decorator [OK]

Common Mistakes:

• Swapping module and function order
• Using wrong separators like '->'
• Incorrect function name casing

3. Given the following pytest test code, what will be the output when running the test?

from unittest.mock import patch
import requests

def fetch_status():
    response = requests.get('https://api.example.com/data')
    return response.status_code

@patch('requests.get')
def test_fetch_status(mock_get):
    mock_get.return_value.status_code = 200
    assert fetch_status() == 200
    print('Test passed')

medium

A. TypeError

B. AssertionError

C. Test passed

D. No output

Solution

1. Step 1: Understand mocking effect on requests.get

   The patch replaces requests.get with a mock that returns an object with status_code 200.

2. Step 2: Check assertion and print statement

   fetch_status() returns 200, matching the assertion, so 'Test passed' is printed.

3. Final Answer:

   Test passed -> Option C

4. Quick Check:

   Mocked return_value.status_code = 200 makes test pass [OK]

Hint: Mock return_value to control function output [OK]

Common Mistakes:

• Forgetting to set return_value.status_code
• Expecting real HTTP call instead of mock
• Missing print output due to assertion failure

4. Identify the error in the following pytest test that mocks an external service call:

from unittest.mock import patch
import myservice

@patch('myservice.call_api')
def test_api(mock_call):
    mock_call.return_value = {'status': 'ok'}
    result = myservice.call_api()
    assert result.status == 'ok'

medium

A. The patch decorator is missing parentheses

B. The assertion should use result['status'] instead of result.status

C. mock_call.return_value should be a string, not a dict

D. The test function is missing a return statement

Solution

1. Step 1: Analyze the mocked return value type

   The mock returns a dictionary {'status': 'ok'}, so result is a dict.

2. Step 2: Check the assertion syntax

   Accessing dict keys requires bracket notation, not dot notation; result.status causes AttributeError.

3. Final Answer:

   The assertion should use result['status'] instead of result.status -> Option B

4. Quick Check:

   Dict keys need brackets, not dot notation [OK]

Hint: Use brackets for dict keys in assertions [OK]

Common Mistakes:

• Using dot notation on dicts
• Forgetting parentheses in patch decorator (not here)
• Expecting return_value must be string always

5. You want to test a function process_data() that calls an external API fetch_data(). The API sometimes returns null. How should you mock fetch_data in pytest to test process_data handles null correctly?

hard

A. Do not mock fetch_data; test process_data only with real data

B. Call the real fetch_data to see if it returns null

C. Mock fetch_data to always raise an exception

D. Use patch to make fetch_data return null and assert process_data handles it

Solution

1. Step 1: Understand the need to test null handling

   Since fetch_data can return null, tests must simulate this to check process_data behavior.

2. Step 2: Use patch to mock fetch_data returning null

   Mocking fetch_data to return null allows controlled testing of process_data's handling of that case.

3. Final Answer:

   Use patch to make fetch_data return null and assert process_data handles it -> Option D

4. Quick Check:

   Mock null return to test edge case handling [OK]

Hint: Mock edge cases like null to test error handling [OK]

Common Mistakes:

• Calling real external API in tests
• Mocking only success cases, ignoring null
• Ignoring exceptions instead of testing them