Mocking Async Calls in Python 3.5: An Easy Guide

Asynchronous programming is becoming increasingly popular in Python 3.5 and higher. It allows faster and more efficient execution of tasks that may otherwise take too long to complete. One of the most common tasks in asynchronous programming is making async calls.

However, testing async calls can be troublesome due to the unpredictability of their execution. Thankfully, mocking async calls can solve this problem by allowing you to control their execution and responses. This makes writing unit tests for async code a breeze!

This article will guide you through the process of mocking async calls in Python 3.5. You’ll learn how to use built-in Python libraries like unittest.mock and asyncio to effectively test your async code. We’ll also cover some best practices and potential pitfalls to watch out for.

If you’re tired of struggling with testing async code or just want to improve your testing skills, this guide is for you! So, buckle up and let’s dive into the world of mocking async calls in Python 3.5.

“Mocking Async Call In Python 3.5” ~ bbaz

Introduction

Asynchronous programming is becoming increasingly important in modern programming practices. With the release of Python 3.5, a new native library for asynchronous programming has been introduced called asyncio. When working with asynchronous programming in Python 3.5, mocking async calls can be a challenging task, but with the help of some amazing libraries, it can be made relatively easy. In this blog, we’ll compare different methods of mocking async calls in Python 3.5 and share our opinions about them.

What is Async Programming in Python 3.5?

Async programming is a programming paradigm that allows other tasks to be executed while waiting for certain tasks to complete. In Python 3.5, the native library for async programming is called asyncio. Asyncio provides a way to write highly-concurrent code in an easy and scalable way.

Why Mocking Async Calls is Important?

In testing, mocking async calls can be crucial because it allows you to test your code without making unnecessary network requests, database queries or any other such tasks. Mocking async calls can also help you in isolating your code while writing unit tests.

Method 1: Using AsyncMock

AsyncMock is a library that provides a simple way to mock asynchronous functions or coroutines in Python 3.5. It is used in Python 3.8 and later versions. You can use AsyncMock to create mock objects for your async functions and coroutines. The library works by overriding the __call__ method of the function and returns the result of its side effect.

Pros of AsyncMock

• Easy to use
• Provides enhanced control over the mocked objects and their side effects

Cons of AsyncMock

• Only available in Python 3.8 and later versions
• Does not work with async functions or coroutines that call other async functions internally.

Method 2: Using Asyncio Mock

Asyncio Mock is another library that provides an easy way to mock async functions and coroutines in Python 3.5. This library allows you to replace any async function with a MagicMock object that you can modify to provide any required responses.

Pros of Asyncio Mock

• Works with both async and non-async functions and coroutines
• Easy to use

Cons of Asyncio Mock

• The MagicMock object does not behave as a true async object, which may make it unsuitable for some use cases.

Method 3: Wrapping Async Functions with Sync Functions

Another common approach to mocking async functions in Python 3.5 is by wrapping them in synchronous functions. This method involves converting all async functions into their synchronous counterparts using the asyncio.run() function. Once the function has been converted, standard mocking techniques for synchronous functions can be applied.

Pros of Wrapping Async Functions with Sync Functions

• Requires no additional libraries or packages
• Can be used with any version of Python 3.x

Cons of Wrapping Async Functions with Sync Functions

• Can be cumbersome to use with complex workflows
• May slow down code execution and impact overall performance
• Can be difficult to manage while dealing with multiple concurrent tasks.

Comparison Table

Method	Pros	Cons
AsyncMock	Easy to use, Control over mock objects and side effects	Only available in Python 3.8+, can’t mock async functions calling other async functions
Asyncio Mock	Works with both async and non-async functions, Easy to Use	The MagicMock object does not behave as a true async object
Wrap Async Functions with Sync Functions	No additional libraries or packages required, works with any version of Python 3.x	Can be cumbersome to use with complex workflows, may slow down code execution, and can be difficult to manage while dealing with multiple concurrent tasks.

Our Opinion

All three methods for mocking async calls have their advantages and disadvantages. If you’re running Python 3.8 or later versions, AsyncMock provides an easy-to-use and comprehensive way to mock async functions, and it gives users more control over the side effects. For those who are working with Python 3.5 or older versions, Asyncio Mock is a viable alternative that works with both async and non-async functions. Finally, wrapping async functions in sync functions is an option but may result in performance drawbacks and prove difficult to manage. Which method you choose will depend on your workflow, personal preferences and factors such as the size of your project, complexity of individual tests and the need for flexibility.

Conclusion

In conclusion, mocking async calls can be a daunting task when working with Python 3.5, but these libraries make it easy. We hope this comparison blog article has provided you with the knowledge you need to choose the right method to mock your async calls in Python 3.5 based on your specific use case.

Thank you for taking the time to read our article on Mocking Async Calls in Python 3.5. We hope that you found it informative and useful in your own software development projects. As we have shown, mocking async calls can greatly simplify testing and debugging, saving valuable time and resources.

Remember, mocking async calls involves creating a fake version of the call that can be executed synchronously, allowing you to better control the behavior of the call in your tests. By following the step-by-step guide we have provided, you can quickly and easily implement this technique in your own code.

If you have any questions or feedback about our article, please don’t hesitate to reach out to us. We are always happy to engage with our readers and help you find solutions to your software development challenges. Thanks again for reading, and we hope to see you back here soon for more informative articles and tutorials.

Mocking async calls in Python 3.5 can be a tricky task for developers who are new to asynchronous programming. Here are some frequently asked questions about mocking async calls in Python 3.5:

1. What is mocking in Python?

   Mocking in Python is the process of creating fake objects or functions that simulate the behavior of real objects or functions. This is useful for testing purposes, as it allows developers to isolate parts of their code and test them independently.

2. What is an async call in Python?

   An async call in Python is a function that runs asynchronously, meaning it does not block the main thread of execution. Instead, it allows other code to run while it waits for a result. This is useful for handling long-running operations, such as network requests or file I/O.

3. Why is mocking async calls important?

   Mocking async calls is important for testing async code, as it allows developers to simulate different scenarios and test their code’s behavior without relying on actual async calls. This can save time and resources, as well as make testing more predictable and reliable.

4. How can I mock an async call in Python 3.5?

   To mock an async call in Python 3.5, you can use the asyncio library’s `create_task` method to create a coroutine object, and then use the `MagicMock` class from the `unittest.mock` module to mock the result of the coroutine. You can then use the `asyncio.run_until_complete` method to run the coroutine and retrieve the mocked result.

5. Are there any best practices for mocking async calls in Python?

   Some best practices for mocking async calls in Python include using the `async with` statement to manage resources, using context managers to handle exceptions, and using the `@asyncio.coroutine` decorator to mark coroutines as async.