20 Python AsyncIO Interview Questions and Answers

Python AsyncIO is a relatively new concept in the world of programming, but it is quickly gaining popularity among developers. AsyncIO is a way to write code that is concurrent and can run in an asynchronous manner. This means that your code can do more than one thing at a time, which can improve the performance of your applications. If you’re interviewing for a position that involves Python AsyncIO, it’s important to be prepared to answer questions about it. In this article, we’ll discuss some common Python AsyncIO interview questions and how you should answer them.

Python AsyncIO Interview Questions and Answers

Here are 20 commonly asked Python AsyncIO interview questions and answers to prepare you for your interview:

1. Can you explain what async IO is in Python?

Async IO is a way of structuring your code so that it can perform multiple tasks simultaneously. This can be useful for tasks that are I/O bound, meaning that they are waiting on data from an external source, such as a file or a network connection. By using async IO, your code can continue to run while it is waiting for the data to arrive, which can improve performance.

2. What are the advantages of using async IO over multithreading or multiprocessing?

Async IO has a number of advantages over multithreading or multiprocessing. First, async IO is non-blocking, which means that a program can continue to run even if one or more of its tasks are waiting for IO. This can lead to more efficient code, as a program is not forced to wait for all tasks to complete before continuing. Additionally, async IO can be used to avoid the overhead of context switching between threads or processes.

3. How do you implement asynchronous event loops in Python?

The asyncio module in Python provides tools for implementing asynchronous event loops. This module is often used in conjunction with the Twisted framework.

4. Why is synchronous programming not a good idea for web applications?

Synchronous programming can lead to issues with blocking, which can make an application unresponsive. Asynchronous programming can help avoid these issues by allowing the program to continue running even if one task is blocked.

5. What does await do in Python?

The await keyword in Python is used to denote a coroutine that will run asynchronously. This means that the code after the await keyword will not run until the coroutine is finished.

6. What’s the difference between coroutines and generators in Python?

Coroutines are similar to generators, but they can both yield and receive values. Generators can only yield values.

7. What is an executor?

An executor is an object that executes tasks. In asyncio, an executor is used to run a task in a separate thread or process.

8. How do you create an executor in Python?

You can create an executor in Python using the concurrent.futures module. This module provides a number of different executor types that you can use to run tasks in parallel.

9. What happens if you use “async” with a function that doesn’t have the @coroutine decorator?

If you use “async” with a function that doesn’t have the @coroutine decorator, then the function will return a coroutine object. However, this coroutine object will not actually do anything until you call the “next” method on it.

10. Is it possible to write async code in python which can be run on both Python 2.7 as well as 3.5+? If yes, then how?

Yes, it is possible to write async code in python which can be run on both Python 2.7 as well as 3.5+. The key is to use the asyncio library, which is included in the standard library as of Python 3.4. This library provides a set of tools which allow you to write async code that is compatible with both Python 2.7 and 3.5+.

11. Are there any limitations when working with AsyncIO in Python?

There are a few potential limitations to be aware of when working with AsyncIO in Python. One is that it can be difficult to debug AsyncIO code due to the fact that the code is often running in multiple threads or processes. Additionally, AsyncIO may not play well with certain libraries or frameworks that are not designed to be used with asynchronous code.

12. What is the best way to avoid running into race conditions while using AsyncIO?

The best way to avoid race conditions while using AsyncIO is to use locks. This will ensure that only one task is running at a time, and that no two tasks are trying to access the same data at the same time.

13. Can you give me some examples where you would use AsyncIO in your application code?

There are many potential use cases for AsyncIO in application code. For example, if you are building a chat application, you could use AsyncIO to handle incoming messages and ensure that they are delivered to the correct recipients in a timely manner. If you are building a web application, you could use AsyncIO to handle requests and responses asynchronously, which would improve the overall performance of the application.

14. Is it possible to call functions from a different thread when using AsyncIO? If yes, then how?

Yes, it is possible to call functions from a different thread when using AsyncIO. This can be done by using the asyncio.run_coroutine_threadsafe() function.

15. What is the maximum number of threads per process in Python?

The maximum number of threads per process in Python is 20.

16. What is GIL? Do you think it poses a problem for multi-threaded apps written in Python?

The GIL is a global interpreter lock that allows only one thread to execute at a time. This can pose a problem for multi-threaded apps written in Python because it can lead to performance issues.

17. What is parallelism? How does it differ from concurrency?

Parallelism is the ability of a program to run multiple tasks at the same time. Concurrency is the ability of a program to handle multiple tasks at the same time, but not necessarily running them all at the same time.

18. What do you understand about context switching?

Context switching is the process of storing and retrieving the state of a process or thread so that it can be resumed at a later time. This allows multiple processes or threads to share a single processor, and is an essential part of any multitasking operating system.

19. What’s the best way to deal with blocking I/O calls in Python?

The best way to deal with blocking I/O calls in Python is to use the asyncio module. This module provides a way to write asynchronous code that can be run in a single thread. This makes it ideal for dealing with blocking I/O calls, as the code can be written in such a way that the blocking call will not cause the rest of the code to pause.

20. Can you explain what a future object is in Python?

A future object is an object that represents the result of an asynchronous operation. Future objects are used to pass around the result of an asynchronous operation from the point at which it is started to the point at which it is needed.