Async vs Threading vs Multiprocessing in Python
Tags: python, threading, multiprocessing, async
Published: 2023-08-20 13:23:10


## Async vs Threading vs Multiprocessing in Python

### Multitasking and concurrency with Python

In Python, there are three main ways to achieve concurrency: asynchronous programming, threading, and multiprocessing. Each approach has its own advantages and disadvantages, and the best choice for a particular application will depend on the specific requirements.

## **Asynchronous programming**


Asynchronous programming is a way of writing code that allows multiple tasks to run concurrently, even if they are blocking. This is done by using coroutines, which are functions that can be suspended and resumed at a later time.

In an asynchronous model, multiple tasks can be executed concurrently. When a long-running function is called, it does not block the execution flow of the program. Instead, the program continues to run and the function is executed in the background. When the function finishes, the program is notified and can access the result.

For example, an application that scrapes a website for data would benefit from using asynchronous programming, as it could run other tasks while waiting for the website to respond.

The main disadvantage of asynchronous programming is that it can be more difficult to write than synchronous code. This is because coroutines can be more complex to manage, and it can be difficult to debug asynchronous code.

Here is a code example of asynchronous programming in Python:

    import asyncio
    
    async def download_file(url):
      """Download a file from a URL."""
      with open(url, "wb") as f:
        response = await asyncio.get(url)
        f.write(response.content)
    
    async def main():
      """Download two files concurrently."""
      tasks = [download_file("https://www.example.com/file1.txt"),
               download_file("https://www.example.com/file2.txt")]
      await asyncio.gather(*tasks)
    
    if __name__ == "__main__":
      asyncio.run(main())


This code downloads two files from the internet concurrently. The download_file() function is an asynchronous function that downloads a file from a URL. The main() function creates two tasks for the download_file() function and then waits for them to complete.

![[sync vs async](https://www.baeldung.com/cs/async-vs-multi-threading)](https://cdn-images-1.medium.com/max/3744/1*audZFWEYvVJZOsZr1f5LiA.png)

## Threading

Threading is a way of running multiple tasks simultaneously within a single process. This is done by creating multiple threads, each of which executes its own code.

The main advantage of threading is that it is relatively easy to write and debug. This is because threads are managed by the Python interpreter, so the programmer does not have to worry about managing them manually.

For example, a web server could use threading to handle multiple requests from different clients at the same time.

Here is a code example of threading in Python:

    import threading
    
    def do_something_threaded():
      """Do something that takes a long time."""
      time.sleep(1)
      print("Done!")
    
    def main():
      """Start two threads."""
      threads = []
      for _ in range(2):
        thread = threading.Thread(target=do_something_threaded)
        threads.append(thread)
      for thread in threads:
        thread.start()
      for thread in threads:
        thread.join()
    
    if __name__ == "__main__":
      main()

This code starts two threads that each do something that takes a long time. The do_something_threaded() function is a threaded function that does something that takes a long time. The main() function creates two threads for the do_something_threaded() function and then waits for them to complete.

## Multiprocessing

Multiprocessing is a way of running multiple tasks simultaneously in separate processes. This is done by creating multiple processes, each of which executes its own code in its own memory space.

The main advantage of multiprocessing is that it is more efficient than threading. This is because processes do not share the same memory space, which eliminates the possibility of race conditions.

Here is a code example of multiprocessing in Python:

    import multiprocessing
    
    def do_something_multiprocessed():
      """Do something that takes a long time."""
      time.sleep(1)
      print("Done!")
    
    def main():
      """Start two processes."""
      processes = []
      for _ in range(2):
        process = multiprocessing.Process(target=do_something_multiprocessed)
        processes.append(process)
      for process in processes:
        process.start()
      for process in processes:
        process

This code starts two processes that each do something that takes a long time. The do_something_multiprocessed() function is a multiprocessed function that does something that takes a long time. The main() function creates two processes for the do_something_multiprocessed() function and then waits for them to complete.

![[multithreading vs multiprocessing](https://www.baeldung.com/cs/async-vs-multi-threading)](https://cdn-images-1.medium.com/max/6036/1*AYX4HrL47oD441FtPDG68A.png)

## **Which is the Best?**

The choice of which approach to concurrency to use in Python will depend on the specific requirements of the application.

* Asynchronous programming is a good choice for applications that perform a lot of blocking I/O operations, such as web scraping, file downloading, and network communication.

* Threading is a good choice for applications that need to run multiple tasks simultaneously in a single process, such as a web server or a game engine.

* Multiprocessing is a good choice for applications that need to run multiple tasks simultaneously in separate processes, such as a scientific computing application or a machine learning model.

Here are some additional considerations when choosing between asynchronous programming, threading, and multiprocessing:

* Performance: Asynchronous programming is generally the most efficient approach, as it allows multiple tasks to run concurrently. However, it can be more difficult to write and debug than threading or multiprocessing.

* Scalability: Threading is a good choice for applications that need to scale to a large number of concurrent tasks. However, it can be less efficient than multiprocessing for a large number of tasks.

* Complexity: Multiprocessing is the most complex approach, but it can be the most efficient for a large number of tasks. However, it can be more difficult to write and debug than threading.

Ultimately, the best approach to concurrency in Python will depend on the specific requirements of the application.

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