Asynchronous Programming in Python – Understanding The Essentials

Python Dependency Management

Asynchronous programming in Python is the process of writing concurrent code that runs asynchronously – i.e. doesn’t take place in real-time. It allows an app instance to execute multiple tasks at the same time, or in parallel. This helps speed up the required processing time because tasks can run simultaneously.

Asynchronous programming can be leveraged in Python to make applications more resilient, flexible, and efficient. Tasks performed asynchronously often  maintain responsiveness in programs and prevent blocking the main thread. This accelerates response time when dealing with multiple tasks at once. 

Let’s dive deeper into what asynchronous programming is, when to perform it, and how to implement it.

What Is Asynchronous Programming?

Asynchronous programming refers to a form of multitasking that allows for faster execution of programs and tasks by dividing a single task into smaller chunks of code. This approach makes it easier for a program to process multiple requests at once, allowing the user to make more efficient use of their time and resources. It allows developers to create complex software applications with minimal effort.

In Python, asynchronous programming is based on the event-driven programming model. This involves using ‘callbacks’, or functions that are triggered as soon as an event occurs. These functions can be used to perform a wide variety of tasks, like making an HTTP request, sending a notification, or even executing some long-running code without blocking the main thread. 

Usually, asynchronous code in Python is directly related to an event loop that needs to be triggered. This loop runs continuously and checks for any new events that need to be processed. Once an event is detected and the loop is triggered, the async code will call the appropriate callback function. 

It’s worth mentioning that there’s a solid number of reliable Python packages like AsyncIO and Twisted, which make it much easier to write asynchronous code. These libraries provide a range of tools that enable developers to create efficient and scalable applications. 

Related: A Comprehensive Guide To Python Dependency Management

When Do I Need Asynchronous Execution?

Asynchronous programming is primarily used for applications that require a high degree of concurrency. In most cases, this includes web applications that need to handle thousands of requests simultaneously, or even some applications that require the execution of long-running tasks. Since asynchronous programming can allow for the creation of more responsive and event-driven apps, it can significantly improve user experience.

Python is an ideal language for developing applications with asynchronous execution. Asynchronous programming allows developers to take advantage of Python’s high-level syntax and object-oriented style. This makes it easier to write code that is more efficient, faster, and easier to maintain. Some use cases that work very well with asynchronous execution include:

  • Web-based applications that need to handle many requests simultaneously.
  • Smooth user experience for real-time applications such as online gaming or video streaming.
  • Data processing applications that need to execute long-running tasks in the background.
  • Distributed systems and microservices architectures.

To get the most out of asynchronous programming in Python, it is important to understand how the event loop works and how to use callbacks. It is also beneficial to understand tools such as AsyncIO and Twisted that can make writing asynchronous code much easier.

Learn More: Most Secure Programming Languages 

Implementing Async Code in Python

Python includes several modules that simplify the process of writing and managing asynchronous code. Thesy provide powerful features such as error handling, cancellation and timeouts, and thread pools. 

The AsyncIO module is the most popular Python library for implementing asynchronous code. It provides a range of tools that make it easier to write and maintain asynchronous code. This includes features such as the event loop, coroutines, futures, and more. Let’s take a look at a simple code snippet as an example use-case of AsyncIO:

import asyncio
async def count():
    await asyncio.sleep(1)

async def main():
    await asyncio.gather(count(), count(), count())

if __name__ == "__main__":
    import time
    s = time.perf_counter()
    elapsed = time.perf_counter() - s
    print(f"{__file__} executed in {elapsed:0.2f} seconds.")


Just like AsyncIO, Twisted library is another popular Python library for asynchronous code. It provides a range of features such as an event-driven networking engine, threading,  process management, and more. However, unlike AsyncIO, Twisted is more server- and network-oriented, as we’ll see in the example below:

from twisted.web import proxy, http
from twisted.internet import reactor
from twisted.python import log
import sys

class ProxyFactory(http.HTTPFactory):
    protocol = proxy.Proxy

reactor.listenTCP(8080, ProxyFactory()) #asynchronous code waiting                           #for a server-side event


To get started, junior developers should familiarize themselves with the basics of asynchronous programming and then explore the various tools available in Python. With practice, developers can write code that is more efficient and easier to maintain. 

Final Thoughts

Asynchronous programming can be an incredibly valuable tool for both new and experienced Python developers. It can make applications more responsive, and improve their user experience. By understanding the basics of asynchronous programming, developers can take advantage of powerful features such as the event loop, coroutines, futures, and more. With practice, developers that master it can create applications that are responsive, efficient, and scalable. 

Guy Bar-Gil / About Author

Guy Bar-Gil is an experienced Head of Product-Led Growth and leads product-led growth at Mend. He loves engaging with people to understand and solve complex problems, with a special passion for product and company strategy. Prior to joining Mend, Guy held positions in R&D teams and served as a combat operator in the IDF.