Why Python Developers Should Care About Asynchronous Programming

Explore what asynchronous programming is, when it should be used, how Python supports it, and measure its performance as compared to synchronous programming.

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In the era of data-driven applications and network-intensiv systems, writing efficient, scalable Python code is more important than ever.

When working with tasks like downloading files, calling APIs, or reading from databases, performance can take a hit if your program spends most of its time waiting.

This is where asynchronous programming shines. It is a way of writing code where tasks that take time — like file downloads or network requests — don’t block the entire program while they finish. Instead, your program continues doing other things.

This model is especially powerful for I/O-bound tasks, where your CPU spends time waiting for external responses (like servers or disks), rather than doing computations.

When should you use it? Asynchronous programming is most beneficial when:

• Your application performs many network or disk I/O operations.

• You're working with concurrent user requests (e.g., in a web server You want to optimise waiting tim in data pipelines or API calls.

• You're building apps that need to scale to thousands of tasks concurrently.

However, if your tasks are CPU-heavy (like model training or image processing), multithreading or multiprocessing is more appropriate.

How Python supports asynchronous programming

Python provides native support for async workflows using:

• async and await keywords to define asynchronous functions (coroutines).