Python is becoming a more and more popular choice among developers for a diverse range of applications. However, as with any language, effectively scaling Python services can pose challenges. This article explains concepts that can be leveraged to better scale your applications. By understanding CPU-bound versus I/O-bound tasks, the implications of the Global Interpreter Lock (GIL), and the mechanics behind thread pools and asyncio, we can better scale Python applications.
CPU-Bound vs. I/O-Bound: The Basics
- CPU-Bound Tasks: These tasks involve heavy calculations, data processing, and transformations, demanding significant CPU power.
- I/O-Bound Tasks: These tasks typically wait on external resources, such as reading from or writing to databases, files, or network operations.