This blog post will introduce you to the exciting new feature in Python 3.12 - Subinterpreters. We’ll start by explaining what subinterpreters are, their history, and how they compare to threads, processes, and greenlets. From there, we’ll delve into why subinterpreters were introduced, discussing the limitations of the Global Interpreter Lock (GIL) and how subinterpreters can improve performance and concurrency in Python. We’ll then provide a step-by-step guide on how to use the Subinterpreters API to create, run code in, share data between, and destroy subinterpreters. We’ll also provide real-world examples of how subinterpreters can be used to improve performance and security in applications. Finally, we’ll discuss the future of subinterpreters, including the proposed PEP 554, and their potential to revolutionize Python’s concurrency model. Whether you’re a seasoned Python developer or just starting out, this post will give you a comprehensive understanding of Python 3.12 subinterpreters and their potential impact on the Python ecosystem.

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Python 3.12 introduces subinterpreters, a new feature that allows for concurrent execution in Python. Subinterpreters run Python code in parallel with the main interpreter, in the same process and address space, but with completely isolated execution resources. They can improve performance and concurrency in Python by bypassing the limitations of the Global Interpreter Lock (GIL). Subinterpreters can be used to create separate environments for improved security and isolation. The future of subinterpreters looks promising with the proposed introduction of a high-level API for managing subinterpreters in the Python standard library.

Python 3.12 Subinterpreters: A New Era of Concurrency

Introduction to Python 3.12 Subinterpreters

Python 3.12 Subinterpreters in the Real World

The Future of Python 3.12 Subinterpreters

This is a very big step forward for Python

I honestly think this article is way too verbose and it contains some mistakes as well. It feels like it was (co)-written by ChatGPT (and that is a bad thing).
Example of mistakes: in the paragraph “Python Subinterpreters vs Threads” it is stated that “each subinterpreter in Python has its own separate memory space” while in the paragraph right below it - “Python Subinterpreters vs Processes” it says that “subinterpreters run in the same process and share the same memory space”.
Those can’t both be true?
When you want to become a good blogger, don’t let ChatGPT write your article. Be concise and to the point and most importantly, check your facts!

Good insight about Python 3.12! It would be more illustrated if you have a comparison table outlining the differences between Subinterpreters and alternative options

What is the difference between sub interpreters and multiprocessing?