Difference between Asyncio and Threadding in python

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Title : Asyncio in python class object oriented #2

Title : Learning Asyncio in python in 12 Minutes

Title : Difference between Asyncio and Threadding in python

Title : Creating Asyncio Decorator in python | Advanced Topic
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[smle_with_me]

In multithreading, creating multiple threads increases the chance of using more CPU cores (assuming each task takes 1 millisecond). However, Python's Global Interpreter Lock (GIL) prevents true parallelism for CPU-bound tasks within a single process. Only one thread can execute Python bytecode at a time. For I/O-bound tasks, other threads can utilize other CPU and execute bytecode meanwhile, creating the illusion of concurrency. However, managing multiple threads can be resource-intensive due to memory overhead.
In contrast, async utilizes a single thread with an event loop. You submit tasks to the event loop, and it executes them sequentially. When a task encounters an I/O operation, the event loop tracks its progress. The main thread then executes other tasks in the queue, if any. Once an I/O task completes, the main thread continues executing the remaining CPU-bound part of the task.

[eduardocasanova-personal]

Once you've tasted Go...you won't want to do multithreading and concurrent programs in any other language. Go routines are just a breeze of fresh air...compared to the rest of the alternatives...no offense :)

[adityasethi]

Who you are wrong. Even in cooperative scheduler decides. User Can never have the same privilege as OS lol. In cooperative user can just give away Gil voluntarily.