This article discusses the differences between multithreading and asyncio in Python programming, especially under the constraints imposed by the Global Interpreter Lock (GIL). Multithreading allows a program to execute multiple tasks concurrently, but in Python, it runs on a single core due to the GIL. Concurrency allows tasks to progress independently, creating the illusion of simultaneous execution. The article uses the analogy of hosting a multi-course dinner to illustrate how tasks can overlap in time, offering insights into the workings of asyncio and threading, highlighting that both give an appearance of concurrency but do not imply true simultaneous processing due to Python's inherent limitations.
In programming, multithreading refers to the ability of a program to execute multiple sequential tasks concurrently. However, multithreading in Python is limited because of the Global Interpreter Lock (GIL).
Concurrency in programming means that the computer is executing multiple tasks at once, or appears to be doing so, even if using a single processor.
Both asyncio and threading enable the appearance of concurrency in Python, but task execution is not truly simultaneous due to the GIL's constraints.
The analogy of hosting a multi-course dinner illustrates concurrency, where tasks overlap in time and progress independently, similar to how asyncio and threading work.
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