Distinguish IO-bound and CPU-bound applications in Python to write code that runs faster
In what cases is the use of libraries for concurrent application development appropriate and can result in increased performance using Python?
To answer this question, we first need to discuss how the Python interpreter works. In the course of the story, when detailing how the interpreter works, we will be referring to CPython, which brings the reference implementation of the Python language and is also the standard and most used interpreter among developers.
CPython is the reference interpreter, created by Guido van Rossum, creator of the Python language. With the popularization of the language, other interpreters were created by the community, such as IronPython (.NET), Jython (JVM), PyPy, Stackless, etc.
When executing the code, the interpreter uses a security mechanism called GIL, or Global Interpreter Lock. This lock structure was originally implemented to eliminate problems related to memory management in competing application scenarios. To execute the interpreter's instructions, it is necessary to obtain control of the GIL by the thread.
This means that only one thread will be running per process during the execution of a Python application.
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Python Programming & Data Handling
In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.