The Python threading module uses threads instead of processes. Threads uniquely run in the same unique memory heap. Whereas Processes run in separate memory heaps. This makes sharing information harder with processes and object instances. One problem arises because threads use the same memory heap, multiple threads can write to the same location in the memory heap which is why the global interpreter lock(GIL) in CPython was created as a mutex to prevent it from happening.
The multithreading library is lightweight, shares memory, responsible for responsive UI and is used well for I/O bound applications. However, the module isn’t killable and is subject to the GIL
Threading library in Python
Multiple threads live in the same process in the same space, each thread will do a specific task, have its own code, own stack memory, instruction pointer, and share heap memory. If a thread has a memory leak it can damage the other threads and parent process.
import threading def calc_square(number): print('Square': , number * number) def calc_quad(): print('Quad': , number * number * number * number) if __name__ == "__main__": number = 7 thread1 = threading.Thread(target=calc_square, args=(number,)) thread2 = threading.Thread(target=calc_quad, args=(number,)) # Will execute both in parallel thread1.start() thread2.start() # Joins threads back to the parent process, which is this # program thread1.join() thread2.join() # This program reduces the time of execution by running tasks in parallel
The multiprocessing library uses separate memory space, multiple CPU cores, bypasses GIL limitations in CPython, child processes are killable(ex. function calls in program) and is much easier to use. Some caveats of the module are a larger memory footprint and IPC’s a little more complicated with more overhead.
Checkout Multiprocessing library in the Python docs
import multiprocessing def calc_square(number): print('Square': , number * number) result = number * number print(result) def calc_quad(): print('Quad': , number * number * number * number) if __name__ == "__main__": number = 7 result = None p1 = multiprocessing.Process(target=calc_square, args=(number,)) p2 = multiprocessing.Process(target=calc_quad, args=(number,)) p1.start() p2.start() p1.join() p2.join() # Wont print because processes run using their own memory location print(result)
An exercise, execute these programs and measure the delta between threads, between process & threading, relative to never using either libraries.
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