Concurrency in Python, the ability to execute multiple tasks simultaneously, is crucial for optimizing performance in modern applications. While Python’s Global Interpreter Lock (GIL) poses challenges to true parallelism, libraries like threading and multiprocessing provide effective ways to leverage concurrency.
In this guide, we’ll delve into the fundamentals of Python concurrency using threading and multiprocessing, offering practical examples to illustrate their usage.
Understanding Threading
Threading allows Python to perform multiple tasks concurrently within a single process. Though threads share the same memory space, they execute independently, making them ideal for I/O-bound operations where tasks often wait for external resources.
Creating Threads
Let’s start with a basic example of threading:
import threading
def print_numbers():
for i in range(1, 6):
print("Thread 1:", i)
def print_letters():
for letter in ['a', 'b', 'c', 'd', 'e']:
print("Thread 2:", letter)
thread1 = threading.Thread(target=print_numbers)
thread2 =…
