Concurrency and Multithreading

Concurrency refers to the ability of a system to handle multiple tasks simultaneously, while parallelism involves the actual execution of those tasks simultaneously on multiple CPU cores. Both concepts aim to improve the efficiency and responsiveness of applications by utilizing the available resources optimally.

Python provides two primary modules for handling concurrency: threading and multiprocessing. Threading allows the execution of multiple threads within the same process, while multiprocessing involves running separate processes with their own memory space.

# Example of threading
import threading

def print_numbers():
for i in range(5):
print(i)

def print_letters():
for letter in ‘ABCDE’:
print(letter)

# Creating threads and starting their execution
thread1 = threading.Thread(target=print_numbers)
thread2 = threading.Thread(target=print_letters)

thread1.start()
thread2.start()

# Waiting for threads to finish
thread1.join()
thread2.join()

Concurrency introduces the possibility of data corruption and race conditions when multiple threads access shared resources concurrently. To prevent such issues, synchronization mechanisms like locks and semaphores ensure proper coordination between threads, while thread safety refers to writing code that can be safely executed by multiple threads without causing errors.

# Example of thread safety with a lock
import threading

counter = 0
lock = threading.Lock()

def increment_counter():
global counter
for _ in range(100000):
# Acquire the lock to safely update the shared resource
with lock:
counter += 1

# Creating threads and starting their execution
thread1 = threading.Thread(target=increment_counter)
thread2 = threading.Thread(target=increment_counter)

thread1.start()
thread2.start()

# Waiting for threads to finish
thread1.join()
thread2.join()

print(counter) # Output: 200000