What methods can be used to prevent or eliminate race conditions when working with threads in multi-process applications?

Race conditions occur when multiple threads access shared resources simultaneously and modify them without synchronization, leading to unpredictable results.

To prevent or eliminate race conditions in Python, you can use:

• Locks — the threading.Lock object allows one thread to acquire a resource, blocking access for others until released.

• Semaphores — limit the number of threads working with a resource simultaneously.

• Monitors and condition variables (Condition) — allow threads to wait for certain conditions.

• Use of thread-safe data structures — for example, from the queue module.

Example with locking:

import threading

lock = threading.Lock()
shared_resource = 0

def increment():
    global shared_resource
    with lock:
        temp = shared_resource
        temp += 1
        shared_resource = temp

threads = [threading.Thread(target=increment) for _ in range(100)]
for t in threads:
    t.start()
for t in threads:
    t.join()

print(shared_resource)  # Expected 100

Thus, thread synchronization prevents simultaneous data modification and ensures the correctness of the program.