Lära Thread Synchronization Basics

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In multithreaded programs, multiple threads often need to access and modify shared data. If two or more threads try to change a shared variable at the same time, you can encounter a race condition. A race condition happens when the outcome of a program depends on the unpredictable timing of threads, which can lead to incorrect or inconsistent results. For example, if two threads both increment a shared counter without coordination, both might read the same value before either writes back, causing one increment to be lost. To prevent this, you need a way to synchronize threads so that only one thread can access the critical section of code at a time.

Python provides synchronization primitives like the Lock from the threading module. A lock ensures that only one thread can enter a critical section at any given moment. When a thread acquires a lock, other threads trying to acquire it will wait until it is released. This prevents simultaneous access to shared resources and eliminates race conditions.


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import threading

counter = 0
lock = threading.Lock()

def increment():
    global counter
    for _ in range(100000):
        with lock:
            counter += 1

threads = []
for _ in range(2):
    t = threading.Thread(target=increment)
    threads.append(t)
    t.start()

for t in threads:
    t.join()

print("Final counter value:", counter)

1. What is a race condition in a multithreaded program, and why is it problematic?

2. What is the purpose of using a Lock in Python threading, and how does it help prevent race conditions?

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