Thread Synchronization Basics
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.
123456789101112131415161718192021import 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)
¡Gracias por tus comentarios!
Pregunte a AI
Pregunte a AI
Pregunte lo que quiera o pruebe una de las preguntas sugeridas para comenzar nuestra charla
Awesome!
Completion rate improved to 6.25Thread Synchronization Basics
Desliza para mostrar el menú
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.
123456789101112131415161718192021import 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)
¡Gracias por tus comentarios!
