How to use Python locks to avoid concurrency errors?

In concurrent programming, multithreading is an effective way to improve program execution efficiency, especially when dealing with I/O-intensive tasks. Python’s multithreading can significantly reduce program execution time. However, concurrent execution of multiple threads also brings about data consistency issues, especially when multiple threads access or modify shared resources simultaneously, which can lead to “race conditions” and unpredictable errors. To avoid these issues, Lock (Lock) Becoming a key tool for solving concurrent access issues.

What is a lock?

Reinsert lock（RLock）It is a special type of lock that allows the same thread to obtain the lock again without deadlock while holding it. If you need to obtain locks multiple times in the same thread, you can useRLock。

import threadinglock = threading.RLock()def recursive_function(n):
    if n > 0:
        lock.acquire()
        print(     The 'f' lock has been obtained:{n}"     )
        recursive_function(n - 1)
        lock.release()     #Start thread to execute recursive function     
thread = threading.Thread(target=recursive_function, args=(5,))
thread.start()
thread.join()

In this example, throughRLockImplemented locking and unlocking operations for recursive functions. The same thread can obtain and release locks multiple times without deadlock.

Non blocking mode of lock