How to Avoid Race Conditions When Sharing Dictionaries in Multiprocessing?

How to Synchronize Access to Shared Dictionaries in Multiprocessing

In a multiprocessing environment, multiple processes may require access to shared data, such as a dictionary. However, if proper synchronization mechanisms are not implemented, race conditions can occur, leading to unreliable or corrupted data.

Consider a scenario where multiple child processes access a global dictionary D while working on a shared queue Q. Initially, the dictionary appears to store the results modified by the child processes. However, once the main process joins Q, the dictionary D becomes empty.

This issue stems from the asynchronous nature of multiprocessing. Each child process has its own memory space, and modifications made to shared variables may not be immediately visible to other processes without proper synchronization.

Using a Manager Object for Synchronization

A common solution to synchronize access to shared data in multiprocessing is to use a Manager object. A Manager provides shared memory segments that can be accessed by all participating processes.

Here's how you can implement synchronization using a Manager:

from multiprocessing import Process, Manager

def f(d):
    d[1] += '1'
    d['2'] += 2

if __name__ == '__main__':
    manager = Manager()

    d = manager.dict()
    d[1] = '1'
    d['2'] = 2

    p1 = Process(target=f, args=(d,))
    p2 = Process(target=f, args=(d,))
    p1.start()
    p2.start()
    p1.join()
    p2.join()

    print(d)

In this example, the Manager object creates a shared dictionary d, which is accessible by both child processes. When a child process modifies the dictionary, the changes are immediately visible to other processes.

Output:

$ python mul.py
{1: '111', '2': 6}

This output demonstrates that the shared dictionary has been successfully synchronized and updated by the child processes.

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