How to Synchronize Access to Shared Data Across Processes in Python?

Synchronizing Access to Shared Data Across Processes: A Multiprocessing Solution

In a scenario where multiple processes concurrently manipulate a global dictionary, synchronization becomes crucial to ensure consistent results. This issue has been encountered in a program that entails multiple processes collaborating on a queue while accessing and modifying a shared dictionary, D.

The discrepancy arises when inspecting the dictionary in a child process, where the modifications are visible. However, upon joining the main process's queue, the dictionary becomes empty, indicating a synchronization/lock issue.

Understanding the Problem

The synchronization challenge stems from the fact that different processes operate on the same dictionary concurrently. Without proper synchronization, changes made by each process may not be reflected consistently across all processes, leading to corrupted or inconsistent data.

Solution: Utilizing a Manager Object

A robust solution involves employing a Manager object from the multiprocessing library. This object provides a mechanism to create and manage shared data structures that can be accessed and modified by multiple processes simultaneously.

The code snippet demonstrates how to implement this approach effectively:

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.dict() method creates a shared dictionary that can be accessed and modified by multiple processes. The processes concurrently modify the dictionary, which is then printed, revealing the updated values.

This approach effectively synchronizes access to the shared dictionary, ensuring that all processes have a consistent view of the data modifications.

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