Memory leak implementing multiprocessing in Python 2.7 when using shared memory
I'm refactoring my project and This might be a silly question, but I've been banging my head against this for hours... I'm experiencing a memory leak when using the `multiprocessing` module in Python 2.7, specifically when trying to share state among multiple processes using `Value` and `Array`. My use case involves a worker pool where each worker needs to update a shared counter. Here's a simplified code snippet showcasing my implementation: ```python from multiprocessing import Process, Value, Array import time def worker(shared_counter): for _ in range(10000): time.sleep(0.001) # Simulate some work with shared_counter.get_lock(): shared_counter.value += 1 if __name__ == '__main__': counter = Value('i', 0) processes = [Process(target=worker, args=(counter,)) for _ in range(4)] for p in processes: p.start() for p in processes: p.join() print('Final counter value:', counter.value) ``` When I run this code, I notice that the memory usage of the process keeps increasing over time until it crashes due to exceeding system limits. I've tried using `get_lock()` to manage access to the shared counter, but it doesn't seem to help. I also verified that Iām not holding onto references to the processes after they join. Is there something I'm missing here? Are there known issues with shared memory in Python 2.7's `multiprocessing` module? Any guidance would be appreciated! I'm working on a API that needs to handle this. What am I doing wrong? The project is a mobile app built with Python. Is there a simpler solution I'm overlooking? Thanks in advance! What's the best practice here?