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Learn Inter-Process Communication (IPC) | Working with Processes
Python Multithreading and Multiprocessing

bookInter-Process Communication (IPC)

When you run multiple processes in Python, each process runs in its own memory space. This means that, unlike threads, processes cannot directly share variables or objects. If you want to exchange data between processes, you need special tools designed for inter-process communication (IPC). One of the most convenient IPC mechanisms in Python is the multiprocessing.Queue. This queue allows you to safely send data from one process to another, handling all the necessary synchronization and serialization behind the scenes. Using a queue, you can implement patterns like producer-consumer, where one process generates data and another consumes it, without worrying about data corruption or race conditions.

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from multiprocessing import Process, Queue def producer(q): for i in range(5): q.put(f"Message {i}") def consumer(q): while not q.empty(): msg = q.get() print(f"Consumed: {msg}") if __name__ == "__main__": q = Queue() p1 = Process(target=producer, args=(q,)) p2 = Process(target=consumer, args=(q,)) p1.start() p1.join() # Wait for producer to finish p2.start() p2.join()
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Which statement best describes the use of multiprocessing.Queue in Python?

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SectionΒ 3. ChapterΒ 2

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bookInter-Process Communication (IPC)

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When you run multiple processes in Python, each process runs in its own memory space. This means that, unlike threads, processes cannot directly share variables or objects. If you want to exchange data between processes, you need special tools designed for inter-process communication (IPC). One of the most convenient IPC mechanisms in Python is the multiprocessing.Queue. This queue allows you to safely send data from one process to another, handling all the necessary synchronization and serialization behind the scenes. Using a queue, you can implement patterns like producer-consumer, where one process generates data and another consumes it, without worrying about data corruption or race conditions.

12345678910111213141516171819
from multiprocessing import Process, Queue def producer(q): for i in range(5): q.put(f"Message {i}") def consumer(q): while not q.empty(): msg = q.get() print(f"Consumed: {msg}") if __name__ == "__main__": q = Queue() p1 = Process(target=producer, args=(q,)) p2 = Process(target=consumer, args=(q,)) p1.start() p1.join() # Wait for producer to finish p2.start() p2.join()
copy
question mark

Which statement best describes the use of multiprocessing.Queue in Python?

Select the correct answer

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 3. ChapterΒ 2
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