[Python] How to Implement an Asynchronous, Distributed Task Queue with Dynamic Worker Scaling...

I’m looking for a way to implement a distributed task queue in Python that allows tasks to be added to a queue and processed asynchronously by workers. The system should support dynamic scaling of workers, where workers can be added or removed depending on the task load, and should only rely on Python’s standard library (no external dependencies).

Specifically, I need help with the following: 1. Asynchronous Task Processing: How can tasks be processed asynchronously without blocking other operations? 2. Distributed Architecture: How can workers be distributed across different machines or processes and coordinate work? 3. Dynamic Worker Scaling: What approach can be used to dynamically scale the number of workers up or down based on the number of tasks in the queue? 4. Error Handling: How can errors be managed across distributed workers and retries be handled?

I’m looking for a clean, efficient solution that makes the best use of Python’s concurrency tools, like asyncio, multiprocessing, or any other relevant features in the standard library.

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