5/ But if you want distributed system with persistence support. Celery handles cross-process or cross-machine tasks with a broker like Redis. Redis alone supports persistent, high-volume queues across systems. Both require setup and add complexity. #SystemDesign

4/ asyncio.Queue: Useful for lightweight, in-process concurrency. Simple to get started right away—no setup, just Python. Best for I/O-bound tasks in a single process (e.g., scrapers, real-time processing). No persistence, not distributed.

3/ Here and example of a web scraper. Multiple coroutines fetch URLs and process responses concurrently, coordinated by a queue.

2/ Useful when chaining tasks like fetching → processing → storing data, rate-limited work like managing API calls, or producer/consumer pattern (fast producer, slow consumer). #AsyncProgramming