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Async programming is about keeping moving efficiently without unnecessary waiting. The idea is to be able to do multiple things at once. We don’t do them at the same time, but make sure that we are never idle.
Adopting asyncio in Python applications can significantly improve the performance and scalability of I/O-bound and network-driven programs. By understanding and applying the concepts of event loops, coroutines, futures, and tasks, developers can write efficient, non-blocking code that can handle thousands of simultaneous connections with ease.
Coroutines:
Coroutines are asynchronous functions declared with async def. These functions can be paused and resumed at await points, allowing I/O operations run in the background.
Event Loop:
The event loop is the core component of asyncio that manages and coordinates asynchronous operations. We can think of it like a traffic controller that orchestrates multiple tasks happening concurrently.
The event loop maintains a queue of tasks and continuously checks for:
- Tasks that are ready to run
- Tasks that are waiting for I/O operations
- Tasks that are scheduled to run in the future
