What are some performance tips for using asyncio in Python 3.10?

What are some performance tips for using asyncio in Python 3.10?

To improve performance with asyncio in Python 3.10, consider using the following tips: 1. Use asyncio.run() for the main entry point. 2. Leverage asyncio.gather() to run multiple coroutines concurrently. 3. Avoid blocking calls; use asynchronous libraries instead. 4. Profile your code to identify bottlenecks.

Can you explain how asyncio.gather() works?

Certainly! asyncio.gather() is used to run multiple coroutines concurrently. It takes in multiple awaitable objects and returns a single future that resolves when all the awaitables are done. This allows you to run tasks in parallel, improving efficiency.

What are some common mistakes to avoid when using asyncio?

Common mistakes include blocking the event loop with synchronous code, not using await properly, and failing to handle exceptions in coroutines. Always ensure your code is non-blocking and properly structured.

How can I profile my asyncio code?

You can use the asyncio built-in debugging features or third-party libraries like py-spy or yappi to profile your asyncio code. These tools help identify performance bottlenecks and optimize your coroutines.