How to Improve Performance of AsyncIO with File I/O in Python 3.9?
Could someone explain I'm sure I'm missing something obvious here, but I'm working with important performance optimization when using `asyncio` for file I/O operations in Python 3.9. My goal is to read multiple large files concurrently, but it seems that the `asyncio` event loop isn't effectively utilizing asynchronous file I/O. Here's a simplified version of my implementation: ```python import asyncio import aiofiles async def read_file(file_path): async with aiofiles.open(file_path, 'r') as file: contents = await file.read() return contents async def main(file_paths): results = await asyncio.gather(*(read_file(path) for path in file_paths)) return results file_list = ['file1.txt', 'file2.txt', 'file3.txt'] asyncio.run(main(file_list)) ``` When I run this code, it does read the files concurrently, but the overall execution time feels slower than reading them sequentially, and I frequently see messages like `Task was destroyed but it is pending!`. I've also tried using `asyncio.sleep(0)` as a way to yield control, but it didn't significantly improve performance. Is there a more efficient way to handle multiple file reads asynchronously using `asyncio`? Should I be looking into other libraries or techniques to enhance performance? Any insights or suggestions would be greatly appreciated! Am I missing something obvious? What's the best practice here? The stack includes Python and several other technologies. Any pointers in the right direction?