What are the main limitations of Python when working with asynchronous programming?

The main limitations of Python when working with asynchronous programming are related to the features of the interpreter and the execution model:

• GIL (Global Interpreter Lock) — restricts the concurrent execution of Python bytecode in a single process, which reduces the efficiency of multithreaded execution, although this is not always critical for asynchronous code.

• Asynchronous at the single-thread level — asyncio and similar libraries implement cooperative multitasking, where context switching occurs only at certain points (await), so blocking code can halt the entire cycle.

• Limited support for asynchronous libraries — not all third-party libraries support async/await, which can complicate integration.

• Debugging and understanding complexity — asynchronous code requires careful design to avoid races, deadlocks, and other issues.

Example of simple asynchronous code in Python:

import asyncio

async def fetch_data():
    await asyncio.sleep(1)
    return 'data'

async def main():
    result = await fetch_data()
    print(result)

asyncio.run(main())

Thus, Python is well-suited for asynchronous programming but requires understanding its limitations and features.