Реализуйте highload RPC ручку /weather с нагрузкой 10k RPS, где функция aiWeatherForecast() работает ~1 секунду

To implement a highload RPC endpoint /weather with a load of 10k RPS and a function aiWeatherForecast() that takes about 1 second, you need to ensure scalability and asynchronicity so as not to block request processing.

Main approaches:

1. Asynchronous request handling — use async/await or event loop to avoid blocking the thread while waiting for the result.
2. Caching — if forecasts do not change every second, cache results to reduce load.
3. Scaling — run multiple instances of the service behind a load balancer.
4. Queues and worker pools — if aiWeatherForecast() is heavy, process requests through a task queue.

Example in Python using asyncio and aiohttp (simplified):

import asyncio
from aiohttp import web

async def aiWeatherForecast():
    await asyncio.sleep(1)  # simulate long operation
    return {"temp": 20, "condition": "sunny"}

async def handle_weather(request):
    forecast = await aiWeatherForecast()
    return web.json_response(forecast)

app = web.Application()
app.router.add_get('/weather', handle_weather)

if __name__ == '__main__':
    web.run_app(app, port=8080)

For 10k RPS, it will be necessary to run multiple server instances, use a load balancer (e.g., Nginx), and possibly optimize aiWeatherForecast() or cache results to avoid running a heavy function on each request.

Also consider using more performant languages or services if Python cannot handle the load.