Три способа выполнить множество задач с asyncio
Функция для примера:
async def do_it(n):
await asyncio.sleep(random.uniform(0.5, 1))
return n
1. Последовательный вызов
async def main():
for i in range(100):
result = await do_it(i)
Такой вызов имеет смысл только тогда, когда результат одной задачи требуется для вызова следующей.
Если они независимы, то это антипаттерн, так как аналогичен простому синхронному вызову по очереди.
2. Упорядоченный результат
async def main():
tasks = [do_it(i) for i in range(100)]
results = await asyncio.gather(*tasks)
Выполняет корутины конкурентно и возвращает результат в виде списка.
Полезен когда требуется получить результаты в том же порядке в котором задачи отправлены.
3. Результат по мере готовности
tasks = [asyncio.create_task(do_it(i)) for i in range(100)]
for cor in asyncio.as_completed(tasks):
result = await cor
Так же выполняет корутины конкурентно, но не гарантирует порядок. Результат возвращается по мере готовности, каждый отдельно.
Полезен когда нужно обработать любой ответ как можно скорее.
#async
http://aiohttp.readthedocs.io/en/stable/web.html#aiohttp-web-websockets
In order to implement a #web_server, first create a #request handler.
A request handler is a coroutine or regular function that accepts a Request instance as its only parameter and returns a Response instance:
#aiohttp#asyncio
from aiohttp import web
async def hello(request):
return web.Response(text="Hello, world")
Next, create an Application instance and register the request handler with the application’s #router on a particular HTTP method and path:
https://github.com/aio-libs/aiohttp/blob/master/docs/web_reference.rst
Server Reference
#asyncio#aiohttp#Request#BaseRequest#Server#client#Router#Resource
#typescript#ai_gateway#gateway#generative_ai#hacktoberfest#langchain#llama_index#llmops#llms#openai#prompt_engineering#router
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