我正在尝试同时调用300个API调用，这样我最多可以在几秒钟内得到结果。
我的伪代码如下：
> 11:14:56 Debug] IdentityServer4.Validation.TokenRequestValidator Start
> token request validation
>
> [11:14:56 Debug] IdentityServer4.Validation.TokenRequestValidator
> Start validation of refresh token request
>
> [11:14:56 Debug]
> IdentityServer4.EntityFramework.Stores.PersistedGrantStore
> MRNR65nTDUALsFTtuD6FKbzcHtXx9WB3xbclR+bdmJs= found in database: False
>
> [11:14:56 Debug] IdentityServer4.Stores.DefaultRefreshTokenStore
> refresh_token grant with value:
> 386dd398df5b20566cc41befd44564221f999e0704b9c6d8ed5b3200a3e6b51e not
> found in store.
>
> [11:14:56 Error] IdentityServer4.Validation.TokenValidator Invalid
> refresh token
>
> [11:14:56 Error] IdentityServer4.Validation.TokenRequestValidator
> Refresh token validation failed. aborting.
这样做，我每秒def function_1():
colors = ['yellow', 'green', 'blue', + ~300 other ones]
loop = asyncio.new_event_loop()
asyncio.set_event_loop(loop)
res = loop.run_until_complete(get_color_info(colors))
async def get_color_info(colors):
loop = asyncio.get_event_loop()
responses = []
for color in colors:
print("getting color")
url = "https://api.com/{}/".format(color)
data = loop.run_in_executor(None, requests.get, url)
r = await data
responses.append(r.json())
return responses
都会被打印出来，并且代码要花很多时间，所以我很确定它们不会同时运行。我在做什么错了？
答案 0 :(得分：8)
aiohttp与原生协同程序（async / await）这是一种典型的模式，可以完成您尝试做的事情。 （Python 3.7 +。）
一个重大变化是，您需要从为同步IO构建的requests移到专门为async /构建的诸如aiohttp之类的包中， await（原生协程）：
import asyncio
import aiohttp # pip install aiohttp aiodns
async def get(
session: aiohttp.ClientSession,
color: str,
**kwargs
) -> dict:
url = f"https://api.com/{color}/"
print(f"Requesting {url}")
resp = await session.request('GET', url=url, **kwargs)
# Note that this may raise an exception for non-2xx responses
# You can either handle that here, or pass the exception through
data = await resp.json()
print(f"Received data for {url}")
return data
async def main(colors, **kwargs):
# Asynchronous context manager. Prefer this rather
# than using a different session for each GET request
async with aiohttp.ClientSession() as session:
tasks = []
for c in colors:
tasks.append(get(session=session, color=c, **kwargs))
# asyncio.gather() will wait on the entire task set to be
# completed. If you want to process results greedily as they come in,
# loop over asyncio.as_completed()
htmls = await asyncio.gather(*tasks, return_exceptions=True)
return htmls
if __name__ == '__main__':
colors = ['red', 'blue', 'green'] # ...
# Either take colors from stdin or make some default here
asyncio.run(main(colors)) # Python 3.7+
有两个不同的元素，一个是协程的异步方面，一个是在您指定任务容器（功能）时引入的并发性：
get，该协程将await与两个 awaitables 结合使用：第一个为.request，第二个为.json。这是异步方面。 await进行这些受IO限制的响应的目的是告诉事件循环，其他get()调用可以轮流通过同一例程运行。await asyncio.gather(*tasks)中。它将等待的get()调用映射到您的每个colors。结果是返回值的汇总列表。请注意，该包装器将等到您的所有所有响应进入并调用.json()。或者，如果您希望在准备就绪时对其进行贪婪地处理，则可以遍历asyncio.as_completed：返回的每个Future对象都代表剩余的等待组中的最早结果。最后，请注意asyncio.run()是Python 3.7中引入的高级“瓷”函数。在早期版本中，您可以大致模拟它：
# The "full" versions makes a new event loop and calls
# loop.shutdown_asyncgens(), see link above
loop = asyncio.get_event_loop()
try:
loop.run_until_complete(main(colors))
finally:
loop.close()
有多种方法可以限制并发率。例如，请参见asyncio.semaphore in async-await function或large numbers of tasks with limited concurrency。