How do I remove the async-everywhere insanity in a program like this?
import asyncio
async def async_coro():
await asyncio.sleep(1)
async def sync_func_1():
# This is blocking and synchronous
await async_coro()
async def sync_func_2():
# This is blocking and synchronous
await sync_func_1()
if __name__ == "__main__":
# Async pollution goes all the way to __main__
asyncio.run(sync_func_2())
I need to have 3 async markers and asyncio.run at the top level just to call one async function. I assume I'm doing something wrong - how can I clean up this code to make it use async less?
FWIW, I'm interested mostly because I'm writing an API using asyncio and I don't want my users to have to think too much about whether their functions need to be def or async def depending on whether they're using a async part of the API or not.
After some research, one answer is to manually manage the event loop:
import asyncio
async def async_coro():
await asyncio.sleep(1)
def sync_func_1():
# This is blocking and synchronous
loop = asyncio.get_event_loop()
coro = async_coro()
loop.run_until_complete(coro)
def sync_func_2():
# This is blocking and synchronous
sync_func_1()
if __name__ == "__main__":
# No more async pollution
sync_func_2()
If you must do that, I would recommend an approach like this:
import asyncio, threading
async def async_coro():
await asyncio.sleep(1)
_loop = asyncio.new_event_loop()
threading.Thread(target=_loop.run_forever, daemon=True).start()
def sync_func_1():
# This is blocking and synchronous
return asyncio.run_coroutine_threadsafe(async_coro(), _loop).result()
def sync_func_2():
# This is blocking and synchronous
sync_func_1()
if __name__ == "__main__":
sync_func_2()
The advantage of this approach compared to one where sync functions run the event loop is that it supports nesting of sync functions. It also only runs a single event loop, so that if the underlying library wants to set up e.g. a background task for monitoring or such, it will work continuously rather than being spawned each time anew.
Related
I am new to asynchronous programming, and while I understand most concepts, there is one relating to the inner runnings of 'await' that I don't quite understand.
Consider the following:
import asyncio
async def foo():
print('start fetching')
await asyncio.sleep(2)
print('done fetcihng')
async def main():
task1 = asyncio.create_task(foo())
asyncio.run(main())
Output: start fetching
vs.
async def foo():
print('start fetching')
print('done fetcihng')
async def main():
task1 = asyncio.create_task(foo())
asyncio.run(main())
Output: start fetching followed by done fetching
Perhaps it is my understanding of await, which I do understand insofar that we can use it to pause (2 seconds in the case above), or await for functions to fully finish running before any further code is run.
But for the first example above, why does await cause 'done fetching' to not run??
asyncio.create_task schedules an awaitable on the event loop and returns immediately, so you are actually exiting the main function (and closing the event loop) before the task is able to finish
you need to change main to either
async def main():
task1 = asyncio.create_task(foo())
await task1
or
async def main():
await foo()
creating a task first (the former) is useful in many cases, but they all involve situations where the event loop will outlast the task, e.g. a long running server, otherwise you should just await the coroutine directly like the latter
I'm trying to build an appllication with python's asyncio module that is scalable and uses other modules based on asyncio also, the idea is to easily add tasks as the application grows, utilizing syncronization primitives for shared resources between the tasks, yet I'm confused regarding which design would best fit the intent.
import asyncio
async def task1():
while True:
# Code from task1
await asyncio.sleep(1)
async def task2():
while True:
# Code from task2
await asyncio.sleep(1)
async def task3():
while True:
# Code from task3
await asyncio.sleep(1)
async def main():
await asyncio.gather(
task1(),
task2(),
task3()
)
if __name__ == "__main__":
asyncio.get_event_loop().run_until_complete(main())
In the above approach each task is a while loop, yielding at the end of each execution,
import asyncio
async def task1():
# Code from task1
await asyncio.sleep(1)
async def task2():
# Code from task2
await asyncio.sleep(1)
async def task3():
# Code from task3
await asyncio.sleep(1)
async def main():
while True:
await asyncio.gather(
task1(),
task2(),
task3()
)
if __name__ == "__main__":
asyncio.get_event_loop().run_until_complete(main())
In this last approach, the gather method is the one being inside a while loop.
I also that perhaps wouldn't be necessary as asyncio tasks are running in a cooperative way, and I would get the same result from awaiting in series each coroutine, saving the usage of mutexes.
Is there anything asynchronous happening besides the asyncio.sleep(1). If not, then why bother using asynchronicity.
In the first piece of code, your three tasks are running independently of each other; you might handle 3 items in task1, 4 items in task2, and none in task3.
In the second, you specifically handle one thing in task1, one thing in task2, one thing in task3, and then start over again. If the tasks run at different speeds, this seems inefficient.
I come from the C++ world, and I'm looking for the equivalent of std::future, std::promise in Python. Is there an equivalent mechanism or another method in Python to achieve the same?
I'm aware of asyncio.Future, but I need it for threading not asyncio.
I'm using a third party library (PJSUA2) which I call directly from my main thread, but which send the results in asynchronous callbacks in context of a worker thread created by the library.
Expecting future/promise support in Python, I was hoping to write my application code like this:
future = wrap_foo(...)
if (future.get() != expected_result):
throw Exception(...)
future1 = wrap_foo(...)
future2 = wrap_bar(...)
I was planning on wrapping all library asynchronous calls with a wrap_xxx function (where the library function is called xxx) taking care of creating the future/promise objects.
I need the ability of having multiple futures pending, so I cannot simply make synchronous wrap_xxx functions which block until the result is ready.
See the asyncio module -
import asyncio
async def main():
print('hello')
await asyncio.sleep(1)
print('world')
asyncio.run(main())
hello
world
It supports coroutines -
import asyncio
import time
async def say_after(delay, what):
await asyncio.sleep(delay)
print(what)
async def main():
print(f"started at {time.strftime('%X')}")
await say_after(1, 'hello')
await say_after(2, 'world')
print(f"finished at {time.strftime('%X')}")
asyncio.run(main())
started at 17:13:52
hello
world
finished at 17:13:55
And tasks -
import asyncio
async def nested():
return 42
async def main():
# Schedule nested() to run soon concurrently
# with "main()".
task = asyncio.create_task(nested())
# "task" can now be used to cancel "nested()", or
# can simply be awaited to wait until it is complete:
print(await task)
asyncio.run(main())
42
And Futures -
import asyncio
async def set_after(fut, delay, value):
# Sleep for *delay* seconds.
await asyncio.sleep(delay)
# Set *value* as a result of *fut* Future.
fut.set_result(value)
async def main():
# Get the current event loop.
loop = asyncio.get_running_loop()
# Create a new Future object.
fut = loop.create_future()
# Run "set_after()" coroutine in a parallel Task.
# We are using the low-level "loop.create_task()" API here because
# we already have a reference to the event loop at hand.
# Otherwise we could have just used "asyncio.create_task()".
loop.create_task(
set_after(fut, 1, '... world'))
print('hello ...')
# Wait until *fut* has a result (1 second) and print it.
print(await fut)
asyncio.run(main())
hello ...
... world
I want to run a simple background task in FastAPI, which involves some computation before dumping it into the database. However, the computation would block it from receiving any more requests.
from fastapi import BackgroundTasks, FastAPI
app = FastAPI()
db = Database()
async def task(data):
otherdata = await db.fetch("some sql")
newdata = somelongcomputation(data,otherdata) # this blocks other requests
await db.execute("some sql",newdata)
#app.post("/profile")
async def profile(data: Data, background_tasks: BackgroundTasks):
background_tasks.add_task(task, data)
return {}
What is the best way to solve this issue?
Your task is defined as async, which means fastapi (or rather starlette) will run it in the asyncio event loop.
And because somelongcomputation is synchronous (i.e. not waiting on some IO, but doing computation) it will block the event loop as long as it is running.
I see a few ways of solving this:
Use more workers (e.g. uvicorn main:app --workers 4). This will allow up to 4 somelongcomputation in parallel.
Rewrite your task to not be async (i.e. define it as def task(data): ... etc). Then starlette will run it in a separate thread.
Use fastapi.concurrency.run_in_threadpool, which will also run it in a separate thread. Like so:
from fastapi.concurrency import run_in_threadpool
async def task(data):
otherdata = await db.fetch("some sql")
newdata = await run_in_threadpool(lambda: somelongcomputation(data, otherdata))
await db.execute("some sql", newdata)
Or use asyncios's run_in_executor directly (which run_in_threadpool uses under the hood):
import asyncio
async def task(data):
otherdata = await db.fetch("some sql")
loop = asyncio.get_running_loop()
newdata = await loop.run_in_executor(None, lambda: somelongcomputation(data, otherdata))
await db.execute("some sql", newdata)
You could even pass in a concurrent.futures.ProcessPoolExecutor as the first argument to run_in_executor to run it in a separate process.
Spawn a separate thread / process yourself. E.g. using concurrent.futures.
Use something more heavy-handed like celery. (Also mentioned in the fastapi docs here).
If your task is CPU bound you could use multiprocessing, there is way to do that with Background task in FastAPI:
https://stackoverflow.com/a/63171013
Although you should consider to use something like Celery if there are lot of cpu-heavy tasks.
Read this issue.
Also in the example below, my_model.function_b could be any blocking function or process.
TL;DR
from starlette.concurrency import run_in_threadpool
#app.get("/long_answer")
async def long_answer():
rst = await run_in_threadpool(my_model.function_b, arg_1, arg_2)
return rst
This is a example of Background Task To FastAPI
from fastapi import FastAPI
import asyncio
app = FastAPI()
x = [1] # a global variable x
#app.get("/")
def hello():
return {"message": "hello", "x":x}
async def periodic():
while True:
# code to run periodically starts here
x[0] += 1
print(f"x is now {x}")
# code to run periodically ends here
# sleep for 3 seconds after running above code
await asyncio.sleep(3)
#app.on_event("startup")
async def schedule_periodic():
loop = asyncio.get_event_loop()
loop.create_task(periodic())
if __name__ == "__main__":
import uvicorn
uvicorn.run(app)
So i have a basic discord bot which accepts input
import discord
import asyncio
import threading
loop = asyncio.new_event_loop()
bot = discord.Client()
def run_asyncio_loop(loop):
asyncio.set_event_loop(loop)
loop.run_forever()
Hangman.set_bot(bot)
#bot.event
async def on_message(message):
bot.loop.create_task(Hangman.main(message))
asyncioLoop = threading.Thread(target = run_asyncio_loop, args = (loop,))
asyncioLoop.start()
bot.run(BotConstants.TOKEN)
In this example it calls the hangman game which does not block anything as i have tested this using asyncio.sleep(n) but when i go to do a something in hangman it blocks it.
class Hangman():
async def main(message):
await Hangman.make_guess(message)
async def update_score(message):
sheetLoaded = Spreadsheet.load_ws(...)
userExists = Spreadsheet.user_exists(...)
if (not userExists):
Spreadsheet.add_user(...)
Spreadsheet.add_score(...)
await Hangman.bot.send_message(message.channel, msg)
elif (not sheetLoaded):
await Hangman.bot.send_message(message.channel, msg)
async def make_guess(message):
# perform guess
if (matched):
await Hangman.bot.send_message(message.channel, msg)
Hangman.GAMES.pop(message.server.id)
await Hangman.update_score(message)
When Hangman.update_score() is called it blocks it. so it won't process any commands until the score has been updated which means for about 5 or so seconds (not long but with lots of users spamming it it's an issue) the bot does not accept any other messages
What am i missing to be able to make the process run in the background while still accept new inputs?
Asyncio is still single-threaded. The only way for the event loop to run is for no other coroutine to be actively executing. Using yield from/await suspends the coroutine temporarily, giving the event loop a chance to work. So unless you call another coroutine using yield (from) or await or return, the process is blocked. You can add await asyncio.sleep(0) in between steps of Hangman.update_score to divide the process blocking in multiple parts, but that will only ensure less "hanging" time, not actually speed up your thread.
To make the process actually run in the background, you could try something along the lines of:
from concurrent.futures import ProcessPoolExecutor
executor = ProcessPoolExecutor(2)
asyncio.ensure_future(loop.run_in_executor(executor, Hangman.update_score(message)))