I've recently been having a run-up with asynchronous functions in Python, and I wonder how one could make a synchronous function into an asynchronous one.
For example, there is the library for translation via google api pygoogletranslation. One could most possibly wonder, how to translate many different words asynchronously. Of course, you could place it into one request, but then google api would consider it a text and treat it accordingly, which will cause incorrect results.
How could one turn this code:
from pygoogletranslation import Translator
translator = Translator()
translations = []
words = ['partying', 'sightseeing', 'sleeping', 'catering']
for word in words:
translations.append(translator.translate(word, src='en', dest='es'))
print(translations)
Into this:
from pygoogletranslation import Translator
import asyncio
translator = Translator()
translation_tasks = []
words = ['partying', 'sightseeing', 'sleeping', 'catering']
for word in words:
asyncio.create_task(translator.translate(word, src='en', dest='es'))
translations = asyncio.run(
asyncio.gather(translation_tasks, return_exceptions=True)
)
print(translations)
Considering the function translate doesn't have a built-in async implementation?
You will have to create an async function and then run it. Though if translate doesn't have built in async support or is blocking, using async will not make it faster. It's probably better to use multithreading/multiprocessing as suggested in the comments.
async def main():
async def one_iteration(word):
output.append(translator.translate(word, src='en', dest='es'))
coros = [one_iteration(word) for word in words]
await asyncio.gather(*coros)
asyncio.run(main())
As mentioned in other answers, calling a blocking function is useless with ayncio. In this particular case, I suggest you use google-cloud-translate, which is the official translate library from Google.
You could have done something like this in your current library:
async def do_task(word):
return translator.translate(word, ...)
def main():
# Create translator
...
asyncio.gather(do_task(word) for word in [])
But this will just run the task in the same way without asyncio. The real gain in asyncio is that, when is something pending or waiting, it can do something else. eg, while waiting for response from server, it can send another request.
How will Python know that some work is pending? Only when the function (coroutine here) notifies the event loop via await keyword. So you definitely need to use a library that natively supports async operations. The above mentioned google-cloud-translate is such a library. You can do:
from google.cloud import translate
async def main():
# Async-supported google translator client
client = translate.TranslationServiceAsyncClient()
words = ['partying', 'sightseeing', 'sleeping', 'catering']
results = await asyncio.gather(*[client.translate_text(parent=f"projects/{project_name}", contents=[word], source_language_code="en", target_language_code="es") for word in words])
print(results)
asyncio.run(main())
You can see that this client actually takes list of strings as input, so you could directly pass the list of strings here. According to docs, the limit for that is 1024. So if your list is bigger, you have to use this for loop.
You might have to set up credentials etc for this client though, which is outside the scope of this question.
To make a function async, you need to define it with async def and change it to use other async functions for anything that might block - for example, instead of requests you'd use aiohttp, and so on. The point of the effort is that the function can then be executed by an event loop along with other such functions. Whenever an async function needs to wait for something, as signaled by the await keyword, it suspends to the event loop and gives others a chance to execute. The event loop will seamlessly coordinate concurrent execution of a possibly large number of such async functions. See e.g. this answer for more details.
If a critical blocking function that you are depending on doesn't have an async implementation, you can use run_in_executor (or, beginning with Python 3.9, asyncio.to_thread) to make it async. Note, however, that such solutions are "cheating" because they use threads under the hood, so they will not provide benefits normally associated by asyncio such as ability to scale beyond the number of threads in the thread pool, or ability to cancel execution of coroutines.
Related
I'm having problems wrapping an external task to parallelize it. I'm a newbie with asyncio so maybe I'm doing something wrong:
I have an animate method that I have also declared as async.
But that calls an external library that uses various iterators etc.
I'm wondering if something in a library is able to block asyncio at the top level?
animate(item) is a problem. if i define another async task it will run multiple calls concurrently and 'gather' later.
So am I doing it wrong, or is it possible the library been written such that it can't simply be parallelized with asyncio?
I also tried wrapping the call to animate with another async method, without luck.
MAX_JOBS = 1 # how long for
ITEMS_PER_JOB = 4 # how many images per job/user request eg for packs
async def main():
for i in range(0, MAX_JOBS):
clogger.info('job index', i)
job = get_next()
await process_job(job)
async def process_job(job):
batch = generate_batch(job)
coros = [animate(item) for idx, item in enumerate(batch)]
asyncio.gather(*coros)
asyncio.run(main())
the animate func has some internals and like
async def animate(options):
for frame in tqdm(animator.render(), initial=animator.start_frame_idx, total=args.max_frames):
pass
OK NVM it seems all libraries have to be written with coroutines, but there are other options like
to_thread
run_in_executor
not sure which is best in 2023 tho
The tasks from asyncio.gather does not work concurrently
Lets say I have a C++ function result_type compute(input_type input), which I have made available to python using cython. My python code executes multiple computations like this:
def compute_total_result()
inputs = ...
total_result = ...
for input in inputs:
result = compute_python_wrapper(input)
update_total_result(total_result)
return total_result
Since the computation takes a long time, I have implemented a C++ thread pool (like this) and written a function std::future<result_type> compute_threaded(input_type input), which returns a future that becomes ready as soon as the thread pool is done executing.
What I would like to do is to use this C++ function in python as well. A simple way to do this would be to wrap the std::future<result_type> including its get() function, wait for all results like this:
def compute_total_results_parallel()
inputs = ...
total_result = ...
futures = []
for input in inputs:
futures.append(compute_threaded_python_wrapper(input))
for future in futures:
update_total_result(future.get())
return total_result
I suppose this works well enough in this case, but it becomes very complicated very fast, because I have to pass futures around.
However, I think that conceptually, waiting for these C++ results is no different from waiting for file or network I/O.
To facilitate I/O operations, the python devs introduced the async / await keywords. If my compute_threaded_python_wrapper would be part of asyncio, I could simply rewrite it as
async def compute_total_results_async()
inputs = ...
total_result = ...
for input in inputs:
result = await compute_threaded_python_wrapper(input)
update_total_result(total_result)
return total_result
And I could execute the whole code via result = asyncio.run(compute_total_results_async()).
There are a lot of tutorials regarding async programming in python, but most of them deal with using coroutines where the bedrock seem to be some call into the asyncio package, mostly calling asyncio.sleep(delay) as a proxy for I/O.
My question is: (How) Can I implement coroutines in python, enabling python to await the wrapped future object (There is some mention of a __await__ method returning an iterator)?
First, an inaccuracy in the question needs to be corrected:
If my compute_threaded_python_wrapper would be part of asyncio, I could simply rewrite it as [...]
The rewrite is incorrect: await means "wait until the computation finishes", so the loop as written would execute the code sequentially. A rewrite that actually runs the tasks in parallel would be something like:
# a direct translation of the "parallel" version
def compute_total_results_async()
inputs = ...
total_result = ...
tasks = []
# first spawn all the tasks
for input in inputs:
tasks.append(
asyncio.create_task(compute_threaded_python_wrapper(input))
)
# and then await them
for task in tasks:
update_total_result(await task)
return total_result
This spawn-all-await-all pattern is so uniquitous that asyncio provides a helper function, asyncio.gather(), which makes it much shorter, especially when combined with a list comprehension:
# a more idiomatic version
def compute_total_results_async()
inputs = ...
total_result = ...
results = await asyncio.gather(
*[compute_threaded_python_wrapper(input) for input in inputs]
)
for result in results:
update_total_result(result)
return total_result
With that out of the way, we can proceed to the main question:
My question is: (How) Can I implement coroutines in python, enabling python to await the wrapped future object (There is some mention of a __await__ method returning an iterator)?
Yes, awaitable objects are implemented using iterators that yield to indicate suspension. But that is way too low-level a tool for what you actually need. You don't need just any awaitable, but one that works with the asyncio event loop, which has specific expectations of the underlying iterator. You need a mechanism to resume the awaitable when the result is ready, where you again depend on asyncio.
Asyncio already provides awaitable objects that can be externally assigned a value: futures. An asyncio future represents an async value that will become available at some point in the future. They are related to, but not semantically equivalent to C++ futures, and should not to be confused with multi-threaded futures from the concurrent.futures stdlib module.
To create an awaitable object that is activated by something that happens in another thread, you need to create a future, and then start your off-thread task, instructing it to mark the future as completed when it finishes execution. Since asyncio futures are not thread-safe, this must be done using the call_soon_threadsafe event loop method provided by asyncio for such situations. In Python it would be done like this:
def run_async():
loop = asyncio.get_event_loop()
future = loop.create_future()
def on_done(result):
# when done, notify the future in a thread-safe manner
loop.call_soon_threadsafe(future.set_result, resut)
# start the worker in a thread owned by the pool
pool.submit(_worker, on_done)
# returning a future makes run_async() awaitable, and
# passable to asyncio.gather() etc.
return future
def _worker(on_done):
# this runs in a different thread
... processing goes here ...
result = ...
on_done(result)
In your case, the worker would be presumably implemented in Cython combined with C++.
class Class1():
def func1():
self.conn.send('something')
data = self.conn.recv()
return data
class Class2():
def func2():
[class1.func1() for class1 in self.classes]
How do I make that last line asynchronously in python? I've been googling but can't understand async/await and don't know which functions I should be putting async in front of. In my case, all the class1.func1 need to send before any of them can receive anything. I was also seeing that __aiter__ and __anext__ need to be implemented, but I don't know how those are used in this context. Thanks!
It is indeed possible to fire off multiple requests and asynchronously
wait for them. Because Python is traditionally a synchronous language,
you have to be very careful about what libraries you use with
asynchronous Python. Any library that blocks the main thread (such as
requests) will break your entire asynchronicity. aiohttp is a common
choice for asynchronously making web API calls in Python. What you
want is to create a bunch of future objects inside a Python list and
await it. A future is an object that represents a value that will
eventually resolve to something.
EDIT: Since the function that actually makes the API call is
synchronous and blocking and you don't have control over it, you will
have to run that function in a separate thread.
Async List Comprehensions in Python
import asyncio
async def main():
loop = asyncio.get_event_loop()
futures = [asyncio.ensure_future(loop.run_in_executor(None, get_data, data)) for data in data_name_list]
await asyncio.gather(*futures) # wait for all the future objects to resolve
# Do something with futures
# ...
loop = asyncio.get_event_loop()
loop.run_until_complete(main())
loop.close()
So I'm trying to use Sanic to to some asynchronous web requests as I have some special ones that take a few seconds to get back, but want to do other requests in the mean time from the client. Here is an example method that seems to still be blocking other calls from the client while its waiting for the lib.getAlarmState() to come back. (lib.getAlartmState() is a call to a C library using pythons ctypes that takes about 3 seconds to return and returns an Int type.)
According to what I'm seeing in documentation for sanic, simply defining the method as async should do what I'm looking to do? I've tried adding an await in front of lib.getAlarmState() but I'm not sure I'm using that quite right.
#app.route('/processjson')
async def processjson(request):
vals = lib.getAlarmState()
return response.json({"alarm:" : vals})
I expect that while the shown method is off doing its thing, I should be able to call other methods from the client and get responses.
That is correct. Sanic will not be able to convert blocking calls to asynchronous. Perhaps, what you can try instead is to run that in a seperate task.
async def get_alarm_state_wrapper():
return lib.getAlarmState()
#app.route('/processjson')
async def processjson(request):
vals = await asyncio.gather(get_alarm_state_wrapper())
return response.json({"alarm:" : vals})
I'm getting the flow of using asyncio in Python 3.5 but I haven't seen a description of what things I should be awaiting and things I should not be or where it would be neglible. Do I just have to use my best judgement in terms of "this is an IO operation and thus should be awaited"?
By default all your code is synchronous. You can make it asynchronous defining functions with async def and "calling" these functions with await. A More correct question would be "When should I write asynchronous code instead of synchronous?". Answer is "When you can benefit from it". In cases when you work with I/O operations as you noted you will usually benefit:
# Synchronous way:
download(url1) # takes 5 sec.
download(url2) # takes 5 sec.
# Total time: 10 sec.
# Asynchronous way:
await asyncio.gather(
async_download(url1), # takes 5 sec.
async_download(url2) # takes 5 sec.
)
# Total time: only 5 sec. (+ little overhead for using asyncio)
Of course, if you created a function that uses asynchronous code, this function should be asynchronous too (should be defined as async def). But any asynchronous function can freely use synchronous code. It makes no sense to cast synchronous code to asynchronous without some reason:
# extract_links(url) should be async because it uses async func async_download() inside
async def extract_links(url):
# async_download() was created async to get benefit of I/O
html = await async_download(url)
# parse() doesn't work with I/O, there's no sense to make it async
links = parse(html)
return links
One very important thing is that any long synchronous operation (> 50 ms, for example, it's hard to say exactly) will freeze all your asynchronous operations for that time:
async def extract_links(url):
data = await download(url)
links = parse(data)
# if search_in_very_big_file() takes much time to process,
# all your running async funcs (somewhere else in code) will be frozen
# you need to avoid this situation
links_found = search_in_very_big_file(links)
You can avoid it calling long running synchronous functions in separate process (and awaiting for result):
executor = ProcessPoolExecutor(2)
async def extract_links(url):
data = await download(url)
links = parse(data)
# Now your main process can handle another async functions while separate process running
links_found = await loop.run_in_executor(executor, search_in_very_big_file, links)
One more example: when you need to use requests in asyncio. requests.get is just synchronous long running function, which you shouldn't call inside async code (again, to avoid freezing). But it's running long because of I/O, not because of long calculations. In that case, you can use ThreadPoolExecutor instead of ProcessPoolExecutor to avoid some multiprocessing overhead:
executor = ThreadPoolExecutor(2)
async def download(url):
response = await loop.run_in_executor(executor, requests.get, url)
return response.text
You do not have much freedom. If you need to call a function you need to find out if this is a usual function or a coroutine. You must use the await keyword if and only if the function you are calling is a coroutine.
If async functions are involved there should be an "event loop" which orchestrates these async functions. Strictly speaking it's not necessary, you can "manually" run the async method sending values to it, but probably you don't want to do it. The event loop keeps track of not-yet-finished coroutines and chooses the next one to continue running. asyncio module provides an implementation of event loop, but this is not the only possible implementation.
Consider these two lines of code:
x = get_x()
do_something_else()
and
x = await aget_x()
do_something_else()
Semantic is absolutely the same: call a method which produces some value, when the value is ready assign it to variable x and do something else. In both cases the do_something_else function will be called only after the previous line of code is finished. It doesn't even mean that before or after or during the execution of asynchronous aget_x method the control will be yielded to event loop.
Still there are some differences:
the second snippet can appear only inside another async function
aget_x function is not usual, but coroutine (that is either declared with async keyword or decorated as coroutine)
aget_x is able to "communicate" with the event loop: that is yield some objects to it. The event loop should be able to interpret these objects as requests to do some operations (f.e. to send a network request and wait for response, or just suspend this coroutine for n seconds). Usual get_x function is not able to communicate with event loop.