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++.
Related
There are many posts on SO asking specific questions about asyncio, but I cannot grasp the right way on what to use for a given situation.
Let's say I want to parse and crawl a number of web pages in parallel. I can do this in at least 3 different ways with asyncio:
with pool.submit:
with ThreadPoolExecutor(max_workers=10) as pool:
result_futures = list(map(lambda x: pool.submit(my_func, x), my_list))
for future in as_completed(result_futures):
results.append(future.result())
return results
With asyncio.gather:
loop = asyncio.get_running_loop()
with ThreadPoolExecutor(max_workers=10) as pool:
futures = [loop.run_in_executor(pool, my_func, x) for x in my_list]
results = await asyncio.gather(*futures)
With just pool.map:
with ThreadPoolExecutor(max_workers=10) as pool:
results = [x for x in pool.map(my_func, arg_list)]
my_func is something like
async def my_func(arg):
async with aiohttp.ClientSession() as session:
async with session.post(...):
...
Could somebody help me understand what would be the differences between those 3 approaches? I understand that I can, for example, handle exceptions independently in the first one, but any other differences?
None of these. ThreadPoolExecutor and run_in_executor will all execute your code in another thread, no matter you use the asyncio loop to watch for their execution. And at that point you might just as well not use asyncio at all: the idea of async is exactly managing to run everything on a single thread - getting some CPU cycles and easing a lot on race-conditions that emerge on multi-threaded code.
If your my_func is using async correctly, all the way (it looks like it is, but the code is incomplete), you have to create an asyncio Task for each call to your "async defined" function. On that, maybe the shortest path is indeed using asyncio.gather:
import asyncio
import aiohttp, ... # things used inside "my_func"
def my_func(x):
...
my_list = ...
results = asyncio.run(asyncio.gather(*(my_func(x) for x in my_list)))
An that is all there is for it.
Now going back to your code, and checking the differences:
your code work almost by chance, as in, you really just passed the async functiona and its parameters to the threadpool executor: on calling any async function in this way, they return imediatelly, with no work done. That means nothing (but some thin boiler plate inner code used to create the co-routines) is executed in your threadpool executors. The values returned by the call that runs in the target threads (i.e. the actual my_func(x) call) are the "co-routines": these are the objects that are to be awaited in the main thread and that will actually performe the network I/O. That is: your "my_func" is a "co-routine function" and when called it retoruns immediately with a "co-routine object". When the co-routine object is awaited the code inside "my_func" is actually executed.
Now, with that out of the way: in your first snippet you call future.result on the concurrent.futures Future: that will jsut give you the co-routine object: that code does not work - if you would write results.append(await future.result()) then, yes, if there are no exceptions in the execution, it would work, but would make all the calls in sequence: "await" stops the execution of the current thread until the awaited object resolves, and since awaiting for the other results would happen in this same code, they will queue and be executed in order, with zero parallelism.
Your pool.map code does the same, and your asyncio.gather code is wrong in a different way: the loop.run_in_executor code will take your call and run it on another thread, and gives you an awaitable object which is suitable to be used with gather. However, awaiting on it will return you the "co-routine object", not the result of the HTTP call.
Your real options regarding getting the exceptions raised in the parallel code are either using asyncio.gather, asyncio.wait or asyncio.as_completed. Check the docs here: https://docs.python.org/3/library/asyncio-task.html
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()
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.
PEP 0492 adds the async keyword to Python 3.5.
How does Python benefit from the use of this operator? The example that is given for a coroutine is
async def read_data(db):
data = await db.fetch('SELECT ...')
According to the docs this achieves
suspend[ing] execution of read_data coroutine until db.fetch awaitable completes and returns the result data.
Does this async keyword actually involve creation of new threads or perhaps the use of an existing reserved async thread?
In the event that async does use a reserved thread, is it a single shared thread each in their own?
No, co-routines do not involve any kind of threads. Co-routines allow for cooperative multi-tasking in that each co-routine yields control voluntarily. Threads on the other hand switch between units at arbitrary points.
Up to Python 3.4, it was possible to write co-routines using generators; by using yield or yield from expressions in a function body you create a generator object instead, where code is only executed when you iterate over the generator. Together with additional event loop libraries (such as asyncio) you could write co-routines that would signal to an event loop that they were going to be busy (waiting for I/O perhaps) and that another co-routine could be run in the meantime:
import asyncio
import datetime
#asyncio.coroutine
def display_date(loop):
end_time = loop.time() + 5.0
while True:
print(datetime.datetime.now())
if (loop.time() + 1.0) >= end_time:
break
yield from asyncio.sleep(1)
Every time the above code advances to the yield from asyncio.sleep(1) line, the event loop is free to run a different co-routine, because this routine is not going to do anything for the next second anyway.
Because generators can be used for all sorts of tasks, not just co-routines, and because writing a co-routine using generator syntax can be confusing to new-comers, the PEP introduces new syntax that makes it clearer that you are writing a co-routine.
With the PEP implemented, the above sample could be written instead as:
async def display_date(loop):
end_time = loop.time() + 5.0
while True:
print(datetime.datetime.now())
if (loop.time() + 1.0) >= end_time:
break
await asyncio.sleep(1)
The resulting coroutine object still needs an event loop to drive the co-routines; an event loop would await on each co-routine in turn, which would execute those co-routines that are not currently awaiting for something to complete.
The advantages are that with native support, you can also introduce additional syntax to support asynchronous context managers and iterators. Entering and exiting a context manager, or looping over an iterator then can become more points in your co-routine that signal that other code can run instead because something is waiting again.
PEP 0492 adds the async keyword to Python 3.5.
How does Python benefit from the use of this operator? The example that is given for a coroutine is
async def read_data(db):
data = await db.fetch('SELECT ...')
According to the docs this achieves
suspend[ing] execution of read_data coroutine until db.fetch awaitable completes and returns the result data.
Does this async keyword actually involve creation of new threads or perhaps the use of an existing reserved async thread?
In the event that async does use a reserved thread, is it a single shared thread each in their own?
No, co-routines do not involve any kind of threads. Co-routines allow for cooperative multi-tasking in that each co-routine yields control voluntarily. Threads on the other hand switch between units at arbitrary points.
Up to Python 3.4, it was possible to write co-routines using generators; by using yield or yield from expressions in a function body you create a generator object instead, where code is only executed when you iterate over the generator. Together with additional event loop libraries (such as asyncio) you could write co-routines that would signal to an event loop that they were going to be busy (waiting for I/O perhaps) and that another co-routine could be run in the meantime:
import asyncio
import datetime
#asyncio.coroutine
def display_date(loop):
end_time = loop.time() + 5.0
while True:
print(datetime.datetime.now())
if (loop.time() + 1.0) >= end_time:
break
yield from asyncio.sleep(1)
Every time the above code advances to the yield from asyncio.sleep(1) line, the event loop is free to run a different co-routine, because this routine is not going to do anything for the next second anyway.
Because generators can be used for all sorts of tasks, not just co-routines, and because writing a co-routine using generator syntax can be confusing to new-comers, the PEP introduces new syntax that makes it clearer that you are writing a co-routine.
With the PEP implemented, the above sample could be written instead as:
async def display_date(loop):
end_time = loop.time() + 5.0
while True:
print(datetime.datetime.now())
if (loop.time() + 1.0) >= end_time:
break
await asyncio.sleep(1)
The resulting coroutine object still needs an event loop to drive the co-routines; an event loop would await on each co-routine in turn, which would execute those co-routines that are not currently awaiting for something to complete.
The advantages are that with native support, you can also introduce additional syntax to support asynchronous context managers and iterators. Entering and exiting a context manager, or looping over an iterator then can become more points in your co-routine that signal that other code can run instead because something is waiting again.