On Mon, Oct 15, 2012 at 2:45 PM, Dino Viehland email@example.com wrote:
They look remarkably similar. The biggest difference I see is that NDB appears to be using an event loop to keep the futures running while we're using add_done_callback (on the yielded futures) to continue stepping the generator function along. So there's not necessary an event loop in our case, and in fact the default context always just executes things synchronously. But frameworks can replace the default context so that work is posted into an event loop of some form.
But do your Futures use threads? NDB doesn't. NDB's event loop doesn't know about Futures; however the @ndb.tasklet decorator does, and the Futures know about the event loop. When you wait for a Future, a callback is added to the Future that will resume the generator when it is done, and in order to run them, the Future passes its callbacks to the event loop to be run.
-----Original Message----- From: firstname.lastname@example.org [mailto:email@example.com] On Behalf Of Guido van Rossum Sent: Monday, October 15, 2012 12:34 PM To: Dino Viehland Cc: firstname.lastname@example.org; Nick Coghlan; email@example.com Subject: Re: [Python-ideas] The async API of the future: yield-from
Wow, sounds very similar to NDB's approach! Please do check out NDB's tasklets and event loop: http://code.google.com/p/appengine-ndb-experiment/source/browse/ndb/tasklets...
On Mon, Oct 15, 2012 at 10:24 AM, Dino Viehland firstname.lastname@example.org wrote:
I'm still catching up to this thread, but we've been investigating Win 8 support for Python and Win 8 has a very asynchronous API design and so we've been interested in much the same space. We've actually come up with an example of the @task decorator (we called it @async) which is built around using yield + the ability to return from generators added in Python 3.3. Our version of this is also based around futures so that an @async API will return a future. The big difference here might be that we always return a future from a call rather than yielding it up the stack. So our API works with just simple yields rather than yield froms. This is what a simple usage of the API looks like:
from concurrent.futures import ThreadPoolExecutor from urllib.request import urlopen executor = ThreadPoolExecutor(max_workers=5) def load_url(url): return urlopen(_url).read() @async def get_image_async(url): buffer = yield executor.submit(load_url, url) return Image(buffer) def main(image_uri): img_future = get_image_async(image_uri) # perform other tasks while the image is downloading img = img_future.result() main("http://www.python.org/images/python-logo.gif")
This example us just using the existing thread pool to run the actual I/O but this will work with anything that will return a future. So inside of an async method anything which is yielded should be a future. The decorator will then attach a callback which will send the result of the future back into the generator, so the "buffer = " line gets the result of the future. Finally the function completes and the future returned from calling get_image_async will have its value set to Image when the StopIteration exception is raised with the return value.
Because we're interested in the GUI side of things here we've also wired this up into Tk so that we can experiment with an existing GUI framework, and I've included the source for the context there. Our thinking here is that different contexts can be created depending upon the framework which you're running in and that the context makes sure the code is running in the right spot, in this case getting back to the GUI thread after an async operation has been completed.
The big outstanding item we're still working through is I/O, but we think the contexts help here too. We're still not quite sure how polling I/O will work, but with the contexts if there's a single thread polling for I/O then the context will get us off the I/O thread and let the polling continue. We are currently thinking that there will need to be a polling thread which handles all of the I/Os, and there could potentially be more than one of these if different libraries aren't cooperating on sharing a single thread.
Here's the code plus the demo Tk app (you'll need your own Holmes.txt file for the sample app to run):
-----Original Message----- From: Python-ideas [mailto:email@example.com] On Behalf Of Calvin Spealman Sent: Monday, October 15, 2012 7:16 AM To: Nick Coghlan Cc: firstname.lastname@example.org Subject: Re: [Python-ideas] The async API of the future: yield-from
On Mon, Oct 15, 2012 at 9:48 AM, Nick Coghlan email@example.com wrote:
On Mon, Oct 15, 2012 at 10:31 PM, Calvin Spealman firstname.lastname@example.org wrote:
Currently, "with yield expr:" is not valid syntax, surprisingly.
It's not that surprising, it's the general requirement that yield expressions must be enclosed in parentheses except when used standalone or in a simple assignment statement.
"with (yield expr):" is valid syntax though, so I'm reluctant to endorse doing anything substantially different if the parentheses are omitted.
Silly oversight on my part, and I agree that the parens shouldn't make the difference in meaning.
I think the combination of "yield from" to delegate control (including exception handling) completely to a subgenerator and "context manager
- for loop + explicit yield" when an operation needs to yield
times and the exception handling behaviour should be left to the caller (as in the "as_completed" case) should cover the necessary behaviours.
I'm still -1 on delegating control to subgenerators with yield-from, versus having the scheduler just deal with them directly. I think it is far less flexible.
I would still like to see a less confusing "with yield expr:" by simply allowing it without parens, but no special meaning. I think it would be really useful in coroutines.
with yield collect() as tasks: yield task1() yield task2() results = yield tasks
-- Nick Coghlan | email@example.com | Brisbane, Australia
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