On Sat, Sep 9, 2017 at 5:05 AM, Antoine Pitrou <solipsis@pitrou.net> wrote:
On Fri, 8 Sep 2017 16:04:27 -0700, Eric Snow <ericsnowcurrently@gmail.com> wrote:
``list()``::
It's called ``enumerate()`` in the threading module. Not sure there's a point in choosing a different name here.
Yeah, in the first version of the PEP it was called "enumerate()". I changed it to "list()" at Raymond's recommendation. The reasoning is that it's less confusing to most people that way. TBH, I'd rather leave it "list()", but could be swayed. Perhaps it would be enough for the PEP to not mention any relationship to "threading"?
The current interpreter (which called ``run()``) will block until the subinterpreter finishes running the requested code. Any uncaught exception in that code will bubble up to the current interpreter.
Why does it block? How is concurrency supposed to be achieved in that model? It would be more flexible if run(code) returned an object that can later be waited on. Something like... a Future :-)
I expect this is more a problem with my description than with the feature. :) I've already re-written this bit to be more clear. It's not that the thread blocks. It's more like a function call, where the current frame is paused while the call is executed. Then it returns to the calling frame. Likewise the interpreter in the current thread gets swapped out with the target interpreter, where the code gets run, and then the original interpreter gets swapped back in. This is how you do it in the C-API and it made sense (to me) to do it the same way in Python.
And why guarantee that it executes in the "current OS thread"? I would say you don't want to specify where it executes exactly, as it opens the door for more sophisticated implementations (such as automatic assignment of subinterpreters inside a pool of threads).
Again, I had explained this poorly in the PEP. The key thing here is that subinterpreters don't do anything special relative to threading. If you want to call "Interpreter.run()" in a thread then you stick it in a "threading.Thread". If you want to auto-assign to a pool of threads then you treat it like any other function you would auto-assign to a pool of threads.
get_fifo(name): list_fifos():
If fifos are uniquely named, why not return a name->fifo mapping?
I suppose we could. Then we could get rid of "get_fifo()" too. I'm still mulling over the right API for the FIFO parts of the PEP.
``FIFOReader(name)``:: [...]
I don't think the method naming choice is very adequate here. The API model for the FIFO objects can either be a (threading or multiprocessing) Queue or a multiprocessing Pipe.
- if a Queue, then it should have a get() / put() pair of methods - if a Pipe, then it should have a recv() / send() pair of methods
Now, since Queues are multi-producer multi-consumer, while Pipes are single-producer single-consumer (they aren't "synchronized"), the better analogy seems to the multiprocessing Pipe here, so I would vote for recv() / send().
But, in any case, definitely not a pop() / push() pair.
Thanks for pointing that out. Prior art, FTW! I'll factor that in.
Has any thought been given to how FIFOs could integrate with async code driven by an event loop (e.g. asyncio)? I think the model of executing several asyncio (or Tornado) applications each in their own subinterpreter may prove quite interesting to reconcile multi-core concurrency with ease of programming. That would require the FIFOs to be able to synchronize on something an event loop can wait on (probably a file descriptor?).
Personally I've given pretty much no thought to the relationship with async. TBH, the FIFO parts of the PEP were added only recently and haven't fully baked yet. I'd be interested more feedback on async relative to PEP, not just with the FIFO bits; my experience with async is pretty limited thus far. -eric