[Python-ideas] Concurrency Modules

[Ludovic Gasc]

Hello,

This discussion is pretty interesting to try to list when each architecture
is the most efficient, based on the need.

However, just a small precision: multiprocess/multiworker isn't antinomic
with AsyncIO: You can have an event loop in each process to try to combine
the "best" of two "worlds".
As usual in IT, it isn't a silver bullet that will care the cancer,
however, at least to my understanding, it should be useful for some
business needs like server daemons.

It isn't a crazy new idea, this design pattern is implemented since a long
time ago at least in Nginx: http://www.aosabook.org/en/nginx.html

If you are interested in to use this design pattern to build a HTTP server
only, you can use easily aiohttp.web+gunicorn:
http://aiohttp.readthedocs.org/en/stable/gunicorn.html
If you want to use any AsyncIO server protocol (aiohttp.web, panoramisk,
asyncssh, irc3d), you can use API-Hour: http://www.api-hour.io

And if you want to implement by yourself this design pattern, be my guest,
if a Python peon like me has implemented API-Hour, everybody on this
mailing-list can do that.

For communication between workers, I use Redis, however, you have plenty of
solutions to do that.
As usual, before to select a communication mechanism you should benchmark
based on your use cases: some results should surprise you.

Have a nice week.

PS: Thank you everybody for EuroPython, it was amazing ;-)

--
Ludovic Gasc (GMLudo)
http://www.gmludo.eu/

2015-07-26 23:26 GMT+02:00 Sven R. Kunze <srkunze at mail.de>:

>  Next update:
>
>
> Improving Performance by Running Independent Tasks Concurrently - A Survey
>
>
>                | processes               | threads                    |
> coroutines
>
> ---------------+-------------------------+----------------------------+-------------------------
> purpose        | cpu-bound tasks         | cpu- & i/o-bound tasks     |
> i/o-bound tasks
>                |                         |
> |
> managed by     | os scheduler            | os scheduler + interpreter | customizable
> event loop
> controllable   | no                      | no                         |
> yes
>                |                         |
> |
> parallelism    | yes                     | depends (cf. GIL)          |
> no
> switching      | at any time             | after any bytecode         | at
> user-defined points
> shared state   | no                      | yes                        |
> yes
>                |                         |
> |
> startup impact | biggest/medium*         | medium                     |
> smallest
> cpu impact**   | biggest                 | medium                     |
> smallest
> memory impact  | biggest                 | medium                     |
> smallest
>                |                         |
> |
> pool module    | multiprocessing.Pool    | multiprocessing.dummy.Pool |
> asyncio.BaseEventLoop
> solo module    | multiprocessing.Process | threading.Thread           |
> ---
>
>
> *
> biggest - if spawn (fork+exec) and always on Windows
> medium - if fork alone
>
> **
> due to context switching
>
>
> On 26.07.2015 14:18, Paul Moore wrote:
>
> Just as a note - even given the various provisos and "it's not that
> simple" comments that have been made, I found this table extremely
> useful. Like any such high-level summary, I expect to have to take it
> with a pinch of salt, but I don't see that as an issue - anyone who
> doesn't fully appreciate that there are subtleties, probably wouldn't
> read a longer explanation anyway.
>
> So many thanks for taking the time to put this together (and for
> continuing to improve it).
>
>  You are welcome. :)
>
> +1 on something like this ending up in the Python docs somewhere.
>
>  Not sure how the process for this is but I think the Python gurus will
> find a way.
>
> _______________________________________________
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>
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