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 firstname.lastname@example.org:
Improving Performance by Running Independent Tasks Concurrently - A Survey
| processes | threads |
---------------+-------------------------+----------------------------+------------------------- 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
* 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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