Hi
does running on tornado imply that you would not consider twisted
http://twistedmatrix.com ?
If not, twisted has exactly this capability hiding long running
queries on whatever db's behind deferToThread().
All right, I was reading its documentation
http://twistedmatrix.com/documents/10.1.0/api/twisted.internet.threads.deferToThread.html
It doesn't tell too much about it: "Run a function in a thread and
return the result as a Deferred.".
Run a function but in what thread? Does it create a new thread for every
invocation? In that case, I don't want to use this. My example case: 10%
from 100 requests/second deal with a database. But it does not mean that
one db-related request will do a single db API call only. They will
almost always do more: start transaction, parse and open query, fetch
with cursor, close query, open another query etc. then commit
transaction. 8 API calls to do a quick fetch + update (usually under
100msec, but it might be blocked by another transaction for a while...)
So we are talking about 80 database API calls per seconds at least. It
would be insane to initialize a new thread for each invocation. And
wrapping these API calls into a single closure function is not useful
either, because that function would not be able to safely access the
state that is stored in the main thread. Unless you protet it with
locks. But it is whole point of async I/O server: to avoid using slow
locks, expensive threads and context switching.
Maybe, deferToThread uses a thread pool? But it doesn't say much about
it. (Am I reading the wrong documentation?) BTW I could try a version
that uses a thread pool.
It is sad, by the way. We have async I/O servers for Python that can be
used for large number of clients, but most external modules/extensions
do not support their I/O loops. Including the extension modules of the
most popular databases. So yes, you can use Twisted or torandoweb until
you do not want to call *some* API functions that are blocking. (By
*some* I mean: much less blocking than non-blocking, but quite a few.)
We also have synchronous Python servers, but we cannot get rid of the
GIL, Python threads are expensive and slow, so they cannot be used for a
large number of clients. And finally, we have messaging services/IPC
like zeromq. They are probably the most expensive, but they scale very
well. But you need more money to operate the underlying hardware. I'm
starting to think that I did not get a quick answer because my use case
(100 clients) fall into to the "heavy weight" category, and the solution
is to invest more in the hardware. :-)
Thanks,
Laszlo
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