Re: Scaling django installation

Check out django-cache-machine. It uses memcache to cache your ORM qureies and updates (invalidates) that cache when they change.

On Fri, Jun 1, 2012 at 10:35 AM, Tim Chase <django.users@tim.thechases.com> wrote:

On 06/01/12 09:17, Subhranath Chunder wrote:
> On Fri, Jun 1, 2012 at 6:57 PM, Tim Chase <django.users@tim.thechases.com>wrote:

>> 2) I/O
>> 2a) disk
>> 2b) network
>> 2c) memory
>>
> Don't think these might be creating much bottleneck in my scenario. But
> still, nothing like getting to exact figures. Again, how would you measure
> it?

usually in I/O operations-per-second.  Additionally, if you use
blocking I/O, the request will have to wait until the I/O has
completed before the request-processing can complete.  If you can
pipeline your high-latency I/O operations, it can produce large
gains.  As for actually measuring the operations, it can be as
simple as noting datetime.datetime.now() before and after the window
of interest (and possibly passing those into your template for a
debugging render).  Without measuring, there's no way to know where
it's slow.

I'm also rashly assuming that you've disabled DEBUG in your settings.

> The focus of the application has been to reduce bottlenecks as much as
> possible.
> Zero or one query, extensive use of memcache, async tasks(via celery), etc.
> it's all there in application layer to reduce the bottlenecks.

Without further details about the particular views that are slowing
things down, it's hard to tell.  Do you have some middleware that's
performing queries?  Are certain views slower than others?  How are
you authenticating (against DB tables or LDAP requests to a remote
server)?

>> There's no single number to measure the complexity
>

> Are we sure. The round-trip response time for a request to the server,
> can't that be used as a single number to measure the complexity?

Well, a view of

 def slowview(request):
   time.sleep(2000)
   return render_to_response(...)

is a slow view, but not terribly complex.

-tkc

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