Thanks Jeremy,
   I'll try to expand a bit, and give some insight into a solution I
crafted this morning after reading your message (it flipped a switch
in my brain)

> How's the data updated?  Need to know how to get the update info to
> the cache.  :)

The data is updated by a crontab job that  runs a python script.  This
script pulls down RSS feeds, scrapes web sites, etc., mines out data
into my format, adds rows to the database using the Django ORM, etc.
every 15 minutes.

> As a hack, you could have a stub view that just decides if it's the
> current month or not, then dispatch either of 2 real views, each with
> its own cache_page.

This is how I implemented it this morning. I think there might a few
edge cases that aren't being handled but what I did is I send everyone
to:

month_detail(...)

which then decides whether or not they are requesting this month or a
previous month, and they're forwarded to the following views,
respectively:

this_month_detail(...) and previous_month_detail(...) which both do a
call to real_month_detail(...) which has all the shared code.

When I set up caching I then just cache this_month_detail for 15
minutes and previous_month_detail for 10 years or so.

> For an actual solution, more detail's needed.
>
> How many other parameters from the request come into play?

Pretty much every parameter that's taken into account comes through my
function arguments, (year, month, entity).  That is, the output of the
view depends wholly on the unique combinations of those 3 items.

> If a small number of permutations, you could bake all the data (that
> is, pull requests into a flat file to be served cheaply later, in
> which "invalidating cache" is deleting a file).

I'd say that there are (3 years)*(12 months)*(65 entities)
permutations (~2300) and that obviously grows every month and as we
add more entities, which happens at the rate of approximately 1-2 a
month.

Do you mean by "baking the requests", caching the output to a file on
disk/db or by pickling the request object (pretty sure you don't mean
this).  If the first is what you mean, how does this differ from using
file-based caching in Django?

> If you decide to use the low-level cache due to too many permutations,
> this is the general approach:
>
> expensive_thing = cache.get(some_key)
> if not expensive_thing:
>     expensive_thing = expensive_process
>    cache.set(some_key, expensive_thing, cache_timeout)
>
> You can, of course, do that as much as you want.

> I have some views that do two or 3 phases, in which I cache a whole
> resultset, then munge or whittle it depending on parameters and cache
> that bit with a more fine-grained key.

I may look into this a bit more to target my intensive bits of that
particular view.


In another vein,  how does everyone deal with invalidating the cache
and the resulting penalty the next client to request that view
receives? These old views can take near 30 seconds to regenerate from
a non-cached state. Say for whatever reason, a month from 5 months ago
recieves a new bit of data and I kill the cache for it.  How do you
regenerate that view?

Is there a way to do it programatically from within Django?

i.e. when I add a new bit of data to an old month in my cron script
and invalidate that month's cache, can I trigger what I might call an
automated rebuild of that template?

I would prefer that the penalty take place during my cron'd script's
execution rather than the user have a perceived delay the next time
that particular view is requested.

Also, thanks a bunch, Jeremy!
Clint

-- 
Clint Ecker
Sr. Web Developer - Stone Ward Chicago
p: 312.464.1443
c: 312.863.9323
---
twitter: clint
skype: clintology
AIM: idiosyncrasyFG
Gtalk: [EMAIL PROTECTED]

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