Hi Feris,
Are you testing it locally, or live? (The datastore can be slow for
puts locally.)
Are you trying to update the same records, or 1000 unique records?
If you are doing 27 updates per second, are you ramping up, and
ramping down your requests when you run tests? Try to simulate a
smooth usage graph, rather than a spikey one.
Have you tried profiling your code live with something like:
import logging
import cProfile, pstats, StringIO
prof = cProfile.Profile()
prof = prof.runctx("self.your_main_function()", globals(),
locals())
stream = StringIO.StringIO()
stats = pstats.Stats(prof, stream=stream)
stats.sort_stats("time") # Or cumulative
stats.print_stats(400) # how many to print
stats.print_callees()
stats.print_callers()
msg = "Profile: \n%s" % stream.getvalue()
logging.info(msg)
What are the 3 fields? Are they indexed?
-Dave
On Oct 5, 7:05 am, "Feris Thia" <[EMAIL PROTECTED]> wrote:
> I'm a bit confuse for what this scale means. Is it scale in terms to server
> large static / cached items ? I have tried to use model puts into datastore
> and it get over quota easily, I test it using a 1000 records - with only 3
> fields and no blob - loop with around 27 records updates per second. Kinda
> slow.. is it supposed to be like that ? What if I developed a social
> networking site with very high updates to datastore ?
>
> Any best practice suggestions ?
>
> Please advice..
>
> Regards,
>
> Feris
>
> On Sun, Oct 5, 2008 at 7:45 PM, Jonathan Feinberg <[EMAIL PROTECTED]>wrote:
>
> > Unless, of course, you can't find anything else with the same scaling
> > properties, ease of use, and pleasure of development. Then do.
>
>
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