Nevermind, I forgot that you had mentioned Django :-) The app ID would still be helpful as well as example code if you feel comfortable sharing.
Thank you, Jeff On Aug 31, 3:16 pm, "Jeff S (Google)" <[email protected]> wrote: > Hi Jeff, > > Ah I see, thanks for the details. I'm looking into this, would you > mind sharing which runtime you are using, and the app ID? > > Cheers, > > Jeff > > On Aug 31, 1:52 pm, Jeff Enderwick <[email protected]> wrote: > > > thanks - I expected that the api calls would use parallel processing, > > but the app/servelet itself is a single thread of execution. > > if I have api_cpu_ms of 74, and cpu_ms of 1500, then that gives 1426ms > > for the non-api (app/servelet usage), yes? > > I'm trying to grok how that would happen in a single thread in 965ms > > of wall-clock time. > > > Jeff > > > On Mon, Aug 31, 2009 at 11:30 AM, Jeff S (Google)<[email protected]> wrote: > > > Hi Jeff > > > > On Fri, Aug 28, 2009 at 1:08 AM, Jeff Enderwick <[email protected]> > > > wrote: > > > >> Trolling my logs, I'm coming across cases where there is extreme > > >> (~10x) variance in cpu_ms for the exact same code flow, same GET URL > > >> and same data (not even any intervening writes to the datastore). I am > > >> logging my db.* function accesses, and I have factored out memcache > > >> too. For example: > > > >> 92ms, 142cpu_ms, 74api_cpu_ms, followed by: > > >> 965ms, 1500cpu_ms, 74api_cpu_ms > > > >> Q1: what could cause such a whopping delta? I am using Django, so > > >> perhaps template compilation? I used cprofile on the SDK with a > > >> similar data/result set, and the first page served was maybe ~2x > > >> subsequent pages in total time. Thoughts? > > > > Your idea of template compilation is along the same lines as my thinking. > > > I > > > can't say difinitively for this case but I would guess that you might be > > > seeing a more expensive first request when a new instance of you app is > > > being spun up. > > > >> Q2: I am assuming the 1st number after the '200' is the wall-clock > > >> time-to-server. As the app is single threaded ... how is it able to > > >> burn 1500ms less 74ms in only 965ms? > > > > Most API calls make calls to distributed services which parallelize work > > > across multiple machines, so it often easy to use more CPU time than wall > > > clock time. If you want to see where the CPU usage is coming from, you can > > > get information about CPU quota levels at any point within your code as > > > documented here: > > > >http://code.google.com/appengine/docs/quotas.html#Monitoring_CPU_Usag... > > > > Thank you, > > > > Jeff > > > >> Thanks! > > --~--~---------~--~----~------------~-------~--~----~ You received this message because you are subscribed to the Google Groups "Google App Engine" group. To post to this group, send email to [email protected] To unsubscribe from this group, send email to [email protected] For more options, visit this group at http://groups.google.com/group/google-appengine?hl=en -~----------~----~----~----~------~----~------~--~---
