Tony, The "update" call is the standard dict.update[1], which should be plenty fast for my purposes.
My data is actually under a kilobyte, so I am quite confused why it would take nearly 1000ms in CPU. Here's an example of the data (in yaml format) with some personally identifying information stripped out: http://emend.appspot.com/?yaml The actual data being cached is slightly larger, but not by much. [1] http://docs.python.org/library/stdtypes.html#dict.update On Jun 22, 11:06 am, Tony <fatd...@gmail.com> wrote: > Without knowing more about your app, I can't say for sure, but it > seems likely that whatever processing takes place in "response.update > (object)" is using your cpu time, which is why you don't see much of a > speedup via caching here. I would suggest profiling the operation to > determine what function call(s) are specifically taking the most > resources. In my experience, you won't notice a large difference in > cpu usage between serializing model instances to memcache vs. adding > identifier information (like db keys) for fetching later. My entities > are small, however, your mileage my vary. I find that the primary > tradeoff in serializing large amounts of info to memcache is in > increased memory pressure and thus lower memcache hit rate, higher > datastore access. > > On Jun 22, 12:48 pm, John Tantalo <john.tant...@gmail.com> wrote: > > > I recently attempted to improve the responsiveness of one of my app's > > more elementary handlers by using memcache to cache the datastore > > lookups. According to my logs, this has had a positive effect on my > > api_cpu_ms, reducing this time to 72 ms. However, the cpu_ms has not > > seen a similar decrease, and hovers around 1000ms. > > > Do memcache gets count towards api_cpu_ms or cpu_ms? Do I need to > > worry about performance issues around deserializing model instances in > > memcache? > > > My caching strategy looks like this: > > > response = dict() # (might not be empty) > > cached = memcache.get(__CACHE_KEY) > > if cached: > > response.update(cached) > > return > > else: > > # datastore calls > > foo = get_foo() > > bar = get_bar() > > # build cache object > > cached = dict(foo=foo, edits=bar) > > response.update(cached) > > # cache > > memcache.set(__CACHE_KEY, cached) > > return > > --~--~---------~--~----~------------~-------~--~----~ You received this message because you are subscribed to the Google Groups "Google App Engine" group. To post to this group, send email to google-appengine@googlegroups.com To unsubscribe from this group, send email to google-appengine+unsubscr...@googlegroups.com For more options, visit this group at http://groups.google.com/group/google-appengine?hl=en -~----------~----~----~----~------~----~------~--~---
