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 -~----------~----~----~----~------~----~------~--~---
