My graph is attached -- interesting, eh?
I don't think we're using memcache in this instance.

:R

On Tue, May 22, 2012 at 2:04 PM, Rishi Arora <[email protected]>wrote:

> Here's what my milliseconds/request looks like, although the #instances
> follows a similar pattern, and translates directly into higher costs (5x
> normal usage today)
>
>
> On Tue, May 22, 2012 at 4:01 PM, Rishi Arora <[email protected]>wrote:
>
>> Perhaps similar to what I'm seeing.  Although I was able to use the
>> appstats tool to determine that the deadlines were being missed
>> specifically because of memcache API calls.  Perhaps you can determine from
>> appstats if its the same for you?
>>
>> If so, please star this issue that I logged:
>>
>> code.google.com/p/googleappengine/issues/detail?id=7554
>>
>>
>>
>> On Tue, May 22, 2012 at 2:02 PM, Robert Morgan <[email protected]>wrote:
>>
>>> Beginning about 18 hours ago, the milliseconds/Request on one of our
>>> apps began to ramp and is now over 100 seconds for a trivial response
>>> (which historically takes 2 seconds). Our other apps are fine, and the AE
>>> status page is clear.
>>>
>>> It's an app that we use for development, it's been around for ages and
>>> still runs the Master-Slave datastore. Python. The ramping just started --
>>> we had not deployed a new server version in about a week, and our client
>>> usage remains low and patterns haven't changed.
>>>
>>> I've poked at it trying to see if I can determine some threshold, but
>>> even a very simple return now fails due to DeadlineExceeded.
>>>
>>> This really feels like it's a system issue -- any ideas on what I can do
>>> next?
>>>
>>> Thanks,
>>> :R
>>>
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>>
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