The query volume doubled since I first started this thread and, at the
current rate, should double again by the end of next week.
I definitely need a solution that can handle in the thousand QPS because
that's where we're heading.
I'm currently running this without consistency using a dedicated memcache.


On Thu, Nov 21, 2013 at 2:00 PM, Jim <jeb62...@gmail.com> wrote:

> If data points for each entity are not coming too fast, you could use
> blobstore/gcs to store your time series for each entity in a blob, then
> store a pointer to that blob in your entity in the data store.  updating is
> expensive but can run off a task queue.  retrieval of the blobs is very
> fast, and then you can quicky parse the blob into memory and compute your
> stats on a given entity.  cross entity stats are trickier and require some
> map-reduce-esque processing.
>
> we use this approach for smart-meter analytics where data points for a
> given entity (meter) don't come any faster than once every 15 min... not
> sure if it would work for you.
>
>
>
> On Thursday, November 21, 2013 8:12:15 AM UTC-6, Mathieu Simard wrote:
>>
>> I need the median value for multiple entities but only compared to
>> themselves.
>> In the future I will probably create the media across entities by doing a
>> median average.
>>
>> On Tuesday, November 19, 2013 9:23:59 PM UTC-5, Jim wrote:
>>>
>>> Are you doing a time-series type analysis where you need the rolling
>>> median value for a specific entity, or do you need the median value across
>>> a range of entities?
>>>
>>>
>>>
>>> On Tuesday, November 12, 2013 2:07:34 PM UTC-6, Mathieu Simard wrote:
>>>>
>>>> Since there is no appengine solution available such as the Redis atomic
>>>> list, I'm left wondering how to implement a cost effective rolling median.
>>>> Has anyone come up with a solution that would be more convenient than
>>>> running a redis instance on Google Compute Engine?
>>>>
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