Thanks for your help.

that sounds promising.

One question I have is how crashing instances are handled:

If I get a warning message like this:

After handling this request, the process that handled this request was
found to be using too much memory and was terminated. This is likely
to cause a new process to be used for the next request to your
application. If you see this message frequently, you may have a memory
leak in your application.


will the instance spin up a new process within milliseconds?


Yes I have a lot of datastore reads, but this is mostly because all queries
are different and caching the responses wont give me much. I use memcache
very heavily. I'm also working on a backend for all these different queries
to be served out of RAM...


On Mon, Sep 5, 2011 at 11:10 AM, renderpaz <[email protected]> wrote:

> The graph of instances is very confusing to me as neither line seems to
> correspond to the number of instances running, or running with > 0 qps, try
> playing with those instance "knobs" they can dramatically effect instance
> count, but for a high qps app like yours, I don't think you will see a
> dramatic change.
>
> Are you running python?
>
> it sounds like your will benefit heavily from Python 2.7 with concurrent
> request handling per instance.  If your requests spend the majority of their
> time waiting, should be no problem to do 4 up, which could really drop your
> instance costs.
>
> I'd then try and tackle your datastore reads.  While I have no idea about
> your app, it seems a bit odd to me that you have more reads that writes, in
> my app I have a 5:1 write to read ratio as I cache just about everything I
> write.  If you aren't confident you will soon read what you write, cache on
> read every time.  Memcache is fast and free, something to think about.
>
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