Sorry, I realize I didn't explain everything: since the external
system can handle an arbitrary load (as long as this load doesn't grow
too fast) and requests get processed in 1 to 2 seconds, we already set
the processing rate to the maximum (200), as well as the concurrent
(200).

The problem is not to limit the number of requests being handled, but
preventing bursts e.g. scaling up smoothly.

On Oct 30, 9:22 pm, Nicholas Verne <nve...@google.com> wrote:
> If your external system is called synchronously by your tasks, you could
> try setting the queue's max_concurrent_requests parameter in queue.yaml/xml
>
> This is documented for Java, but the same general rules apply for Python or
> Go 
> applications.http://code.google.com/appengine/docs/java/config/queue.html#Defining...
>
> Nick Verne
>
>
>
>
>
>
>
> On Mon, Oct 31, 2011 at 3:13 PM, Pol <p...@everpix.net> wrote:
> > Hi,
>
> > Through a task queue, we manage calls from GAE to an external system,
> > which is fully-scalable but takes minutes to do so. The task queue
> > rate is therefore set to 200/s.
>
> > So what happens is that sometimes we get bursts of activity, and the
> > task queue sends 200 requests at once: the vast majority fails in the
> > external system as it can't handle this sudden request in load. But
> > the external system doesn't start scaling up as there are no more
> > requests coming and it's scaling based on the CPU load. Then suddenly
> > you get another burst of requests from the task queue retrying, and
> > then again the majority fails and so on.
>
> > So how can we configure a task queue to have a very high rate *but*
> > prevent it to get to this rate too fast? I noticed the bucket size
> > parameter, but I'm not sure how to use it properly.
>
> > Thanks,
>
> > - Pol
>
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