Thanks for the replies.
Robert,
The rates come in bursts peaking over a few seconds and tail off quickly
over the course of a few minutes. They are generated by users quickly
clicking links in web browsers that use AJAX to connect to the GAE cloud. I
would like to get the sequential order of these clicks over all users.
I had a thought, I could have a 'Request' entity as so:
class Request(db.Model):
request_id = db.StringProperty()
timestamp = db.DateTimeProperty(auto_now_add=True)
count = db.IntegerProperty(long)
The timestamp property is taken from the instances clock and count property
is updated with memcache atomic increment for each new request. I could then
use a task to select the Request entities ordered by timestamp and in memory
also order by count. The count property takes precedence over the timestamp.
The worst case scenario, where memcache is flushed before each new request,
would degenerate to a resolution of the clock differences between each GAE
instance.
I don't expect I would hit the monotonically increasing value performance
limitation as Ikai explained. However all I would need to do is not index
timestamp and count properties and reindex them at a later date. Then the
limitation would be how quickly can I call memcache increment. I don't
expect this would be an issue either...
In the general case, I think this method would provide the greatest accuracy
and tolerate the occasional memcache flush. Relative to the latency
variations inherent in the web, this could be a winner. If I needed to be
even more specific, I could use Calvin's idea to make sure each GAE
instance's clock isn't minutes different from other instances to minimise
the disruption when memcache is flushed.
What do you think?
Best wishes,
Dan
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