Hello,

I've started writing GAE app that will have a 3D lattice object model with a 
couple of thousand nodes per user. Each request will typically need to read 
and analyse 1-2% of the records to walk the model in order to find the 
target node and half a dozen writes to update it and it's neighbours.

With the new pricing I'm now thinking of storing the model as a single large 
serialised object, with one read to get the whole thing and one write to put 
it back after manipulation in memory.

It *seems *wrong to be reading and writing so much **unchanged** data but 
we're being charged per datastore API operation not by actual volume.The CPU 
cost of serialising and de-serialising is, apparently, irrelevant.

I must surely be missing something?

(It does mean that if I decided to migrate away from GAE I can replace all 
the clever storage with a flat file!)

If we're charged per record on bulk fetches (still to be decided?) then this 
strategy might be good for iterating over any list. Storing a serialised 
copy of the whole collection along side the individual records would add one 
(large) read and one (large) write per record update but would allow 
iteration for one read rather than "N".

Admittedly I'm new to GAE and haven't really studied this through yet, but 
Google's policy of charging a "real" monetary value for "real" resources, 
CPU time, disk space, network bandwidth, via an "abstract" resource concept 
like datastore API calls seems like asking for trouble. Developers will 
always try to optimise their costs so Google needs to set charges based on 
what _they_ want optimised. I can't see how minimising the number of 
individual API calls can possibly be a real target for Google.

I think I'll investigate other hosting possibilities before committing a lot 
of time to learning this one. I can't guess at how much it's going to cost 
and I haven't the heart to fight.

Bob


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