One last point, in the CAP theorem proof, "partitions" really just means loss of packets to/from one node. That is the smallest/general case. I don't know wher

One last point, in the CAP theorem proof, "partitions" really just means loss of packets to/from one node. That is the smallest/general case. I don't know where you got this idea from. The formal model in Gilbert and Lynch's proof is arbitrary packet loss, which is equivalent to an arbitrary pattern of isolation between groups (of any size) of nodes. See section 2.3: http://lpd.epfl.ch/sgilbert/pubs/BrewersConjecture-SigAct.pdf http://lpd.epfl.ch/sgilbert/pubs/BrewersConjecture-SigAct.pd... Even if one can handle single node failure well (eg: dynamo systems) it may or may not be able to handle full split-brain mode. Just so we're clear, Dynamo does handle totally isolated components during partition. This is an explicit goal of the paper, and it provides specific guarantees about availability in those circumstances. That is a lot harder and there isn't any real CS to help you here (not that the CAP theorem is helpful) There is a great deal of CS to help you deal with partitions. You might start with Lamport, Lynch, Gray, Brewer, Chandra, Cheriton & Skeen, etc...