In CAP theorem terms, Redis has picked zero (remember CAP theorem is pick at most two).
That might be true, but describing something in the CAP framework is not the only salient thing you can say about a distributed storage system. It is characterizing the failure modes.
You also have to think about what happens in the normal case. In the normal case you have a consistency vs. latency tradeoff. People have written about this, but unfortunately I don't think this broader way of thinking hasn't the attention it deserves:
"If you examine PNUTS through the lens of CAP, it would seem that the designers have no idea what they are doing (I assure you this is not the case). Rather than giving up just one of consistency or availability, the system gives up both!"
"The reason is that CAP is missing a very important letter: L. PNUTS gives up consistency not for the goal of improving availability. Instead, it is to lower latency. "
That might be true, but describing something in the CAP framework is not the only salient thing you can say about a distributed storage system. It is characterizing the failure modes.
You also have to think about what happens in the normal case. In the normal case you have a consistency vs. latency tradeoff. People have written about this, but unfortunately I don't think this broader way of thinking hasn't the attention it deserves:
http://dbmsmusings.blogspot.com/2011/12/replication-and-late...
http://dbmsmusings.blogspot.com/2010/04/problems-with-cap-an...
"If you examine PNUTS through the lens of CAP, it would seem that the designers have no idea what they are doing (I assure you this is not the case). Rather than giving up just one of consistency or availability, the system gives up both!"
"The reason is that CAP is missing a very important letter: L. PNUTS gives up consistency not for the goal of improving availability. Instead, it is to lower latency. "