[vii]

It's not surprising that a simple theorem doesn't deal with practical trade-offs. In my experience if you feel you are running up against the CAP theorem you need to take a moment to think about the real needs of your application and looking for a more detailed theorem isn't going to guide you well, instead you need to think about what you actually want to happen when various kinds of failure occur. In finance for example, often trading systems will choose to be available and accept transactions when there is a failure and then reconcile afterwards - swapping the risk of making mistakes but in a trusted environment to prevent the potentially catastrophic condition of not being able to trade.

The article claims that the CAP theorem only prohibits linearisability as a consistency model. The original CAP paper talks about "Atomic consistency" which is clearly at least linearisability. However as the proof formalizes, it's obvious that if you accept writes to totally split and separated systems which cannot communicate together (network partition), you cannot be consistent across the two parts. This is confusing because this kind of consistency only loosely matches the definitions in the ACID acronym which are geared around invalid database states in a single machine database, not about different requests to different database machines giving inconsistent answers.

Linearizability versus serialisability (and strict serialisability) are explained well by Peter Bailis http://www.bailis.org/blog/linearizability-versus-serializab...

[maxov]

That link is really great, thanks! I was actually looking for a clear comparison between consistency definitions.

I did not realize that consistency and atomicity also are overloaded between databases and distributed systems. That certainly adds to the confusion! I must have missed it when reading the original article.

More real-world experience is also really valuable, and an application-first approach to handling distributed systems works really well. I guess my hope is that eventually the theory becomes robust, matching the real world enough so that we won't have the problems of mis-applied theorems. IMO the best theories are intuitive and help us expose holes we could not catch in the real world.