There are two posts: 1) the link you posted. It looks at the application as a whole, not really at the datastore alone. 2) Another one on the big data store themselves, and more generally of the different definitions of 'availability'. It's here http://blog.thislongrun.com/2015/04/cap-availability-high-av...

I will update the posts to make this clearer. As well, it's a series and these posts are the most complex of the series :-). Many points are more detailed in the previous posts.

For an existence proof of things he claims doesn't exist consider Cassandra. It is eventually consistent, and you can configure it to be each of those things he says can't happen by tuning the read, write consistency. For CA choose all, CP choose quorum, for AP choose one. QED.

My understanding is that you can't "choose" CA, because partitions can never be entirely eliminated. If CA was really an option, who wouldn't pick it?

So if Cassandra is actually able to do that, then can you expand a little more on how it accomplishes that?

http://codahale.com/you-cant-sacrifice-partition-tolerance/

(Sadly, Coda deferred to someone important after writing this article, someone whom I don't think bothered to think through how much more beautiful Coda's explanation of a "partition" is than the AFAICT impossible-to-discern one that supposedly was being used in the original paper :/. I frankly think the final "update" to the article should be ignored. FWIW, in the first "update", it is pointed out that Eric Brewer actually have a very positive shout-out to Coda's article.)