“For analytical things, eventual consistency is ok (as long as you can know after you have run them if they were consistent or not). For real world involving money or resources it’s not necessarily the case.” — Michael “Monty” Widenius. In a recent interview, I asked Justin Sheehy, Chief Technology Officer at Basho Technologies, maker [...]

“For analytical things, eventual consistency is ok (as long as you can know after you have run them if they were consistent or not). For real world involving money or resources it’s not necessarily the case.” — Michael “Monty” Widenius. In a recent interview, I asked Justin Sheehy, Chief Technology Officer at Basho Technologies, maker [...]

The CAP Theorem has become a convenient excuse for throwing data consistency under the bus. It is automatically assumed that every distributed system falls prey to CAP and therefore must sacrifice one of the three objectives, with consistency being the consistent fall guy. This automatic assumption is simply false. I am not debating the validity of the CAP Theorem, but instead positing that the onset of CAP limitations—what I call the CAP event horizon—does not start as soon as you move to a second master database node. Certain approaches can, in fact, extend the CAP event horizon.
Physics tells us that different properties apply at different scales. For example, quantum physics displays properties that do not apply at larger scale. We see similar nuances in scaling databases. For example, if you are running a master slave database, using synchronous replication with a single
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Very nice and interesting post from Michael Stonebraker explaining how errors dictate CAP Theorem (Consistency, Availability and Partition-tolerance); as only one objective from the CAP can be achieved during normal error conditions as NoSQL system seems to relax the consistency model as CAP theorem anyway proves that one can’t get all 3 at the same [...]