Teams from Princeton and CMU are working together to solve one of the most difficult problems in the repertoire: scalable geo-distributed data stores. Major companies like Google and Facebook have been working on multiple datacenter database functionality for some time, but there's still a general lack of available systems that work for complex data scenarios.

The ideas in this paper--Don’t Settle for Eventual: Scalable Causal Consistency for Wide-Area Storage with COPS--are different. It's not another eventually consistent system, or a traditional transaction oriented system, or a replication based system, or a system that punts on the issue. It's something new, a causally consistent system that achieves ALPS system properties. Move over CAP, NoSQL, etc, we have another acronym: ALPS - Available (operations always complete successfully), Low-latency (operations complete quickly (single digit milliseconds)), Partition-tolerant (operates with a partition), and Scalable (just add more servers to add more capacity). ALPS is the recipe for an always-on data store: operations always complete, they are always successful, and they are always fast.

ALPS sounds great, but we want more, we want consistency guarantees as well. Fast and wrong is no way to go through life. Most current systems achieve low latency by avoiding synchronous operation across the WAN, directing reads and writes to a local datacenter, and then using eventual consistency to maintain order. Causal consistency promises another way.

Intrigued? Let's learn more about causal consistency and how it might help us build bigger and better distributed systems.