CAP theorem states that when designing and deploying applications in distributed environments, you can only optimize for 2 out of the following 3 properties:

  1. Consistency: a system operates fully or not at all, all nodes agree (the system’s behaviour is indistinguishable from a centralized system)
  2. Availability: system is always able to answer a request
  3. Partition Tolerance: if data is distributed and some nodes fail, the whole system can continue to function

One way to illustrate this is to imagine a set of clusters trying to agree on a value. There is a network partition between two groups of nodes in the cluster called A and B. They all initially have a value x = 0. A client ever issues a command x = 1 to a node i in A and sometime in the future, a client issues another command return x.

  1. If node i ever returns 1, this violates consistency as B cannot have heard of the update from A.
  2. If it only ever answers 0, this violates availability as x = 1 was never appropriately set.


  • ACID stands for atomicity, consistency, isolation, durability
    • Prioritizes C and A
    • Immediate consistency limits scale-out performance
  • BASE stands for basically available, soft state, eventual consistency
    • Prioritizes A and P
    • Scale-out performance is greatly enhanced
    • Fine when nature of the data can tolerate some imprecision in query results