CAP Theory

The CAP Theorem states that, in a distributed system (a collection of interconnected nodes that share data.), you can only have two out of the following three guarantees across a write/read pair: Consistency, Availability, and Partition Tolerance - one of them must be sacrificed. However, as you will see below, you don’t have as many options here as you might think.

• Consistency - A read is guaranteed to return the most recent write for a given client.
• Availability - A non-failing node will return a reasonable response within a reasonable amount of time (no error or timeout).
• Partition Tolerance - The system will continue to function when network partitions occur.

Given that networks aren’t completely reliable, you must tolerate partitions in a distributed system, period. Fortunately, though, you get to choose what to do when a partition does occur. According to the CAP theorem, this means we are left with two options: Consistency and Availability.

CP - Consistency/Partition Tolerance

Wait for a response from the partitioned node which could result in a timeout error. The system can also choose to return an error, depending on the scenario you desire. Choose Consistency over Availability when your business requirements dictate atomic reads and writes.

AP - Availability/Partition Tolerance

Return the most recent version of the data you have, which could be stale. This system state will also accept writes that can be processed later when the partition is resolved. Choose Availability over Consistency when your business requirements allow for some flexibility around when the data in the system synchronizes. Availability is also a compelling option when the system needs to continue to function in spite of external errors (shopping carts, etc.)

Building distributed systems provide many advantages, but also adds complexity. Understanding the trade-offs available to you in the face of network errors, and choosing the right path is vital to the success of your application. Failing to get this right from the beginning could doom your application to failure before your first deployment.