The CAP theorem, also known as Brewer's theorem, is a fundamental principle in the field of distributed system design that outlines the trade-offs between three key properties: Consistency, Availability, and Partition Tolerance. It was proposed by Eric Brewer in 2000 and formally proved by Seth Gilbert and Nancy Lynch of MIT in 2002[8][9].
Consistency
Consistency in the context of the CAP theorem means that all nodes in the distributed system see the same data at the same time. Essentially, any read operation on the system should return the most recent write operation acknowledged to the client, ensuring that all clients have a consistent view of the data[9].
Availability
Availability refers to the system's ability to always respond to requests, regardless of the state of any individual node within the system. This means that every request receives a response, without guaranteeing that the returned data is the most recent version if some nodes are not currently reachable[9].
Partition Tolerance
Partition tolerance means that the system continues to operate despite any number of communication breakdowns between nodes in the system. This property is crucial because it addresses the reality of network failures in distributed systems, ensuring that the system can still function even when network partitions occur[9].
Trade-offs
According to the CAP theorem, a distributed system can only provide two of the three properties (Consistency, Availability, Partition Tolerance) fully at the same time. This means that:
CA (Consistency and Availability): Systems that prioritize consistency and availability typically are not partition-tolerant. They can suffer from failures if there is a network partition.
CP (Consistency and Partition Tolerance): Systems that prioritize consistency and partition tolerance may sacrifice availability. During a partition or network failure, these systems may not be ab...
