CAP Theorem Analysis

The CAP theorem, also known as Brewer's theorem, is a fundamental concept in distributed systems that states that it is impossible for a distributed data store to simultaneously provide consistency (C), availability (A), and partition tolerance (P).

• Consistency (C): Consistency refers to all nodes in a distributed system having the same view of the data at the same time. In this system, achieving strict consistency across all nodes is not a priority. Instead, eventual consistency is guaranteed, which means that over time, all nodes will converge to the same state. Nodes periodically exchange information and update their routing tables to achieve this convergence.

• Availability (A): Availability implies that the system remains responsive and accessible to users even in the presence of node failures or network partitions. The system prioritizes availability by ensuring that nodes can continue to operate and provide services even when some nodes are unavailable or unreachable. This is achieved through data redundancy and replication, where multiple copies of data are stored on different nodes.

• Partition Tolerance (P): Partition tolerance refers to the system's ability to continue functioning and providing services despite network partitions or failures. The system is designed to be partition-tolerant, allowing the network to continue operating and maintaining its functionality even when nodes are temporarily disconnected or isolated due to network issues.

All these points are analyzed assuming that the system's recovery capacity is not exceeded. If this point were exceeded, the system's availability would start to be affected. However, this would only happen in the case of a certain number of simultaneous failures since the system can recover from eventual failures without any problems.