CAP theorem, (also called Brewer's theorem after computer scientist Eric Brewer,) is a concept in distributed computing that states that it is impossible for a distributed system to simultaneously provide all three of the following guarantees:

1. Consistency: all nodes in the system see the same data at the same time.
2. Availability: every request to the system receives a response, without guarantee that it contains the most recent version of the data.
3. Partition tolerance: the system continues to function despite arbitrary partitioning due to network failures.

In other words, CAP theorem states that a distributed system can provide at most two of the above guarantees. This means that trade-offs must be made between consistency, availability, and partition tolerance, depending on the specific requirements and constraints of the system.

It is important to note that CAP theorem is a theoretical concept and there is ongoing debate about its practical implications in real-world systems. Nevertheless, it provides a useful framework for thinking about the design and behavior of distributed systems.

Example

Consider a social media website with multiple servers spread across different geographical locations. The database stores user information, such as their name, email address, and posts.

• If the system prioritizes Consistency, it would ensure that all nodes in the database see the same data at the same time. This means that if a user updates their information on one server, the change would be immediately reflected on all other servers. This would provide a consistent view of the data to all users, but might result in lower Availability, as the system may become unavailable if one of the servers goes down or if there is a network partition.

• If the system prioritizes Availability, it would ensure that every request to the database receives a response, even if the response is not the most recent version of the data. This means that if a user updates their information on one server, the change might not be immediately reflected on all other servers. This would provide high availability, as the system would continue to function even if one of the servers goes down or if there is a network partition, but might result in lower Consistency, as users might see different versions of the data.

• If the system prioritizes Partition Tolerance, it would ensure that the system continues to function despite network failures or partitions. This means that if a network failure causes a partition in the system, some nodes might not be able to communicate with each other, but the system as a whole would continue to operate. This would provide high partition tolerance, but might result in lower Consistency and Availability, as users might not be able to access the latest version of the data and the system might become unavailable if the partition affects a critical component of the system.

This example demonstrates how the trade-offs inherent in CAP theorem affect the design and behavior of a distributed database system.