CAP Theorem in DBMS: System Design Concept

CAP Theorem is one of the important concepts used in Distributed Systems. In this blog, we’ll cover basic concepts related to the CAP theorem and its applicability to various systems.

What is CAP Theorem?

CAP Theorem is necessary for designing networked shared data systems. It allows a distributed database system to have only two of these functionalities: Consistency, Availability, and Partition Tolerance. We can make trade-offs between the three available functionalities based on the unique use case for our system.

Let’s understand the functionalities of all three concepts.

Consistency

Consistency refers to the requirement that all nodes in a distributed system have the same data at a given point in time. It ensures that all clients accessing the system see the same data, regardless of which node they are connected to. When a client performs a read operation, it should receive the most recent write operation value, ensuring that all nodes in the system return the same data. To maintain consistency, it is the responsibility of the node where the write operation is performed to instantly replicate the data to all other nodes in the system.

Ensuring consistency is important for maintaining the integrity and accuracy of the data in a distributed system. It requires careful coordination and synchronization of data updates across all nodes in the system. Failure to maintain consistency can result in conflicting or outdated data being returned to clients, leading to potential errors or inconsistencies in the system.

Availability

Availability refers to the requirement that a distributed system is always able to respond to client requests for data. This means that the system must be operational at all times, even if one or more nodes are down or experiencing issues. When a client requests data, it should receive a response, regardless of the state of any individual node in the system.

Ensuring availability is important for maintaining the functionality of a distributed system. It allows clients to access the data they need, even if some nodes in the system are unavailable or experiencing issues. In order to achieve availability, a distributed system must have redundant nodes or mechanisms in place to ensure that it can continue operating even if some nodes fail or are unavailable.

Partition Tolerance

Partition tolerance refers to the ability of a distributed system to continue operating even if there is a failure or delay in the communication between nodes. This is an essential requirement for a distributed system, as it allows the system to continue functioning even if some nodes or communication channels are unavailable.

In order to achieve partition tolerance, a distributed system must have mechanisms in place to replicate data across multiple nodes and networks. This ensures that the system can continue operating even if some nodes or communication channels fail or experience delays.

Why CAP theorem important?

In a distributed system, data is stored over multiple nodes and communication occurs over a network. However, network failures can disrupt the system's ability to access and update data. To address this issue, it is important for the system to have partition tolerance, which allows it to continue operating even if some nodes or communication channels are unavailable.

When designing a distributed system, most of the time it is important to consider the trade-off between consistency and availability. If consistency is prioritized, the system may not be able to return the most recent version of the data if it cannot guarantee that the information is up to date. On the other hand, if availability is prioritized, the system will return the most recent available version of the data, even if it may not be completely up to date.

The CAP theorem states that it is impossible for a distributed system to simultaneously achieve consistency, availability, and partition tolerance. Therefore, it is important to understand the requirements of the system and choose a data management approach that meets its critical needs. It is also important to be aware of the CAP theorem when designing any cloud application or networked system.

CAP Theorem Database Architecture

Distributed networks heavily depend on NoSQL databases as they offer horizontal scalability, and they are highly distributed. Hence, they can easily and rapidly scale across a growing network of multiple interconnected nodes. But as discussed above, one can only have two of the three available functionalities. The different combinations and their use cases are discussed below:

• CP System: This system focuses more on consistency and partition tolerance. So these systems are not available most of the time. When any issue occurs in the system, it has to shut down the non-consistent node until the partition is resolved, and during that time, it is not available.
• AP System: This type of database focuses more on availability and partition tolerance rather than consistency. When any issue occurs in the system, then it will no longer remain in a consistent state. However, all the nodes remain available, and affected nodes might return a previous version of data, and the system will take some time to become consistent.
• CA System: This type of database focuses more on consistency and availability across all nodes than partition tolerance. Fault-Tolerance is the basic necessity of any distributed system, and hence it is almost rare to use a CA type of architecture for any practical purpose.

Use Cases of CAP Theorem

MongoDB is a popular NoSQL database management system that focuses on a "CP" database style, which means it maintains consistency while compromising on availability. In other words, MongoDB prioritizes data consistency over the ability to always respond to client requests for data. In the event of a network partition, MongoDB will maintain consistency by ensuring that all nodes in the system have the same data, but it may compromise availability by not being able to respond to all client requests.

Cassandra is another popular NoSQL database that focuses on an "AP" database style, which concentrates entirely on availability and partition tolerance rather than consistency. In other words, Cassandra will prioritize the ability to always respond to client requests and maintain partition tolerance. However, Cassandra does provide eventual consistency by figuring out all the inconsistencies in a certain period of time.

Microservices-based applications often rely on the CAP theorem to design the most efficient databases for the application. For example, if horizontal scalability is essential to the application with eventual consistency, an "AP" database like Cassandra can help meet deployment requirements and simplify deployment. On the other hand, if the application depends heavily on data consistency, such as in a payment service, it may be better to opt for a relational database like PostgreSQL, which focuses on a "CP" database style.

Conclusion

Distributed systems allow us to achieve a relatively higher level of computing power, availability and give the scope of scalability. It is essential to design the systems by considering real-life practical consequences and choosing the most appropriate design suitable for our application. It is a complex architecture that requires effective network management. So it becomes essential to understand the complexity incurred in distributed systems, make the appropriate trade-offs for the task, and select the right tool.

Thanks to Suyash for his contribution in creating the first version of this content. If you have any queries/doubts/feedback, please write us at contact@enjoyalgorithms.com. Enjoy learning, Enjoy system design, Enjoy algorithms!