CAP Theorem in System Design

CAP Theorem is the most important concept used in Distributed Systems. In this blog, we’ll cover all the basic concepts related to the CAP theorem and its applicability to various systems. So let’s dive deep into understanding the critical concepts behind working of CAP Theorem :)

What is the CAP Theorem?

CAP Theorem is one of the essential concepts necessary for designing networked shared data systems. CAP Theorem is a concept that allows a distributed database system to have any of only two of the three functionalities: Consistency, Availability, and Partition Tolerance. CAP Theorem is an essential concept that helps make trade-offs between the three available functionalities, based on our unique use case that we need for our system.

Key Concepts

CAP theorem is very useful in a distributed system in which we need to store data over multiple nodes and simultaneously need to communicate over the network. Due to massive dependency on-network calls, it is prevalent for a distributed system to fall into the trap of network failures; hence partition tolerance is essential. So, in that case, we have to choose judiciously between consistency or availability as per our requirement. Whenever consistency is preferred over availability in the system, it is challenging for the system to return the most recent writing. It will return an error if specific information cannot be guaranteed to be up to date. Similarly, whenever availability is preferred over consistency, then the system will return the most recent available version of the information. Hence with the development of various complex systems, it becomes essential to understand the CAP theorem when designing any cloud app or networked system. It becomes convenient to choose a data management system that meets the critical requirements of the system.

Let’s understand the functionalities of all three concepts.

Consistency

Consistency means that everything should go on in a very well-coordinated manner and with proper synchronization. It ensures that all clients should see the same data simultaneously, regardless of which node they are connected with. Performing a read operation will return the most recent write operation value, causing all nodes to return the same data. Whenever data is written on the node, it is the node’s utmost responsibility to instantly pass on the data to all other nodes in the system. 

Availability

Availability means that the system is always there and ready whenever any request is made to it. Whenever any client requests data, it should get a response, even if one or more nodes are down. Hence, to achieve the availability in a distributed system, every time the system must remain operational. Every client should get a response, regardless of the state of any individual node in the system, which necessitates the nodes’ availability all the time. 

Partition Tolerance

Partition Tolerance is necessary for any distributed system; we always need to choose between availability and consistency. Partition Tolerance corresponds to the condition that the system should work irrespective of any harm or breakdown of nodes. This condition states that the system should continue to run, irrespective of delaying and inconsistency. It is the necessity of the system to remain fault-tolerant. It should sustain any kind of network failure by sufficiently replicating the data records across various possible configurations of nodes to prevent the system from any type of failure.

CAP theorem and 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 that consists of multiple interconnected nodes. But as discussed above, one can only have any two of the three available functionalities. The different combinations and their use cases are discussed below:

  • CP System: This type of system focuses more on consistency and partition tolerance. 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 very rare to use a CA type of architecture for any practical purpose.

Use Cases of CAP Theorem

  1. MongoDB is a very popular NoSQL database management system and treated as a CP database. It resolves network partitions by maintaining consistency while compromising on availability.
  2. Cassandra is also a very popular NoSql database which focuses on AP database style. It entirely focuses on availability and partition tolerance rather than consistency. But Cassandra provides eventual consistency by figuring out all the inconsistencies in a certain period of time.
  3. Microservices-based applications are also heavily dependent 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, then an AP database like Cassandra can help meet the requirements and simplification of deployment. On the other hand, if the application depends heavily on data consistency as in a payment service, it would be better to opt for a relational database like PostgreSQL.

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 all the real-life practical consequences and choose the most appropriate design suitable for our application. Distributed systems are highly complex architectures that require effective network management as single-network counterparts. Hence it becomes essential to understand the complexity incurred in distributed systems, make the appropriate trade-offs for the task, and select the right tool for the job.

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