What is Availability vs Consistency in terms of CAP theorem?

The CAP theorem, also known as Brewer's theorem, is a fundamental theorem in distributed computing that states any distributed data store can only provide two of the following three guarantees at the same time: Consistency, Availability, and Partition Tolerance. Understanding Availability and Consistency in the context of the CAP theorem is crucial for designing distributed systems.

Availability

• Definition: Availability in the CAP theorem refers to the guarantee that every request to the system receives a response, regardless of the state of any individual node in the system. This doesn't necessarily mean that every request is successful, but rather that every request gets a response – without the guarantee that it contains the most recent write.
• Real-World Example: A distributed database system is considered available if it continues to operate and respond to requests even when one or more nodes fail or become unreachable. For instance, in an e-commerce system designed for high availability, users can view products and make purchases even if some parts of the system are down.

Consistency

• Definition: Consistency in the CAP theorem means that all nodes see the same data at the same time. Put another way, after a write operation has been acknowledged, any subsequent read operation should return that value.
• Real-World Example: In a banking system, consistency is crucial for transactions. If you deposit money into your account, you expect that the next time you check your balance, it will reflect this deposit across all bank branches and ATMs, no matter where they are located.

CAP Theorem and Distributed Systems:

• Partition Tolerance: This is a necessity in any distributed system. It refers to the system's ability to continue operating despite network partitions or communication breakdowns between nodes in the system.
• Trade-offs:
  • If a system chooses Consistency and Partition Tolerance (CP), it may sacrifice availability, meaning that if a network partition happens, some users might not be able to access the data until the partition is resolved.
  • If a system chooses Availability and Partition Tolerance (AP), it can lead to temporary inconsistencies in the system, where not all nodes have the same data at the same time.

Conclusion:

In distributed system design, understanding the trade-offs between availability and consistency is crucial. The choice depends on the specific requirements of the application. Systems where transactions require strict accuracy (like financial systems) often prioritize consistency, whereas systems that need to serve user requests under all circumstances (like social media platforms) might prioritize availability. Partition tolerance, given the nature of distributed systems, is not something that can be compromised.