CAP Theorem

The CAP theorem, also known as Brewer’s theorem, is a concept in distributed computing that describes the trade-offs between three important properties of distributed systems: Consistency, Availability, and Partition Tolerance.

The CAP theorem, formulated by computer scientist Eric Brewer in 2000, helps in understanding the fundamental challenges of designing and operating distributed systems. Let’s explore each of these properties in detail:

Consistency ©

This property ensures that all nodes in a distributed system have a consistent view of the data. In other words, every read request to the system will return the most recent write. In a strongly consistent system, if a write operation has been acknowledged, all subsequent read operations will reflect that write.

• Strong Consistency: All read and write operations are guaranteed to be immediately consistent with each other, regardless of network delays or failures.

Availability (A)

Availability ensures that every request (read or write) made to a distributed system receives a response, without any guarantee of the data’s consistency. In the context of availability, “availability” means that the system is responsive and operational, even if it returns stale or potentially inconsistent data.

• High Availability: The system remains operational and responds to requests even in the presence of network failures or other system issues.

Partition Tolerance (P)

Partition tolerance addresses the system’s ability to function and make progress in the presence of network partitions or communication failures between nodes in a distributed system.

• Partition Tolerance: The system continues to operate, even when network communication between nodes is unreliable or fails.

The CAP Theorem Trade-offs

The CAP theorem asserts that in a distributed system, it is impossible to simultaneously achieve all three properties (Consistency, Availability, and Partition Tolerance) to their fullest extent. Instead, you must make trade-offs between these properties. The CAP theorem describes three common scenarios based on the trade-offs:

CP Systems (Consistency and Partition Tolerance)

In CP systems, consistency is prioritized, even at the expense of availability. When a network partition occurs, the system might choose to become temporarily unavailable rather than risk delivering inconsistent data. This is typical in traditional relational databases.

CA Systems (Consistency and Availability)

CA systems prioritize both consistency and availability. They do not tolerate network partitions and always aim for strong consistency. These systems are suitable for applications where consistency is critical and network partitions are rare or negligible.

AP Systems (Availability and Partition Tolerance)

AP systems prioritize availability and partition tolerance over strict consistency. These systems may provide eventual consistency, where data may take some time to propagate and become consistent across all nodes. They are often used in distributed, fault-tolerant databases like NoSQL databases.

Real-World Examples

• Amazon DynamoDB : DynamoDB is an example of an AP system. It prioritizes availability and partition tolerance, ensuring that read and write operations are always possible, even in the presence of network partitions. It provides eventual consistency.
• Google Cloud Spanner : Spanner is an example of a CP system. It offers strong consistency and horizontal scalability, but it sacrifices availability in case of network partitions.
• Cassandra : Cassandra is an example of an AP system. It focuses on high availability and partition tolerance, and it provides tunable consistency levels, allowing users to choose the level of consistency they require.

Understanding the CAP theorem is crucial when designing and choosing distributed systems. It helps architects and engineers make informed decisions about the trade-offs between consistency, availability, and partition tolerance based on the specific needs of their applications and the challenges they face in their distributed environments.