Have you ever wondered why some online services can show inconsistent data when you switch between devices? Or why certain systems seem to slow down or become unavailable during heavy traffic? These behaviors are not just random quirks—they're a direct result of the trade-offs distributed systems must make, as outlined by the CAP theorem.

The CAP Theorem, also known as Brewer’s Theorem, is a fundamental concept in distributed systems that describes the trade-offs between three key properties: Consistency, Availability, and Partition Tolerance. According to the CAP Theorem, a distributed system can only guarantee two of the three properties at the same time, but not all three.

The Three Properties of CAP:

1. Consistency (C):

   • Every read from the database must return the most recent write or an error, means whenever data is written to one node, it must instantly replicated to all other node in the system before the write is deemed as successful.
   • In a consistent system, all nodes in a distributed system have the same data at any given time.
   • Example: Imagine updating your profile picture on a social media platform. In a consistent system, everyone who views your profile will immediately see the updated picture.

2. Availability (A):

   • The system remains available to respond to requests, even if some of the nodes in the system fail.
   • In an available system, every request (read or write) will receive a response, even if the data might be slightly out of date.
   • Example: If one of the servers storing your profile picture goes down, other servers will still handle requests, even if they show the old picture.

3. Partition Tolerance (P):

   • The system continues to function even if communication between parts of the system is lost (i.e., network partitions).
   • In a partition-tolerant system, even if some parts of the system are not connected to each other due to network issues, the system can still operate.
   • Example: If a data center loses connection to another data center, users connected to each data center can still interact with the system independently.

CAP Theorem: The Trade-off

The CAP theorem states that in the presence of a network partition, a distributed system must choose between consistency and availability because it's impossible to guarantee both at the same time. Let's explore the combinations:

1. Consistency + Partition Tolerance (CP):

   • The system prioritizes consistency but sacrifices availability during network partitions.
   • Example: Traditional databases like HBase ensure consistency across nodes but may become unavailable during a network partition to avoid serving outdated or inconsistent data.

2. Availability + Partition Tolerance (AP):

   • The system remains available even in the presence of network partitions, but it may return stale (inconsistent) data.
   • Example: Systems like Cassandra or DynamoDB prioritize availability. Even if some parts of the system are partitioned or disconnected, the system will still process requests, though some data might be slightly out of sync.

3. Consistency + Availability (CA):

   • In theory, this combination is possible in systems without network partitions, like single-node databases.
   • Example: A traditional relational database running on a single machine can provide both consistency and availability. However, in distributed systems where network partitions can happen, this is not practical.

CAP Theorem in Real Life

In real-world distributed systems, designers often make trade-offs based on the needs of the application. Here’s how different systems implement the CAP theorem:

• CP Systems (Consistency + Partition Tolerance):

  • HBase, MongoDB (strong consistency mode), Zookeeper: These systems prioritize consistency and can handle network partitions, but they might sacrifice availability during failures.
  • Use Case: Financial systems, where data accuracy is crucial, and it's better for the system to be temporarily unavailable than return incorrect data.

• AP Systems (Availability + Partition Tolerance):

  • Cassandra, DynamoDB, CouchDB: These systems prioritize availability and partition tolerance, allowing the system to continue running, but they might return stale or inconsistent data.
  • Use Case: Social media platforms or e-commerce websites, where temporary inconsistencies (like seeing an outdated post or product availability) are acceptable, but the system must always be online.

• CA Systems (Consistency + Availability):

  • Systems that run on a single server or in environments without network partitioning can guarantee both consistency and availability.
  • Use Case: Small-scale applications or systems with low traffic that don’t require a distributed architecture.

The common misunderstanding is thinking that you always have to abandon one of the three properties, but this is only true during a network partition. Outside of a partition, systems can often guarantee all three properties to a high degree (though absolute guarantees might vary depending on system design).

Conclusion

In summary, the CAP theorem offers a fundamental insight into the design of distributed systems, reminding us that perfect consistency, availability, and partition tolerance cannot all be guaranteed simultaneously when network partitions occur. However, outside of such failures, it is possible to balance both consistency and availability without compromise. By understanding CAP's real implications, engineers can better assess the needs of their applications and choose appropriate trade-offs.

I hope this blog helped you understand the CAP theorem better. Thanks for reading.