Understanding the CAP Theorem
The CAP Theorem, also known as Brewer's Theorem, is a fundamental principle in distributed data systems. It states that it is impossible for a distributed data store to simultaneously provide more than two out of the following three guarantees:
C: Consistency
Consistency means that all nodes in the distributed system see the same data at the same time. When data is written to one node, all subsequent reads from any other node will return that same written data. This ensures data uniformity across the cluster.
A: Availability
Availability means that the system remains operational and responsive, even if one or more nodes fail. Every request received by a non-failing node in the system must result in a response. It does not guarantee that the response contains the most recent write, but it ensures the system is always up to serve requests.
P: Partition Tolerance
Partition tolerance means that the system continues to operate despite network partitions that may cause messages to be lost or delayed between nodes. In a distributed system, network partitions are inevitable, so partition tolerance is usually a mandatory requirement.
The Trade-offs
Since partition tolerance (P) is generally a must-have for distributed systems (as network failures are a reality), the CAP theorem essentially means that system designers must choose between strong consistency (C) and high availability (A) when a network partition occurs.
- CP (Consistency and Partition Tolerance): These systems sacrifice availability during a partition. If communication between nodes is lost, some nodes might become unavailable to ensure that data consistency is maintained across the nodes that are still communicating.
- AP (Availability and Partition Tolerance): These systems sacrifice consistency during a partition. All nodes remain available, but they might return stale or conflicting data until the partition is resolved and data is reconciled. This is often referred to as eventual consistency.
- CA (Consistency and Availability): This model can only exist in systems where network partitions are impossible, such as a single-node database. For distributed systems, CA is not a practical choice.
Most NoSQL databases are designed to be distributed and therefore must be partition tolerant. The choice between prioritizing consistency or availability depends heavily on the application's requirements. For instance, financial systems often prioritize consistency, while a social media feed might prioritize availability. For further insights into system design, especially in distributed environments, Understanding Microservices Architecture provides relevant perspectives.
We will see how different Popular NoSQL Databases make these trade-offs in practice.