CAP Theorem

The CAP Theorem is a fundamental concept in distributed systems, including NoSQL databases, which states that a distributed data system can simultaneously guarantee at most two out of the following three properties:

1. Consistency (C): Every read receives the most recent write or an error. This means all nodes see the same data at the same time, providing a single, up-to-date view of the data.
2. Availability (A): Every request receives a response about whether it was successful or failed. This ensures the system remains operational and responsive, even under high loads or some node failures.
3. Partition Tolerance (P): The system continues to function despite arbitrary message loss or network failures that partition the network into isolated segments.

Since network partitions (failures) are inevitable in distributed environments, systems must tolerate partition tolerance (P). The CAP Theorem then presents a trade-off between consistency and availability during such partitions:

• If a system prioritizes Consistency and Partition Tolerance (CP), it may become unavailable during network partitions to ensure data remains consistent (e.g., MongoDB in default mode, Redis).
• If a system prioritizes Availability and Partition Tolerance (AP), it remains available during partitions but may return stale or inconsistent data temporarily (e.g., Cassandra, CouchDB).
• The combination of Consistency and Availability (CA) without partition tolerance is not feasible in distributed NoSQL systems due to inherent network failures.

NoSQL databases typically emphasize horizontal scaling across multiple nodes, making partition tolerance essential. They use the CAP theorem trade-offs to decide whether to favor consistency or availability based on application needs.

To summarize for NoSQL:

• CAP Theorem highlights that NoSQL distributed databases can offer only two of the three guarantees: Consistency, Availability, and Partition Tolerance.
• Partition Tolerance is always essential in NoSQL.
• NoSQL databases are often designed as either CP or AP systems, according to their intended use case.
• For example, MongoDB by default offers CP (Consistency and Partition Tolerance), but can be tuned for more availability at the cost of consistency.

This framework helps inform choices in designing and configuring NoSQL databases to meet specific application demands, balancing between consistent data and system availability under network partitions.

A distributed database cannot guarantee all three CAP properties—Consistency, Availability, and Partition Tolerance—simultaneously because of fundamental trade-offs that arise due to network partitions and latency in distributed systems.

Here's why: