What is the CAP theorem, and how does it apply to document databases?

The CAP theorem, also known as Brewer’s theorem, explains the trade-offs in distributed systems when handling network partitions. It states that a distributed database can provide at most two of three guarantees: Consistency (all nodes see the same data at the same time), Availability (every request receives a response), and Partition Tolerance (the system continues operating despite network failures). In practice, partition tolerance is unavoidable in distributed systems, so the real choice is between consistency and availability during a partition. For example, if a network split occurs, a system prioritizing consistency (CP) might reject requests to avoid serving stale data, while one favoring availability (AP) might return potentially outdated data but stay responsive.

Document databases, like MongoDB or CouchDB, implement CAP trade-offs based on their design. MongoDB, for instance, is often classified as a CP system. It prioritizes consistency and partition tolerance by default: during a network partition, a MongoDB primary node will stop accepting writes if it loses contact with replica set members, ensuring data remains consistent but sacrificing availability until the partition resolves. In contrast, CouchDB leans toward AP. It allows writes to any node during a partition, accepting temporary inconsistencies (e.g., conflicting document versions) to maintain availability. These conflicts must later be resolved manually or via application logic. Another example is Amazon DynamoDB, which offers tunable consistency: developers can choose between strong consistency (CP-like) for critical operations or eventual consistency (AP-like) for lower latency.

When working with document databases, developers must align their choice with application requirements. For systems like financial ledgers where consistency is critical, a CP-oriented database ensures data integrity but risks downtime during partitions. For high-traffic applications like social media feeds, an AP database prioritizes uptime, accepting eventual consistency. Modern document databases often provide tools to mitigate these trade-offs. For example, MongoDB’s retryable writes handle transient failures, while CouchDB’s conflict resolution mechanisms help reconcile divergent data. Understanding CAP helps developers anticipate how their database will behave under stress and design fallback strategies, such as caching or idempotent operations, to balance user experience and data reliability.