Interview Answer:
CAP Theorem is a foundational concept in distributed databases stating that a system cannot simultaneously guarantee Consistency, Availability, and Partition Tolerance. Partition tolerance is non-negotiable in distributed systems because node or network failures are inevitable. Therefore, designers must choose between prioritizing consistency or availability during network partitions.
CP Systems (Consistency + Partition Tolerance) ensure strong consistency. If a partition occurs, the system may reject requests to preserve correctness. Examples include HBase and MongoDB (in some configurations). These systems are ideal when data accuracy is more important than uptime.
AP Systems (Availability + Partition Tolerance) prioritize serving requests even if nodes disagree temporarily. They allow eventual consistency where replicas synchronize in the background. Systems like Cassandra, DynamoDB, and Riak fall in this category and are suitable for high-traffic, globally distributed applications.
CAP Theorem influences how databases are architected, replicated, and partitioned. Modern systems often allow configurable consistency levels so developers can balance availability and correctness per operation. While CAP simplifies distributed thinking, real-world systems also consider latency, durability, and throughput, making database design a multidimensional challenge.
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