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Distributed System
CAP theorem

CAP theorem

What is CAP?

  • Consistency (C): All nodes see the same data at the same time. After a write, all reads return the latest value.
  • Availability (A): Every request receives a response (success or failure), without guaranteeing the data is up-to-date.
  • Partition Tolerance (P): The system continues operating despite network failures between nodes.

Real-World Trade-offs

In distributed systems, network partitions are inevitable, making P a requirement rather than a choice.

The real decision is between CP and AP: when a network partition occurs, should the system prioritize Consistency (reject requests to maintain data accuracy) or Availability (continue serving requests despite potential inconsistency)?

Use Case

KEP-3157 RV semantic

  • RV="" (CP approach): Performs quorum read from etcd to ensure strong consistency, but may block during network partitions.
  • RV=“0” (AP approach): Serves from apiserver’s watch cache for immediate response and high availability, tolerating eventual consistency.