What does the CAP theorem state about a distributed data store?

It can provide all three of C, A and P at once if nodes are fast enough

It must always give up consistency

It can provide only one of the three guarantees

Why must a designer of a real distributed system choose between consistency and availability?

Because network partitions will happen, so partition tolerance is a must and the trade-off is between C and A during a partition

Because partitions are rare, so P can be ignored

Because consistency and availability are the same thing

Because ACID forbids availability

Two replicas lose connection. How does an AP system behave compared with a CP system?

AP accepts writes on both sides and reconciles later; CP may stop accepting writes on one side to avoid conflicts

AP stops all writes; CP accepts writes on both sides

Both accept writes on both sides and ignore conflicts

Both shut down completely until the network is repaired

2073 Bhadra · 5 marks

Short note: CAP theorem

Short answer

The CAP theorem limits a distributed data store to two of Consistency, Availability, and Partition Tolerance. Since network partitions are inevitable, systems must trade off: CP (e.g., HBase) preserves consistency, possibly rejecting requests; AP (e.g., Cassandra) remains available but returns potentially stale data (eventual consistency).

Write a short note on CAP theorem.

The CAP theorem (Brewer's theorem) states that a distributed data store can provide at most two of these three guarantees at the same time: Consistency, Availability and Partition tolerance.

Consistency: every read returns the latest write, so all nodes show the same data.
Availability: every request to a working node gets a response (not an error), even if some nodes fail.
Partition tolerance: the system continues to operate when messages between nodes are lost or delayed because of a network split.

In a real distributed system, network partitions will happen, so partition tolerance is a must. The designer must choose, during a partition, between consistency and availability.

   C ---- A        CA: RDBMS (single node)
    \    /         CP: HBase, MongoDB, Redis
      P            AP: Cassandra, CouchDB, Dynamo

Type	Gives up	Behaviour	Examples
CA	P	Works only if network is reliable	MySQL, Oracle
CP	A	May reject requests to keep data correct	HBase, MongoDB
AP	C	Always responds, data may be stale (eventual consistency)	Cassandra, DynamoDB

Example: Two replicas of a shopping database lose connection. A CP system stops accepting writes on one side to avoid conflicting values. An AP system accepts writes on both sides and reconciles later.

The theorem explains why NoSQL systems use BASE (Basically Available, Soft state, Eventually consistent) instead of strict ACID.

✓ Answer checked by Claude Opus 5.5 (high effort)

NoSQL term and CAP theorem · Big Data Technologies, chapter 4

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Short note: CAP theorem

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