Advanced Software Architecture #CAP-theorem #PACELC #consistency #distributed-systems

CAP Theorem Language

Replication lag: the delay between a write on the primary and its application on a secondary
Read preference: MongoDB configuration controlling which node the client reads from ( primary , primaryPreferred , secondary , nearest )
Write concern: the acknowledgement level required before a write is confirmed to the client ( w:1 = primary only, w:majority = majority of replica set)
Stale read: a read that returns data not yet reflecting the latest committed write

5 exercises — practise the vocabulary of distributed systems consistency: why partition tolerance is not optional, AP vs. CP system behaviour, the PACELC model, linearizability vs. eventual consistency, and responding to CAP challenges in design interviews.

CAP theorem quick reference

Partition Tolerance (P): cannot be opted out of in any multi-node distributed system — partitions will occur
The real choice: CP (prefer consistency, may reject requests during partition) or AP (prefer availability, may return stale data)
Strong consistency / linearizability: every read returns the most recently completed write — from any node
Eventual consistency: all replicas converge to the same value eventually, given no new writes
PACELC: extends CAP to model latency vs. consistency even when there is no partition
CA systems: not valid in distributed systems — only achievable on a single node

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