Distributed Systems Series — Part 4.2: Fault Tolerance & High Availability Two Goals That Sound the Same and Are Not Fault tolerance and high availability are the two most frequently conflated concepts in distributed systems engineering. Engineers use them interchangeably in architecture discussions, design documents, and system reviews. This conflation is not just imprecise — … Read more
Engineering guidelines for replication, consistency, and consensus in distributed systems — with a complete design review checklist covering failure design, consistency model selection, replication configuration, consensus placement, performance, and observability.
Performance trade-offs in distributed replication and consensus — write latency, tail latency, write vs read scalability, consensus throughput limits, batching and pipelining, geographic distribution costs, and backpressure design.
How quorums work in distributed systems — the W + R > N rule explained with worked examples, strict vs sloppy quorums, read repair and practical configuration guidance for Cassandra, DynamoDB and Riak.
CAP is not a design choice you make once — it is a constraint that surfaces when the network fails. This post explains CAP correctly, debunks common myths, introduces PACELC, and gives engineers a practical framework for applying CAP thinking per operation.
Consistency models are contracts between a system and its clients. This post explains the full spectrum from linearisability to eventual consistency — what each guarantees, what it costs, and when to use it.