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
Distributed Systems Series — Part 4.1: Fault Tolerance & High Availability Why Failure Vocabulary Matters Before Failure Mechanisms Part 4 covers how distributed systems survive failures. But before designing survival mechanisms — redundancy, failure detection, circuit breakers, chaos engineering — engineers must be precise about what kinds of failures they are designing for. A retry … 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.
Paxos vs Raft explained — how both consensus algorithms work, why Paxos is hard to implement, how Raft’s leader election and log replication work step by step, and why Raft dominates production systems like etcd, CockroachDB, and TiKV.
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.