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.
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.
Why is consensus hard in distributed systems? Learn the Two Generals Problem, FLP impossibility, crash-stop vs Byzantine failures, and why consensus must stay off the data path — with production examples from etcd, Raft, and Google Spanner.
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.