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.
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.
Leader-based, multi-leader, and leaderless replication explained — with synchronous vs asynchronous replication, replication lag, quorum configuration, and real production examples from PostgreSQL, Kafka, Cassandra, and Google Spanner.
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.
Why is replication necessary in distributed systems? Learn the five motivations — availability, fault tolerance, durability, performance, and geographic distribution — and why replication converts hardware failures into coordination problems that consistency and consensus must solve.