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.
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.
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.
A practical guide to designing communication and coordination in distributed systems, based on real failure modes and proven principles.
Coordination services provide strong consistency and fault tolerance for leader election, locks, and configuration in distributed systems.