Latest Consistency Articles

Distributed systems often store the same data on multiple nodes to improve availability, fault tolerance, read scalability, and geographic performance . Replication introduces a difficult question: when one copy changes, how quickly must every other copy reflect that update?

Database transactions and locks are both used to protect data correctness, but they solve different problems. Transactions define failure boundaries, while locks define concurrency behavior.

The CAP theorem explains one of the most important trade-offs in distributed system design: when a network partition happens, a system must choose between consistency and availability . This article explains what CAP really means, why partition tolerance is unavoidable in distributed systems, and ho

Consistency in system design describes how fresh and correct data should be when users, services, or replicas read it after a write. Some systems require every read to reflect the latest committed value, while others can tolerate stale data for better latency, availability, or scalability.

Consistency patterns describe how systems behave when multiple users, services, or processes read and write shared data at the same time.