Consistency patterns define how distributed systems synchronize data across replicas to ensure clients have a reliable view of the data.
In distributed systems with replicated data (for fault tolerance or performance), different consistency strategies balance availability, latency, and correctness. These patterns are central to understanding trade-offs in system design, especially under the CAP theorem.
Use consistency patterns when your system replicates data and you need to decide how quickly and accurately updates should reflect across all nodes.
You need to know
Weak Consistency – No guarantees that a read will return the latest write. It's fast and available but may return stale data. Useful for VoIP, video chat, and realtime multiplayer games.
Eventual Consistency – Writes will propagate to all replicas eventually. Reads may lag but will converge. Great for high availability and partition tolerance (e.g., DNS, social feeds).
Strong Consistency – All reads after a write will return the updated value. Requires coordination and usually impacts availability or latency (e.g., RDBMS, file systems).
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