Core jargon for software architecture, delivery, and engineering culture.
The system keeps responding to requests even when part of it has failed — one of the three properties in the CAP theorem.
A network of geographically distributed servers that cache and serve static assets from wherever is closest to the user.
A pattern that detects when a downstream service is failing and stops sending it requests temporarily, instead of letting every caller time out and pile up.
Every read returns the most recent write, so all clients see the same data at the same time — one of the three properties in the CAP theorem.
Updates spread to all copies of the data over time, so reads may briefly return stale data before every copy catches up.
Automatically switching operation to a standby system the moment the primary one fails, with minimal disruption.
When a cluster of machines needs one to be in charge, they run a protocol to automatically agree on which one — and pick a new one if it goes down.
A component that distributes incoming requests across multiple server instances so no single one gets overwhelmed.
A durable buffer that holds messages between a producer and a consumer, decoupling them so the producer doesn't block waiting for the consumer to be ready.
The system keeps operating even when a network failure splits it into isolated groups that can no longer communicate.
Keeping multiple copies of the same data on different machines, for redundancy and to spread read traffic.
The mechanism by which services in a distributed system find each other's current network addresses, which change as instances start and stop.
Splitting a dataset across multiple machines — often by a key like user ID — so each machine holds only a slice of the total data.