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Replication

Replicazione

Replication is the maintenance of identical copies of the same data on multiple machines. It serves two intertwined goals: resilience (if a node dies, the data survives elsewhere) and performance (more copies serve more readers, including geographically closer ones). Together with sharding, which divides data, replication, which multiplies it, is the foundation of distributed databases.

Definition

How it works

In the most common model, primary-replica, writes go to the primary node and propagate to replicas, which serve reads. Propagation can be synchronous (the write is confirmed once all copies have it: safer, slower) or asynchronous (immediate confirmation, with the risk of reading slightly stale data: so-called eventual consistency). The CAP theorem, formulated by Eric Brewer, frames the unavoidable trade-off between consistency and availability when the network partitions.

Applications

In AI, replication operates on several planes: vector indexes are replicated to sustain search traffic, model weights are copied across dozens of inference nodes, and training checkpoints are replicated on redundant storage. Beyond AI it protects bank accounts, medical records and any data that must outlive a dying disk.

History & etymology

The term comes from the Latin replicare, to fold back and hence to repeat. Database replication spread through the 1980s and 90s with commercial distributed systems; a key theoretical reference is Leslie Lamport's work on distributed consensus, whose Paxos algorithm (published 1998) and its successor Raft (2014) are the mechanisms by which replicas agree on a single shared truth.

How it's used in Grace

Grace replicates the value of your skills across multiple supports: your score on the platform, your position in the weekly leagues and the certification with its verifiable URL, available to anyone at any time. In the scenarios on critical data, replication is the concept to bring up when discussing continuity.

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