Data updating replication software
executed on separate devices, or it could be replicated in time, if it is executed repeatedly on a single device.Replication in space or in time is often linked to scheduling algorithms The access to a replicated entity is typically uniform with access to a single, non-replicated entity.
Whether one replicates data or computation, the objective is to have some group of processes that handle incoming events.On the other side, if any replica processes a request and then distributes a new state, then this is a multi-primary scheme (called multi-master in the database field).In the multi-primary scheme, some form of distributed concurrency control must be used, such as distributed lock manager.Backup differs from replication in that it saves a copy of data unchanged for a long period of time.Replicas, on the other hand, undergo frequent updates and quickly lose any historical state.Usually, the scale up goes with two dimensions, horizontal and vertical: horizontal scale-up has more data replicas, vertical scale-up has data replicas located further away in distance.
Problems raised by horizontal scale-up can be alleviated by a multi-layer multi-view access protocol.
The storage industry narrows the definitions, so mirroring is a local (short-distance) operation.
A replication is extendable across a computer network, so the disks can be located in physically distant locations, and the master-slave database replication model is usually applied.
For instance, if a record is changed on two nodes simultaneously, an eager replication system would detect the conflict before confirming the commit and abort one of the transactions.
A lazy replication system would allow both transactions to commit and run a conflict resolution during resynchronization.
If we replicate data, these processes are passive and operate only to maintain the stored data, reply to read requests, and apply updates.