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Replication (computing)
Replication in computing refers to maintaining multiple copies of data, processes, or resources to ensure consistency across redundant components. This fundamental technique spans databases, file systems, and distributed systems, serving to improve availability, fault-tolerance, accessibility, and performance. Through replication, systems can continue operating when components fail (failover), serve requests from geographically distributed locations, and balance load across multiple machines. The challenge lies in maintaining consistency between replicas while managing the fundamental tradeoffs between data consistency, system availability, and network partition tolerance – constraints known as the CAP theorem.
Replication in computing can refer to:
Replication in space or in time is often linked to scheduling algorithms.
Access to a replicated entity is typically uniform with access to a single non-replicated entity. The replication itself should be transparent to an external user. In a failure scenario, a failover of replicas should be hidden as much as possible with respect to quality of service.
Computer scientists further describe replication as being either:
When one leader replica is designated via leader election to process all the requests, the system is using a primary-backup or primary-replica scheme, which is predominant in high-availability clusters. In comparison, if any replica can process a request and distribute a new state, the system is using a multi-primary or multi-master scheme. In the latter case, some form of distributed concurrency control must be used, such as a distributed lock manager.
Load balancing differs from task replication, since it distributes a load of different computations across machines, and allows a single computation to be dropped in case of failure. Load balancing, however, sometimes uses data replication (especially multi-master replication) internally, to distribute its data among machines.
Backup differs from replication in that the saved copy of data remains unchanged for a long period of time. Replicas, on the other hand, undergo frequent updates and quickly lose any historical state. Replication is one of the oldest and most important topics in the overall area of distributed systems.
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Replication (computing)
Replication in computing refers to maintaining multiple copies of data, processes, or resources to ensure consistency across redundant components. This fundamental technique spans databases, file systems, and distributed systems, serving to improve availability, fault-tolerance, accessibility, and performance. Through replication, systems can continue operating when components fail (failover), serve requests from geographically distributed locations, and balance load across multiple machines. The challenge lies in maintaining consistency between replicas while managing the fundamental tradeoffs between data consistency, system availability, and network partition tolerance – constraints known as the CAP theorem.
Replication in computing can refer to:
Replication in space or in time is often linked to scheduling algorithms.
Access to a replicated entity is typically uniform with access to a single non-replicated entity. The replication itself should be transparent to an external user. In a failure scenario, a failover of replicas should be hidden as much as possible with respect to quality of service.
Computer scientists further describe replication as being either:
When one leader replica is designated via leader election to process all the requests, the system is using a primary-backup or primary-replica scheme, which is predominant in high-availability clusters. In comparison, if any replica can process a request and distribute a new state, the system is using a multi-primary or multi-master scheme. In the latter case, some form of distributed concurrency control must be used, such as a distributed lock manager.
Load balancing differs from task replication, since it distributes a load of different computations across machines, and allows a single computation to be dropped in case of failure. Load balancing, however, sometimes uses data replication (especially multi-master replication) internally, to distribute its data among machines.
Backup differs from replication in that the saved copy of data remains unchanged for a long period of time. Replicas, on the other hand, undergo frequent updates and quickly lose any historical state. Replication is one of the oldest and most important topics in the overall area of distributed systems.