Replication (computing)

Replication in computing refers to maintain 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 refers to maintain 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. == Terminology == Replication in computing can refer to: Data replication, where the same data is stored on multiple storage devices Computation replication, where the same computing task is executed many times. Computational tasks may be: Replicated in space, where tasks are executed on separate devices Replicated in time, where tasks are executed repeatedly on a single device 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: Active replication, which is performed by processing the same request at every replica Passive replication, which involves processing every request on a single replica and transferring the result to the other replicas 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. Data replication and computation replication both require processes to handle incoming events. Processes for data replication are passive and operate only to maintain the stored data, reply to read requests and apply updates. Computation replication is usually performed to provide fault-tolerance, and take over an operation if one component fails. In both cases, the underlying needs are to ensure that the replicas see the same events in equivalent orders, so that they stay in consistent states and any replica can respond to queries. == Database replication == Database replication involves maintaining copies of the same data on multiple machines, typically implemented through three main approaches: single-leader, multi-leader, and leaderless replication. In single-leader (also called primary/replica) replication, one database instance is designated as the leader (primary), which handles all write operations. The leader logs these updates, which then propagate to replica nodes. Each replica acknowledges receipt of updates, enabling subsequent write operations. Replicas primarily serve read requests, though they may serve stale data due to replication lag – the delay in propagating changes from the leader. In multi-master replication (also called multi-leader), updates can be submitted to any database node, which then propagate to other servers. This approach is particularly beneficial in multi-data center deployments, where it enables local write processing while masking inter-data center network latency. However, it introduces substantially increased costs and complexity which may make it impractical in some situations. The most common challenge that exists in multi-master replication is transactional conflict prevention or resolution when concurrent modifications occur on different leader nodes. Most synchronous (or eager) replication solutions perform conflict prevention, while asynchronous (or lazy) solutions have to perform conflict resolution. For instance, if the same 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 re-synchronization. Conflict resolution methods can include techniques like last-write-wins, application-specific logic, or merging concurrent updates. However, replication transparency can not always be achieved. When data is replicated in a database, they will be constrained by CAP theorem or PACELC theorem. In the NoSQL movement, data consistency is usually sacrificed in exchange for other more desired properties, such as availability (A), partition tolerance (P), etc. Various data consistency models have also been developed to serve as Service Level Agreement (SLA) between service providers and the users. There are several techniques for replicating data changes between nodes: Statement-based replication: Write requests (such as SQL statements) are logged and transmitted to replicas for execution. This can be problematic with non-deterministic functions or statements having side effects. Write-ahead log (WAL) shipping: The storage engine's low-level write-ahead log is replicated, ensuring identical data structures across nodes. Logical (row-based) replication: Changes are described at the row level using a dedicated log format, providing greater flexibility and independence from storage engine internals. ==See also== Change data capture Fault-tolerant computer system Log shipping Multi-master replication Optimistic replication Shard (data) State machine replication Virtual synchrony ==References==

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