Terminology and Models of Replication
– Replication in computing can refer to data replication or computation replication.
– Data replication involves storing the same data on multiple storage devices.
– Computation replication involves executing the same computing task multiple times.
– Computational tasks can be replicated in space or in time.
– Replication in space refers to executing tasks on separate devices.
– Replication in time refers to executing tasks 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.
– Three widely cited models for data replication are transactional replication, state machine replication, and virtual synchrony.
– Transactional replication is used for replicating transactional data, such as a database.
– State machine replication assumes that the replicated process is a deterministic finite automaton and that atomic broadcast of every event is possible.
– Virtual synchrony involves a group of processes that cooperate to replicate in-memory data or coordinate actions.
– State machine replication is usually implemented using the Paxos algorithm.
– Virtual synchrony defines a distributed entity called a process group.
– Database replication can be used on many database management systems (DBMS).
– Multi-master replication allows updates to be submitted to any database node and ripple through to other servers.
Replication in Distributed Systems
– Replication transparency is achieved when data is replicated between database servers and users cannot tell or know which server they are using.
– Replication becomes more complex when it scales up horizontally and vertically.
– Problems raised by horizontal scale-up can be alleviated by a multi-layer, multi-view access protocol.
– Replication in disk storage aims to prevent damage from failures or disasters.
– Replication is one of the oldest and most important topics in distributed systems.
– Replication ensures that replicas see the same events in equivalent orders, maintaining consistent states.
– Replication transparency may not always be achieved due to constraints imposed by the CAP theorem or PACELC theorem.
– Various data consistency models have been developed to serve as Service Level Agreements (SLA) between service providers and users.
– Latency determines the distance between sites or the type of replication used.
– Write operations can be handled asynchronously or synchronously.
– Synchronous replication guarantees zero data loss but decreases overall performance.
– Asynchronous replication increases performance but may result in data loss.
– Semi-synchronous replication offers better performance but lacks durability in case of local storage failure.
– Replication is used in distributed fault-tolerant file systems.
– Some commercial synchronous replication systems continue operating locally when the remote replica fails.
– Wide-area network (WAN) optimization techniques can address latency limitations.
– Replication is performed at the logical level rather than the storage block level.
– Different software-based methods are used for file-based replication.
– Synchronous and asynchronous modes are available for file-level replication.
– File-level replication allows for informed decisions based on file location and type.
– Only changed data is replicated, reducing bandwidth usage.
– Capture with a kernel driver involves intercepting filesystem functions to capture file operations.
– Captured operations are transmitted to another machine for replication.
– Synchronous mode waits for replication acknowledgment, while asynchronous mode does not.
– File-level replication allows for more granular data transmission.
– Batch replication involves comparing and synchronizing source and destination file systems.
– Rsync is a notable implementation of batch replication.
Performance and Optimization
– Measurement of achieved performance levels of web applications.
– Data replication strategies with performance objectives.
– Dangers of replication and a solution.
– Chain replication for high throughput and availability.
– Object storage on CRAQ for read-mostly workloads.
– WANdisco’s active replication scheme.
– Spread Toolkit supporting virtual synchrony model.
– C-Ensemble and Quicksilver as alternatives to Spread Toolkit.
– Modern multi-primary replication protocols optimizing for failure-free operation.
– ITTIA DB SQL™ Users Guide on replication conflict resolution.
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
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