Eventual consistency is a consistency model used in distributed computing to achieve high availability that informally guarantees that, if no new updates are made to a given data item, eventually all accesses to that item will return the last updated value. Eventual consistency, also called optimistic replication, is widely deployed in distributed systems, and has origins in early mobile computing projects. A system that has achieved eventual consistency is often said to have converged, or achieved replica convergence. Eventual consistency is a weak guarantee – most stronger models, like linearizability are trivially eventually consistent, but a system that is merely eventually consistent does not usually fulfill these stronger constraints. Eventually-consistent services are often classified as providing BASE semantics, in contrast to traditional ACID guarantees. In chemistry BASE is opposite to ACID, which helps remembering the acronym. According to the same resource, these are the rough definitions of each term in BASE:
asically vailable: basic reading and writingoperations are available as much as possible, but without any kind of consistency guarantees
oft state: without consistency guarantees, after some amount of time, we only have some probability of knowing the state, since it may not yet have converged
ventually consistent: If the system is functioning and we wait long enough after any given set of inputs, we will eventually be able to know what the state of the database is, and so any further reads will be consistent with our expectations
Eventual consistency is sometimes criticized as increasing the complexity of distributed software applications. This is partly because eventual consistency is purely a liveness guarantee and does not make safety guarantees: an eventually consistent system can return any value before it converges.
Conflict resolution
In order to ensure replica convergence, a system must reconcile differences between multiple copies of distributed data. This consists of two parts:
exchanging versions or updates of data between servers ; and
choosing an appropriate final state when concurrent updates have occurred, called reconciliation.
The most appropriate approach to reconciliation depends on the application. A widespread approach is "last writer wins". Another is to invoke a user-specified conflicthandler. Timestamps and vector clocks are often used to detect concurrency between updates. Some people use "first writer wins" in situations where "last writer wins" is unacceptable. Reconciliation of concurrent writes must occur sometime before the next read, and can be scheduled at different instants:
Read repair: The correction is done when a read finds an inconsistency. This slows down the read operation.
Write repair: The correction takes place during a write operation, if an inconsistency has been found, slowing down the write operation.
Asynchronous repair: The correction is not part of a read or write operation.
Strong eventual consistency
Whereas eventual consistency is only a liveness guarantee, strong eventual consistency adds the safety guarantee that any two nodes that have received the same set of updates will be in the same state. If, furthermore, the system is monotonic, the application will never suffer rollbacks. Conflict-free replicated data types are a common approach to ensuring SEC.
