Eventual consistency

Summary

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. Eventually-consistent services are often classified as providing BASE semantics (basically-available, soft-state, eventual consistency), in contrast to traditional ACID (atomicity, consistency, isolation, durability). In chemistry, a base is the opposite of an acid, which helps in remembering the acronym. According to the same resource, these are t

Official source

https://en.wikipedia.org/wiki/Eventual_consistency

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The weakest failure detector for eventual consistency

Rachid Guerraoui, Pierre Sens

In its classical form, a consistent replicated service requires all replicas to witness the same evolution of the service state. If we consider an asynchronous messagepassing environment in which processes might fail by crashing, and assume that a majority of processes are correct, then the necessary and sufficient information about failures for implementing a general state machine replication scheme ensuring consistency is captured by the Omega failure detector. This paper shows that in such a message-passing environment, Omega is also the weakest failure detector to implement an eventually consistent replicated service, where replicas are expected to agree on the evolution of the service state only after some (a priori unknown) time. In fact, we show that Omega is the weakest to implement eventual consistency in any message-passing environment, i.e., under any assumption on when and where failures might occur. Ensuring (strong) consistency in any environment requires, in addition to Omega, the quorum failure detector S. Our paper thus captures, for the first time, an exact computational difference between building a replicated state machine that ensures consistency and one that only ensures eventual consistency.

2019

The Weakest Failure Detector for Eventual Consistency

Rachid Guerraoui, Pierre Sens

In its classical form, a consistent replicated service requires all replicas to witness the same evolution of the service state. Assuming a message-passing environment with a majority of correct processes, the necessary and sufficient information about failures for implementing a general state machine replication scheme ensuring consistency is captured by the ω failure detector. This paper shows that in such a message-passing environment, ω is also the weakest failure detector to implement an eventually consistent replicated service, where replicas are expected to agree on the evolution of the service state only after some (a priori unknown) time. In fact, we show that ω is the weakest to implement eventual consistency in any message-passing environment, i.e., under any assumption on when and where failures might occur. Ensuring (strong) consistency in any environment requires, in addition to ω, the quorum failure detector Σ. Our paper thus captures, for the first time, an exact computational difference between building a replicated state machine that ensures consistency and one that only ensures eventual consistency. © Copyright 2015 ACM.

ACM Press2015

Rewriting Numeric CSPs for Consistency Algorithms

Claudio Lottaz

1999