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Replica Divergence in Data-Centric Consistency Models

机译:数据以数据为中心的稠度模型分歧

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Eventual consistency is demanded nowadays in highly scalable and available geo-replicated services. According to the CAP theorem, when network partitions may arise a distributed service should choose between being strongly consistent or being highly available. Since scalable services must be available, relaxed consistency is the regular choice. Eventual consistency is not a regular data-centric consistency model, but only a state convergence property to be added to a relaxed consistency model. This paper discusses which data-centric consistency models are not implicitly convergent and, because of this, provide an adequate basis for building eventually consistent services.
机译:现在在高度可扩展和可用的地理复制服务中需要最终的一致性。根据帽定理,当网络分区可能出现分布式服务时,应该选择强烈一致或高度可用。由于必须可用的可扩展服务,因此宽松的一致性是常规选择。最终的一致性不是常规的数据中心一致性模型,而是仅将状态融合属性添加到放松的一致性模型中。本文讨论了哪些以数据为中心的一致性模型并不隐含地收敛,因此为此提供了适当的建设最终一致的服务。

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