Causal consistency (CC) is an attractive consistency model for geo-replicated data stores because it hits a sweet spot in the ease-of-programming versus performance trade-off. We present a new approach for implementing CC in geo-replicated data stores, which we call Optimistic Causal Consistency (OCC). OCC's main design goal is to maximize data freshness. The optimism in our approach lies in the fact that the updates replicated to a remote data center are made visible immediately, without checking if their causal dependencies have been received. Servers perform the dependency check needed to enforce CC only upon serving a client operation, rather than on receipt of a replicated data item as in existing systems. OCC offers a significant gain in data freshness, which is of crucial importance for various types of applications, such as real-time systems. OCC's potentially blocking behavior makes it vulnerable to network partitions. We therefore propose a recovery mechanism that allows an OCC system to fall back on a pessimistic protocol to continue operating during network partitions. We implement POCC, the first causally consistent geo-replicated multi-master key-value data store designed to maximize data freshness. We show that POCC improves data freshness, while offering comparable or better performance than its pessimistic counterparts.

中文翻译：

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地理复制键值存储的乐观因果一致性

因果一致性 (CC) 是地理复制数据存储的一种有吸引力的一致性模型，因为它在易于编程与性能权衡方面达到了最佳点。我们提出了一种在地理复制数据存储中实施 CC 的新方法，我们称之为乐观因果一致性 (OCC)。OCC 的主要设计目标是最大限度地提高数据新鲜度。我们方法的乐观之处在于，复制到远程数据中心的更新立即可见，而无需检查是否已收到它们的因果依赖关系。服务器仅在为客户端操作提供服务时执行强制 CC 所需的依赖性检查，而不是像在现有系统中那样接收复制的数据项。OCC 显着提高了数据新鲜度，这对于各种类型的应用程序至关重要，比如实时系统。OCC 潜在的阻塞行为使其容易受到网络分区的影响。因此，我们提出了一种恢复机制，允许 OCC 系统回退到悲观协议以在网络分区期间继续运行。我们实施 POCC，这是第一个因果一致的地理复制多主键值数据存储，旨在最大限度地提高数据新鲜度。我们表明 POCC 提高了数据新鲜度，同时提供了与其悲观对应物相当或更好的性能。第一个因果一致的地理复制多主键值数据存储，旨在最大限度地提高数据新鲜度。我们表明 POCC 提高了数据新鲜度，同时提供了与其悲观对应物相当或更好的性能。第一个因果一致的地理复制多主键值数据存储，旨在最大限度地提高数据新鲜度。我们表明 POCC 提高了数据新鲜度，同时提供了与其悲观对应物相当或更好的性能。