CausalSpartan:使用混合逻辑时钟的分布式数据存储的因果一致性

Mohammad Roohitavaf, M. Demirbas, S. Kulkarni
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引用次数: 32

摘要

因果一致性是一种中间一致性模型,即使在存在网络分区的情况下,也可以与高可用性和高性能需求一起实现。在分区数据存储的上下文中,已经证明使用时钟进行隐式依赖跟踪比通过发送依赖检查消息进行显式依赖跟踪更有效。现有的基于时钟的解决方案依赖于紧密同步的单调心理时钟。这些要求使得当前的协议容易受到时钟异常的影响。在本文中,我们提出了一种新的基于时钟的算法,CausalSpartan,它使用混合逻辑时钟(hlc)代替物理时钟。我们表明,使用hlc,在没有任何开销的情况下,我们使系统对物理时钟异常具有鲁棒性。这种改进在查询放大上下文中更为显著,其中单个查询会导致多个GET/PUT操作。我们还表明,与现有的基于时钟的方法相比,CausalSpartan减少了给定数据项的可见性延迟。反过来,这减少了协作应用程序的完成时间,在协作应用程序中,访问两个不同副本的两个客户机编辑数据存储的相同项。与以前的协议一样,CausalSpartan假设给定的客户机不访问多个副本。我们表明,在存在网络分区的情况下,如果要提供因果一致性以及对本地更新的即时可用性,这个假设(在其他几部作品中提出)是必不可少的。
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CausalSpartan: Causal Consistency for Distributed Data Stores Using Hybrid Logical Clocks
Causal consistency is an intermediate consistency model that can be achieved together with high availability and high-performance requirements even in presence of network partitions. In the context of partitioned data stores, it has been shown that implicit dependency tracking using clocks is more efficient than explicit dependency tracking by sending dependency check messages. Existing clock-based solutions depend on monotonic psychical clocks that are closely synchronized. These requirements make current protocols vulnerable to clock anomalies. In this paper, we propose a new clock-based algorithm, CausalSpartan, that instead of physical clocks, utilizes Hybrid Logical Clocks (HLCs). We show that using HLCs, without any overhead, we make the system robust on physical clock anomalies. This improvement is more significant in the context of query amplification, where a single query results in multiple GET/PUT operations. We also show that CausalSpartan decreases the visibility latency for a given data item comparing to existing clock-based approaches. In turn, this reduces the completion time of collaborative applications where two clients accessing two different replicas edit same items of the data store. Like previous protocols, CausalSpartan assumes that a given client does not access more than one replica. We show that in presence of network partitions, this assumption (made in several other works) is essential if one were to provide causal consistency as well as immediate availability to local updates.
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