Tunable Causal Consistency：规范与实现
Tunable Causal Consistency: Specification and Implementation
摘要：
为了实现高可用性和低延迟，分布式数据存储通常在多个站点（称为副本）地理上复制数据。然而，这引入了数据一致性问题。由于存在网络分区时一致性、可用性和延迟之间的基本权衡，不存在一刀切的一致性模型。为了满足不同应用程序的需求，许多流行的数据存储提供了可调的一致性，允许客户端指定每个操作的一致性级别。在本文中，我们提出了可调因果一致性（TCC）。它允许客户端从众所周知的四个会话保证中为每个操作选择所需的会话保证，即，读你的写，单调的读，单调的写，写跟随着读。具体来说，我们首先提出了一个形式化的规范TCC的扩展（可见，ar）的框架最初提出的Burckhardt等人。然后，我们设计了一个TCC协议，并开发了一个原型分布式键值存储称为TCCSTORE。我们在阿里云上评估TCCSTORE。对于所有工作负载，延迟小于38 ms，吞吐量高达每秒约2800次操作。我们还表明，TCC实现更好的性能比因果一致性，并需要一个可以忽略不计的开销时，与最终的一致性。
英文摘要：
To achieve high availability and low latency, distributed data stores often geographically replicate data at multiple sites called replicas. However, this introduces the data consistency problem. Due to the fundamental tradeoffs among consistency, availability, and latency in the presence of network partition, no a one-size-fits-all consistency model exists. To meet the needs of different applications, many popular data stores provide tunable consistency, allowing clients to specify the consistency level per individual operation. In this paper, we propose tunable causal consistency (TCC). It allows clients to choose the desired session guarantee for each operation, from the well-known four session guarantees, i.e., read your writes, monotonic reads, monotonic writes, and writes follow reads. Specifically, we first propose a formal specification of TCC in an extended (vis,ar) framework originally proposed by Burckhardt et al. Then we design a TCC protocol and develop a prototype distributed key-value store called TCCSTORE. We evaluate TCCSTORE on Aliyun. The latency is less than 38ms for all workloads and the throughput is up to about 2800 operations per second. We also show that TCC achieves better performance than causal consistency and requires a negligible overhead when compared with eventual consistency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
