分布式事务性键值存储中的参数化和运行时可调快照隔离

Hengfeng Wei, Yu Huang, Jian Lu
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引用次数: 1

摘要

为了提高分布式事务性键值存储的性能,已经提出了快照隔离(SI)的几个宽松变体。然而,这些宽松的变体没有提供SI异常严重程度的规范或控制。它们还被设计为在整个系统生命周期中静态使用。为了克服这些缺点,我们提出了参数化和运行时可调快照隔离的思想。我们首先定义了一个新的事务一致性模型,称为放松版本快照隔离(RVSI),它可以正式和定量地指定它可能产生的相对于SI的异常。为此,我们将SI分解为三个“视图属性”,为每个属性引入一个参数来量化三种可能的异常之一:k1-BV (k1-版本有界后向视图),k2-FV (k2-版本有界前向视图)和k3-SV (k3-版本有界快照视图)。然后,我们跨多个数据中心实现一个名为Chameleon的原型分区复制分布式事务性键值存储。在实现RVSI的同时,变色龙允许每个事务在运行时动态调整其一致性级别。实验表明,当应用程序愿意容忍某些异常时,RVSI有助于降低事务中断率。我们还评估了k1-BV、k2-FV和k3-SV在不同场景下对降低事务中断率的个别影响。我们发现它取决于客户端和副本之间的问题延迟,k1和k2在降低事务中止率方面起主要作用。
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Parameterized and Runtime-Tunable Snapshot Isolation in Distributed Transactional Key-Value Stores
Several relaxed variants of Snapshot Isolation (SI) have been proposed for improved performance in distributed transactional key-value stores. These relaxed variants, however, provide no specification or control of the severity of the anomalies with respect to SI. They have also been designed to be used statically throughout the whole system life cycle. To overcome these drawbacks, we propose the idea of parameterized and runtime-tunable snapshot isolation. We first define a new transactional consistency model called Relaxed Version Snapshot Isolation (RVSI), which can formally and quantitatively specify the anomalies it may produce with respect to SI. To this end, we decompose SI into three "view properties", for each of which we introduce a parameter to quantify one of three kinds of possible anomalies: k1-BV (k1-version bounded backward view), k2-FV (k2-version bounded forward view), and k3-SV (k3-version bounded snapshot view). We then implement a prototype partitioned replicated distributed transactional key-value store called Chameleon across multiple data centers. While achieving RVSI, Chameleon allows each transaction to dynamically tune its consistency level at runtime. The experiments show that RVSI helps to reduce the transaction abort rates when applications are willing to tolerate certain anomalies. We also evaluate the individual impacts of k1-BV, k2-FV, and k3-SV on reducing the transaction abort rates in various scenarios. We find that it depends on the issue delays between clients and replicas which of k1 and k2 plays a major role in reducing transaction abort rates.
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