为线性化和无冲突的每个副本本地读取收集共识

Qing Li, Binjie Zhang, Yong Jiang, Dan Zhao, Yuan Yang, Zhenhui Yuan
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引用次数: 0

摘要

强一致性键值应用程序的最佳读取策略是启用每个副本的本地读取,每个副本都有能力在本地提供读取服务。不幸的是,当前的每个副本本地读取方案被两个问题所困扰。首先,当工作负载倾斜时,某些方案必须违反每个副本的本地读取,从而降低吞吐量。其次,目前大多数方案依赖于租赁或专用硬件来保证线性化,这给部署带来了困难。在本文中,我们提出了一种线性读协议,它解决了当前方案的问题。在lean中,副本节点总是在本地提供读取服务,我们要求客户端验证线性性。为了实现验证,lean设计了一种新颖的读取算法,该算法允许客户端从副本中收集共识提示,并使副本能够轻量级和快速地为验证做出贡献。我们使用一个广泛使用的软件栈来实现lean。我们的3副本评估表明,在高读取工作负载下,lean的吞吐量最多是未复制应用程序吞吐量的2.1倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gleaning the Consensus for Linearizable and Conflict-Free Per-Replica Local Reads
The optimal read strategy for strong consistent key-value applications is to enable the per-replica local reads that each replica has the ability to serve reads locally. Unfortunately, current schemes for the per-replica local reads are perplexed by two issues. First, some schemes have to violate the per-replica local reads when the workload is skewed, degrading the throughput. Second, most of current schemes rely on leases or a specialized hardware to guarantee the linearizability, bringing difficulties to the deployment. In this paper, we proposes Glean, a linearizable read protocol that solves the issues of current schemes. In Glean, replica nodes always serve reads locally and we ask clients to validate the linearizability. To achieve the validation, Glean designs a novel read algorithm that allows the client to glean a consensus hint from replicas and enables replicas to contribute to the validation lightweight and fast. We implement Glean with a widely-used software stack. Our 3-replica evaluation shows that the throughput of Glean is at most 2.1 × to the throughput of an unreplicated application under heavy-read workloads.
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