PBS在工作:用一致性指标推进数据管理

Peter D. Bailis, S. Venkataraman, M. Franklin, J. Hellerstein, I. Stoica
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引用次数: 11

摘要

最近的大量工作已经提出了用于量化分布式数据存储的数据一致性属性的分析和经验技术。在本演示中,我们将开始探索它们支持的广泛的新数据库功能,包括动态查询调优、一致性sla、监视和管理。我们的演示将展示应用程序程序员和数据库管理员如何利用这些特性。我们描述了三种主要的应用场景,并给出了支持它们的系统架构。我们还描述了我们将概率有界过期(probabilisbounded Staleness, PBS)预测集成到Cassandra(一个流行的NoSQL存储)中的经验,并概述了一个演示平台,该平台将允许SIGMOD与会者体验实时一致性指标的重要性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PBS at work: advancing data management with consistency metrics
A large body of recent work has proposed analytical and empirical techniques for quantifying the data consistency properties of distributed data stores. In this demonstration, we begin to explore the wide range of new database functionality they enable, including dynamic query tuning, consistency SLAs, monitoring, and administration. Our demonstration will exhibit how both application programmers and database administrators can leverage these features. We describe three major application scenarios and present a system architecture for supporting them. We also describe our experience in integrating Probabilistically Bounded Staleness (PBS) predictions into Cassandra, a popular NoSQL store and sketch a demo platform that will allow SIGMOD attendees to experience the importance and applicability of real-time consistency metrics.
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