2020年的分布式数据管理原则？
Principles of Distributed Data Management in 2020?
摘要：
随着高速网络、快速商用硬件和Web的出现，分布式数据源已经变得无处不在。第三版的Özsu-Valduriez教科书《分布式数据库系统原理》[10]反映了分布式数据管理和分布式数据库系统的发展。在这个新版本中，分布式数据管理的基本原则仍然可以基于早期版本的三个维度：数据源的分布性，异构性和自治性。回顾过去，对基本原则和通用技术的关注不仅有助于理解和教授材料，而且还有助于实现无限数量的变化。这些通用技术的主要应用显然是分布式和并行DBMS版本。如今，为了支持重要的数据密集型应用（例如，社交网络、网络数据分析、科学应用等）的需求，新的分布式数据管理技术和系统（例如MapReduce、Hadoop、SciDB、Peanut、Pig Latin等）正在出现，并受到研究界的广泛关注。虽然它们在特定应用程序的一致性/灵活性/性能权衡方面做得很好，但它们似乎是临时的，可能会损害数据互操作性。我讨论的关键问题是：新兴解决方案背后的基本原则是什么？有没有什么通用的架构模型来解释这些原则？我们是否需要新的基础来看待数据分布？
英文摘要：
With the advents of high-speed networks, fast commodity hardware, and the web, distributed data sources have become ubiquitous. The third edition of the Özsu-Valduriez textbook Principles of Distributed Database Systems [10] reflects the evolution of distributed data management and distributed database systems. In this new edition, the fundamental principles of distributed data management could be still presented based on the three dimensions of earlier editions: distribution, heterogeneity and autonomy of the data sources. In retrospect, the focus on fundamental principles and generic techniques has been useful not only to understand and teach the material, but also to enable an infinite number of variations. The primary application of these generic techniques has been obviously for distributed and parallel DBMS versions. Today, to support the requirements of important data-intensive applications (e.g. social networks, web data analytics, scientific applications, etc.), new distributed data management techniques and systems (e.g. MapReduce, Hadoop, SciDB, Peanut, Pig latin, etc.) are emerging and receiving much attention from the research community. Although they do well in terms of consistency/flexibility/performance trade-offs for specific applications, they seem to be ad-hoc and might hurt data interoperability. The key questions I discuss are: What are the fundamental principles behind the emerging solutions? Is there any generic architectural model, to explain those principles? Do we need new foundations to look at data distribution?
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。
