引言文献是由 Rick Cattell 撰写的论文,论文讨论了可扩展的结构化数据的、非结构化的(包括基于键值对的、基于文档的和面对列的)数据存储方案(注:NOSQL是支撑大数据应用的关键所在。事实上,将 NOSQL 翻译为“非结构化”不甚准确,因为 NOSQL 更为常见的解释是:Not Only SQL(不仅仅是结构化),换句话说,NOSQL 并不是站在结构化 SQL 的对立面,而是既可包括结构化数据,也可包括非结构化数据)。论文信息Scalable SQL and NoSQL Data StoresRick Cattell Originally published in 2024, last revised December 2024摘要ABSTRACTIn this paper , we examine a number of SQL and so— called “NoSQL” data stores designed to scale simple OLTP-style application loads over many servers。Originally motivated by Web 2。0 applications, these systems are designed to scale to thousands or millions of users doing updates as well as reads , in contrast to traditional DBMSs and data warehouses。We contrast the new systems on their data model, consistency mechanisms, storage mechanisms, durability guarantees, availability, query support, and other dimensions. These systems typically sacrifice some of these dimensions, e.g。 database-wide transaction consistency, in order to achieve others, e 。g。 higher availability and scalability.在这篇文献中,我们验证了许多 SQL 和所谓的‘NoSQL’数据存储(它设计于支持简单的 OLTP 风格的应用,能够用于扩展在很多服务器上)它最先由 Web 2。0 应用引起,与传统的数据库管理系统和数据仓库对比,这些系统设计为可扩展到数以千计或数以百万计的用户做更新,同时读取。我们对比了新系统上的数据模型,一致性机制, 存储机制,持久性保证,可用性,支持的查询以及其它属性,这些系统典型的牺牲(为了实现其它属性而去掉)了一些属性。如数据库常有的事务一致性,牺牲了这个是为了其它的属性,如高可用,可扩展.Note: Bibliographic references for systems are not listed, but URLs for more information can be found in the System References table at the end of this paper。...