III: Small: Using Empirical Generalization to Develop Predictive Models of DBMS Processing

III：小：使用经验概括来开发 DBMS 处理的预测模型

基本信息

批准号：
1016205
负责人：
Richard Snodgrass
金额：
$ 47.41万
依托单位：
University of Arizona
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-01 至 2015-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1016205&HistoricalAwards=false
关键词：
III Small Using Empirical Generalization

III Small Using Empirical Generalization

项目摘要

Database management systems (DBMSes) are now an essential component of a vibrant information technology industry. Despite research and development efforts over several decades, DBMSes are not well understood. There is surprisingly little known about quite basic questions such as, how often does the optimizer pick the wrong plan for a query? does adding a physical operator for an algebraic operation always improve the effectiveness of query optimization, or is there a limit to the number of operators that can be practically accommodated? how do throughput and disk utilization depend on multiprogramming level? or when does thrashing occur? This project extends an existing laboratory information management system to develop and thoroughly test predictive models of centralized DBMSes. These models concern the role of schema complexity, effective operator set, and cardinality estimation errors on the plan chosen by the optimizer, the structure of the optimizer search space, and the interaction of multiprogramming level on throughput, disk utilization, and response time in predicting thrashing. These models predict important characteristics of DBMSes that share a common architecture, quantify the relative contributions of identified causal factors, and determine fundamental limits of that architecture.These models can be used to further improve DBMSes through engineering efforts that benefit from the fundamental understanding that this perspective can provide. Additionally, this novel research infrastructure, being made available to the community and to students via a web portal, encourages a culture of empirical generalization and the sharing of experimental results: http://www.cs.arizona.edu/projects/soc/sodb/

数据库管理系统（DBMSE）现在是充满活力的信息技术行业的重要组成部分。尽管经过数十年的研究和发展工作，但DBMSES尚未得到充分的理解。关于基本问题，诸如Optimizer多久为查询选择错误的计划的频率众所周知，鲜为人知？为代数操作添加物理操作员是否总是提高查询优化的有效性，或者对实际上可以容纳的操作员数量有限制？吞吐量和磁盘利用如何取决于多编程水平？还是什么时候发生的？该项目扩展了现有的实验室信息管理系统，以开发和彻底测试集中式DBMSES的预测模型。这些模型涉及架构复杂性，有效运算符集和基数估计误差的作用，对优化器选择的计划，优化器搜索空间的结构以及多编程水平在吞吐量，磁盘利用率上的相互作用以及预测触及的响应时间。这些模型预测了共享共同体系结构的DBMS的重要特征，量化了确定的因果因素的相对贡献，并确定了该体系结构的基本限制。这些模型可用于通过从基本理解中可以提供的基本理解来进一步改善DBMSES。此外，这种新颖的研究基础设施是通过网络门户提供给社区和学生的，它鼓励经验概括的文化和实验结果的共享：http：//www.cs.arizona.edu/projects/soc/soc/sodb/