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

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 (DBMS) 现在是充满活力的信息技术行业的重要组成部分。尽管进行了数十年的研究和开发工作，但 DBMS 仍未得到很好的理解。令人惊讶的是，人们对非常基本的问题知之甚少，例如优化器多久会为查询选择错误的计划？为代数运算添加物理运算符是否总能提高查询优化的有效性，或者实际可容纳的运算符数量是否有限制？吞吐量和磁盘利用率如何取决于多道程序设计级别？或者什么时候会发生颠簸？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 DBMS 的预测模型。这些模型涉及模式复杂性、有效运算符集和优化器选择的计划的基数估计错误、优化器搜索空间的结构以及预测中多道程序设计级别对吞吐量、磁盘利用率和响应时间的交互作用。殴打。这些模型预测共享通用架构的 DBMS 的重要特征，量化已识别因果因素的相对贡献，并确定该架构的基本限制。这些模型可用于通过工程工作进一步改进 DBMS，这些工作受益于对这一点的基本理解。视角可以提供。此外，这种新颖的研究基础设施通过门户网站向社区和学生开放，鼓励经验概括和实验结果共享的文化：http://www.cs.arizona.edu/projects/soc/索德布/