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

数据库管理系统（dbms）现在是充满活力的信息技术产业的重要组成部分。尽管已经进行了几十年的研究和开发工作，但是人们对dbms的理解并不好。令人惊讶的是，很少有人知道一些非常基本的问题，比如，优化器为查询选择错误计划的频率是多少？为代数操作添加物理操作符是否总能提高查询优化的效率，或者实际可容纳的操作符数量是否有限制？吞吐量和磁盘利用率如何依赖于多道编程级别？或者什么时候发生鞭打？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、有效操作符集和优化器选择的计划上的基数估计错误的作用、优化器搜索空间的结构以及多编程级别对吞吐量、磁盘利用率和响应时间的交互作用。这些模型预测共享公共体系结构的dbms的重要特征，量化已确定的因果因素的相对贡献，并确定该体系结构的基本限制。这些模型可用于通过工程工作进一步改进dbms，这些工程工作受益于此透视图所提供的基本理解。此外，这个新颖的研究基础设施通过门户网站提供给社区和学生，鼓励经验概括和实验结果共享的文化：http://www.cs.arizona.edu/projects/soc/sodb/

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/