Query optimization in a memory-resident domain relational calculus database system

Query optimization in a memory-resident domain relational calculus database system
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内存驻留域关系演算数据库系统中的查询优化

DOI:
10.1145/77643.77646
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发表时间:
1990
期刊:
ACM Trans. Database Syst.
影响因子:
--
通讯作者:
R. Krishnamurthy
R. Krishnamurthy
中科院分区:
--
文献类型:
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作者:
K. Whang;R. Krishnamurthy

文献摘要

被引文献

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我们提出了优化内存驻留数据库系统中的查询的技术。内存驻留数据库系统中的优化技术与传统磁盘驻留数据库系统中的优化技术有很大不同。在本文中,我们讨论了此类系统中查询优化的以下方面,并提出了具体的解决方案:(1)开发 CPU 密集型成本模型的新方法; (2) 主存查询处理的新优化策略; (3) 对利用数据内存驻留的连接算法和访问结构的新见解; (4)操作系统的调度算法对内存驻留假设的影响。我们提出了一个有趣的结果,即在内存驻留数据库系统中处理查询的主要成本是由谓词的评估引起的。我们使用 IBM Research 正在开发的 Office-by-Example (OBE) 来讨论优化技术。我们还提供了性能测量的结果,这些结果在当前的技术水平下被证明是非常出色的。尽管最近对内存驻留数据库系统进行了研究,但这些系统中的查询优化方面尚未得到很好的研究。我们相信本文揭示了内存驻留数据库系统中的查询优化问题,并提出了实用的解决方案。
We present techniques for optimizing queries in memory-resident database systems. Optimization techniques in memory-resident database systems differ significantly from those in conventional disk-resident database systems. In this paper we address the following aspects of query optimization in such systems and present specific solutions for them: (1) a new approach to developing a CPU-intensive cost model; (2) new optimization strategies for main-memory query processing; (3) new insight into join algorithms and access structures that take advantage of memory residency of data; and (4) the effect of the operating system's scheduling algorithm on the memory-residency assumption. We present an interesting result that a major cost of processing queries in memory-resident database systems is incurred by evaluation of predicates. We discuss optimization techniques using the Office-by-Example (OBE) that has been under development at IBM Research. We also present the results of performance measurements, which prove to be excellent in the current state of the art. Despite recent work on memory-resident database systems, query optimization aspects in these systems have not been well studied. We believe this paper opens the issues of query optimization in memory-resident database systems and presents practical solutions to them.