Rule- and Cost-Based Optimization of OLAP Workloads on Distributed RDBMS with Column-Oriented Storage Function

Database systems have recently utilized both row-and a column-oriented storage systems, also termed as hybrid storage, as their storage devices for large scale data management. The hybrid storage based database systems should ideally be operated on distributed computing environments for query optimization, however, studies on query optimization of such database systems are not available in literature. Therefore, the selection of the storage type and the accurate estimation of query workload are critical factors for efficient query processing on distributed computing environments. In this paper, we describe a novel storage selection method of a RDBMS with a column-oriented storage function, which is a type of DBMSs with the hybrid storage, on distributed computing environments. Our storage selection method is designed for efficient query processing based on rule-and cost-based optimization in the research field of RDBMS, and it can help to improve query optimization of RDBMSs with the hybrid storage.