Query Optimization over Parallel Relational Data Warehouses in Distributed Environments by Simultaneous Fragmentation and Allocation

摘要

Parallel database technology has already shown its efficiency in supporting high-performance Online Analytical Processing (OLAP) applications. This scenario implies achieving query optimization over relational Data Warehouses (RDW) on top of which typical OLAP functionalities, such as roll-up, drill-down and aggregate query answering, can be implemented. As a result, it follows the emerging need for a comprehensive methodology able to support the design of RDW over parallel and distributed environments in all the phases, including data partitioning, fragment allocation, and data replication. Existing design approaches have an important limitation: fragmentation and allocation phases are performed in an isolated manner. In order to overcome this limitation, in this paper we propose a new methodology for designing parallel RDW over distributed environments, for query optimization purposes. The methodology is illustrated on database clusters, as a noticeable case of distributed environments. Contrary to state-of-the-art approaches where allocation is performed after fragmentation, in our approach we propose allocating fragments just during the partitioning phase. Also, a naive replication algorithm that takes into account the heterogeneous characteristics of our reference architecture is proposed.