Aggregate-based query processing in a parallel data warehouse server

In the last years data warehousing has emerged as a fundamental database technology providing the basis for online analytical processing (OLAP). In general, analytical queries involve aggregations of large data sets. This results in serious performance problems if ad-hoc queries are to be answered online. One method to avoid performance bottlenecks is to use parallel hardware, i.e. SMP or MPP machines which are able to cope with the data volume. Another optimization approach specific to data warehousing is to preaggregate some of the results in order to avoid scanning the base relations. The prototypical OLAP system CUBESTAR PARALLEL SERVER combines both approaches. In order to achieve high query performance with low hardware costs, we present a technique for the dynamic, i.e. query-behavior and load-dependent, use and management of multidimensional aggregates in a shared-nothing workstation cluster.