Modelling test data for performance evaluation of large parallel database machines

Parallel servers offer improved processing power for relational database systems and provide system scalability. In order to support the users of these systems, new ways of assessing the performance of such machines are required. If these assessments are to show how the machines perform under commercial workloads they need to be based upon models which have a real commercial basis. This paper shows how a realistic model of a financial application has been developed and how a set of tools has been created which allow the implementation of the model on any commercial database system. The tools allow the generation of large quantities of test data in a manner which renders it amenable to subsequent independent analysis. The test data thus generated forms the basis for the performance tuning of parallel database machines