Modelling test data for performance evaluation of large parallel database machines

Abstract

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.

Consistent database sampling as a database prototyping approach

“…The data values are strictly randomly generated using several probability distributions. The approach presented in [10] is also concerned with database performance evaluation. It shows that generic database benchmarking tests prove unsatisfactory for high-performance databases, because their underlying schemes are too simplistic and the data volumes being considered are too small § .…”

“…It shows that generic database benchmarking tests prove unsatisfactory for high-performance databases, because their underlying schemes are too simplistic and the data volumes being considered are too small § . Bates et al [10] paper proposed an alternative, more realistic, benchmark model suitable for large parallel databases. It describes a toolkit used to generate database prototypes based on more realistic requirements, in terms of workload, semantic information, and data values.…”

“…This new version increases the data volumes according to technological advances. However, the business model still enforces a significantly simpler set of integrity constraints than that used in [10], so the claim of excessive simplicity made by Bates et al can still be considered to hold.…”

A formal framework for database sampling

“…The data values are strictly randomly generated using several probability distributions. The approach presented in [4] is also concerned with database performance evaluation. It shows that generic database benchmarking tests prove unsatisfactory for high-performance databases because their underlying schemes are too simplistic and the data volumes being considered too small 1 .…”

“…It shows that generic database benchmarking tests prove unsatisfactory for high-performance databases because their underlying schemes are too simplistic and the data volumes being considered too small 1 . Bates [4] paper proposes an alternative, more realistic, benchmark model suitable for large parallel databases, as well as a toolkit used to generate database prototypes based on more real requirements, in terms of workload, semantic information, and data values.…”