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DBMbench: Microbenchmarking Database Systems in a Small, yet Real World

Tuesday September 16, 2003
3rd floor Intel Research Pittsburgh (417 S. Craig Street)
4:00 pm

Minglong Shao
Carnegie Mellon University

Database benchmarks are widely used to test functionality and evaluate the overall performance of database systems. However, the complexity involved in establishing the experimental environment and in conducting detailed analysis makes conventional benchmarks inappropriate for use in research that evaluates low-level hardware, such as processor microarchitecture and memory hierarchy performance behavior of database systems. It is often necessary to scale down benchmark parameters such as dataset size or query complexity. Scaling down is only valid, however, if the new benchmarks preserve the typical characteristics of the original benchmark for the purpose that they are used. In this talk, I will introduce a systematic scaling framework to scale down conventional benchmarks and present extrapolation rules to predict the processor and memory behavior of full-scale benchmarks. I will also propose the DBMbench consisting of two substantially scaled-down microbenchmarks: MTPC-H and MTPC-C that capture the processor and memory performance behavior of DSS and OLTP workloads.


Minglong Shao is a Ph.D. candidate in the Department of Computer Science at Carnegie Mellon University. She received her B.A. from the Department of Computer Science and Technology, Tsinghua University. Currently, she is working with Professor Anastassia Ailamaki on database performance characterization. Her research focuses on techniques to improve database performance throughout the memory hierarchy.


Department of Electrical and Computer EngineeringCarnegie Mellon UniversitySchool of Computer Science