Efficient Update Data Generation for DBMS Benchmarks

Michael Frank, Meikel Poess, and Tilmann Rabl.

In ICPE '12: Proceedings of the ACM International Conference on Performance Engineering, 2012.

Abstract

It is without doubt that industry standard benchmarks have been proven to be crucial to the innovation and productivity of the computing industry. They are important to the fair and standardized assessment of performance across different vendors, different system versions from the same vendor and across different architectures. Good benchmarks are even meant to drive industry and technology forward. Since at some point, after all reasonable advances have been made using a particular benchmark even good benchmarks become obsolete over time. This is why standard consortia periodically overhall their existing benchmarks or develop new benchmarks. An extremely time and resource consuming task in the creation of new benchmarks is the development of benchmark generators, especially because benchmarks tend to become more and more complex. The parallel data generation framework (PDGF) is a generic data generator that is capable of generating the data for the initial load of arbitrary relational schemas. It was, however, not able to generate data for the actual workload, i.e. transactions, incremental load etc., mainly because it did not understand the notion of updates. Updates are data changes that occur over time, e.g. a customer changes address, switches job, gets married or has children. Many benchmarks, need to reflect these changes during their workloads. In this paper we describe extensions to the first version of PDGF that enables the generation of update data.

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