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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Quantitative Contention Generation for Performance Evaluation on OLTP Databases
verfasst von : Chunxi Zhang, Rong Zhang, Weining Qian, Ke Shu, Aoying Zhou
Erschienen in: Web and Big Data
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Abstract
Although we have achieved significant progress in improving the scalability of transactional database systems (OLTP), the presence of contention operations in workloads is still the fundamental limitation in improving throughput. The reason is that the overhead of managing conflict transactions with concurrency control mechanism is proportional to the amount of contentions. As a consequence, contention workload generation is urgent to evaluate performance of modern OLTP database systems. Though we have kinds of standard benchmarks which provide some ways in simulating resource contention, e.g. skew distribution control of transactions, they can not control the generation of contention quantitatively; even worse, the simulation effectiveness of these methods is affected by the scale of data. So in this paper we design a scalable quantitative contention generation method with fine contention granularity control, which is expected to generate resource contention specified by contention ratio and contention intensity.