Scalable and quantitative contention generation for performance evaluation on OLTP databases  

作　　者：Chunxi ZHANG Yuming LI Rong ZHANG Weining QIAN Aoying ZHOU 

机构地区：[1]School of Data Science and Engineering,East China Normal University,Shanghai 200062,China

出　　处：《Frontiers of Computer Science》2023年第2期15-31,共17页中国计算机科学前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.62072179);ECNUOceanBase Joint Lab of Distributed Database System and 2020 the Key Software Adaptation and Verification Project(Database).

摘　　要：Massive scale of transactions with critical requirements become popular for emerging businesses,especially in E-commerce.One of the most representative applications is the promotional event running on Alibaba's platform on some special dates,widely expected by global customers.Although we have achieved significant progress in improving the scalability of transactional database systems(OLTP),the presence of contention operations in workloads is still one of the fundamental obstacles to performance improving.The reason is that the overhead of managing conflict transactions with concurrency control mechanisms is proportional to the amount of contentions.As a consequence,generating contented workloads is urgent to evaluate performance of modern OLTP database systems.Though we have kinds of standard benchmarks which provide some ways in simulating contentions,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.We conduct a comprehensive set of experiments on popular opensourced DBMSs compared with the latest contention simulation method to demonstrate the effectiveness of our generation work.

关 键 词：high contention OLTP database performance evaluation database benchmarking 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]