BIPeC: A Combined Change-Point Analyzer to Identify Performance Regressions in Large-scale Database Systems

Zhan Lyu, Thomas Bach, Yong Li, Nguyen Minh Le, Lars Hoemke
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引用次数: 0

Abstract

Performance testing in large-scale database systems like SAP HANA is a crucial yet labor-intensive task, involving extensive manual analysis of thousands of measurements, such as CPU time and elapsed time. Manual maintenance of these metrics is time-consuming and susceptible to human error, making early detection of performance regressions challenging. We address these issues by proposing an automated approach to detect performance regressions in such measurements. Our approach integrates Bayesian inference with the Pruned Exact Linear Time (PELT) algorithm, enhancing the detection of change points and performance regressions with high precision and efficiency compared to previous approaches. Our method minimizes false negatives and ensures SAP HANA's system's reliability and performance quality. The proposed solution can accelerate testing and contribute to more sustainable performance management practices in large-scale data management environments.
BIPeC:用于识别大规模数据库系统性能回归的变化点组合分析器
SAP HANA 等大型数据库系统的性能测试是一项重要而又劳动密集型的任务,需要对 CPU 时间和已用时间等数以千计的测量数据进行大量手动分析。对这些指标的手动维护既耗时又容易出现人为错误,因此及早发现性能退步具有挑战性。为了解决这些问题,我们提出了一种自动方法来检测这些测量指标的性能退步情况。我们的方法将贝叶斯推理与剪枝精确线性时间(PELT)算法结合在一起,与以前的方法相比,具有更高的精度和效率,从而提高了对变化点和性能回归的检测能力。我们的方法最大程度地减少了误判,确保了 SAPHANA 系统的可靠性和性能质量。所提出的解决方案可以加快测试速度,并有助于在大规模数据管理环境中采用更可持续的性能管理实践。
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