Change Point Detection for MongoDB Time Series Performance Regression
Mark Leznik, Md Shahriar Iqbal, Igor A. Trubin, Arne Lochner, Pooyan Jamshidi, André Bauer · 2022
Commits to the MongoDB software repository trigger a collection of automatically run tests. Here, the identification of commits responsible for performance regressions is paramount. Previously, the process relied on manual inspection of time series graphs to identify significant changes, later replaced with a threshold-based detection system. However, neither system was sufficient for finding changes in performance in a timely manner. This work describes our recent implementation of a change point detection system built upon time series features, a voting system, the Perfomalist approach, and XGBoost. The algorithm produces a list of change points representing significant changes from a given history of performance results. We are able to automatically detect change points and achieve an 83% accuracy, all while reducing the human effort in the process.