PyRCA: A Library for Metric-based Root Cause Analysis
Chenghao Liu Wenzhuo Yang Himanshu Mittal Manpreet Singh Doyen Sahoo Steven C. H. Hoi
We introduce PyRCA, an open-source Python machine learning library of Root
Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps). It
provides a holistic framework to uncover the complicated metric causal
dependencies and automatically locate root causes of incidents. It offers a
unified interface for multiple commonly used RCA models, encompassing both
graph construction and scoring tasks. This library aims to provide IT
operations staff, data scientists, and researchers a one-step solution to rapid
model development, model evaluation and deployment to online applications. In
particular, our library includes various causal discovery methods to support
causal graph construction, and multiple types of root cause scoring methods
inspired by Bayesian analysis, graph analysis and causal analysis, etc. Our GUI
dashboard offers practitioners an intuitive point-and-click interface,
empowering them to easily inject expert knowledge through human interaction.
With the ability to visualize causal graphs and the root cause of incidents,
practitioners can quickly gain insights and improve their workflow efficiency.
This technical report introduces PyRCA's architecture and major
functionalities, while also presenting benchmark performance numbers in
comparison to various baseline models. Additionally, we demonstrate PyRCA's
capabilities through several example use cases.