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PyRCA: A Python Machine Learning Library for Root Cause Analysis
| Date | Stars |
|---|---|
| 2026-07-31 | 563 |
| 2026-08-05 | 565 |
| 2026-08-06 | 565 |
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# PyRCA: A Python library for Root Cause Analysis <div align="center"> <a href="#"> <img src="https://img.shields.io/badge/Python-3.7, 3.8, 3.9-blue"> </a> <a href="https://pypi.python.org/pypi/sfr-pyrca"> <img alt="PyPI" src="https://img.shields.io/pypi/v/sfr-pyrca.svg"/> </a> <a href="https://opensource.salesforce.com/PyRCA/"> <img alt="Documentation" src="https://github.com/salesforce/PyRCA/actions/workflows/docs.yml/badge.svg"/> </a> <a href="https://pepy.tech/project/sfr-pyrca"> <img alt="Downloads" src="https://static.pepy.tech/badge/sfr-pyrca"> </a> <a href="https://arxiv.org/abs/2306.11417"> <img alt="DOI" src="https://zenodo.org/badge/DOI/10.48550/ARXIV.2306.11417.svg"/> </a> </div> ## Table of Contents 1. [Introduction](#introduction) 2. [Installation](#installation) 3. [Getting Started](#getting-started) 4. [Documentation](https://opensource.salesforce.com/PyRCA/) 5. [Tutorial](https://github.com/salesforce/PyRCA/tree/main/examples) 6. [Example](#application-example) 7. [Benchmarks](#benchmarks) 8. [How to Contribute](#how-to-contribute) ## Introduction The adoption of microservices architectures is growing at a rapid pace, making multi-service applications the standard paradigm in real-world IT applications. Typically, a multi-service application consists of hundreds of interacting services, making it increasingly challenging to detect service failures and identify their root causes. Root cause analysis (RCA) methods typically rely on KPI metrics, traces, or logs monitored on these services to determine the root causes when a system failure is detected. Such methods can aid engineers and SREs in the troubleshooting process. PyRCA is a Python machine-learning library designed to facilitate root cause analysis by offering various state-of-the-art RCA algorithms and an end-to-end pipeline for building RCA solutions. At present, PyRCA primarily focuses on metric-based RCA, including two types of algorithms: (1) identifying anomalous metrics in parallel with the observed anomaly through metric data analysis, such as ε-diagnosis, and (2) identifying root causes based on a topology/causal graph representing the causal relationships between the observed metrics, such as Bayesian inference and Random Walk. PyRCA also provides a convenient tool for building causal graphs from the observed time series data and domain knowledge, enabling users to develop graph-based solutions quickly. Furthermore, PyRCA offers a benchmark for evaluating various RCA methods, which is valuable for industry and academic research. The following list shows the supported RCA methods in our library: 1. [ε-Diagnosis](https://dl.acm.org/doi/10.1145/3308558.3313653) 2. Bayesian Inference-based RCA (BI) 3. Random Walk-based RCA (RW) 4. [Root Cause Discovery method (RCD)](https://openreview.net/pdf?id=weoLjoYFvXY) 5. [Hypothesis Testing-based RCA (HT)](https://dl.acm.org/doi/10.1145/3534678.3539041) We will continue improving this library to make it more comprehensive in the future. In the future, PyRCA will support trace and log-based RCA methods as well. ## Installation You can install ``pyrca`` from PyPI by calling ``pip install sfr-pyrca``. You may install from source by cloning the PyRCA repo, navigating to the root directory, and calling ``pip install .``, or ``pip install -e .`` to install in editable mode. You may install additional dependencies: - **For plotting & visualization**: Calling ``pip install sfr-pyrca[plot]``, or ``pip install .[plot]`` from the root directory of the repo. - **Install all the dependencies**: Calling ``pip install sfr-pyrca[all]``, or ``pip install .[all]`` from the root directory of the repo. ## Getting Started PyRCA provides a unified interface for training RCA models and finding root causes. To apply a certain RCA method, you only need to specify: - **The selected RCA method**: e.g., ``BayesianNetwork``, ``EpsilonDiagnosis``. - **The method configuration**:
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b06b786965396c7a, llm:Description: 'PyRCA: A Python Machine Learning Library for Root Cause Analysis' (repository description).
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