rcabench-platform 0.1.11

An experiment framework for Root Cause Analysis

rcabench-platform

An experiment framework for Root Cause Analysis (RCA), supporting fast development of RCA algorithms and their evaluation on various datasets.

Development Guide

Requirements

Operating System

This project is primarily developed and tested on Ubuntu 24.04 LTS or later versions. Other Linux distributions and macOS environments should be compatible with minimal configuration adjustments.

Windows is not officially supported. While some functionality may work in Windows environments (especially through WSL), we cannot guarantee full compatibility or provide dedicated support.

Toolchain

Toolchain	Version
uv	^0.7.5
just	^1.21.0
Docker Engine	*
Docker Compose	*

IDE

Recommended setup

• Visual Studio Code with the following extensions:
  • Python Extension Pack
  • Pylance
  • Ruff
  • Git Graph
  • Data Wrangler

Git

Commit Message

We follow the Conventional Commits specification.

Branching Strategy

When you are developing a new feature, create a new branch from main and name it according to the following convention:

<your github id>/feat/<feature-name>

This branch prefixed with your github id, is your own working branch. You can force-push to it freely. Do anything you want in this branch.

When you are done with the feature, create a pull request to main and invite other developers to review your code. If the code is approved, it will be merged into main. Then you can start a new branch from main and continue your work.

The main branch is the default branch for this repository. main is protected and should not be used for development. Before merging any changes into main, ensure that the following conditions are met:

• The branch is up to date with main.
• The branch is free of merge conflicts.
• The basic checks passed successfully.
• The changes will not break other developers' workflow.

Workflow

Download source code

git clone [e-mail]:LGU-SE-Internal/rcabench-platform.git
cd rcabench-platform

Run basic checks

just dev

If the basic checks pass, then your python environment is ready for development.

Local development services

docker compose up -d

docker compose down

We have the following localhost services running in the background:

• neo4j: for graph visualization

Link datasets

Mount JuiceFS to your machine:

sudo juicefs mount redis://10.10.10.38:6379/1 /mnt/jfs -d --cache-size=1024

See infra/README.md for more details.

Link the datasets to the project directory:

mkdir -p data
cd data
ln -s /mnt/jfs/rcabench_platform_datasets ./

Commands

Test if the environment is set up correctly by running the following command:

./main.py self test

Notebooks

Edit the SDG Visualization notebook:

./notebooks/sdg.py