/chaos_genius

Chaos Genius is an open-source business observability platform. Chaos Genius enables teams to monitor thousands of KPIs, across multiple data sources, and perform automated root cause analysis for any deviation in the KPIs.

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Open source analytics engine to detect & diagnose the root-cause of outliers in
high-dimensional business & system metrics


✨ What is Chaos Genius?

Chaos Genius is an open source analytics engine to help humans detect & diagnose the root-cause of outliers and change in high-dimensional datasets.

Using Chaos Genius, users can segment large datasets by key performance metrics (e.g. Daily Active Users, Cloud Costs, Failure Rates) and setup drill-downs for explanation of deviation or anomalies across important dimensions (e.g., countryID, DeviceID, ProductID, DayofWeek).

The key use-cases for Chaos Genius include monitoring & alerting for various business, system and data quality metrics.

Chaos Genius comes with a UI that offers simple point-and-click functionality for various tasks like adding data sources, defining the key performance metrics with dimensions and setting up alerts for anamalous behavior and deviations.

See more on our website.

⚙️ Quick Start

git clone https://github.com/chaos-genius/chaos_genius

cd chaos_genius

docker-compose up

Visit http://localhost:8080

Follow this Quick Start guide or read our Documentation for more details.

💫 Key Features

🔸 Automated DeepDrills

Identify the key drivers of change in defined metrics (e.g. Sales) across a large number of high cardinality dimensions (e.g. CountryID, ProductID, BrandID, Device_type).

  • Techniques: Statistical Filtering, A* like path based search to deal with combinatorial explosion

DD

🔸 Anomaly Detection

Modular anomaly detection toolkit for monitoring high-dimensional time series with ability to select from different models. Tackle variations caused by seasonality, trends and holidays in the time series data.

  • Models: Prophet, EWMA, EWSTD, Neural Prophet, ARIMA (coming soon)

Anomaly

🔸 Smart Alerts

Actionable alerts with self-learning thresholds. Configurations to setup alert frequency & reporting to combat alert fatigue.

  • Channels: Email, Slack, Webhooks (coming soon)

:octocat: Community

For any help, discussions and suggestions feel free to reach out to the Chaos Genius team and the community here:

  • GitHub (report bugs, contribute, follow roadmap)

  • Slack (discuss with the community and Chaos Genius team)

  • Book Office Hours (set up time with the Chaos Genius team for any questions or help with setup)

  • Blog (follow us on latest trends on Data, Machine Learning, Open Source and more)

🚦 Roadmap

Our goal is to make Chaos Genius production ready for all organisations irrespective of their data infrasturcture, data sources and scale requirements. With that in mind we have created a roadmap for Chaos Genius. If you see something missing or wish to make suggestions, please drop us a line on our Community Slack or raise an issue.

🌱 Contributing

Want to contribute? Get started with:

  • Show us some love - Give us a 🌟!

  • Submit an issue.

  • Share a part of the documentation that you find difficult to follow.

  • Translate our Readme.

  • Create a pull request. Here's a list of issues to start with. Please review our contribution guidelines before opening a pull request. Thank you for contributing!

❤️ Contributors

Thanks goes to these wonderful people (emoji key):


pshrimal21

📆 📖 🤔 🎨

Harshit Surana

💻 🔣 🔬 🐛

Manas Solanki

💻 👀 🔧 🐛

Kartikay Bagla

💻 🚧 🔬

Varun P

💻 🚧 🔬

Keshav Pradeep

💻 🔣 📖

Daj Katal

🔌 📖

Amatullah Sethjiwala

💻 🔣 ⚠️

juzarbhori

💻 🎨

Amogh Dhar Diwan

💻 🔣 🐛

Samyak Sarnayak

💻 📦 🐛

Aayush Naik

💻 🐛 📦

Kshitij Agarwal

💻 🔧 🐛

Bhargav S. Kumar

💻 📦 🐛

moghankumar06

💻 🎨

Santhoshkumar1023

💻 🎨

Mansi-Chauhan27

🔌

davidhayter-karhoo

🐛

Marijn van Aerle

🐛

gxu-kangaroo

🐛

RamneekKaur983

💻

arvind-27

🔣

Josh Taylor

🐛

ChartistDev

💻 🎨 🐛 👀

This project follows the all-contributors specification. Contributions of any kind welcome!

📜 License

Chaos Genius is licensed under the MIT license.