Website • Docs • Blog • LinkedIn • Community Slack
Open source analytics engine to detect & diagnose the root-cause of outliers in
high-dimensional business & system metrics
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.
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.
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
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)
Actionable alerts with self-learning thresholds. Configurations to setup alert frequency & reporting to combat alert fatigue.
- Channels: Email, Slack, Webhooks (coming soon)
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)
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.
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.
-
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!
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specification. Contributions of any kind welcome!
Chaos Genius is licensed under the MIT license.