tourradar-testcases/CueObserve

Timeseries Anomaly detection and Root Cause Analysis on data in SQL data warehouses and databases

Overview

CueObserve helps you monitor your metrics. Know when, where, and why a metric isn't right.

CueObserve uses timeseries Anomaly detection to find where and when a metric isn't right. It then offers one-click Root Cause analysis so that you know why a metric isn't right.

CueObserve works with data in your SQL data warehouses and databases. It currently supports Snowflake, BigQuery, Redshift, Druid, Postgres, MySQL, SQL Server and ClickHouse.

Getting Started

Install via Docker

wget https://raw.githubusercontent.com/cuebook/CueObserve/latest_release/docker-compose.yml -q -O cueobserve-docker-compose.yml
docker-compose -f cueobserve-docker-compose.yml up -d

Now visit http://localhost:3000 in your browser.

Demo Video

How it works

You write a SQL GROUP BY query, map its columns as dimensions and measures, and save it as a virtual Dataset.

You then define one or more anomaly detection jobs on the dataset.

When an anomaly detection job runs, CueObserve does the following:

1. Executes the SQL GROUP BY query on your data warehouse and stores the result as a Pandas dataframe.
2. Generates one or more timeseries from the dataframe, as defined in your anomaly detection job.
3. Generates a forecast for each timeseries using Prophet.
4. Creates a visual card for each timeseries. Marks the card as an anomaly if the last data point is anomalous.

Features

• Automated SQL to timeseries transformation.
• Run anomaly detection on the aggregate metric or split it by any dimension. Limit the split to significant dimension values.
• Use Prophet or simple mathematical rules to detect anomalies.
• In-built Scheduler. CueObserve uses Celery as the executor and celery-beat as the scheduler.
• Slack alerts when anomalies are detected.
• Monitoring. Slack alert when a job fails. CueObserve maintains detailed logs.

Limitations

• Currently supports Prophet for timeseries forecasting.
• Not being built for real-time anomaly detection on streaming data.

Support

For general help using CueObserve, read the documentation, or go to Github Discussions.

To report a bug or request a feature, open an issue.

Contributing

We'd love contributions to CueObserve. Before you contribute, please first discuss the change you wish to make via an issue or a discussion. Contributors are expected to adhere to our code of conduct.