# Overview

[![CueObserve Logo](https://2208403543-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mdq_hGRVa-mnRCfA6lY%2Fuploads%2Fgit-blob-ea62511e4184fd4e0d1483303cb3f7e1cd69f267%2FcueObserve.png?alt=media)](https://cueobserve.cuebook.ai)

[![](https://api.codeclimate.com/v1/badges/a70e071b59d5dbc38846/maintainability)](https://codeclimate.com/github/cuebook/CueObserve/maintainability) [![](https://api.codeclimate.com/v1/badges/a70e071b59d5dbc38846/test_coverage)](https://codeclimate.com/github/cuebook/CueObserve/test_coverage) [![Test Coverage](https://github.com/cuebook/cueobserve/actions/workflows/pr_checks.yml/badge.svg) ](https://github.com/cuebook/cueobserve/actions/workflows/pr_checks.yml)[![License](https://img.shields.io/github/license/cuebook/cueobserve)](https://github.com/cuebook/cueobserve/blob/main/LICENSE.md)

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.

![CueObserve Anomaly](https://2208403543-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mdq_hGRVa-mnRCfA6lY%2Fuploads%2Fgit-blob-7eb84674abdb58b3c05950218361a6731940c13a%2FOverview_Anomaly.png?alt=media) ![CueObserve RCA](https://2208403543-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mdq_hGRVa-mnRCfA6lY%2Fuploads%2Fgit-blob-b3afff0e4cbda6467e5270816dac2c8fc889fc62%2FOverview_RCA.png?alt=media)

### Getting Started

Install via docker-compose

```
mkdir -p ~/cuebook
wget https://raw.githubusercontent.com/cuebook/CueObserve/latest_release/docker-compose-prod.yml -q -O ~/cuebook/docker-compose-prod.yml
wget https://raw.githubusercontent.com/cuebook/CueObserve/latest_release/.env -q -O ~/cuebook/.env
cd ~/cuebook
```

```
docker-compose -f docker-compose-prod.yml --env-file .env up -d
```

Now visit [localhost:3000](http://localhost:3000) in your browser. 

### Demo Video

[![Watch CueObserve video](http://img.youtube.com/vi/VZvgNa65GQU/hqdefault.jpg)](http://www.youtube.com/watch?feature=player_embedded\&v=VZvgNa65GQU)

### How it works

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

![Dataset SQL](https://2208403543-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mdq_hGRVa-mnRCfA6lY%2Fuploads%2Fgit-blob-430f4c563836d1ccf8866fb811a49d21753635cc%2FDataset_SQL_cropped.png?alt=media)

![Dataset Schema Map](https://2208403543-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mdq_hGRVa-mnRCfA6lY%2Fuploads%2Fgit-blob-4398ba8a4763876c856c8afb0caa72ba4b0c0e3a%2FDataset_Mapping_cropped.png?alt=media)

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

![Anomaly Definition](https://2208403543-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-Mdq_hGRVa-mnRCfA6lY%2Fuploads%2Fgit-blob-1bfd214b7a5c6afcba8e509c5dafa16b15c68cee%2FAnomalyDefinitions.png?alt=media)

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](https://github.com/facebook/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](https://cueobserve.cuebook.ai/), or go to [Github Discussions](https://github.com/cuebook/cueobserve/discussions).

To report a bug or request a feature, open an [issue](https://github.com/cuebook/cueobserve/issues).

### Contributing

We'd love contributions to CueObserve. Before you contribute, please first discuss the change you wish to make via an [issue](https://github.com/cuebook/cueobserve/issues) or a [discussion](https://github.com/cuebook/cueobserve/discussions). Contributors are expected to adhere to our [code of conduct](https://github.com/cuebook/cueobserve/blob/main/CODE_OF_CONDUCT.md).