If you plan to work on CueObserve code and make changes, this documentation will give you a high level overview of the components used and how to modify them.


CueObserve has multi-service architecture, with services as mentioned:

  1. Frontend single-page application written on ReactJS. It's code can be found in ui folder and runs on http://localhost:3000/.

  2. API is based on Django (python framework) & uses REST API. It is the main service, responsible for connections, authentication and anomaly.

  3. Alerts micro-service, currently responsible for sending alerting/notifications only to slack. It's code is in alerts-api folder and runs on localhost:8100.

  4. Celery to execute the tasks asynchronously. Tasks like anomaly detection are handled by Celery.

  5. Celery beat scheduler to trigger the scheduled tasks.

  6. Redis to handle the task queue of Celery.

Getting code & starting development servers

Get the code by cloning our open source github repo

git clone
cd CueObserve
docker-compose -f docker-compose-dev.yml --env-file up --build 

docker-compose's build command will pull several components and install them on local, so this will take a few minutes to complete.

Backend Development

The code for the backend is in /api directory. As mentioned in the overview it is based on Django framework.

Configure environment variables

Configure environment variables as you need for the backend server :

export ENVIRONMENT=dev

export POSTGRES_DB_HOST="localhost"
export POSTGRES_DB_USERNAME="postgres"
export POSTGRES_DB_PASSWORD="postgres"
export POSTGRES_DB_SCHEMA="cue_observe"
export POSTGRES_DB_PORT=5432


export `=False 

Change the values based on your running PostgreSQL instance. If you do not wish to use PostgreSQL as your database for development, comment lines 4-8 and CueObserve will create a SQLite database file at the location api/db/db.sqlite3.

The backend server can be accessed on http://localhost:8000/.

Celery Development

CueObserve uses Celery for executing asynchronous tasks like anomaly detection. There are three components needed to run an asynchronous task, i.e. Redis, Celery and Celery Beat. Redis is used as the message queue by Celery, so before starting Celery services, Redis server should be running. Celery Beat is used as the scheduler and is responsible to trigger the scheduled tasks. Celery workers are used to execute the tasks.


At the moment, we have test cases only for the backend service, test cases for UI are in our roadmap.

Backend for API and services is tested using PyTest. To run test cases exec into cueo-backend and run command


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