ROS-backend

Backend for Resource Optimization Service

The Red Hat Insights resource optimization service enables RHEL customers to assess and monitor their public cloud usage and optimization. The service exposes workload metrics for CPU, memory, and disk-usage and compares them to resource limits recommended by the public cloud provider. Currently ROS only provides suggestions for AWS RHEL instances. To enable ROS, customers need to perform a few prerequisite steps on targeted systems via Ansible playbook.

Underneath, ROS uses Performance Co-Pilot (PCP) to monitor and report workload metrics.

How it works

DB Schema

Getting Started

This project uses poetry to manage the development and production environments.

Once you have poetry installed, do the following:

The latest version is supported on Python 3.11, install it and then switch to 3.11 version:

poetry env use python3.11

There are some package dependencies, install those:

dnf install tar gzip gcc python3.11-devel libpq-devel

Install the required dependencies:

poetry install

Afterwards you can activate the virtual environment by running:

poetry shell

A list of configurable environment variables is present inside .env.example file.

Dependencies

The application depends on several parts of the insights platform. These dependencies are provided by the docker-compose.yml file in the scripts directory.

To run the dependencies, just run following command:

cd scripts && docker-compose up insights-inventory-mq db-ros insights-engine

Running the ROS application

Within docker

To run the full application ( With ros components within docker)

docker-compose up ros-processor ros-api

On host machine

In order to properly run the application from the host machine, you may optionally modify your /etc/hosts file for convenience. Check the README.md file in scripts directory for details and important networking considerations.

Initialize the database

Run the following commands to execute the db migration scripts.

export FLASK_APP=manage.py
flask db upgrade
flask seed

Running the processor locally

The processor component connects to kafka, and listens on topics for system archive uploads/ system deletion messages.

python -m ros.processor.main

Running the web api locally

The web api component provides a REST api view of the app database.

python -m ros.api.main

Running the Tests

It is possible to run the tests using pytest:

poetry install
poetry run pytest --cov=ros tests

Available API endpoints

Resource Optimization REST API documentation can be found at /api/ros. It is accessible at raw OpenAPI definition here. On a local instance it can be accessed on http://localhost:8000/api/ros/v1/openapi.json.

For local development setup, remember to use the x-rh-identity header encoded from account number and org_id, the one used while running make insights-upload-data command.