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DevOps Apprenticeship: Project Exercise

System Requirements

The project uses poetry for Python to create an isolated environment and manage package dependencies. To prepare your system, ensure you have an official distribution of Python version 3.7+ and install Poetry using one of the following commands (as instructed by the poetry documentation):

Poetry installation (Bash)

curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py | python -

Poetry installation (PowerShell)

(Invoke-WebRequest -Uri https://raw.githubusercontent.com/python-poetry/poetry/master/install-poetry.py -UseBasicParsing).Content | python -

Dependencies

The project uses a virtual environment to isolate package dependencies. To create the virtual environment and install required packages, run the following from your preferred shell:

$ poetry install

You'll also need to clone a new .env file from the .env.template to store local configuration options. This is a one-time operation on first setup:

$ cp .env.template .env  # (first time only)

The .env file is used by flask to set environment variables when running flask run. This enables things like development mode (which also enables features like hot reloading when you make a file change). There's also a SECRET_KEY variable which is used to encrypt the flask session cookie.

Running the App

Once the all dependencies have been installed, start the Flask app in development mode within the Poetry environment by running:

$ poetry run flask run

You should see output similar to the following:

 * Serving Flask app "app" (lazy loading)
 * Environment: development
 * Debug mode: on
 * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
 * Restarting with fsevents reloader
 * Debugger is active!
 * Debugger PIN: 226-556-590

Now visit http://localhost:5000/ in your web browser to view the app.

Testing

There are unit tests to check correct data is returned from the viewmodel properties as well as tests for database reading and writing. and an integration test to check the index.html data is populated using mockmongo data

To run pytests from command line poetry run pytest -rA This will run and provide output from all tests and their names.

to run individual tests use the poetry run pytest -k "keyword" command where keyword is a term within the test title which you wish to run. ie poetry run pytest -k "open"

Pytest tests can also be run from VSCode testing tab in the side bar if configured with correct extensions.

Automated Deployment

There are files required to support automated Ansible deployment located in repository /deployment directory. These files will need to be copied to the ec2-user home directory on the ansible controller node and executed from this directory using the following command

ansible-playbook ansible-playbook.yml -i ansible-inventory

Required variables can be established following process detailed in Trello environmental variables section

Docker configuration

Build dev and production images using dockerfile included in repository by running following command from the repo directory (ensure that the .env file is created and populated with relevant environmental variables.)

Development:

docker build --target development --tag todo-app:dev .

Production(gunicorn):

docker build --target production --tag todo-app:prod .

Run built images using the following commands:

Development: Bound to localhost port 5000 (localhost:5000 in browser)

docker run  --name todo-app-dev -d  --mount type=bind,source="$(pwd)"/todo_app,target=/app/todo_app -p 5000:5000 --env-file .env todo-app:dev

Live: Bound to localhost port 8000 (localhost:8000 in browser)

docker run  --name todo-app-prod -d -p 8000:8000 --env-file .env todo-app:prod

Terraform Deployment

As part of CI/CD pipeline Azure infrastructure is automatically provisioned via the supplied terraform configuration files.

Authorisation and Access

OAuth configuration is in place to restrict access to only users authenticated. Attempts to access the App will be redirected to the GitHib login page.

There are currently two levels of access restrictions

  • Reader
  • Writer

By default authenticated users will be provided Reader (read only rights). Administrator rights are currently hard coded to a single user account.

Testing / demo of access rights

To elevate rights for testing/demo purposes add your UserID to the administrators variable list in app.py.
You used ID can be established by browsing the /PrivilegeError endpoint (once you are logged in, else no ID will be returned)

Logging

Python logging is configured to both console and Loggly. To utilise external Loggly logging for own deployment generate and update the LOGGLY_TOKEN env variable with token from your own account.

LOG_LEVEL is set as an environmental Variable and can be edited to the below values to allow more granular log capture. (Default is INFO)

  • DEBUG
  • INFO
  • WARNING
  • ERROR