DevOps Apprenticeship: Project Exercise

If you are using GitPod for the project exercise (i.e. you cannot use your local machine) then you'll want to launch a VM using the following link. Note this VM comes pre-setup with Python & Poetry pre-installed.

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.

Also need to populate the following Trello related variables: TRELLO_KEY TRELLO_SECRET TRELLO_TOKEN TRELLO_USERNAME TRELLO_BOARDID

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.

Running the tests:

This will run both the unit and integration tests

poetry run pytest

Docker

To build and run a development docker container, run the following commands in a docker prompt:

docker build --target development --tag todo-app:dev .
docker run -d -p 7000:8000 --env-file .env.dev todo-app:dev

The container should then be running on http://localhost:7000/

To build and run a production docker container, run the following commands in a docker prompt:

docker build --target production --tag todo-app:prod .
docker run -d -p 7001:8000 --env-file .env.prod todo-app:prod

The container should then be running on http://localhost:7001/

Testing in docker containers

To build and run a test docker container, run the following command in a docker prompt:

docker build --target test --tag todo-app:test .
docker run --env-file .env.test todo-app:test

If all the tests run successfully, you should see terminal output something like this:

============================= test session starts ==============================
platform linux -- Python 3.10.7, pytest-7.1.3, pluggy-1.0.0
rootdir: /tests
collected 4 items

tests/test_client.py .                                                    [ 25%]
tests/test_viewmodel.py ...                                               [100%]

============================== 4 passed in 0.33s ===============================

Deployment to Azure App Service (with containers)

Note: image is stored in Docker Hub repo: https://hub.docker.com/r/paulcurren/todo-app

App Service Plan: ASP-Cohort22PauCurProjectExercise-930f

Web App: pdc-todo-app

Azure App Service URL: https://pdc-todo-app.azurewebsites.net

Deploy via Terraform

az login --use-device-code or use ARM_CLIENT_ID etc. environment variables terraform apply

Browse to: https://app-module12-pdc.azurewebsites.net/

Use Azure Blob Storage for State

Create storage account:

az storage account create --resource-group Cohort22_PauCur_ProjectExercise --name module12asa --sku Standard_LRS --encryption-services blob

Create blob container:

az storage container create --name tfstate --account-name module12asa

Get the key (Powershell _ Azure CLI):

$ACCOUNT_KEY=$(az storage account keys list --resource-group Cohort22_PauCur_ProjectExercise --account-name module12asa --query '[0].value' -o tsv) $env:ARM_ACCESS_KEY=$ACCOUNT_KEY

Add Terraform to CI/CD

Create service principal:

az ad sp create-for-rbac --role Contributor --scopes subscriptions/d33b95c7-af3c-4247-9661-aa96d47fccc0/resourceGroups/Cohort22_PauCur_ProjectExercise

Following secrets added to GitHub:

From the state storage account:

• ARM_ACCESS_KEY

From the service principal:

• ARM_CLIENT_ID
• ARM_CLIENT_SECRET
• ARM_TENANT_ID
• ARM_SUBSCRIPTION_ID

From the GitHub application registration:

• OAUTH_CLIENT_ID
• OAUTH_CLIENT_SECRET

From Loggly:

• LOGGLY_TOKEN