appmod-demos

This repository is a companion to a presentation given the Cognizant organization on Sept. 23 2021 entitled "Modern Application development – Container services, AKS and Functions"

Intitial Setup and Prerequisites

Visual Studio Code

Install Visual Studio Code from the following link: https://code.visualstudio.com/Download

Install the following VsCode extensions:

• Azure Account
• Azure Tools
• C#

Azure CLI

For Windows Choclatey is the easiest way to install, follow these instructions to install Choclatey, https://chocolatey.org/install

Execute the following script in Windows Powershell or Windows Powershell ISE.
Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))

Ensure choco was added to your User or system path, if not add.
C:\ProgramData\chocolatey\bin

Open a command prompt and Install the Azure CLI
choco install azure-cli

In advanced settings update your user PATH environment variable to include:
C:\Program Files (x86)\Microsoft SDKs\Azure\CLI2\wbin
After completing keep the dialog open as you will be adding more here.

Donet CLI

To install the dotnet CLI, install .Net Core from https://docs.microsoft.com/en-us/dotnet/core/install/windows?tabs=net50

Ensure the dotnet CLI is added to your user or system PATH
C:\Program Files (x86)\dotnet\

Docker Desktop

Windows: https://www.docker.com/products/docker-desktop
Install docker desktop, this will also require the Windows Subsystem for Linx (WSL2) to be installed.
This will most likely require a restart.

Mac: https://hub.docker.com/editions/community/docker-ce-desktop-mac?utm_source=docker&utm_medium=webreferral&utm_campaign=dd-smartbutton&utm_location=header

Docker CLI

When installing Docker Desktop the Docker CLI will also be installed

Kubectl

Follow the instructions at https://kubernetes.io/docs/tasks/tools/install-kubectl-windows/ to install
Add the following to your User PATH environment variable
%USERPROFILE%\.azure-kubectl
%USERPROFILE%\.azure-kubelogin

Azure Functions Core Tools

Follow the instructions at: https://docs.microsoft.com/en-us/azure/azure-functions/functions-run-local?tabs=v3%2Cwindows%2Ccsharp%2Cportal%2Cbash%2Ckeda#v2 to install the Azure Functions Core Tools Ensure your User or system PATH is updated with C:\Program Files\Microsoft\Azure Functions Core Tools\

Restart all terminals and VsCode instances

This ensures that your PATH environment variable is correct

Azure

First, ensure you have an subscription that you can deploy your resources to
Ensure you can login to Azure using az login
Use az account show --output table to find what your active directory and subscription is.

List your subscriptions with:
az account list --output table
Set your active subscription with:
az account set --subscription "My Demos"

Read more here: https://docs.microsoft.com/en-us/cli/azure/manage-azure-subscriptions-azure-cli
Look at your registered providers with:
Mac/Linux: az provider list --output table | grep " Registered"
Windows: az provider list --output table | findstr /C:" Registered"
Ensure the following providers are registered:

• Microsoft.Storage
• Microsoft.OperationsManagement
• Microsoft.OperationalInsights
• Microsoft.ContainerService
• Microsoft.ManagedIdentity
• microsoft.insights
• Microsoft.ContainerRegistry
• Microsoft.Compute
• Microsoft.Network

If not registered register with:
az provider register --namespace {provider-string}
e.g. az provider register --namespace Microsoft.Network

Containers: Docker and Azure Container Registry

Ensure that docker cli is working

1. open a new terminal
2. run docker --version
3. if not working follow the docker desktop install instructions

Instructions

1. Open VsCode
2. Create a folder/directory and use VsCode Open Folder
3. Open a new terminal from the terminal menu
4. Your terminal should be in the folder you opened, if not cd to it
5. Run dotnet new webapi -o forecast.api -n forecast.api
6. cd to the new directory cd forecast.api
7. Terminal: run vscode ., this will open vscode to the new application directory
8. VsCode Command Pallette (ctl+shift+P): find add docker files to workspace and click
9. A new dockerfile should be added to your directory, view to ensure it looks correct
10. Terminal: docker image list, no images should exist
11. VsCode Command Pallette (ctl+shift+P): Docker Images: Build Image
12. Terminal: Run docker image list, you should now see your image
13. Terminal:
    docker run -p 5000:5000 --name forecastapi forecastapi
14. Menu/Terminal->New Terminal: call api with curl
    curl http://localhost:5000/WeatherForecast
15. Ensure you see a response from the API with weather forecast data
16. Terminal: show running containers docker ps
17. Terminal: stop running container
    docker stop forecastapi
18. Create ACR in the the terminal with az cli
    Create a resource group
    az group create --location eastus --resource-group rg-ipldemo
19. From here on you will use a name for your ACR such as: {YourInitials}IplDemo JklAcrDemo to ensure no DNS conflicts
20. Terminal: Create an Azure Container Registry resource
    az acr create --location eastus --name {YourAcrName} --resource-group rg-ipldemo --sku basic
21. Terminal: Update Docker CLI config to include our new ACR repository az acr login --name {YourAcrName}
22. Terminal: publish our local image to ACR
    docker tag forecastapi:latest {YourAcrName}.azurecr.io/forecastapi:latest
docker push {YourAcrName}.azurecr.io/forecastapi:latest
23. Navigate to the Azure Portal -> All Resources
24. Find your ACR
25. Select repositories in the left menu
26. View your published image

Azure Kubernetes Service (AKS)

1. Create a new AKS cluster
   az group create --name rg-k8s --location eastus az aks create --resource-group rg-k8s --name aks-ipldemo --node-count 1 --enable-addons monitoring --generate-ssh-keys az aks get-credentials --resource-group rg-k8s --name aks-ipldemo
2. Give AKS access to ACR to pull images
   az aks update -n aks-ipldemo -g rg-k8s --attach-acr {YourAcrName}
3. Azure portal: Find your cluster, namespaces menu, see there are no namespaces
4. VsCode: review forecast.yml from the same directory as this Readme
   Notice the service, deployment, and loadbalancer
5. VsCode->Terminal: View running pods
   kubectl get pods
6. VsCode->Terminal: view k8s namespaces kubectl get namespaces
7. VsCode: Open Folder, folder that contains the forecast.yml file
8. VsCode->Terminal: Create our k8s resources kubectl apply -f forecast.yml
9. Azure portal: Find and view your AKS cluster
   Click workloads and now see a running pod, forecast, browse around and you can more details
10. Azure portal: View the updated load balancer
    Find your resource group such as: MC_rg-k8s_aks-ipldemo_eastus
    Click the Load Balancer
    Click the Load Balancing Rules menu
    Click the one load balancing rule and notice it load balacing port 80

Azure Functions and dotnet CLI

1. Terminal: Create a new Azure function shell project
   func init forecast.func --dotnet
2. Terminal:
   cd forecast.func
code .
3. Terminal: create a new HTTP triggered function in your project
   func new --name weatherforecast --template "HTTP trigger" --authlevel "anonymous"
4. VsCode: View the created files in the File Explorer
5. VsCode: Copy the contents of azurefuncforecast.cs to weatherforecast.cs file
6. VsCode: F5 to debug the project
7. Ctrl+Click the URL in the output window to show the forecast data
8. Terminal: Create a new Azure Function resource (Watch for replacement {your initials})
   az group create --name rg-azurefunc --location eastus
az storage account create --name stazurefunc{your initials} --location eastus --resource-group rg-azurefunc --sku Standard_LRS
az functionapp create --resource-group rg-azurefunc --consumption-plan-location eastus --runtime dotnet --functions-version 3 --name forecastfunc --storage-account stazurefunc{your initials}
9. Terminal: Publish your file to the Azure Function func azure functionapp publish forecastfunc
10. hit the url in the output window
11. go to the azure portal
12. view the function app in All Resources
    show the function, click it
    show various aspects of the function
    cant be modified since its deployed with a package

Clean Up

1. delete resource group
   az group delete --resource-group rg-ipldemo
2. delete k8s deployment + service
   kubectl delete deployment forecast kubectl delete service forecast
3. delete images from acr
   docker rmi $(docker images -q) -f docker system prune
4. Delete Azure Function
   az group delete --resource-group rg-azurefunc
5. Delete AKS cluster
   az group delete --resource-group rg-k8s