Serverless job processing on Azure Container Apps

This scenario project is about running a queue based job engine written in Java. The objective is ensure the following requirements:

• Minimal amount of code beeing written by using Dapr
• Fast scale up time of container runtime by using Quarkus native images
• Automatic scale to 0 using Keda in Azure Container Apps
• Ensuring that each instance is only processing one job at a time
• Publishin application and runtime metrics via Micrometer
• Scrapping metrics via telegraf sidecar and publish them to Azure Monitor
• Using Azure Managed Grafana to build dashboards on jobengine execution times
• Use the workload profiles to run multiple replicas on the same host to save costs

Local installation

Install locally

• Install dapr using this guide https://docs.dapr.io/getting-started/install-dapr-cli/
• Init dapr using this guide https://docs.dapr.io/getting-started/install-dapr-selfhost/
• Install Quarkus using this guide https://quarkus.io/get-started/
• Add VSCode Extension for Quarkus https://marketplace.visualstudio.com/items?itemName=redhat.vscode-quarkus

Debug locally

• Start the "Debug Quarkus application (src/Engine) with Dapr Local Components" Debug mode in VSCode
• Trigger the Dapr input binding

curl -X POST -H 'Content-Type: application/json' http://localhost:8080/consume -d '{ message: "hello world" }'

• Create message in queue

curl -X POST -H 'Content-Type: application/json' http://localhost:8080/publish -d '{ message: "hello world" }'

Deployment of the Azure resources

Manual deployment of azure resources with azure cli

DEPLOYMENT_NAME="dzaca31" # here the deployment
LOCATION="northcentralus" # azure region 
bash ./deploy-aca-infra-bicep.sh $DEPLOYMENT_NAME $LOCATION
bash ./deploy-aks-infra-bicep.sh $DEPLOYMENT_NAME $LOCATION

Manual deployment of app into existing Azure Container App Environment with azure cli

DEPLOYMENT_NAME="dzaca27" # here the deployment
LOCATION="westeurope" # azure region 
GHUSER="denniszielke" # replace with your user name
IMAGE_TAG="latest"
bash ./deploy-aca-apps-bicep.sh $DEPLOYMENT_NAME $GHUSER $IMAGE_TAG

Debug locally with azure resource components

DEPLOYMENT_NAME="dzaca64" # here the deployment
bash ./create-config.sh $DEPLOYMENT_NAME

• Start the "Debug Quarkus application (src/Engine) with Dapr Azure Components" Debug mode in VSCode

Publish messages locally

dapr publish --publish-app-id engine --topic requests --pubsub requests --data '{"guid":"balasbla", "message": "helloe"}'

Set up workload Identity for your GitHub Actions to use federated trust

Official documentation: https://docs.microsoft.com/en-us/azure/active-directory/develop/workload-identity-federation-create-trust-github?tabs=azure-portal

For this repository to your own account.

We will create a service principal and grant it permissions on a dedicated resource group

DEPLOYMENT_NAME="dzaca64" # here the deployment
RESOURCE_GROUP=$DEPLOYMENT_NAME # here enter the resources group
LOCATION="westeurope" # azure region 
AZURE_SUBSCRIPTION_ID=$(az account show --query id -o tsv) # here enter your subscription id
GHUSER="denniszielke" # replace with your user name
GHREPO="serverless-job-engine" # here the repo name
AZURE_TENANT_ID=$(az account show --query tenantId -o tsv)
GHREPO_BRANCH=":ref:refs/heads/main"
az group create -n $RESOURCE_GROUP -l $LOCATION -o none

AZURE_CLIENT_ID=$(az ad sp create-for-rbac --name "$DEPLOYMENT_NAME" --role contributor --scopes "/subscriptions/$AZURE_SUBSCRIPTION_ID/resourceGroups/$RESOURCE_GROUP" -o json | jq -r '.appId')

AZURE_CLIENT_OBJECT_ID="$(az ad app show --id ${AZURE_CLIENT_ID} --query objectId -o tsv)"

az rest --method POST --uri "https://graph.microsoft.com/beta/applications/$AZURE_CLIENT_OBJECT_ID/federatedIdentityCredentials" --body "{'name':'$DEPLOYMENT_NAME','issuer':'https://token.actions.githubusercontent.com','subject':'repo:$GHUSER/$GHREPO$GHREPO_BRANCH','description':'GitHub Actions for $DEPLOYMENT_NAME','audiences':['api://AzureADTokenExchange']}"

Monitoring builtin and custom Java metrics using Micrometer

Using the telegraf sidecar we can public builtin and custom metrics via Micrometer, scrap the /q/metrics endpoint via a dedicated telegraf sidecar, send metrics to the Azure Monitor and query the results via Azure Managed Grafana: