A quick look at Azure App Configuration and the Python Provider

When developing an application, it is highly likely that it needs to be configured with all sorts of settings. A simple list of key/value pairs is usually all you need. Some of the values can be read by anyone (e.g., a public URL) while some values should be treated as secrets (e.g., a connection string).

Azure App Configuration is a service to centrally manage these settings in addition to feature flags. In this post, we will look at storing and retrieving application settings and keeping feature flags for another time. I will also say App Config instead of App Configuration to save some keystrokes. ๐Ÿ˜‰

We will do the following:

  • Retrieve key-value pairs for multiple applications and environments from one App Config instance
  • Use Key Vault references in App Config and retrieve these from Key Vault directly
  • Use the Python provider client to retrieve key-value pairs and store them in a Python dictionary

Why use App Configuration at all?

App Configuration helps by providing a fully managed service to store configuration settings for your applications separately from your code. Storing configuration separate from code is a best practice that most developers should follow.

Although you could store configuration values in files, using a service like App Config provides some standardization within or across developer teams.

Some developers store both configuration values and secrets in Key Vault. Although that works, App Config is way more flexible in organizing the settings and retrieving lists of settings with key and label filters. If you need to work with more than a few settings, I would recommend using a combination of App Config and Key Vault.

In what follows, I will show how we store settings for multiple applications and environments in the same App Config instance. Some of these settings will be Key Vault references.

Read https://learn.microsoft.com/en-us/azure/azure-app-configuration/overview before continuing to know more about App Config.

Provisioning App Config

Provisioning App Configuration is very easy from the portal or the Azure CLI. With the Azure CLI, use the following commands to create a resource group and an App Configuration instance in that group:

az group create -n RESOURCEGROUP -l LOCATION
az appconfig create -g RESOURCEGROUP  -n APPCONFIGNAME -l LOCATION

After deployment, we can check the portal and navigate to Configuration Explorer.

App Configuration in the Azure Portal

In Configuration Explorer, you can add the configuration values for your apps. They are just key/value pairs but they can be further enriched with labels, content types, and tags.

Note that there is a Free and a Standard tier of App Config. See https://azure.microsoft.com/en-us/pricing/details/app-configuration/ for more information. In production, you should use the Standard tier.

Storing configuration and secrets for multiple apps and environments

To store configuration values for multiple applications, you will have to identify the application in the key. App Configuration, oddly, has no knowledge of applications. For example, a key could be app1:setting1. You decide on the separator between the app name (app1 here) and its setting (setting1). In your code, you can easily query all settings for your app with a key filter (e.g. “app1:”. I will show an example of using a key filter later with the Python provider.

If you want to have different values for a key per environment (like dev, prd, etc…), you can add a label for each environment. To retrieve all settings for an environment, you can use a label filter. I will show an example of using a label filter later.

Suppose you want to use app1:setting1 in two environments: dev and prd. How do you create the key-value pairs? One way is to use the Azure CLI. You can also create them with the portal or from Python, C#, etc… With the CLI:

az appconfig kv set --name APPCONFIGNAME  --key app1:setting1 --value "value1" --label dev

APPCONFIG name is the name of your App Config instance. Just the name, not the full URL. For the prd environment:

az appconfig kv set --name APPCONFIGNAME  --key app1:setting1 --value "value2" --label prd

In Configuration Explorer, you will now see:

app1:setting1 for two environments (via labels)

For more examples of using the Azure CLI, see https://learn.microsoft.com/en-us/azure/azure-app-configuration/scripts/cli-work-with-keys.

In addition to these plain key-value pairs, you can also create Key Vault references. Let’s create one from the portal. In Configuration Explorer, click + Create and select Key Vault reference. You will get the following UI that allows you to create the reference. Make sure you have a Key Vault with a secret called dev-mysecret if you want to follow along. Below, set the label to dev. I forgot that in the screenshot below:

Creating a Key Vault Reference

Above, I am using the same naming convention for the key in App Config: app1:mysecret. Notice though that the secret I am referencing in Key Vault contains the environment and a dash (-) before the actual secret name. If you use one Key Vault per app instead of a Key Vault per app and environment, you will have to identify the environment in the secret name in some way.

After creating the reference, you will see the following in Configuration explorer:

Configuration explorer with one Key Vault reference

Note that the Key Vault reference has a content type. The content type is application/vnd.microsoft.appconfig.keyvaultref+json;charset=utf-8. You can use the content type in your code to know if the key contains a reference to a Key Vault secret. That reference will be something like https://kv-app1-geba.vault.azure.net/secrets/dev-mysecret. You can then use the Python SDK for Azure Key Vault to retrieve the secret from your code. Azure App Config will not do that for you.

You can use content types in other ways as well. For example, you could store a link to a storage account blob and use a content type that informs your code it needs to retrieve the blob from the account. Of course, you will need to write code to retrieve the blob. App Config only contains the reference.

Reading settings

There are many ways to read settings from App Config. If you need them in an Azure Pipeline, for instance, you can use the Azure App Configuration task to pull keys and values from App Config and set them as Azure pipeline variables.

If you deploy your app to Kubernetes and you do not want to read the settings from your code, you can integrate App Configuration with Helm. See https://learn.microsoft.com/en-us/azure/azure-app-configuration/integrate-kubernetes-deployment-helm for more information.

In most cases though, you will want to read the settings directly from your code. There is an SDK for several languages, including Python. The SDK has all the functionality you need to read and write settings.

Next to the Python SDK, there is also a Python provider which is optimized to read settings from App Config and store them in a Python dictionary. The provider has several options to automatically trim app names from keys and to automatically retrieve a secret from Key Vault if the setting in App Config is a Key Vault reference.

To authenticate to App Config, the default is access keys with a connection string. You can find the connection string in the Portal:

App Config Connection string for read/write or just read

You can also use Azure AD (it’s always enabled) and disable access keys. In this example, I will use a connection string to start with:

Before we connect and retrieve the values, ensure you install the provider first:

pip install azure-appconfiguration-provider

Above, use pip or pip3 depending on your installation of Python.

In your code, ensure the proper imports:

from azure.appconfiguration.provider import (
    AzureAppConfigurationProvider,
    SettingSelector,
    AzureAppConfigurationKeyVaultOptions
)
from azure.keyvault.secrets import SecretClient
from azure.identity import DefaultAzureCredential

To authenticate to Azure Key Vault with Azure AD, we can use DefaultAzureCredential():

try:
    CREDENTIAL = DefaultAzureCredential(exclude_visual_studio_code_credential=True)
except Exception as ex:
    print(f"error setting credentials: {ex}")

Note: on my machine, I had an issue with the VS Code credential feature so I turned that off.

Next, use a SettingSelector from the provider to provide a key filter and label filter. I want to retrieve key-value pairs for an app called app1 and an environment called dev:

app = 'app1'
env = 'dev'
selects = {SettingSelector(key_filter=f"{app}:*", label_filter=env)}

Next, when I retrieve the key-value pairs, I want to strip app1: from the keys:

trimmed_key_prefixes = {f"{app}:"}

In addition, I want the provider to automatically go to Key Vault and retrieve the secret:

key_vault_options = AzureAppConfigurationKeyVaultOptions(secret_resolver=retrieve_secret)

retrieve_secret refers to a function you need to write to retrieve the secret and add custom logic. There are other options as well.

def retrieve_secret(uri):
    try:
        # uri is in format: https://<keyvaultname>.vault.azure.net/secrets/<secretname>
        # retrieve key vault uri and secret name from uri
        vault_uri = "https://" + uri.split('/')[2]
        secret_name = uri.split('/')[-1]
        print(f"Retrieving secret {secret_name} from {vault_uri}...")
 
        # retrieve the secret from Key Vault; CREDENTIAL was set globally
        secret_client = SecretClient(vault_url=vault_uri, credential=CREDENTIAL)
 
        # get secret value from Key Vault
        secret_value = secret_client.get_secret(secret_name).value
 
    except Exception as ex:
        print(f"retrieving secret: {ex}", 1)

    return secret_value

Now that we have all the options, we can retrieve the key-value pairs.

connection_string = 'YOURCONNSTR'
app_config = AzureAppConfigurationProvider.load(
    connection_string=connection_string, selects=selects, key_vault_options=key_vault_options, 
    trimmed_key_prefixes=trimmed_key_prefixes)

print(app_config)

Now we have a Python dictionary app_config with all key-value pairs for app1 and environment dev. The key-value pairs are a mix of plain values from App Config and Key Vault.

You can now use this dictionary in your app in whatever way you like.

If you would like to use the same CREDENTIAL to connect to App Config, you can also use:

endpoint = 'APPCONFIGNAME.azconfig.io' # no https://
app_config = AzureAppConfigurationProvider.load(
    endpoint=endpoint, credential=CREDENTIAL, selects=selects, key_vault_options=key_vault_options, 
    trimmed_key_prefixes=trimmed_key_prefixes)

Ensure the credential you use has the App Configuration Data Reader role to read the key-value pairs.

Here’s all the code in a gist: https://gist.github.com/gbaeke/9b075a87a1198cdcbcc2b2028492085b. Ensure you have the key-value pairs as above and provide the connection string to the connection_string variable.

Conclusion

In this post, we showed how to retrieve key-value pairs with the Python provider from one App Config instance for multiple applications and environments.

The application is stored as a prefix in the key (app1:). The environment is a label (e.g., dev), allowing us to have the same setting with different values per environment.

Some keys can contain a reference to Key Vault to allow your application to retrieve secrets from Key Vault as well. I like this approach to have a list of all settings for an app and environment, where the value of the key can be an actual value or a reference to some other entity like a secret, a blob, or anything else.

First steps with Crossplane

Image Source: crossplane.io

Although Crossplane has been around for a while, I never got around to trying it. Crossplane has many capabilities. However, in this post, I will focus on the following aspects:

  • Installing Crossplane on a Kubernetes cluster (AKS); you can install on a local cluster as well (e.g., k3s, kind, minikube, …) but then you would need Azure Arc for Kubernetes to install the microsoft.flux extension (I will be using GitOps with Flux via that extension)
  • Adding and configuring providers for Azure and Kubernetes: providers allow you to deploy to Azure and Kubernetes (and much more) from Crossplane
  • Deploying Azure infrastructure with Crossplane using a fully declarative GitOps approach

Introduction

Crossplane basically allows you to build a control plane that you or your teams can use to deploy infrastructure and applications. This control plane is built on Kubernetes. In short, suppose I want to deploy an Azure resource group with Crossplane, I would create the below YAML file and apply it with kubectl apply -f filename.yaml.

This is, in essence, a fully declarative approach to deploying Azure infrastructure using Kubernetes. There are other projects, such as the Azure Service Operator v2, that do something similar.

apiVersion: azure.jet.crossplane.io/v1alpha2
kind: ResourceGroup
metadata:
  name: rg-crossplane
spec:
  forProvider:
    location: "westeurope"
    tags:
      provisioner: crossplane
  providerConfigRef:
    name: default

In order to enable this functionality, you need the following:

  • Install Crossplane on your Kubernetes cluster
  • Add a provider that can create Azure resources; above the jet provider for Azure is used; more about providers later
  • Configure the provider with credentials; in this case Azure credentials

In a diagram:

Install Crossplane from git with Flux on AKS; deploy an Azure resource group and another AKS cluster from Crossplane; create a namespace on that new cluster

Combination with GitOps

Although you can install and configure Crossplane manually and just use kubectl to add custom resources, I wanted to add Crossplane and custom resources using GitOps. To that end, I am using Azure Kubernetes Service (AKS) with the microsoft.flux extension. For more information to enable and install the extension, see my Flux v2 quick guide.

โš ๏ธ The git repository I am using with Flux v2 and Crossplane is here: https://github.com/gbaeke/crossplane/tree/blogpost. This refers to the blogpost branch, which should match the content of this post. Tbe main branch might be different.

The repo contains several folders that match Flux kustomizations:

  • infra folder: installs Crossplane and Azure Key Vault to Kubernetes; an infra kustomization will point to this folder
  • secrets folder: creates a secret with Azure Key Vault to Kubernetes from Azure Key Vault; the secrets kustomization will point to this folder
  • crossplane-apps folder: installs Azure resources and Kubernetes resources with the respective Crossplane providers; the apps kustomization will point to this folder

Note: if you do not know what Flux kustomizations are and how Flux works, do check my Flux playlist: https://www.youtube.com/playlist?list=PLG9qZAczREKmCq6on_LG8D0uiHMx1h3yn. The videos look at the open source version of Flux and not the microsoft.flux extension. To learn more about that extension, see https://www.youtube.com/watch?v=w_eoJbgDs3g.

Installing Crossplane

The infra customization installs Crossplane and Azure Key Vault to Kubernetes. The latter is used to sync a secret from Key Vault that contains credentials for the Crossplane Azure provider. More details are in the diagram below:

As noted above, the installation of Crossplane is done with Flux. First, there is the HelmRepository resource that adds the Crossplane Helm repository to Flux.

apiVersion: source.toolkit.fluxcd.io/v1beta2
kind: HelmRepository
metadata:
  namespace: config-infra
  name: crossplane
spec:
  interval: 1m0s
  url: https://charts.crossplane.io/stable

Next, there is the HelmRelease that installs Crossplane. Important: target namespace is crossplane-system (bottom line):

apiVersion: helm.toolkit.fluxcd.io/v2beta1
kind: HelmRelease
metadata:
  name: crossplane
  namespace: config-infra
spec:
  chart:
    spec:
      chart: crossplane
      reconcileStrategy: ChartVersion
      sourceRef:
        kind: HelmRepository
        name: crossplane
        namespace: config-infra
  install:
    createNamespace: true
  interval: 1m0s
  targetNamespace: crossplane-system

For best results, in the YAML above, set the namespace of the resource to the namespace you use with the AKS k8s-configuration. The resources to install Azure Key Vault to Kubernetes are similar.

To install the Crossplane jet provider for Azure:

---
apiVersion: pkg.crossplane.io/v1alpha1
kind: ControllerConfig
metadata:
  name: jet-azure-config
  labels:
    app: crossplane-provider-jet-azure
spec:
  image: crossplane/provider-jet-azure-controller:v0.9.0
  args: ["-d"]
---
apiVersion: pkg.crossplane.io/v1
kind: Provider
metadata:
  name: crossplane-provider-jet-azure
spec:
  package: crossplane/provider-jet-azure:v0.9.0
  controllerConfigRef:
    name: jet-azure-config

Above, debugging is turned on for the provider. This is optional. The provider actually runs in the crossplane-system namespace:

jet provider

The provider is added via the Provider resource (second resource in the YAML manifest).

We can now create the AKS k8s-configuration, which creates a Flux source and a kustomization:

RG=your AKS resource group
CLUSTER=your AKS cluster name (to install Crossplane to)

az k8s-configuration flux create -g $RG -c $CLUSTER \
  -n cluster-config --namespace config-infra -t managedClusters \
  --scope cluster -u https://github.com/gbaeke/crossplane \
  --branch main  \
  --kustomization name=infra path=./infra prune=true

The Flux source will be the repo specified with -u. There is one kustomization: infra. Pruning is turned on. With pruning, removing manifests from the repo results is removing them from Kubernetes.

The k8s-configuration should result in:

Don’t mind the other Kustomizations; will be added later; this is the GitOps view in the properties of the cluster in the Azure Portal

Crossplane is now installed with two providers. We can now configure the Azure provider with credentials.

Configuring Azure Credentials

You need to create a service principal by following the steps in https://crossplane.io/docs/v1.9/cloud-providers/azure/azure-provider.html. I compacted the resulting JSON with:

cat <path-to-JSON> | jq -c

The output of the above command was added to Key Vault:

Azure creds in Key Vault

The Key Vault I am using uses the Azure RBAC permission model. Ensure that the AKS cluster’s kubelet identity has at least the Key Vault Secrets User role. It is a user-assigned managed identity with a name like clustername-agentpool.

To actually create a Kubernetes secret from this Key Vault secret, the secrets folder in the git repo contains the manifest below:

apiVersion: spv.no/v2beta1
kind: AzureKeyVaultSecret
metadata:
  name: azure-creds 
  namespace: crossplane-system
spec:
  vault:
    name: kvgebadefault # name of key vault
    object:
      name: azure-creds # name of the akv object
      type: secret # akv object type
  output: 
    secret: 
      name: azure-creds # kubernetes secret name
      dataKey: creds # key to store object value in kubernetes secret

This creates a Kubernetes secret in the crossplane-system namespace with name azure-creds and a key creds that holds the credentials JSON.

Secret as seen in k9s
the decoded secret as shown in k9s

To add the secret(s) as an extra kustomization, run:

RG=your AKS resource group
CLUSTER=your AKS cluster name

az k8s-configuration flux create -g $RG -c $CLUSTER \
  -n cluster-config --namespace config-infra -t managedClusters \
  --scope cluster -u https://github.com/gbaeke/crossplane \
  --branch main  \
  --kustomization name=infra path=./infra prune=true \
  --kustomization name=secrets path=./secrets prune=true dependsOn=["infra"]

Note that the secrets kustomization is dependent on the infra kustomization. After running this command, ensure the secret is in the crossplane-system namespace. The k8s-configuration uses the same source but now has two kustomizations.

Deploying resources with the Jet provider for Azure

Before explaining how to create Azure resources, a note on providers. As a novice Crossplane user, I started with the following Azure provider: https://github.com/crossplane-contrib/provider-azure. This works well but it is not so simple for contributors to ensure the provider is up-to-date with the latest and greatest Azure features. For example, if you deploy AKS, you cannot use managed identity, the cluster uses availability sets etc…

To improve this, Terrajet was created. It is a code generation framework that can generate Crossplane CRDs (custom resource definitions) and sets up the provider to use Terraform. Building on top of Terraform is an advantage because it is more up-to-date with new cloud features. That is the reason why this post uses the jet provider. When we later create an AKS cluster, it will take advantage of managed identity and other newer features.

Note: there is also a Terraform provider that can take Terraform HCL to do anything you want; we are not using that in this post

Ok, let’s create a resource group and deploy AKS. First, we have to configure the provider with Azure credentials. The crossplane-apps folder contains a file called jet-provider-config.yaml:

apiVersion: azure.jet.crossplane.io/v1alpha1
kind: ProviderConfig
metadata:
  name: default
spec:
  credentials:
    source: Secret
    secretRef:
      namespace: crossplane-system
      name: azure-creds
      key: creds

The above ProviderConfig tells the provider to use the credentials in the Kubernetes secret we created earlier. We know we are configuring the jet provider from the apiVersion: azure.jet.crossplane.io/v1alpha1.

With that out of the way, we can create the resource group and AKS cluster. Earlier in this post, the YAML to create the resource group was already shown. To create a basic AKS cluster called clu-cp in this group, aks.yaml is used:

apiVersion: containerservice.azure.jet.crossplane.io/v1alpha2
kind: KubernetesCluster
metadata:
  name: clu-cp
spec:
  writeConnectionSecretToRef:
    name: example-kubeconfig
    namespace: crossplane-system
  forProvider:
    location: "westeurope"
    resourceGroupNameRef:
      name: rg-crossplane
    dnsPrefix: "clu-cp"
    defaultNodePool:
      - name: default
        nodeCount: 1
        vmSize: "Standard_D2_v2"
    identity:
      - type: "SystemAssigned"
    tags:
      environment: dev
  providerConfigRef:
    name: default

Above, we refer to our resource group by name (resourceGroupNameRef) and we write the credentials to our cluster to a secret (writeConnectionSecretToRef). That secret will contain keys with the certificate and private key, but also a kubeconfig key with a valid kubeconfig file. We can use that later to connect and deploy to the cluster.

To see an example of connecting to the deployed cluster and creating a namespace, see k8s-provider-config.yaml and k8s-namespace.yaml in the repo. The resource k8s-provider-config.yaml will use the example-kubeconfig secret created above to connect to the AKS cluster that we created in the previous steps.

To create a kustomization for the crossplane-apps folder, run the following command:

RG=your AKS resource group
CLUSTER=your AKS cluster name

az k8s-configuration flux create -g $RG -c $CLUSTER \
  -n cluster-config --namespace config-infra -t managedClusters \
  --scope cluster -u https://github.com/gbaeke/crossplane \
  --branch main  \
  --kustomization name=infra path=./infra prune=true \
  --kustomization name=secrets path=./secrets prune=true dependsOn=["infra"] \
  --kustomization name=apps path=./crossplane-apps prune=true dependsOn=["secrets"]

This folder does not contain a kustomization.yaml file. Any manifest you drop in it will be applied to the cluster! The k8s-kustomization now has the same source but three kustomizations:

infra, secrets and apps kustomizations

After a while, an AKS cluster clu-cp should be deployed to resource group rg-crossplane:

AKS deployed by Crossplane running on another AKS cluster

To play around with this, I recommend using Visual Studio Code and the GitOps extension. When you make a change locally and push to main, to speed things up, you can reconcile the git repository and the apps kustomization manually:

Reconcile the GitRepository source and kustomization from the GitOps extension for Visual Studio Code

Conclusion

In this post, we looked at installing and configuring Crossplane on AKS via GitOps and the microsoft.flux extension. In addition, we deployed a few Azure resources with Crossplane and its jet provider for Azure. We only scratched the surface here but I hope this gets you started quickly when evaluating Crossplane for yourself.

Approving a private endpoint connection with Azure CLI

In my previous post, I wrote about App Services with Private Link and used Azure Front Door to publish the web app. Azure Front Door Premium (in preview), can create a Private Endpoint and link it to your web app via Azure Private Link. When that happens, you need to approve the pending connection in Private Link Center.

The pending connection would be shown here, ready for approval

Although this is easy to do, you might want to automate this approval. Automation is possible via a REST API but it is easier via Azure CLI.

To do so, first list the private endpoint connections of your resource, in my case that is a web app:

az network private-endpoint-connection list --id /subscriptions/SUBID/resourceGroups/RGNAME/providers/Microsoft.Web/sites/APPSERVICENAME

The above command will return all private endpoint connections of the resource. For each connection, you get the following information:

 {
    "id": "PE CONNECTION ID",
    "location": "East US",
    "name": "NAME",
    "properties": {
      "ipAddresses": [],
      "privateEndpoint": {
        "id": "PE ID",
        "resourceGroup": "RESOURCE GROUP NAME OF PE"
      },
      "privateLinkServiceConnectionState": {
        "actionsRequired": "None",
        "description": "Please approve this connection.",
        "status": "Pending"
      },
      "provisioningState": "Pending"
    },
    "resourceGroup": "RESOURCE GROUP NAME OF YOUR RESOURCE",
    "type": "YOUR RESOURCE TYPE"
  }

To approve the above connection, use the following command:

az network private-endpoint-connection approve --id  PE CONNECTION ID --description "Approved"

The –id in the approve command refers to the private endpoint connection ID, which looks like below for a web app:

/subscriptions/YOUR SUB ID/resourceGroups/YOUR RESOURCE GROUP/providers/Microsoft.Web/sites/YOUR APP SERVICE NAME/privateEndpointConnections/YOUR PRIVATE ENDPOINT CONNECTION NAME

After running the above command, the connection should show as approved:

Approved private endpoint connection

When you automate this in a pipeline, you can first list the private endpoint connections of your resource and filter on provisioningState=”Pending” to find the ones you need to approve.

Hope it helps!

Azure App Services with Private Link

In one of my videos on my YouTube channel, I discuss Azure App Services with Private Link. The video describes how it works and provides an example of deploying the infrastructure with Bicep. The Bicep templates are on GitHub.

If you want to jump straight to the video, here it is:

In the rest of this blog post, I provide some more background information on the different pieces of the solution.

Azure App Service

Azure App Service is a great way to host web application and APIs on Azure. It’s PaaS (platform as a service), so you do not have to deal with the underlying Windows or Linux servers as they are managed by the platform. I often see AKS (Azure Kubernetes Service) implementations to host just a couple of web APIs and web apps. In most cases, that is overkill and you still have to deal with Kubernetes upgrades, node patching or image replacements, draining and rebooting the nodes, etc… And then I did not even discuss controlling ingress and egress traffic. Even if you standardize on packaging your app in a container, Azure App Service will gladly accept the container and serve it for you.

By default, Azure App Service gives you a public IP address and FQDN (Fully Qualified Domain Name) to reach your app securely over the Internet. The default name ends with azurewebsites.net but you can easily add custom domains and certificates.

Things get a bit more complicated when you want a private IP address for your app, reachable from Azure virtual networks and on-premises networks. One solution is to use an App Service Environment. It provides a fully isolated and dedicated environment to run App Service apps such as web apps and APIs, Docker containers and Functions. You can create an internal ASE which results in an Internal Load Balancer in front of your apps that is configured in a subnet of your choice. There is no need to configure Private Endpoints to make use of Private Link. This is often called native virtual network integration.

At the network level, an App Service Environment v2, works as follows:

External ASE
ASE networking (from Microsoft website)

Looking at the above diagram, an ILB ASE (but also an External ASE) also makes it easy to connect to back-end systems such as on-premises databases. The outbound connection to internal resources originates from an IP in the chosen integration subnet.

The downside to ASE is that its isolated instances (I1, I2, I3) are rather expensive. It also takes a long time to provision an ASE but that is less of an issue. In reality though , I would like to see App Service Environments go away and replaced by “regular” App Services with toggles that give you the options you require. You would just deploy App Services and set the options you require. In any case, native virtual network integration should not depend on dedicated or shared compute. One can only dream right? ๐Ÿ˜‰

Note: App Service Environment v3, in preview at the time of this writing, provides a simplified deployment experience and also costs less. See App Service Environment v3 public preview – Azure App Service

As an alternative to an ASE for a private app, consider a non-ASE App Service that, in production, uses Premium V2 or V3 instances. The question then becomes: “How do you get a private IP address?” That’s where Private Link comes in…

Azure Private Link with App Service

Azure Private Link provides connectivity to Azure services (such as App Service) via a Private Endpoint. The Private Endpoint creates a virtual network interface card (NIC) on a subnet of your choice. Connections to the NICs IP address end up at the Private Link service the Private Endpoint is connected to. Below is an example with Azure SQL Database where one Private Endpoint is mapped, via Azure Private Link, to one database. The other databases are not reachable via the endpoint.

Private Endpoint connected to Azure SQL Database (PaaS) via Private Link (source: Microsoft website)

To create a regular App Service that is accessible via a private IP, we can do the same thing:

  • create a private endpoint in the subnet of your choice
  • connect the private endpoint to your App Service using Private Link

Both actions can be performed at the same time from the portal. In the Networking section of your App Service, click Configure your private endpoint connections. You will see the following screen:

Private Endpoint connection of App Service

Now click Add to create the Private Endpoint:

Creating the private endpoint

The above creates the private endpoint in the default subnet of the selected VNET. When the creation is finished, the private endpoint will be connected to App Service and automatically approved. There are scenarios, such as connecting private endpoints from other tenants, that require you to approve the connection first:

Automatically approved connection

When you click on the private endpoint, you will see the subnet and NIC that was created:

Private Endpoint

From the above, you can click the link to the network interface (NIC):

Network interface created by the private endpoint

Note that when your delete the Private Endpoint, the interface gets deleted as well.

Great! Now we have an IP address that we can use to reach the App Service. If you use the default name of the web app, in my case https://web-geba.azurewebsites.net, you will get:

Oops, no access on the public name (resolves to public IP)

Indeed, when you enable Private Link on App Service, you cannot access the website using its public IP. To solve this, you will need to do something at the DNS level. For the default domain, azurewebsites.net, it is recommended to use Azure Private DNS. During the creation of my Private Endpoint, I turned on that feature which resulted in:

Private DNS Zone for privatelink.azurewebsites.net

You might wonder why this is a private DNS zone for privatelink.azurewebsites.net? From the moment you enable private link on your web app, Microsoft modifies the response to the DNS query for the public name of your app. For example, if the app is web-geba.azurewebsites.net and you query DNS for that name, it will respond with a CNAME of web-geba.privatelink.azurewebsites.net. If that cannot be resolved, you will still get the public IP but that will result in a 403.

In my case, as long as the DNS servers I use can resolve web-geba.privatelink.azurewebsites.net and I can connect to 10.240.0.4, I am good to go. Note however that the DNS story, including Private DNS and your own DNS servers, is a bit more complex that just checking a box! However, that is not the focus of this blogpost so moving on… ๐Ÿ˜‰

Note: you still need to connect to the website using https://web-geba.azurewebsites.net in your browser

Outbound connections to internal resources

One of the features of App Service Environments, is the ability to connect to back-end systems in Azure VNETs or on-premises. That is the result of native VNET integration.

When you enable Private Link on a regular App Service, you do not get that. Private Link only enables private inbound connectivity but does nothing for outbound. You will need to configure something else to make outbound connections from the Web App to resources such as internal SQL Servers work.

In the network configuration of you App Service, there is another option for outbound connectivity to internal resources – VNet integration.

VNET Integration

In the Networking section of App Service, find the VNet integration section and click Click here to configure. From there, you can add a VNet to integrate with. You will need to select a subnet in that VNet for this integration to work:

Outbound connectivity for App Service to Azure VNets

There are quite some things to know when it comes to VNet integration for App Service so be sure to check the docs.

Private Link with Azure Front Door

Often, a web app is made private because you want to put a Web Application Firewall (WAF) in front of the app. Typically, that goal is achieved by putting Azure Application Gateway (AG) with WAF in front of an internal App Services Environment. As as alternative to AG, you can also use virtual appliances such as Barracuda WAF for Azure. This works because the App Services Environment is a first-class citizen of your Azure virtual network.

There are multiple ways to put a WAF in front of a (non-ASE) App Service. You can use Front Door with the App Service as the origin, as long as you restrict direct access to the origin. To that end, App Services support access restrictions.

With Azure Front Door Premium, in preview at the time of this writing (June 2021), you can use Private Link as well. In that case, Azure Front Door creates a private endpoint. You cannot control or see that private endpoint because it is managed by Front Door. Because the private endpoint is not in your tenant, you will need to approve the connection from the private endpoint to your App Service. You can do that in multiple ways. One way is Private Link Center Pending Connections:

Pending Connections

If you check the video at the top of this page, this is shown here.

Conclusion

The combination of Azure networking with App Services Environments (ASE) and “regular” App Services (non-ASE) can be pretty confusing. You have native network integration for ASE, private access with private link and private endpoints for non-ASE, private DNS for private link domains, virtual network service endpoints, VNet outbound configuration for non-ASE etc… Most of the time, when I am asked for the easiest and most cost-effective option for a private web app in PaaS, I go for a regular non-ASE App Service and use Private Link to make the app accessible from the internal network.

Building a GitHub Action with Docker

While I was investigating Kyverno, I wanted to check my Kubernetes deployments for compliance with Kyverno policies. The Kyverno CLI can be used to do that with the following command:

kyverno apply ./policies --resource=./deploy/deployment.yaml

To do this easily from a GitHub workflow, I created an action called gbaeke/kyverno-cli. The action uses a Docker container. It can be used in a workflow as follows:

# run kyverno cli and use v1 instead of v1.0.0
- name: Validate policies
  uses: gbaeke/kyverno-action@v1
  with:
    command: |
      kyverno apply ./policies --resource=./deploy/deployment.yaml

You can find the full workflow here. In the next section, we will take a look at how you build such an action.

If you want a video instead, here it is:

GitHub Actions

A GitHub Action is used inside a GitHub workflow. An action can be built with Javascript or with Docker. To use an action in a workflow, you use uses: followed by a reference to the action, which is just a GitHub repository. In the above action, we used uses: gbaeke/kyverno-action@v1. The repository is gbaeke/kyverno-action and the version is v1. The version can refer to a release but also a branch. In this case v1 refers to a branch. In a later section, we will take a look at versioning with releases and branches.

Create a repository

An action consists of several files that live in a git repository. Go ahead and create such a repository on GitHub. I presume you know how to do that. We will add several files to it:

  • Dockerfile and all the files that are needed to build the Docker image
  • action.yml: to set the name of our action, its description, inputs and outputs and how it should run

Docker image

Remember that we want a Docker image that can run the Kyverno CLI. That means we have to include the CLI in the image that we build. In this case, we will build the CLI with Go as instructed on https://kyverno.io. Here is the Dockerfile (should be in the root of your git repo):

FROM golang:1.15
COPY src/ /
RUN git clone https://github.com/kyverno/kyverno.git
WORKDIR kyverno
RUN make cli
RUN mv ./cmd/cli/kubectl-kyverno/kyverno /usr/bin/kyverno
ENTRYPOINT ["/entrypoint.sh"]

We start from a golang image because we need the go tools to build the executable. The result of the build is the kyverno executable in /usr/bin. The Docker image uses a shell script as its entrypoint, entrypoint.sh. We copy that shell script from the src folder in our repository.

So go ahead and create the src folder and add a file called entrypoint.sh. Here is the script:

#!/usr/bin/env bash
set -e
set -o pipefail
echo ">>> Running command"
echo ""
bash -c "set -e;  set -o pipefail; $1"

This is just a bash script. We use the set commands in the main script to ensure that, when an error occurs, the script exits with the exit code from the command or pipeline that failed. Because we want to run a command like kyverno apply, we need a way to execute that. That’s why we run bash again at the end with the same options and use $1 to represent the argument we will pass to our container. Our GitHub Action will need a way to require an input and pass that input as the argument to the Docker container.

Note: make sure the script is executable; use chmod +x entrypoint.sh

The action.yml

Action.yml defines our action and should be in the root of the git repo. Here is the action.yml for our Docker action:

name: 'kyverno-action'
description: 'Runs kyverno cli'
branding:
  icon: 'command'
  color: 'red'
inputs:
  command:
    description: 'kyverno command to run'
    required: true
runs:
  using: 'docker'
  image: 'Dockerfile'
  args:
    - ${{ inputs.command }}

Above, we give the action a name and description. We also set an icon and color. The icon and color is used on the GitHub Marketplace:

command icon and color as defined in action.yml (note that this is the REAL action; in this post we call the action kyverno-action as an example)

As stated earlier, we need to pass arguments to the container when it starts. To achieve that, we define a required input to the action. The input is called command but you can use any name.

In the run: section, we specify that this action uses Docker. When you use image: Dockerfile, the workflow will build the Docker image for you with a random name and then run it for you. When it runs the container, it passes the command input as an argument with args: Multiple arguments can be passed, but we only pass one.

Note: the use of a Dockerfile makes running the action quite slow because the image needs to be built every time the action runs. In a moment, we will see how to fix that.

Verify that the image works

On your machine that has Docker installed, build and run the container to verify that you can run the CLI. Run the commands below from the folder containing the Dockerfile:

docker build -t DOCKER_HUB_USER/kyverno-action:v1.0.0 .

docker run DOCKER_HUB_USER/kyverno-action:v1.0.0 "kyverno version"

Above, I presume you have an account on Docker Hub so that you can later push the image to it. Substitute DOCKER_HUB_USER with your Docker Hub username. You can of course use any registry you want.

The result of docker run should be similar to the result below:

>>> Running command

Version: v1.3.5-rc2-1-g3ab75095
Time: 2021-04-04_01:16:49AM
Git commit ID: main/3ab75095b70496bde674a71df08423beb7ba5fff

Note: if you want to build a specific version of the Kyverno CLI, you will need to modify the Dockerfile; the instructions I used build the latest version and includes release candidates

If docker run was successful, push the image to Docker Hub (or your registry):

docker push DOCKER_HUB_USER/kyverno-action:v1.0.0

Note: later, it will become clear why we push this container to a public registry

Publish to the marketplace

You are now ready to publish your action to the marketplace. One thing to be sure of is that the name of your action should be unique. Above, we used kyverno-action. When you run through the publishing steps, GitHub will check if the name is unique.

To see how to publish the action, check the following video:

video starts at the marketplace publishing step

Note that publishing to the marketplace is optional. Our action can still be used without it being published. Publishing just makes our action easier to discover.

Using the action

At this point, you can already use the action when you specify the exact release version. In the video, we created a release called v1.0.0 and optionally published it. The snippet below illustrates its use:

- name: Validate policies
  uses: gbaeke/kyverno-action@v1.0.0
  with:
    command: |
      kyverno apply ./policies --resource=./deploy/deployment.yaml

Running this action results in a docker build, followed by a docker run in the workflow:

The build step takes quite some time, which is somewhat annoying. Let’s fix that! In addition, we will let users use v1 instead of having to specify v1.0.0 or v1.0.1 etc…

Creating a v1 branch

By creating a branch called v1 and modifying action.yml to use a Docker image from a registry, we can make the action quicker and easier to use. Just create a branch in GitHub and call it v1. We’ll use the UI:

create the branch here; if it does not exist there will be a create option (here it exists already)

Make the v1 branch active and modify action.yml:

In action.yml, instead of image: ‘Dockerfile’, use the following:

image: 'docker://DOCKER_HUB_USER/kyverno-action:v1.0.0'

When you use the above statement, the image will be pulled instead of built from scratch. You can now use the action with @v1 at the end:

# run kyverno cli and use v1 instead of v1.0.0
- name: Validate policies
  uses: gbaeke/kyverno-action@v1
  with:
    command: |
      kyverno apply ./policies --resource=./deploy/deployment.yaml

In the worflow logs, you will see:

The action now pulls the image from Docker Hub and later runs it

Conclusion

We can conclude that building GitHub Actions with Docker is quick and fun. You can build your action any way you want, using the tools you like. Want to create a tool with Go, or Python or just Bash… just do it! If you do want to build a GitHub Action with JavaScript, then be sure to check out this article on devblogs.microsoft.com.

Managed Identity on Azure Arc Servers

Azure Arc โ€“ Azure Management | Microsoft Azure

When you install the Azure Arc agent on any physical or virtual server, either Windows or Linux, the machine suddenly starts living in a cloud world:

  • it appears in the Azure Portal
  • you can apply resource tags
  • you can check for security and regulatory compliance with Azure Policy
  • you can enable Update management
  • and much, much more…

Check Microsoft’s documentation for more information about Azure Arc for Servers to find out more. Below is a screenshot of such an Azure Arc-enabled Windows Server 2019 machine running on-premises with Insights enabled (on my laptop ๐Ÿ˜€):

Azure Arc-enabled Windows Server 2019

A somewhat lesser-known feature of Azure Arc is that these servers also have Managed Server Identity (MSI). After you have installed the Azure Arc agent, which normally installs to Program Files\AzureConnectedMachineAgent, two environment variables are set:

  • IMDS_ENDPOINT=http://localhost:40342
  • IDENTITY_ENDPOINT=http://localhost:40342/metadata/identity/oauth2/token

IMDS stands for Instance Metadata Service. On a regular Azure virtual machine, this service listens on the non-routable IP address of 169.254.169.254. On the virtual machine, you can make HTTP requests to that IP address without any issue. The traffic never leaves the virtual machine.

On an Azure Arc-enabled server, which can run anywhere, using the non-routable IP address is not feasible. Instead, the IMDS listens on a port on localhost as indicated by the environment variables.

The service can be used for all sorts of things. For example, I can make the following request (PowerShell):

Invoke-RestMethod -Headers @{"Metadata"="true"} -Method GET -Uri http://localhost:40342/metadata/instance?api-version=2020-06-01 | ConvertTo-Json

The result will be a JSON structure with most of the fields empty. That is not surprising since this is not an Azure VM and most fields are Azure-related (vmSize, fault domain, update domain, …). But it does show that the IMDS works, just like on a regular Azure VM.

Although there are many other things you can do, one of its most useful features is providing you with an access token to access Azure Resource Manager, Key Vault, or other services.

There are many ways to obtain an access token. The documentation contains an example in PowerShell that uses the environment variables and Invoke-WebRequest to get a token for https://management.azure.com.

A common requirement is code that needs to retrieve secrets from Azure Key Vault. Now we know that we can acquire a token via the IMDS, let’s see how we can do this with the Azure SDK for Python, which has full support for the IMDS on Azure Arc-enabled machines. The code below does the trick:

from azure.identity import ManagedIdentityCredential
from azure.keyvault.secrets import SecretClient

credentials = ManagedIdentityCredential()

secret_client = SecretClient(vault_url="https://gebakv.vault.azure.net", credential=credentials)
secret = secret_client.get_secret("notsecret")
print(secret.value)

Of course, you need Python installed with the following packages (use pip install):

  • azure-identity
  • azure-keyvault

Yes, the above code is all you need to use the managed identity of the Azure Arc-enabled server to authenticate to Key Vault and obtain the secret called notsecret. The functionality that makes the Python SDK work with Azure Arc can be seen here.

Of course, you need to make sure that the managed identity has the necessary access rights to Key Vault:

Managed Identity has Get permissions on Secrets

I have not looked at MSI Azure Arc support in the other SDKs but the Python SDK sure makes it easy!

Azure Key Vault Provider for Secrets Store CSI Driver

In the previous post, I talked about akv2k8s. akv2k8s is a Kubernetes controller that synchronizes secrets and certificates from Key Vault. Besides synchronizing to a regular secret, it can also inject secrets into pods.

Instead of akv2k8s, you can also use the secrets store CSI driver with the Azure Key Vault provider. As a CSI driver, its main purpose is to mount secrets and certificates as storage volumes. Next to that, it can also create regular Kubernetes secrets that can be used with an ingress controller or mounted as environment variables. That might be required if the application was not designed to read the secret from the file system.

In the previous post, I used akv2k8s to grab a certificate from Key Vault, create a Kubernetes secret and use that secret with nginx ingress controller:

certificate in Key Vault ------akv2aks periodic sync -----> Kubernetes secret ------> nginx ingress controller

Let’s briefly look at how to do this with the secrets store CSI driver.

Installation

Follow the guide to install the Helm chart with Helm v3:

helm repo add csi-secrets-store-provider-azure https://raw.githubusercontent.com/Azure/secrets-store-csi-driver-provider-azure/master/charts
helm install csi-secrets-store-provider-azure/csi-secrets-store-provider-azure --generate-name

This will install the components in the current Kubernetes namespace.

Easy no?

Syncing the certificate

Following the same example as with akv2aks, we need to point at the certificate in Key Vault, set the right permissions, and bring the certificate down to Kubernetes.

You will first need to decide how to access Key Vault. You can use the managed identity of your AKS cluster or be more granular and use pod identity. If you have setup AKS with a managed identity, that is the simplest solution. You just need to grab the clientId of the managed identity like so:

az aks show -g <resource group> -n <aks cluster name> --query identityProfile.kubeletidentity.clientId -o tsv

Next, create a file with the content below and apply it to your cluster in a namespace of your choosing.

apiVersion: secrets-store.csi.x-k8s.io/v1alpha1
kind: SecretProviderClass
metadata:
  name: azure-gebakv
  namespace: YOUR NAMESPACE
spec:
  provider: azure
  secretObjects:
  - secretName: nginx-cert
    type: kubernetes.io/tls
    data:
    - objectName: nginx
      key: tls.key
    - objectName: nginx
      key: tls.crt
  parameters:
    useVMManagedIdentity: "true"
    userAssignedIdentityID: "CLIENTID YOU OBTAINED ABOVE" 
    keyvaultName: "gebakv"         
    objects:  |
      array:
        - |
          objectName: nginx
          objectType: secret        
    tenantId: "ID OF YOUR AZURE AD TENANT"

Compared to the akv2k8s controller, the above configuration is a bit more complex. In the parameters section, in the objects array, you specify the name of the certificate in Key Vault and its object type. Yes, you saw that correctly, the objectType actually has to be secret for this to work.

The other settings are self-explanatory: we use the managed identity, set its clientId and in keyvaultName we set the short name of our Key Vault.

The settings in the parameters section are actually sufficient to mount the secret/certificate in a pod. With the secretObjects section though, we can also ask for the creation of regular Kubernetes secrets. Here, we ask for a secret of type kubernetes.io/tls with name nginx-cert to be created. You need to explicitly set both the tls.key and the tls.crt value and correctly reference the objectName in the array.

The akv2k8s controller is simpler to use as you only need to point it to your certificate in Key Vault (and specify it’s a certificate, not a secret) and set a secret name. There is no need to set the different values in the secret.

Using the secret

The advantage of the secrets store CSI driver is that the secret is only mounted/created when an application requires it. That also means we have to instruct our application to mount the secret explicitly. You do that via a volume as the example below illustrates (part of a deployment):

spec:
      containers:
      - name: realtimeapp
        image: gbaeke/fluxapp:1.0.2
        volumeMounts:
          - mountPath: "/mnt/secrets-store"
            name: secrets-store-inline
            readOnly: true
        env:
        - name: REDISHOST
          value: "redis:6379"
        resources:
          requests:
            cpu: 25m
            memory: 50Mi
          limits:
            cpu: 150m
            memory: 150Mi
        ports:
        - containerPort: 8080
      volumes:
      - name: secrets-store-inline
        csi:
          driver: secrets-store.csi.k8s.io
          readOnly: true
          volumeAttributes:
            secretProviderClass: "azure-gebakv"

In the above YAML, the following happens:

  • in volumes: we create a volume called secrets-store-inline and use the csi driver to mount the secrets we specified in the SecretProviderClass we created earlier (azure-gebakv)
  • in volumeMounts: we mount the volume on /mnt/secrets-store

Because we used secretObjects in our SecretProviderClass, this mount is accompanied by the creation of a regular Kubernetes secret as well.

When you remove the deployment, the Kubernetes secret will be removed instead of lingering behind for all to see.

Of course, the pods in my deployment do not need the mounted volume. It was not immediately clear to me how to avoid the mount but still create the Kubernetes secret (not exactly the point of a CSI driver ๐Ÿ˜€). On the other hand, there is a way to have the secret created as part of ingress controller creation. That approach is more useful in this case because we want our ingress controller to use the certificate. More information can be found here. In short, it roughly works as follows:

  • instead of creating and mounting a volume in your application pod, a volume should be created and mounted on the ingress controller
  • to do so, you modify the deployment of your ingress controller (e.g. ingress-nginx) with extraVolumes: and extraVolumeMounts: sections; depending on the ingress controller you use, other settings might be required

Be aware that you need to enable auto rotation of secrets manually and that it is an alpha feature at this point (December 2020). The akv2k8s controller does that for you out of the box.

Conclusion

Both the akv2k8s controller and the Secrets Store CSI driver (for Azure) can be used to achieve the same objective: syncing secrets, keys and certificates from Key Vault to AKS. In my experience, the akv2k8s controller is easier to use. The big advantage of the Secrets Store CSI driver is that it is a broader solution (not just for AKS) and supports multiple secret stores. Next to Azure Key Vault, it also supports Hashicorp’s Vault for example. My recommendation: for Azure Key Vault and AKS, keep it simple and try akv2k8s first!

Deploy and bootstrap your Kubernetes cluster with Azure DevOps and GitOps

A while ago, I published a post about deploying AKS with Azure DevOps with extras like Nginx Ingress, cert-manager and several others. An Azure Resource Manager (ARM) template is used to deploy Azure Kubernetes Service (AKS). The extras are installed with Helm charts and Helm installer tasks. I mainly use it for demo purposes but I often refer to it in my daily work as well.

Although this works, there is another approach that combines an Azure DevOps pipeline with GitOps. From a high level point of view, that works as follows:

  • Deploy AKS with an Azure DevOps pipeline: declarative and idempotent thanks to the ARM template; the deployment is driven from an Azure DevOps pipeline but other solutions such as GitHub Actions will do as well (push)
  • Use a GitOps tool to deploy the GitOps agents on AKS and bootstrap the cluster by pointing the GitOps tool to a git repository (pull)

In this post, I will use Flux v2 as the GitOps tool of choice. Other tools, such as Argo CD, are capable of achieving the same goal. Note that there are ways to deploy Kubernetes using GitOps in combination with the Cluster API (CAPI). CAPI is quite a beast so let’s keep this post a bit more approachable. ๐Ÿ˜‰

Let’s start with the pipeline (YAML):

# AKS deployment pipeline
trigger: none

variables:
  CLUSTERNAME: 'CLUSTERNAME'
  RG: 'CLUSTER_RESOURCE_GROUP'
  GITHUB_REPO: 'k8s-bootstrap'
  GITHUB_USER: 'GITHUB_USER'
  KEY_VAULT: 'KEYVAULT_SHORTNAME'

stages:
- stage: DeployGitOpsCluster
  jobs:
  - job: 'Deployment'
    pool:
      vmImage: 'ubuntu-latest'
    steps: 
    # DEPLOY AKS
    - task: AzureResourceGroupDeployment@2
      inputs:
        azureSubscription: 'SUBSCRIPTION_REF'
        action: 'Create Or Update Resource Group'
        resourceGroupName: '$(RG)'
        location: 'YOUR LOCATION'
        templateLocation: 'Linked artifact'
        csmFile: 'aks/deploy.json'
        csmParametersFile: 'aks/deployparams.gitops.json'
        overrideParameters: '-clusterName $(CLUSTERNAME)'
        deploymentMode: 'Incremental'
        deploymentName: 'aks-gitops-deploy'
       
    # INSTALL KUBECTL
    - task: KubectlInstaller@0
      name: InstallKubectl
      inputs:
        kubectlVersion: '1.18.8'

    # GET CREDS TO K8S CLUSTER WITH ADMIN AND INSTALL FLUX V2
    - task: AzureCLI@1
      name: RunAzCLIScripts
      inputs:
        azureSubscription: 'AzureMPN'
        scriptLocation: 'inlineScript'
        inlineScript: |
          export GITHUB_TOKEN=$(GITHUB_TOKEN)
          az aks get-credentials -g $(RG) -n $(CLUSTERNAME) --admin
          msi="$(az aks show -n CLUSTERNAME -g CLUSTER_RESOURCE_GROUP | jq .identityProfile.kubeletidentity.objectId -r)"
          az keyvault set-policy --name $(KEY_VAULT) --object-id $msi --secret-permissions get
          curl -s https://toolkit.fluxcd.io/install.sh | sudo bash
          flux bootstrap github --owner=$(GITHUB_USER) --repository=$(GITHUB_REPO) --branch=main --path=demo-cluster --personal

A couple of things to note here:

  • The above pipeline contains several strings in UPPERCASE; replace them with your own values
  • GITHUB_TOKEN is a secret defined in the Azure DevOps pipeline and set as an environment variable in the last task; it is required for the flux bootstrap command to configure the GitHub repo (e.g. deploy key)
  • the AzureResourceGroupDeployment task deploys the AKS cluster based on parameters defined in deployparams.gitops.json; that file is in a private Azure DevOps git repo; I have also added them to the gbaeke/k8s-bootstrap repository for reference
  • The AKS deployment uses a managed identity versus a service principal with manually set client id and secret (recommended)
  • The flux bootstrap command deploys an Azure Key Vault to Kubernetes Secrets controller that requires access to Key Vault; the script in the last task retrieves the managed identity object id and uses az keyvault set-policy to grant get key permissions; if you delete and recreate the cluster many times, you will have several UNKNOWN access policies at the Key Vault level

The pipeline is of course short due to the fact that nginx-ingress, cert-manager, dapr, KEDA, etc… are all deployed via the gbaeke/k8s-bootstrap repo. The demo-cluster folder in that repo contains a source and four kustomizations:

  • source: reference to another git repo that contains the actual deployments
  • k8s-akv2k8s-kustomize.yaml: deploys the Azure Key Vault to Kubernetes Secrets controller (akv2k8s)
  • k8s-secrets-kustomize.yaml: deploys secrets via custom resources picked up by the akv2k8s controller; depends on akv2k8s
  • k8s-common-kustomize.yaml: deploys all components in the ./deploy folder of gbaeke/k8s-common (nginx-ingress, external-dns, cert-manager, KEDA, dapr, …)

Overall, the big picture looks like this:

Note that the kustomizations that point to ./akv2k8s and ./deploy actually deploy HelmReleases to the cluster. For instance in ./akv2k8s, you will find the following manifest:

---
apiVersion: helm.toolkit.fluxcd.io/v2beta1
kind: HelmRelease
metadata:
  name: akv2k8s
  namespace: flux-system
spec:
  chart:
    spec:
      chart: akv2k8s
      sourceRef:
        kind: HelmRepository
        name: akv2k8s-repo
  interval: 5m0s
  releaseName: akv2k8s
  targetNamespace: akv2k8s

This manifest tells Flux to deploy a Helm chart, akv2k8s, from the HelmRepository source akv2k8s-repo that is defined as follows:

---
apiVersion: source.toolkit.fluxcd.io/v1beta1
kind: HelmRepository
metadata:
  name: akv2k8s-repo
  namespace: flux-system
spec:
  interval: 1m0s
  url: http://charts.spvapi.no/

It is perfectly valid to use a kustomization that deploys manifests that contain resources of kind HelmRelease and HelmRepository. In fact, you can even patch those via a kustomization.yaml file if you wish.

You might wonder why I deploy the akv2k8s controller first, and then deploy a secret with the following manifest (upercase strings to be replaced):

apiVersion: spv.no/v1
kind: AzureKeyVaultSecret
metadata:
  name: secret-sync 
  namespace: flux-system
spec:
  vault:
    name: KEYVAULTNAME # name of key vault
    object:
      name: SECRET # name of the akv object
      type: secret # akv object type
  output: 
    secret: 
      name: SECRET # kubernetes secret name
      dataKey: values.yaml # key to store object value in kubernetes secret

The external-dns chart I deploy in later steps requires configuration to be able to change DNS settings in Cloudflare. Obviously, I do not want to store the Cloudflare secret in the k8s-common git repo. One way to solve that is to store the secrets in Azure Key Vault and then grab those secrets and convert them to Kubernetes secrets. The external-dns HelmRelease can then reference the secret to override values.yaml of the chart. Indeed, that requires storing a file in Key Vault which is easy to do like so (replace uppercase strings):

az keyvault secret set --name SECRETNAME --vault-name VAULTNAME --file ./YOURFILE.YAML

You can call the secret what you want but the Kubernetes secret dataKey should be values.yaml for the HelmRelease to work properly.

There are other ways to work with secrets in GitOps. The Flux v2 documentation mentions SealedSecrets and SOPS and you are of course welcome to use that.

Take a look at the different repos I outlined above to see the actual details. I think it makes the deployment of a cluster and bootstrapping the cluster much easier compared to suing a bunch of Helm install tasks and manifest deployments in the pipeline. What do you think?

An introduction to Flux v2

If you have read my blog and watched my Youtube channel, you know I have worked with Flux in the past. Flux, by weaveworks, is a GitOps Kubernetes Operator that ensures that your cluster state matches the desired state described in a git repository. There are other solutions as well, such as Argo CD.

With Flux v2, GitOps on Kubernetes became a lot more powerful and easier to use. Flux v2 is built on a set of controllers and APIs called the GitOps Toolkit. The toolkit contains the following components:

  • Source controller: allows you to create sources such as a GitRepository or a HelmRepository; the source controller acts on several custom resource definitions (CRDs) as defined in the docs
  • Kustomize controller: runs continuous delivery pipelines defined with Kubernetes manifests (YAML) files; although you can use kustomize and define kustomization.yaml files, you do not have to; internally though, Flux v2 uses kustomize to deploy your manifests; the kustomize controller acts on Kustomization CRDs as defined here
  • Helm controller: deploy your workloads based on Helm charts but do so declaratively; there is no need to run helm commands; see the docs for more information
  • Notification controller: responds to incoming events (e.g. from a git repo) and sends outgoing events (e.g. to Teams or Slack); more info here

If you throw it all together, you get something like this:

GitOps Toolkit components that make up Flux v2 (from https://toolkit.fluxcd.io/)

Getting started

To get started, you should of course look at the documentation over at https://toolkit.fluxcd.io. I also created a series of videos about Flux v2. The first one talks about Flux v2 in general and shows how to bootstrap a cluster.

Part 1 in the series about Flux v2

Although Flux v2 works with other source control systems than GitHub, for instance GitLab, I use GitHub in the above video. I also use kind, to make it easy to try out Flux v2 on your local machine. In subsequent videos, I use Azure Kubernetes Services (AKS).

In Flux v2, it is much easier to deploy Flux on your cluster with the flux bootstrap command. Flux v2 itself is basically installed and managed via GitOps principles by pushing all Flux v2 manifests to a git repository and running reconciliations to keep the components running as intended.

Kustomize

Flux v1 already supported kustomize but v2 takes it to another level. Whenever you want to deploy to Kubernetes with YAML manifests, you will create a kustomization, which is based on the Kustomization CRD. A kustomization is defined as below:

apiVersion: kustomize.toolkit.fluxcd.io/v1beta1
kind: Kustomization
metadata:
  name: realtimeapp-dev
  namespace: flux-system
spec:
  healthChecks:
  - kind: Deployment
    name: realtime-dev
    namespace: realtime-dev
  - kind: Deployment
    name: redis-dev
    namespace: realtime-dev
  interval: 1m0s
  path: ./deploy/overlays/dev
  prune: true
  sourceRef:
    kind: GitRepository
    name: realtimeapp-infra
  timeout: 2m0s
  validation: client

A kustomization requires a source. In this case, the source is a git repository called realtimeapp-infra that was already defined in advance. The source just points to a public git repository on Github: https://github.com/gbaeke/realtimeapp-infra.

The source contains a deploy folder, which contains a bases and an overlays folder. The kustomization points to the ./deploy/overlays/dev folder as set in path. That folder contains a kustomization.yaml file that deploys an application in a development namespace and uses the base from ./deploy/bases/realtimeapp as its source. If you are not sure what kustomize exactly does, I made a video that tries ๐Ÿ˜‰ to explain it:

An introduction to kustomize

It is important to know that you do not need to use kustomize in your source files. If you point a Flux v2 kustomization to a path that just contains a bunch of YAML files, it will work equally well. You do not have to create a kustomization.yaml file in that folder that lists the resources (YAML files) that you want to deploy. Internally though, Flux v2 will use kustomize to deploy the manifests and uses the deployment order that kustomize uses: first namespaces, then services, then deployments, etc…

The interval in the kustomization (above set at 1 minute) means that your YAML files are applied at that interval, even if the source has not changed. This ensures that, if you modified resources on your cluster, the kustomization will reset the changes to the state as defined in the source. The source itself has its own interval. If you set a GitRepository source to 1 minute, the source is checked every 1 minute. If the source has changes, the kustomizations that depend on the source will be notified and proceed to deploy the changes.

A GitRepository source can refer to a specific branch, but can also refer to a semantic versioning tag if you use a semver range in the source. See checkout strategies for more information.

Deploying YAML manifests

If the above explanation of sources and kustomizations does not mean much to you, I created a video that illustrates these aspects more clearly:

In the above video, the source that points to https://github.com/gbaeke/realtimeapp-infra gets created first (see it at this mark). Next, I create two kustomizations, one for development and one for production. I use a kustomize base for the application plus two overlays, one for dev and one for production.

What to do when the app container images changes?

Flux v1 has a feature that tracks container images in a container registry and updates your cluster resources with a new image based on a filter you set. This requires read/write access to your git repository because Flux v1 set the images in your source files. Flux v2 does not have this feature yet (November 2020, see https://toolkit.fluxcd.io/roadmap).

In my example, I use a GitHub Action in the application source code repository to build and push the application image to Docker Hub. The GitHub action triggers a build job on two events:

  • push to main branch: build a container image with a short sha as the tag (e.g. gbaeke/flux-rt:sha-94561cb
  • published release: build a container image with the release version as the tag (e.g. gbaeke/flux-rt:1.0.1)

When the build is caused by a push to main, the update-dev-image job runs. It modifies kustomization.yaml in the dev overlay with kustomize edit:

update-dev-image:
    runs-on: ubuntu-latest
    if: contains(github.ref, 'heads')
    needs:
    - build
    steps:
    - uses: imranismail/setup-kustomize@v1
      with:
        kustomize-version: 3.8.6
    - run: git clone https://${REPO_TOKEN}@github.com/gbaeke/realtimeapp-infra.git .
      env:
        REPO_TOKEN: ${{secrets.REPO_TOKEN}}
    - run: kustomize edit set image gbaeke/flux-rt:sha-$(git rev-parse --short $GITHUB_SHA)
      working-directory: ./deploy/overlays/dev
    - run: git add .
    - run: |
        git config user.email "$EMAIL"
        git config user.name "$GITHUB_ACTOR"
      env:
        EMAIL: ${{secrets.EMAIL}}
    - run: git commit -m "Set dev image tag to short sha"
    - run: git push

Similarly, when the build is caused by a release, the image is updated in the production overlay’s kustomization.yaml file.

Conclusion

If you are interested in GitOps as an alternative for continuous delivery to Kubernetes, do check out Flux v2 and see if it meets your needs. I personally like it a lot and believe that they are setting the standard for GitOps on Kubernetes. I have not covered Helm deployments, monitoring and alerting features yet. I will create additional videos and posts about those features in the near future. Stay tuned!

GitOps with Kubernetes: a better way to deploy?

I recently gave a talk at TechTrain, a monthly event in Mechelen (Belgium), hosted by Cronos. The talk is called “GitOps with Kubernetes: a better way to deploy” and is an introduction to GitOps with Weaveworks Flux as an example.

You can find a re-recording of the presentation on Youtube:

%d bloggers like this: