Taking Azure Container Apps for a spin

At Ignite November 2021, Microsoft released Azure Container Apps as a public preview. It allows you to run containerized applications on a serverless platform, in the sense that you do not have to worry about the underlying infrastructure.

The underlying infrastructure is Kubernetes (AKS) as the control plane with additional software such as:

  • Dapr: distributed application runtime to easily work with state, pub/sub and other Dapr building blocks
  • KEDA: Kubernetes event-driven autoscaler so you can use any KEDA supported scaler, in addition to scaling based on HTTP traffic, CPU and memory
  • Envoy: used to provide ingress functionality and traffic splitting for blue-green deployment, A/B testing, etc…

Your apps actually run on Azure Container Instances (ACI). ACI was always meant to be used as raw compute to build platforms with and this is a great use case.

Note: there is some discussion in the community whether ACI (via AKS virtual nodes) is used or not; I will leave it in for now but in the end, it does not matter too much as the service is meant to hide this complexity anyway

Azure Container Apps does not care about the runtime or programming model you use. Just use whatever feels most comfortable and package it as a container image.

In this post, we will deploy an application that uses Dapr to save state to Cosmos DB. Along the way, we will explain most of the concepts you need to understand to use Azure Container Apps in your own scenarios. The code I am using is on GitHub and written in Go.

Configure the Azure CLI

In this post, we will use the Azure CLI exclusively to perform all the steps. Instead of the Azure CLI, you can also use ARM templates or Bicep. If you want to play with a sample that deploys multiple container apps and uses Bicep, be sure to check out this great Azure sample.

You will need to have the Azure CLI installed and also add the Container Apps extension:

az extension add \
  --source https://workerappscliextension.blob.core.windows.net/azure-cli-extension/containerapp-0.2.0-py2.py3-none-any.whl

The extension allows you to use commands like az containerapp create and az containerapp update.

Create an environment

An environment runs one or more container apps. A container app can run multiple containers and can have revisions. If you know how Kubernetes works, each revision of a container app is actually a scaled collection of Kubernetes pods, using the scalers discussed above. Each revision can be thought of as a separate Kubernetes Deployment/ReplicaSet that runs a specific version of your app. Whenever you modify your app, depending on the type of modification, you get a new revision. You can have multiple active revisions and set traffic weights to distribute traffic as you wish.

Container apps, revisions, pods, and containers

Note that above, although you see multiple containers in a pod in a revision, that is not the most common use case. Most of the time, a pod will have only one application container. That is entirely up to you and the rationale behind using one or more containers is similar to multi-container pods in Kubernetes.

To create an environment, be sure to register or re-register the Microsoft.Web provider. That provider has the kubeEnvironments resource type, which represents a Container App environment.

az provider register --namespace Microsoft.Web

Next, create a resource group:

az group create --name rg-dapr --location northeurope

I have chosen North Europe here, but the location of the resource group does not really matter. What does matter is that you create the environment in either North Europe or Canada Central at this point in time (November 2021).

Every environment needs to be associated with a Log Analytics workspace. You can use that workspace later to view the logs of your container apps. Let’s create such a workspace in the resource group we just created:

az monitor log-analytics workspace create \
  --resource-group rg-dapr \
  --workspace-name dapr-logs

Next, we want to retrieve the workspace client id and secret. We will need that when we create the Container Apps environment. Commands below expect the use of bash:

LOG_ANALYTICS_WORKSPACE_CLIENT_ID=`az monitor log-analytics workspace show --query customerId -g rg-dapr -n dapr-logs --out tsv`
LOG_ANALYTICS_WORKSPACE_CLIENT_SECRET=`az monitor log-analytics workspace get-shared-keys --query primarySharedKey -g rg-dapr -n dapr-logs --out tsv`

Now we can create the environment in North Europe:

az containerapp env create \
  --name dapr-ca \
  --resource-group rg-dapr \
  --logs-workspace-id $LOG_ANALYTICS_WORKSPACE_CLIENT_ID \
  --logs-workspace-key $LOG_ANALYTICS_WORKSPACE_CLIENT_SECRET \
  --location northeurope

The Container App environment shows up in the portal like so:

Container App Environment in the portal

There is not a lot you can do in the portal, besides listing the apps in the environment. Provisioning an environment is extremely quick, in my case a matter of seconds.

Deploying Cosmos DB

We will deploy a container app that uses Dapr to write key/value pairs to Cosmos DB. Let’s deploy Cosmos DB:

uniqueId=$RANDOM
az cosmosdb create \
  --name dapr-cosmosdb-$uniqueId \
  --resource-group rg-dapr \
  --locations regionName='northeurope'

az cosmosdb sql database create \
    -a dapr-cosmosdb-$uniqueId \
    -g rg-dapr \
    -n dapr-db

az cosmosdb sql container create \
    -a dapr-cosmosdb-$uniqueId \
    -g rg-dapr \
    -d dapr-db \
    -n statestore \
    -p '/partitionKey' \
    --throughput 400

The above commands create the following resources:

  • A Cosmos DB account in North Europe: note that this uses session-level consistency (remember that for later in this post 😉)
  • A Cosmos DB database that uses the SQL API
  • A Cosmos DB container in that database, called statestore (can be anything you want)

In Cosmos DB Data Explorer, you should see:

statestore collection will be used as a State Store in Dapr

Deploying the Container App

We can use the following command to deploy the container app and enable Dapr on it:

az containerapp create \
  --name daprstate \
  --resource-group rg-dapr \
  --environment dapr-ca \
  --image gbaeke/dapr-state:1.0.0 \
  --min-replicas 1 \
  --max-replicas 1 \
  --enable-dapr \
  --dapr-app-id daprstate \
  --dapr-components ./components-cosmosdb.yaml \
  --target-port 8080 \
  --ingress external

Let’s unpack what happens when you run the above command:

  • A container app daprstate is created in environment dapr-ca
  • The container app will have an initial revision (revision 1) that runs one container in its pod; the container uses image gbaeke/dapr-state:1.0.0
  • We turn off scaling by setting min and max replicas to 1
  • We enable ingress with the type set to external. That configures a public IP address and DNS name to reach our container app on the Internet; Envoy proxy is used under the hood to achieve this; TLS is automatically configured but we do need to tell the proxy the port our app listens on (–target-port 8080)
  • Dapr is enabled and requires that our app gets a Dapr id (–enable-dapr and –dapr-app-id daprstate)

Because this app uses the Dapr SDK to write key/value pairs to a state store, we need to configure this. That is were the –dapr-components parameter comes in. The component is actually defined in a file components-cosmosdb.yaml:

- name: statestore
  type: state.azure.cosmosdb
  version: v1
  metadata:
    - name: url
      value: YOURURL
    - name: masterkey
      value: YOURMASTERKEY
    - name: database
      value: YOURDB
    - name: collection
      value: YOURCOLLECTION

In the file, the name of our state store is statestore but you can choose any name. The type has to be state.azure.cosmosdb which requires the use of several metadata fields to specify the URL to your Cosmos DB account, the key to authenticate, the database, and collection.

In the Go code, the name of the state store is configurable via environment variables or arguments and, by total coincidence, defaults to statestore 😉.

func main() {
	fmt.Printf("Welcome to super api\n\n")

	// flags
	... code omitted for brevity
	// State store name
	f.String("statestore", "statestore", "State store name")

The flag is used in the code that writes to Cosmos DB with the Dapr SDK (s.config.Statestore in the call to daprClient.SaveState below):

// write data to Dapr statestore
	ctx := r.Context()
	if err := s.daprClient.SaveState(ctx, s.config.Statestore, state.Key, []byte(state.Data)); err != nil {
		w.WriteHeader(http.StatusInternalServerError)
		fmt.Fprintf(w, "Error writing to statestore: %v\n", err)
		return
	} else {
		w.WriteHeader(http.StatusOK)
		fmt.Fprintf(w, "Successfully wrote to statestore\n")
	}

After running the az containerapp create command, you should see the following output (redacted):

{
  "configuration": {
    "activeRevisionsMode": "Multiple",
    "ingress": {
      "allowInsecure": false,
      "external": true,
      "fqdn": "daprstate.politegrass-37c1a51f.northeurope.azurecontainerapps.io",
      "targetPort": 8080,
      "traffic": [
        {
          "latestRevision": true,
          "revisionName": null,
          "weight": 100
        }
      ],
      "transport": "Auto"
    },
    "registries": null,
    "secrets": null
  },
  "id": "/subscriptions/SUBID/resourceGroups/rg-dapr/providers/Microsoft.Web/containerApps/daprstate",
  "kind": null,
  "kubeEnvironmentId": "/subscriptions/SUBID/resourceGroups/rg-dapr/providers/Microsoft.Web/kubeEnvironments/dapr-ca",
  "latestRevisionFqdn": "daprstate--6sbsmip.politegrass-37c1a51f.northeurope.azurecontainerapps.io",
  "latestRevisionName": "daprstate--6sbsmip",
  "location": "North Europe",
  "name": "daprstate",
  "provisioningState": "Succeeded",
  "resourceGroup": "rg-dapr",
  "tags": null,
  "template": {
    "containers": [
      {
        "args": null,
        "command": null,
        "env": null,
        "image": "gbaeke/dapr-state:1.0.0",
        "name": "daprstate",
        "resources": {
          "cpu": 0.5,
          "memory": "1Gi"
        }
      }
    ],
    "dapr": {
      "appId": "daprstate",
      "appPort": null,
      "components": [
        {
          "metadata": [
            {
              "name": "url",
              "secretRef": "",
              "value": "https://ACCOUNTNAME.documents.azure.com:443/"
            },
            {
              "name": "masterkey",
              "secretRef": "",
              "value": "MASTERKEY"
            },
            {
              "name": "database",
              "secretRef": "",
              "value": "dapr-db"
            },
            {
              "name": "collection",
              "secretRef": "",
              "value": "statestore"
            }
          ],
          "name": "statestore",
          "type": "state.azure.cosmosdb",
          "version": "v1"
        }
      ],
      "enabled": true
    },
    "revisionSuffix": "",
    "scale": {
      "maxReplicas": 1,
      "minReplicas": 1,
      "rules": null
    }
  },
  "type": "Microsoft.Web/containerApps"
}

The output above gives you a hint on how to define the Container App in an ARM template. Note the template section. It defines the containers that are part of this app. We have only one container with default resource allocations. It is possible to set environment variables for your containers but there are none in this case. We will set one later.

Also note the dapr section. It defines the app’s Dapr id and the components it can use.

Note: it is not a good practice to enter secrets in configuration files as we did above. To fix that:

  • add a secret to the Container App in the az containerapp create command via the --secrets flag. E.g. --secrets cosmosdb='YOURCOSMOSDBKEY'
  • in components-cosmosdb.yaml, replace value: YOURMASTERKEY with secretRef: cosmosdb

The URL for the app is https://daprstate.politegrass-37c1a51f.northeurope.azurecontainerapps.io. When I browse to it, I just get a welcome message: Hello from Super API on Container Apps.

Every revision also gets a URL. The revision URL is https://daprstate–6sbsmip.politegrass-37c1a51f.northeurope.azurecontainerapps.io. Of course, this revision URL gives the same result. Our app has only one revision.

Save state

The application has a /state endpoint you can post a JSON payload to in the form of:

{
  "key": "keyname",
  "data": "datatostoreinkey"
}

We can use curl to try this:

curl -v -H "Content-type: application/json" -d '{ "key": "cool","data": "somedata"}' 'https://daprstate.politegrass-37c1a51f.northeurope.azurecontainerapps.io/state'

Trying the curl command will result in an error because Dapr wants to use strong consistency with Cosmos DB and we configured it for session-level consistency. That is not very relevant for now as that is related to Dapr and not Container Apps. Switching the Cosmos DB account to strong consistency will fix the error.

Update the container app

Let’s see what happens when we update the container app. We will add an environment variable WELCOME to change the welcome message that the app displays. Run the following command:

az containerapp update \
  --name daprstate \
  --resource-group rg-dapr \
  --environment-variables WELCOME='Hello from new revision'

The template section in the JSON output is now:

"template": {
    "containers": [
      {
        "args": null,
        "command": null,
        "env": [
          {
            "name": "WELCOME",
            "secretRef": null,
            "value": "Hello from new revision"
          }
        ],
        "image": "gbaeke/dapr-state:1.0.0",
        "name": "daprstate",
        "resources": {
          "cpu": 0.5,
          "memory": "1Gi"
        }
      }
    ]

It is important to realize that, when the template changes, a new revision will be created. We now have two revisions, reflected in the portal as below:

Container App with two revisions

The new revision is active and receives 100% of the traffic. When we hit the / endpoint, we get Hello from new revision.

The idea here is that you deploy a new revision and test it before you make it active. Another option is to send a small part of the traffic to the new revision and see how that goes. It’s not entirely clear to me how you can automate this, including automated tests, similar to how progressive delivery controllers like Argo Rollouts and Flagger work. Tip to the team to include this! 😉

The az container app create and update commands can take a lot of parameters. Use az container app update –help to check what is supported. You will also see several examples.

Check the logs

Let’s check the container app logs that are sent to the Log Analytics workspace attached to the Container App environment. Make sure you still have the log analytics id in $LOG_ANALYTICS_WORKSPACE_CLIENT_ID:

az monitor log-analytics query   --workspace $LOG_ANALYTICS_WORKSPACE_CLIENT_ID   --analytics-query "ContainerAppConsoleLogs_CL | where ContainerAppName_s == 'daprstate' | project ContainerAppName_s, Log_s, TimeGenerated | take 50"   --out table

This will display both logs from the application container and the Dapr logs. One of the log entries shows that the statestore was successfully initialized:

... msg="component loaded. name: statestore, type: state.azure.cosmosdb/v1"

Conclusion

We have only scratched the surface here but I hope this post gave you some insights into concepts such as environments, container apps, revisions, ingress, the use of Dapr and logging. There is much more to look at such as virtual network integration, setting up scale rules (e.g. KEDA), automated deployments, and much more… Stay tuned!

Kubernetes Blue-Green deployments with Argo Rollouts

In this post, we will take a look at 🟦/🟩 blue-green deployments in Kubernetes. With blue-green deployments, you deploy a new version of an application or service next to the live and stable version. After manual or automatic checks, you promote the new version to become the live version. Switching between versions is simply a networking change. This could be a change in a router configuration or, in the case of Kubernetes, a change in a Kubernetes service.

Note: there often is confusion about what is the 🟦 blue and what is the 🟩 green service; usually the green service is the live and stable one; the blue service is the newly deployed preview service you intend to promote; some documents switch it around; I sometimes do that as well, for instance on my YouTube channel 😉

A Kubernetes deployment resource does not have a StrategyType for blue-green deployments. It only supports RollingUpdate or Recreate. You can easily work around that with multiple deployments and services, as discussed by Nills Franssens here: Simple Kubernetes blue-green deployments.

When I need to do blue-green, I prefer using a progressive delivery controller such as Argo Rollouts or Flagger. They are both excellent pieces of software that make it easy to do blue-green deployments, in addition to canary deployments and automated tests. In this post, we will look at Argo Rollouts.

Want to see a video instead?

Installing Argo Rollouts

Installing Argo Rollouts is documented here. For a quick install, just do:

kubectl create namespace argo-rollouts
kubectl apply -n argo-rollouts -f https://github.com/argoproj/argo-rollouts/releases/latest/download/install.yaml

Argo Rollouts comes with a kubectl plugin for its CLI. Install it with brew install argoproj/tap/kubectl-argo-rollouts. That allows you to run the CLI with kubectl argo rollouts. If you do not use brew, install the plugin manually.

Deploy your application with a Rollout

Argo Rollouts uses a replacement for a Deployment resource: a Rollout. The YAML for a Rollout is almost identical to a Deployment except that the apiVersion and Kind are different. In the spec you can add a strategy section to specify whether you want a blueGreen or a canary rollout. Below is an example of a rollout for a simple API:

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: superapi
spec:
  replicas: 2
  selector:
    matchLabels:
      app: superapi
  template:
    metadata:
      labels:
        app: superapi
    spec:
      containers:
      - name: superapi
        image: ghcr.io/gbaeke/super:1.0.2
        resources:
          requests:
            memory: "128Mi"
            cpu: "50m"
          limits:
            memory: "128Mi"
            cpu: "50m"
        env:
          - name: WELCOME
            valueFrom:
              configMapKeyRef:
                name: superapi-config
                key: WELCOME
        ports:
        - containerPort: 8080
  strategy:
    blueGreen:
      activeService: superapi-svc-active
      previewService: superapi-svc-preview
      autoPromotionEnabled: false

You will notice that the blueGreen strategy requires two services: an activeService and a previewService. Both settings refer to a Kubernetes service resource. Below is the activeService (previewService is similar and uses the same selector):

kind: Service
apiVersion: v1
metadata:
  name:  superapi-svc-active
spec:
  selector:
    app:  superapi
  type:  ClusterIP
  ports:
  - name:  http
    port:  80
    targetPort:  8080

The only thing we have to do, in this example, is to deploy the rollout and the two services with kubectl apply. In this post, however, we will use Kustomize to deploy everything.

Deploying a rollout with Kustomize

To deploy the rollout and its services with Kustomize, we can use the kustomization.yaml below:

apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
namespace: blue-green

nameSuffix: -geba
namePrefix: dev-

commonLabels:
  app: superapi
  version: v1
  env: dev


configurations:
  - https://argoproj.github.io/argo-rollouts/features/kustomize/rollout-transform.yaml

resources:
  - namespace.yaml
  - rollout.yaml
  - service-active.yaml
  - service-preview.yaml

configMapGenerator:
- name: superapi-config
  literals:
    - WELCOME=Hello from v1!
    - PORT=8080   

With Kustomize, we can ensure we deploy our resources to a specific namespace. Above, that is the blue-green namespace. We also add a prefix and suffix to the names of Kubernetes resources we create and we add labels as well (commonLabels). For this to work properly with a rollout, you have to add the configurations section. Without it, Kustomize will not know what to do with the rollout resource (kind=rollout).

Note that we also use a configMapGenerator that creates a ConfigMap that sets a welcome message. If you look at the rollout spec, you will see that the pod template uses it to set the WELCOME environment variable. The API that we deploy will respond with that message when you hit the root, for instance with curl.

To deploy with Kustomize, we can run kubectl apply -k . from the folder holding kustomization.yaml and the manifests in the resources list.

Checking the initial rollout with the UI

When we initially deploy our application, there is only one version of our app. The rollout uses a ReplicaSet to deploy two pods, similarly to a Deployment. Both the activeService and the previewService point to these two pods.

Argo Rollouts has a UI you can start with kubectl argo rollouts dashboard -n blue-green. The rollout is visualized as below:

Initial rollout of the application

In a tool like Octant, the resource viewer shows the relationships between the actual Kubernetes resources:

Resource viewer in Octant

Above, you can clearly see the Rollout creates a ReplicaSet which, in turn, creates the Pods (click image to enlarge). Both services point to the same pods.

Upgrading to a new version

We will now upgrade to a new version of the application: v2. To simulate this, we can simply modify the WELCOME message in the ConfigMapGenerator in kustomization.yaml. When we run kubectl apply -k . again, Kustomize will create a new ConfigMap with a different name (containing a hash) and will update that name in the pod template of the rollout. When you update the pod template of the rollout, the rollout knows it needs to upgrade with the blue-green strategy. This, again, is identical to how a deployment behaves. In the UI, we now see:

Rollout after introducing v2 changes

There are now two revisions, both backed by a ReplicaSet. Each ReplicaSet controls two pods. One set of pods is for the active service, the other set for the preview. We can click on the rollout to see those details:

Details of the rollout

Above, we can clearly see that revision one is the stable and active service. That is our initial v1 deployment. Revision 2 is the preview service, the v2 deployment. We can port forward to that service and view the welcome message:

Port forward to the preview service

In Octant, this is what we see in Resource Viewer:

Rollout after introducing v2 changes

Above, we can clearly see the rollout now uses two ReplicaSets to run the active and preview pods. The rollout also modified the service selectors and the labels on the pods by adding a label like rollouts-pod-template-hash:758d6b4845. Each revision has its own hash.

Promotion

Currently, the rollout is in a paused state. The Argo Rollouts UI shows this but you can also view this with the CLI by running kubectl argo rollouts get rollout dev-superapi-geba:

Getting the status of the rollout with the CLI

Above the status is paused with a message of BlueGreenPause. You can clearly see the green service is the stable and active one (v1) and the blue service is the preview service (v2). We can now promote the preview service to become stable and active.

To promote the service, in the web UI, click Promote and then Sure?. With the CLI, just run kubectl argo rollouts promote dev-superapi-geba. When you run the get command again, you will see:

Rollout after promotion of v2

Above, you can see the status as ✔️ Healthy. Revision 2 is now stable and active. Revision 1 will be scaled down by setting the number of pods in the ReplicaSet to 0. In the web UI, you now see:

Rollout after promotion of Revision 2

Note that it is still possible to rollback to revision one by clicking the Rollback button or using the CLI. That will keep Revision 2 active and create a Revision 3 for you to preview. After clicking Promote and Sure? again, you will then make Revision 3 active which is the initial v1 service.

Conclusion

If you have the need for blue-green deployments, it is highly recommended to use a progressive delivery controller like Argo Rollouts. It makes the whole process more intuitive and gives you fine control over upgrade, abort, promote and rollback operations. Above, we looked at blue-green with a manual pause, check, and promote. There are other options, such as analysis based on metrics with an automatic promotion that we will look at in later posts.

Trying out WebAssembly on Azure Kubernetes Service

Introduction

In October 2021, Microsoft announced the public preview of AKS support for deploying WebAssembly System Interface (WASI) workloads in Kubernetes. You can read the announcement here. In short, that means we can run another type of workload on Kubernetes, besides containers!

WebAssembly is maybe best known for the ability to write code with languages such as C#, Go and Rust that can run in the browser, alongside JavaScript code. One example of this is Blazor, which allows you to build client web apps with C#.

Besides the browser, there are ways to run WebAssembly modules directly on the operating system. Because WebAssembly modules do not contain machine code suitable for a specific operating system and CPU architecture, you will need a runtime that can interpret the WebAssembly byte code. At the same time, WebAssembly modules should be able to interface with the operating system, for instance to access files. In other words, WebAssembly code should be able to access specific parts of the operating system outside the sandbox it is running in by default.

The WebAssembly System Interface (or WASI) allows WebAssembly modules to interact with the outside world. It allows you to declare what the module is allowed to see and access.

One example of a standalone runtime that can run WebAssembly modules is wasmtime. It supports interacting with the host environment via WASI as discussed above. For example, you can specify access to files on the host via the –dir flag and be very specific about what files and folders are allowed.

An example with Rust

In what follows, we will create Hello World-style application with Rust. You do not have to know anything about Rust to follow along. As a matter of fact, I do not know that much about Rust either. I just want a simple app to run on Azure Kubernetes Service later. Here’s the source code:

use std::env;

fn main() {
  println!("Content-Type: text/plain\n");
  println!("Hello, world!");

  printenv();
  
}

fn printenv() {
  for (key, value) in env::vars() {
    println!("{}: {}", key, value);
  }
}

Note: Because I am a bit more comfortable with Go, I first created a demo app with Go and used TinyGo to build the WebAssembly module. That worked great with wasmtime but did not work well on AKS. There is probably a good explanation for that. I will update this post when I learn more.

To continue with the Rust application, it is pretty clear what it does: it prints the Content-Type for a HTTP response, a Hello, World! message, and all environment variables. Why we set the Content-Type will become clearer later on!

To build this app, we need to target wasm32-wasi to build a WebAssembly module that supports WASI as well. You can run the following commands to do so (requires that Rust is installed on your system):

rustup target add wasm32-wasi
cargo build --release --target wasm32-wasi

The rustup command should only be run once. It adds wasm32-wasi as a supported target. The cargo build command then builds the WebAssembly module. On my system, that results in a file in the target/wasm32-wasi/release folder called sample.wasm (name comes from a setting in cargo.toml) . With WebAssembly support in VS Code, I can right click the file and use Show WebAssembly:

Showing the WebAssembly Module in VS Code (WebAssembly Toolkit for VS Code extension)

We can run this module with cargo run but that runs the app directly on the operating system. In my case that’s Ubuntu in Windows 11’s WSL2. To run the WebAssembly module , you can use wasmtime:

wasmtime sample.wasm

The module will not read the environment variables from the host. Instead, you pass environment variables from the wasmtime cli like so (command and result shown below):

wasmtime --env test=hello sample.wasm

Content-Type: text/plain

Hello, world!
test: hello

Publishing to Azure Container Registry

A WebAssembly can be published to Azure Container Registry with wasm-to-oci (see GitHub repo). The command below publishes our module:

wasm-to-oci push sample.wasm <ACRNAME>.azurecr.io/sample:1.0.0

Make sure you are logged in to ACR with az acr login -n <ACRNAME>. I also enabled anonymous pull on ACR to not run into issues with pulls from WASI-enabled AKS pools later. Indeed, AKS will be able to pull these artefacts to run them on a WASI node.

Here is the artefact as shown in ACR:

WASM module in ACR with mediaType = application/vnd.wasm.content.layer.v1+wasm

Running the module on AKS

To run WebAssembly modules on AKS nodes, you need to enable the preview as described here. After enabling the preview, I deployed a basic Kubernetes cluster with one node. It uses kubenet by default. That’s good because Azure CNI is not supported by WASI node pools.

az aks create -n wademo -g rg-aks --node-count 1

After finishing the deployment, I added a WASI nodepool:

az aks nodepool add \
    --resource-group rg-aks \
    --cluster-name wademo \
    --name wasipool \
    --node-count 1 \
    --workload-runtime wasmwasi

The aks-preview extension (install or update it!!!) for the Azure CLI supports the –workload-runtime flag. It can be set to wasmwasi to deploy nodes that can execute WebAssembly modules. The piece of technology that enables this is the krustlet project as described here: https://krustlet.dev. Krustlet is basically a WebAssembly kubelet. It stands for Kubernetes Rust Kubelet.

After running the above commands, the command kubectl get nodes -o wide will look like below:

NAME                                STATUS   ROLES   AGE    VERSION         INTERNAL-IP   EXTERNAL-IP   OS-IMAGE             KERNEL-VERSION     CONTAINER-RUNTIME
aks-nodepool1-23291395-vmss000000   Ready    agent   3h6m   v1.20.9         10.240.0.4    <none>        Ubuntu 18.04.6 LTS   5.4.0-1059-azure   containerd://1.4.9+azure
aks-wasipool-23291395-vmss000000    Ready    agent   3h2m   1.0.0-alpha.1   10.240.0.5    <none>        <unknown>            <unknown>          mvp

As you can see it’s early days here! 😉 But we do have a node that can run WebAssembly! Let’s try to run our module by deploying a pod via the manifest below:

apiVersion: v1
kind: Pod
metadata:
  name: sample
  annotations:
    alpha.wagi.krustlet.dev/default-host: "0.0.0.0:3001"
    alpha.wagi.krustlet.dev/modules: |
      {
        "sample": {"route": "/"}
      }
spec:
  hostNetwork: true
  containers:
    - name: sample
      image: <ARCNAME>.azurecr.io/sample:1.0.0
      imagePullPolicy: Always
  nodeSelector:
    kubernetes.io/arch: wasm32-wagi
  tolerations:
    - key: "node.kubernetes.io/network-unavailable"
      operator: "Exists"
      effect: "NoSchedule"
    - key: "kubernetes.io/arch"
      operator: "Equal"
      value: "wasm32-wagi"
      effect: "NoExecute"
    - key: "kubernetes.io/arch"
      operator: "Equal"
      value: "wasm32-wagi"
      effect: "NoSchedule"

Wait a moment! There is a new acronym here: WAGI! WASI has no network primitives such as sockets so you should not expect to build a full webserver with it. WAGI, which stands for WebAssembly Gateway Interface, allows you to run WASI modules as HTTP handlers. It is heavily based on CGI, the Common Gateway Interface that allows mapping HTTP requests to executables (e.g. a Windows or Linux executable) via something like IIS or Apache.

We will need a way to map a route such as / to a module and the response to a requests should be HTTP responses. That is why we set the Content-Type in the example by simply printing it to stdout. WAGI will also set several environment variables with information about the incoming request. That is the reason we print all the environment variables. This feels a bit like the early 90’s to me when CGI was the hottest web tech in town! 😂

The mapping of routes to modules is done via annotations, as shown in the YAML. This is similar to the modules.toml file used to start a Wagi server manually. Because the WASI nodes are tainted, tolerations are used to allow the pod to be scheduled on such nodes. With the nodeSelector, the pod needs to be scheduled on such a node.

To run the WebAssembly module, apply the manifest above to the cluster as usual (assuming the manifest is in pod.yaml:

kubectl apply -f pod.yaml

Now run kubectl get pods. If the status is Registered vs Running, this is expected. The pod will not be ready either:

NAME    READY   STATUS       RESTARTS   AGE
sample  0/1     Registered   0          108m

In order to reach the workload from the Internet, you need to install nginx with a value.yaml file that contains the internal IP address of the WASI node as documented here.

After doing that, I can curl the public IP address of the nginx service of type LoadBalancer:

~ curl IP

Hello, world!
HTTP_ACCEPT: */*
QUERY_STRING: 
SERVER_PROTOCOL: HTTP/1.0
GATEWAY_INTERFACE: CGI/1.1
REQUEST_METHOD: GET
SERVER_PORT: 3001
REMOTE_ADDR: 10.240.0.4
X_FULL_URL: http://10.240.0.5:3001/
X_RAW_PATH_INFO: 
CONTENT_TYPE: 
SERVER_NAME: 10.240.0.5
SCRIPT_NAME: /
AUTH_TYPE: 
PATH_TRANSLATED: 
PATH_INFO: 
CONTENT_LENGTH: 0
X_MATCHED_ROUTE: /
REMOTE_HOST: 10.240.0.4
REMOTE_USER: 
SERVER_SOFTWARE: WAGI/1
HTTP_HOST: 10.240.0.5:3001
HTTP_USER_AGENT: curl/7.58.0

As you can see, WAGI has set environment variables that allows your handler to know more about the incoming request such as the HTTP User Agent.

Conclusion

Although WebAssembly is gaining in popularity to build browser-based applications, it is still early days for running these workloads on Kubernetes. WebAssembly will not replace containers anytime soon. In fact, that is not the actual goal. It just provides an additional choice that might make sense for some applications in the future. And as always, the future will arrive sooner than expected!

Azure AD pod-managed identities in AKS revisited

A long time ago, I wrote a blog post about assigning managed identities to pods in Azure Kubernetes Services (AKS) to authenticate to Azure Storage. The implementation was based on the aad-pod-identity project on GitHub. You can look at the walkthrough to see how it worked.

Microsoft recently released a preview that enables you to turn on pod identity during cluster creation. It uses the same building blocks as before but makes it fully supported and part of AKS (although preview now). To create a basic cluster with pod identity enabled, you can use the following commands:

az group create -n RESOURCEGROUP -l LOCATION
az aks create -g RESOURCEGROUP -n CLUSTERNAME --enable-managed-identity --enable-pod-identity --network-plugin azure

Note: you need to use Azure CNI networking here; kubenet will not work

Before you deploy the cluster, make sure you follow the prerequisites in the documentation (Before you begin). At the time of writing (December 2020), the section in the documentation that tells you how to create the AKS cluster does not use the Azure CNI plugin. Make sure you add that!

What does –enable-pod-identity do?

When you use –enable-pod-identity, you should see nmi pods on your cluster in the kube-system namespace:

NMI pods

These pods are created from a DaemonSet so you will have one pod per cluster node (Linux nodes only ). When your application wants to use a managed identity, it does a request to the Instance Metadata Service (IMDS) endpoint which is 169.254.169.254. Requests to that IP address are intercepted by the NMI pods via iptables rules. The NMI pod that intercepts the request then makes an Azure AD Authentication Library (ADAL) request to Azure AD to obtain a token for the managed identity and returns it to your application.

Next to the NMI pods, other things are added as well, such as custom resource definitions. Some of those are discussed below.

How to request the token?

It’s great to know that the NMI pods intercept requests to the IMDS endpoint but how do you make such a request? I put together a small example in Python in the following git repository: https://github.com/gbaeke/python-msi. The code is in the rg-api folder in server.py:

from azure.identity import DefaultAzureCredential
from azure.mgmt.resource import ResourceManagementClient, SubscriptionClient
from fastapi import FastAPI

app = FastAPI()

try:
    credentials = DefaultAzureCredential()
    subscription_client = SubscriptionClient(credentials)
    subscription = next(subscription_client.subscriptions.list())
    subscription_id = subscription.subscription_id
    resource_client = ResourceManagementClient(credentials, subscription_id)
except:
    print("error obtaining credentials")

@app.get("/")
def read_root():
    groups=[]
    try:
        for resource_group in resource_client.resource_groups.list():
            groups.append(resource_group.name)
    except:
        print("error obtaining groups")
    
    return groups

The code does the following:

  • use the azure-identity Python library to obtain credentials via DefaultAzureCredential() function. Note that that function tries multiple authentication options. If you run the code on your local computer and you are logged on to Azure with the Azure CLI, it will also work
  • use the azure-mgmt-resource Python library to enumerate resource groups in the current subscription
  • create a very simple API with FastAPI to ask for the list of resource groups; we can use a kubectl port forward later to obtain the JSON response; if authentication fails, the call will return an empty list instead of HTTP errors as you normally would

On my system, this is the result of the call when pod identity is working:

A bunch of resource groups in my test subscription… messy as usual

The repo also contains a Dockerfile to build a container with the app. I built and pushed that container to Docker Hub as gbaeke/rgapi.

Creating and using the identity

If we want the pod that runs the above code to use a specific identity, we have to create the identity and then tell the pod to use it. To create the managed identity, use the following command:

 az identity create --resource-group  rg-clu-msi --name rgapi 

The output of this command contains an id field that we need in another command later. The result of the above command is a User Assigned Managed Identity called rgapi. I already granted the Contributor role at the subscription level.

User Assigned Managed Identity rgapi

Note that this has nothing to do with AKS. To create a pod identity to use in AKS, you will need to run another command:

az aks pod-identity add --resource-group rg-clu-msi --cluster-name clu-msi --namespace  rgapi  --name rgapi --identity-resource-id "id field from previous command" 

The above command creates a pod identity called rgapi in the namespace rgapi. This namespace will be created if it does not exist. You can see the pod identity by running the below command:

 kubectl get azureidentities.aadpodidentity.k8s.io

If you look inside such an object, you would find the reference to the managed identity by its resource id (the id field from earlier). There are other custom resource definitions used by pod identity that we will not bother with now.

Now we need to create a pod and associate it with the pod identity. You can do so with the following YAML:

apiVersion: v1
kind: Pod
metadata:
  name: rgapi
  namespace: rgapi
  labels:
    aadpodidbinding: rgapi
spec:
  containers:
  - name: rgapi
    image: gbaeke/rgapi
  nodeSelector:
    kubernetes.io/os: linux

The important bit above is the aadpodidbinding label which refers to the pod identity we created earlier. When the above pod gets scheduled, it will call out to the IMDS endpoint. You should see that in the logs of the NMI pod on the same node as your application pod. For example:

no clientID or resourceID in request. rgapi/rgapi has been matched with azure identity rgapi/rgapi
status (200) took 12677813 ns for req.method=GET reg.path=/metadata/identity/oauth2/token req.remote=10.240.0.36

The first line indicates that I did not specifically set a clientID in my request but that the request is matched to the rgapi identity. The second line shows the NMI pod requesting a token for the identity from the Azure AD token endpoint.

Great! We now have a pod running that can retrieve resource groups with our custom managed identity. We did not have to add credentials manually or grab them from Key Vault. Our pod automatically picks up the pod identity. 🎉

Conclusion

Although it is still not super simple (is identity ever simple really?), the new method to enable pod identities is a definite improvement. It is currently in preview so it should not be used in production. Once it goes GA however, you will have a fully supported method of using user assigned managed identity with your pods and use specific identities per pod following least privilege methods.

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 😀). 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!

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 😉 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!

Docker without Docker: a look at Podman

I have been working with Docker for quite some time. More and more however, I see people switching to tools like Podman and Buildah and decided to give that a go.

I installed a virtual machine in Azure with the following Azure CLI command:

az vm create \
  	--resource-group RESOURCEGROUP \
  	--name VMNAME \
  	--image UbuntuLTS \
	--authentication-type password \
  	--admin-username azureuser \
  	--admin-password PASSWORD \
	--size Standard_B2ms

Just replace RESOURCEGROUP, VMNAME and PASSWORD with the values you want to use and you are good to go. Note that the above command results in Ubuntu 18.04 at the time of writing.

SSH into that VM for the following steps.

Installing Podman

Installation of Podman is easy enough. The commands below do the trick:

. /etc/os-release
echo "deb https://download.opensuse.org/repositories/devel:/kubic:/libcontainers:/stable/xUbuntu_${VERSION_ID}/ /" | sudo tee /etc/apt/sources.list.d/devel:kubic:libcontainers:stable.list
curl -L https://download.opensuse.org/repositories/devel:/kubic:/libcontainers:/stable/xUbuntu_${VERSION_ID}/Release.key | sudo apt-key add -
sudo apt-get update
sudo apt-get -y upgrade 
sudo apt-get -y install podman

You can find more information at https://podman.io/getting-started/installation.

Where Docker uses a client/server model, with a privileged Docker daemon and a docker client that communicates with it, Podman uses a fork/exec model. The container process is a child of the Podman process. This also means you do not require root to run a container which is great from a security and auditing perspective.

You can now just use the podman command. It supports the same arguments as the docker command. If you want, you can even create a docker alias for the podman command.

To check if everything is working, run the following command:

podman run hello-world

It will pull down the hello-world image from Docker Hub and display a message.

I wanted to start my gbaeke/nasnet container with podman, using the following command:

podman run  -p 80:9090 -d gbaeke/nasnet

Of course, the above command will fail. I am not running as root, which means I cannot bind a process to a port below 1024. There are ways to fix that but I changed the command to:

podman run  -p 9090:9090 -d gbaeke/nasnet

The gbaeke/nasnet container is large, close to 3 GB. Pulling the container from Docker Hub went fast but Podman took a very long time during the Storing signatures phase. While the command was running, I checked disk space on the VM with df and noticed that the machine’s disk was quickly filling up.

On WSL2 (Windows Subsystem for Linux), I did not have trouble with pulling large images. With the docker info command, I found that it was using overlay2 as the storage driver:

Docker on WSL2 uses overlay2

You can find more information about Docker and overlay2, see https://docs.docker.com/storage/storagedriver/overlayfs-driver/.

With podman, run podman info to check the storage driver podman uses. Look for graphDriverName in the output. In my case, podman used vfs. Although vfs is well supported and runs anywhere, it does full copies of layers (represented by directories on your filesystem) in the image which results in using a lot of diskspace. If the disk is not super fast, this will result in long wait times when pulling an image and waste of disk space.

Without getting bogged down in the specifics of the storage drivers and their pros and cons, I decided to switch Podman from vfs to fuse-overlayfs. Fuse stands for Filesystem in Userspace, so fuse-overlayfs is the implementation of overlayfs in userspace (using FUSE). It supports deduplication of layers which will result in less consumption of disk space. This should be very noticeable when pulling a large image.

IMPORTANT: remove the containers folder in ~/.local/share to clear out container storage before installing overlayfs. Use the command below;

rm -rf ~/.local/share/containers

Installing fuse-overlayfs

The installation instructions are at https://github.com/containers/fuse-overlayfs. I needed to use the static build because I am running Ubuntu 18.04. On newer versions of Ubuntu, you can use apt install libfuse3-dev.

It’s of no use here to repeat the static build steps. Just head over to the GitHub repo and follow the steps. When asked to clone the git repo, use the following command:

git clone https://github.com/containers/fuse-overlayfs.git

The final step in the instructions is to copy fuse-overlayfs (which was just built with buildah) to /usr/bin.

If you now run podman info, the graphDrivername should be overlay. There’s nothing you need to do to make that happen:

overlay storage driver with /usr/bin/fuse-overlayfs as the executable

When you now run the gbaeke/nasnet container, or any sufficiently large container, the process should be much smoother. I can still take a couple of minutes though. Note that at the end, your output will be somewhat like below:

Output from podman run -p 9090:9090 -d gbaeke/nasnet

Now you can run podman ps and you should see the running container:

gbaeke/nasnet container is running

Go to http://localhost:9090 and you should see the UI. Go ahead and classify an image! 😉

Conclusion

Installing and using Podman is easy, especially if you are familiar with Docker somewhat. I did have trouble with performance and disk storage with large images but that can be fixed by swapping out vfs with something like overlayfs. Be aware that there are many other options and that it is quite complex under the hood. But with the above steps, you should be good to go.

Will I use podman from now on? Probably not as Docker provides me all I need for now and a lot of tools I use are dependent on it.

From MQTT to InfluxDB with Dapr

In a previous post, we looked at using the Dapr InfluxDB component to write data to InfluxDB Cloud. In this post, we will take a look at reading data from an MQTT topic and storing it in InfluxDB. We will use Dapr 0.10, which includes both components.

To get up to speed with Dapr, please read the previous post and make sure you have an InfluxDB instance up and running in the cloud.

If you want to see a video instead:

MQTT to Influx with Dapr

Note that the video sends output to both InfluxDB and Azure SignalR. In addition, the video uses Dapr 0.8 with a custom compiled Dapr because I was still developing and testing the InfluxDB component.

MQTT Server

Although there are cloud-based MQTT servers you can use, let’s mix it up a little and run the MQTT server from Docker. If you have Docker installed, type the following:

docker run -it -p 1883:1883 -p 9001:9001 eclipse-mosquitto

The above command runs Mosquitto and exposes port 1883 on your local machine. You can use a tool such as MQTT Explorer to send data. Install MQTT Explorer on your local machine and run it. Create a connection like in the below screenshot:

MQTT Explorer connection

Now, click Connect to connect to Mosquitto. With MQTT, you send data to topics of your choice. Publish a json message to a topic called test as shown below:

Publish json data to the test topic

You can now click the topic in the list of topics and see its most recent value:

Subscribing to the test topic

Using MQTT with Dapr

You are now ready to read data from an MQTT topic with Dapr. If you have Dapr installed, you can run the following code to read from the test topic and store the data in InfluxDB:

const express = require('express');
const bodyParser = require('body-parser');

const app = express();
app.use(bodyParser.json());

const port = 3000;

// mqtt component will post messages from influx topic here
app.post('/mqtt', (req, res) => {
    console.log("MQTT Binding Trigger");
    console.log(req.body)

    // body is expected to contain room and temperature
    room = req.body.room
    temperature = req.body.temperature

    // room should not contain spaces
    room = room.split(" ").join("_")

    // create message for influx component
    message = {
        "measurement": "stat",
        "tags": `room=${room}`,
        "values": `temperature=${temperature}`
    };
    
    // send the message to influx output binding
    res.send({
        "to": ["influx"],
        "data": message
    });
});

app.listen(port, () => console.log(`Node App listening on port ${port}!`));

In this example, we use Node.js instead of Python to illustrate that Dapr works with any language. You will also need this package.json and run npm install:

{
  "name": "mqttapp",
  "version": "1.0.0",
  "description": "",
  "main": "app.js",
  "scripts": {
    "test": "echo \"Error: no test specified\" && exit 1"
  },
  "author": "",
  "license": "ISC",
  "dependencies": {
    "body-parser": "^1.18.3",
    "express": "^4.16.4"
  }
}

In the previous post about InfluxDB, we used an output binding. You use an output binding by posting data to a Dapr HTTP URI.

To use an input binding like MQTT, you will need to create an HTTP server. Above, we create an HTTP server with Express, and listen on port 3000 for incoming requests. Later, we will instruct Dapr to listen for messages on an MQTT topic and, when a message arrives, post it to our server. We can then retrieve the message from the request body.

To tell Dapr what to do, we’ll create a components folder in the same folder that holds the Node.js code. Put a file in that folder with the following contents:

apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
  name: mqtt
spec:
  type: bindings.mqtt
  metadata:
  - name: url
    value: mqtt://localhost:1883
  - name: topic
    value: test

Above, we configure the MQTT component to list to topic test on mqtt://localhost:1883. The name we use (in metadata) is important because that needs to correspond to our HTTP handler (/mqtt).

Like in the previous post, there’s another file that configures the InfluxDB component:

apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
  name: influx
spec:
  type: bindings.influx
  metadata:
  - name: Url
    value: http://localhost:9999
  - name: Token
    value: ""
  - name: Org
    value: ""
  - name: Bucket
    value: ""

Replace the parameters in the file above with your own.

Saving the MQTT request body to InfluxDB

If you look at the Node.js code, you have probably noticed that we send a response body in the /mqtt handler:

res.send({
        "to": ["influx"],
        "data": message
    });

Dapr is written to accept responses that include a to and a data field in the JSON response. The above response simply tells Dapr to send the message in the data field to the configured influx component.

Does it work?

Let’s run the code with Dapr to see if it works:

dapr run --app-id mqqtinflux --app-port 3000 --components-path=./components node app.js

In dapr run, we also need to specify the port our app uses. Remember that Dapr will post JSON data to our /mqtt handler!

Let’s post some JSON with the expected fields of temperature and room to our MQTT server:

Posting data to the test topic

The Dapr logs show the following:

Logs from the APP (appear alongside the Dapr logs)

In InfluxDB Cloud table view:

Data stored in InfluxDB Cloud (posted some other data points before)

Conclusion

Dapr makes it really easy to retrieve data with input bindings and send that data somewhere else with output bindings. There are many other input and output bindings so make sure you check them out on GitHub!

Using the Dapr InfluxDB component

A while ago, I created a component that can write to InfluxDB 2.0 from Dapr. This component is now included in the 0.10 release. In this post, we will briefly look at how you can use it.

If you do not know what Dapr is, take a look at https://dapr.io. I also have some videos on Youtube about Dapr. And be sure to check out the video below as well:

Let’s jump in and use the component.

Installing Dapr

You can install Dapr on Windows, Mac and Linux by following the instructions on https://dapr.io/. Just click the Download link and select your operating system. I installed Dapr on WSL 2 (Windows Subsystem for Linux) on Windows 10 with the following command:

wget -q https://raw.githubusercontent.com/dapr/cli/master/install/install.sh -O - | /bin/bash

The above command just installs the Dapr CLI. To initialize Dapr, you need to run dapr init.

Getting an InfluxDB database

InfluxDB is a time-series database. You can easily run it in a container on your local machine but you can also use InfluxDB Cloud. In this post, we will simply use a free cloud instance. Just head over to https://cloud2.influxdata.com/signup and signup for an account. Just follow the steps and use a free account. It stores data for maximum 30 days and has some other limits as well.

You will need the following information to write data to InfluxDB:

  • Organization: this will be set to the e-mail account you signed up with; it can be renamed if you wish
  • Bucket: your data is stored in a bucket; by default you get a bucket called e-mail-prefix’s Bucket (e.g. geert.baeke’s Bucket)
  • Token: you need a token that provides the necessary access rights such as read and/or write

Let’s rename the bucket to get a feel for the user interface. Click Data, Buckets and then Settings as shown below:

Getting to the bucket settings

Click Rename and follow the steps to rename the bucket:

Renaming the bucket

Now, let’s create a token. In the Load Data screen, click Tokens. Click Generate and then click Read/Write Token. Describe the token and create it like below:

Creating a token

Now click the token you created and copy it to the clipboard. You now have the organization name, a bucket name and a token. You still need a URL to connect to but that just the URL you see in the browser (the yellow part):

URL to send your data

Your URL will depend on the cloud you use.

Python code to write to InfluxDB with Dapr

The code below requires Python 3. I used version 3.6.9 but it will work with more recent versions of course.

import time
import requests
import os

dapr_port = os.getenv("DAPR_HTTP_PORT", 3500)

dapr_url = "http://localhost:{}/v1.0/bindings/influx".format(dapr_port)
n = 0.0
while True:
    n += 1.0
    payload = { 
        "data": {
            "measurement": "temp",
            "tags": "room=dorm,building=building-a",
            "values": "sensor=\"sensor X\",avg={},max={}".format(n, n*2)
            }, 
        "operation": "create" 
    }
    print(payload, flush=True)
    try:
        response = requests.post(dapr_url, json=payload)
        print(response, flush=True)

    except Exception as e:
        print(e, flush=True)

    time.sleep(1)

The code above is just an illustration of using the InfluxDB output binding from Dapr. It is crucial to understand that a Dapr process needs to be running, either locally on your system or as a Kubernetes sidecar, that the above program communicates with. To that end, we get the Dapr port number from an environment variable or use the default port 3500.

The Python program uses the InfluxDB output binding simply by posting data to an HTTP endpoint. The endpoint is constructed as follows:

dapr_url = "http://localhost:{}/v1.0/bindings/influx".format(dapr_port)

The dapr_url above is set to a URI that uses localhost over the Dapr port and then uses the influx binding by appending /v1.0/bindings/influx. All bindings have a specific name like influx, mqtt, etc… and that name is then added to /v1.0/bindings/ to make the call work.

So far so good, but how does the binding know where to connect and what organization, bucket and token to use? That’s where the component .yaml file comes in. In the same folder where you save your Python code, create a folder called components. In the folder, create a file called influx.yaml (you can give it any name you want). The influx.yaml contents is shown below:

apiVersion: dapr.io/v1alpha1
kind: Component
metadata:
  name: influx
spec:
  type: bindings.influx
  metadata:
  - name: Url
    value: YOUR URL
  - name: Token
    value: "YOUR TOKEN HERE"
  - name: Org
    value: "YOUR ORG"
  - name: Bucket
    value: "YOUR BUCKET"

Of course, replace the uppercase values above with your own. We will later tell Dapr to look for files like this in the components folder. Automatically, because you use the influx binding in your Python code, Dapr will go look for the file above (type: bindings.influx) and retrieve the required metadata. If any of the metadata is not set or if the file is missing or improperly formatted, you will get an error.

To actually use the binding, we need to post some data to the URI we constructed. The data we send is in the payload variable as shown below:

 payload = { 
        "data": {
            "measurement": "temp",
            "tags": "room=dorm,building=building-a",
            "values": "sensor=\"sensor X\",avg={},max={}".format(n, n*2)
            }, 
        "operation": "create" 
    }

It requires a measurement field, a tags and a values field and uses the InfluxDB line protocol to send the data. You can find more information about that here.

The data field in the payload is specific to the Influx component. The operation field is required by this Dapr component as it is written to listen for create operations.

Running the code

On your local machine, you will need to run Dapr together with your code to make it work. You use dapr run for this. To run the Python code (saved to app.py in my case), run the command below from the folder that contains the code and the components folder:

dapr run --app-id influx -d ./components python3 app.py

This starts Dapr and our application with app id influx. With -d, we point to the components file.

When you run the code, Dapr logs and your logs will be printed to the screen. In InfluxDB Cloud, we can check the data from the user interface:

Date Explorer (Note: other organization and bucket than the one used in this post)

Conclusion

Dapr can be used in the cloud and at the edge, in containers or without. In both cases, you often have to write data to databases. With Dapr, you can now easily write data as time series to InfluxDB. Note that Dapr also has an MQTT input and output binding. Using the same simple technique you learned in this post, you can easily read data from an MQTT topic and forward it to InfluxDB. In a later post, we will take a look at that scenario as well. Or check this video instead: https://youtu.be/2vCT79KG24E. Note that the video uses a custom compiled Dapr 0.8 with the InfluxDB component because this video was created during development.

Dapr Service Invocation between an HTTP Python client and a GRPC Go server

Recently, I published several videos about Dapr on my Youtube channel. The videos cover the basics of state management, PubSub and service invocation.

The Getting Started with state management and service invocation:

Let’s take a closer look at service invocation with HTTP, Python and Node.

Service Invocation with HTTP

Service invocation (from Dapr docs)

The services you write (here Service A and B) talk to each other using the Dapr runtime. On Kubernetes, you talk to a Dapr sidecar deployed alongside your service container. On your development machine, you run your services via dapr run.

If you want to expose a method on Service B and you use HTTP, you just need to expose an HTTP handler or route. For example, with Express in Node you would use something like:

const express = require('express');
const app = express();
app.post('/neworder', (req, res) => { your code }

You then run your service and annotate it with the proper Dapr annotations (Kubernetes):

annotations:
        dapr.io/enabled: "true"
        dapr.io/id: "node"
        dapr.io/port: "3000"

On your local machine, you would just run the service via dapr run:

dapr run --app-id node --app-port 3000 node app.js

In the last example, the Dapr id is node and we indicate that the service is listening on port 3000. To invoke the method from service A, it can use the following code (Python example shown):

dapr_port = os.getenv("DAPR_HTTP_PORT", 3500)
dapr_url = "http://localhost:{}/v1.0/invoke/node/method/neworder".format(dapr_port)
message = {"data": {"orderId": 1234}}
response = requests.post(dapr_url, json=message, timeout=5)

As you can see, service A does not contact service B directly. It just talks to its Dapr sidecar on localhost (or Dapr on your dev machine) and asks it to invoke the neworder method via a service that uses Dapr id node. It is also clear that both service A and B use HTTP only. Because you just use HTTP to expose and invoke methods, you can use any language or framework.

You can find a complete example here with Node and Python.

Service Invocation with HTTP and GRPC

Dapr has SDKs available for C#, Go and other languages. You might prefer those over the generic HTTP approach. In the case of Go, the SDK uses GRPC to interface with the Dapr runtime. With Dapr in between, one service can use HTTP while another uses GRPC.

Let’s take a look at a service that exposes a method (HelloFromGo) from a Go application. The full example is here. Instead of creating an HTTP route with the name of your method, you use an OnInvoke handler that looks like this (only the start is shown, see the full code):

func (s *server) OnInvoke(ctx context.Context, in *commonv1pb.InvokeRequest) (*commonv1pb.InvokeResponse, error) {
	var response string

	switch in.Method {
	case "HelloFromGo":

		response = s.HelloFromGo()

Naturally, you also have to implement an HelloFromGo() method as well:

// HelloFromGo is a simple demo method to invoke
func (s *server) HelloFromGo() string {
	return "Hello"

}

Another service can use any language or framework and invoke the above method with a POST to the following URL if the Dapr id of the Go service is goserver:

http://localhost:3500/v1.0/invoke/goserver/method/HelloFromGo

A POST to the above URL tells Dapr to execute the OnInvoke method via GRPC which will run the HelloFromGo function. It is perfectly possible to include a payload in your POST and have the OnInvoke handler to process that payload. The full example is here which also includes sending and processing a JSON payload and sending back a text response. You will need to somewhat understand how GRPC works and also understand protocol buffers. A good book on GRPC is the following one: https://learning.oreilly.com/library/view/grpc-up-and/9781492058328/.

Conclusion

Dapr allows you to choose between HTTP and GRPC interfaces to interact with the runtime. You can choose whatever is most comfortable to you. One team can use HTTP with Python, JavaScript etc… while other teams use GRPC with their language of choice. Whatever you choose, the Dapr runtime will make sure service invocation just works allowing you to focus on the code.

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