AutoScaling your Kubernetes cluster on AWS

One of the challenges I have faced in the last few months is the autoscaling of my Kubernetes cluster. This is perfectly working on Google Cloud, however as my cluster is deployed on AWS I have no such fortune. However since recently the autoscaling support for AWS has been made possible due to this little contribution that was made:

In this post I want to describe how you can autoscale your kubernetes cluster based on the container workload. This is a very important principle because normal autoscaling by AWS can not do this on metrics available to the cluster, it can only do it on for example memory or cpu utilisation. What we want is that in case a container cannot be scheduled on a cluster because there are not enough resources the cluster gets scaled out.

Defining resource usage

One of the very important things to do when you are defining your deployments against Kubernetes is to define the resource usage of your containers. In each deployment object it is therefore imperative that you specify the resource allocation that is expected. Kubernetes will then use this to allocate this to a node that has enough capacity. In this exercise we will deploy two containers that both at their limit require 1.5GB of memory, this looks as following in fragment of one of the deployment descriptors:

      - name: amq
        image: rmohr/activemq:latest
            memory: "1500Mi"
            cpu: "500m"

Setting up scaling

So given this we will start out with a cluster of one node of type m3.medium which has 3.75GB of memory, we do this on purpose with a limited initial cluster to test out our autoscaling.

If you execute kubectl get nodes we see the following response:

NAME                                       STATUS    AGE   Ready     10m

In order to apply autoscaling we need to deploy a specific deployment object and container that checks the Kubernetes Cluster for unscheduled workloads and if needed will trigger an AWS autoscale group. This deployment object looks as following:

apiVersion: extensions/v1beta1
kind: Deployment
  name: cluster-autoscaler
    app: cluster-autoscaler
  replicas: 1
      app: cluster-autoscaler
        app: cluster-autoscaler
        - image:
          name: cluster-autoscaler
              cpu: 100m
              memory: 300Mi
              cpu: 100m
              memory: 300Mi
            - ./cluster-autoscaler
            - --v=4
            - --cloud-provider=aws
            - --skip-nodes-with-local-storage=false
            - --nodes=MIN_SCALE:MAX_SCALE:ASG_NAME
            - name: AWS_REGION
              value: us-east-1
            - name: ssl-certs
              mountPath: /etc/ssl/certs/ca-certificates.crt
              readOnly: true
          imagePullPolicy: "Always"
        - name: ssl-certs
            path: "/etc/ssl/certs/ca-certificates.crt"

Note: The image we are using is a google supplied image with the autoscaler script on there, you can check here for the latest version:

In the above deployment object ensure to replace the MIN_SCALE and MAX_SCALE settings for the autoscaling and ensure the right autoscaling group (ASG_NAME) is set. Please note that the minimum and maximum scaling rule need to be allowed in the AWS scaling group as the scaling process cannot modify the auto scaling group rule itself.

AWS Policy
In AWS we need to ensure there is an IAM policy in place that allows all resources to query the auto scaling groups and modify the desired capacity of the group. I have used the below role definition, which is very wide:

    "Version": "2012-10-17",
    "Statement": [
            "Effect": "Allow",
            "Action": [
            "Resource": "*"

Make sure to attach this to the role that is related to the worked nodes, in my core-os kube-aws generated cluster this is something like ‘testcluster-IAMRoleController-GELKOS5QWHRU’ where testcluster is my clustername.

Deploying the autoscaler
Now let’s deploy the autoscaler just like any other deployment object against the Kubernetes cluster

kubectl create -f autoscaler.yaml

Let’s check next that the autoscaler is working:

➜  test kubectl get po
NAME                                  READY     STATUS    RESTARTS   AGE
cluster-autoscaler-2111878067-nhr01   1/1       Running   0          2m

We can also check the logs using kubectl logs cluster-autoscaler-2111878067-nhr01

No unschedulable pods
Scale down status: unneededOnly=true lastScaleUpTime=2017-01-06 08:44:01.400735149 +0000 UTC lastScaleDownFailedTrail=2017-01-06 08:44:01.400735354 +0000 UTC schedulablePodsPresent=false
Calculating unneded nodes
Node - utilization 0.780000
Node is not suitable for removal - utilization to big (0.780000)
Node - utilization 0.954000
Node is not suitable for removal - utilization to big (0.954000)

We can see the autoscaler checks regulary the workload on the nodes and check if they can be scaled down and check if additional worker nodes are needed.

Let’s try it out

Now that we have deployed our autoscaling container let’s start to schedule our workload against AWS. In this case we will deploy two objects, being ActiveMQ and Cassandra where both require a 1.5GB memory footprint. The combined deployment plus the system containers will cause the scheduler of Kubernetes to determine there is no capacity available, and in this case Cassandra cannot be scheduled as can be seen in below snippet from kubectl describe po/cassandra-2599509460-g3jzt:

FailedScheduling	pod (cassandra-2599509460-g3jzt) failed to fit in any node

When we check in the logs of the autoscaler we can see the below:

Estimated 1 nodes needed in testcluster-AutoScaleWorker-19KN6Y4AR18Z
Scale-up: setting group testcluster-AutoScaleWorker-19KN6Y4AR18Z size to 2
Event(api.ObjectReference{Kind:"Pod", Namespace:"default", Name:"cassandra-2599509460-mt275", UID:"af53ac7b-d3ec-11e6-bd28-0add02d2d0c1", APIVersion:"v1", ResourceVersion:"2224", FieldPath:""}): type: 'Normal' reason: 'TriggeredScaleUp' pod triggered scale-up, group: testcluster-AutoScaleWorker-19KN6Y4AR18Z, sizes (current/new): 1/2

It is scheduling an additional worker by increasing the desired capacity of our auto scaling group in AWS. After a small wait we can see the additional node has been made available:

NAME                                       STATUS    AGE   Ready     27m    Ready     11s

And a short while after the node came up we also see that the pod with Cassandra has become active:

NAME                                  READY     STATUS    RESTARTS   AGE
amq-4187240469-hlkhh                  1/1       Running   0          20m
cassandra-2599509460-mt275            1/1       Running   0          8m
cluster-autoscaler-2111878067-nhr01   1/1       Running   0          11m


We have been able to autoscale our AWS deployed Kubernetes cluster which is extremely useful. I can use this in production to quickly scale out and down my cluster. But perhaps even more important for my case in development i can use it to during idle moments run a minimum size cluster and during workloads it scales back up to full capacity, saving me quite some money.


Remote Controlling Nao robot using a Raspberry Pi Robot

Today I want to take some time to write about the next step I am currently taking to have both my self-build Raspberry PI robot and the Nao robot interact with each other on a useful basis. You might have already seen some posts before like about robot interaction or perhaps the model train one However both these posts did not really demonstrate a practical use-case.

Recently I presented about this topic at the Devoxx conference in Antwerp where I attempt to demonstrate how to control one robot from another using Kubernetes, Helm and Minikube combined with some IoT glue 🙂 The scenario I demonstrated was to create a Robotic Arm from my Raspberry PI robot that I use to remote control a Nao robot.

Robot arm
In order to have some form of remote control I have created a Robot Arm which i can use as a sort of joystick. I have created the robot from the same parts as described in this post ( The robot arm is controller via a Raspberry PI that has a bit of Java software to connect it to MQTT to send servo position changes and to receive commands from MQTT to execute motions on the robot arm.

The robot looks like this:

Nao Robot
For the Nao robot I have written a customer Java controller that connects to the remote API of Nao. This controller software does nothing else but allowing remote control of the Nao robot by listening to commands coming from MQTT.

Connecting the Robots

Like before in previous setups I will be using my custom Robot Cloud deployment setup for this experiment. I will be deploying a number of micro-services to a Kubernetes cluster that is running on AWS. The most important public services are the MQTT message bus which is where the robots are sending status (sensors/servo’s) towards and received commands from (animations, walk commands etc.). For more detail on the actual services and their deployment you can check here

The most important part of bridging the gap between the robots is to have a specific container that receives updates from the servo’s on the robot arm. Based on events from those servo’s (move the joystick forward) I want to trigger the Nao robot to start walking. The full code with a lot more detail is available in this git repository:


It’s quite a complex setup, but the conclusion is that by using my Kubernetes deployed Robot Cloud stack I can use the robot Arm to control the Nao robot. If you want to see a bit more with a live demo you can check out my Devoxx presentation here:

One thing I could not demo at Devoxx was the interaction with a real Nao Robot, I have made a recording how that would look and also put this on youtube here:

Deployment using Kubernetes Helm

One of the challenges I face in my development setup is that I want to quickly and often create and deploy my robotics stack. I often want to change and redeploy my entire stack from scratch, because I want to iterate quickly and also reduce my costs as much as possible. My Jenkins jobs have helped a great deal here, and automation is definitely key. However I have recently started experimenting with Kubernetes Helm which is a package manager for Kubernetes which has made this even easier for me.

Kubernetes Helm

Helm is a package manager that allows you to define a package with all its dependent deployment objects for Kubernetes. With helm and this package you can then ask a cluster to install the entire package in one go instead of passing individual deployment commands. This means for me that instead of asking Kubernetes to install each of my several micro-services to be installed I simply ask it to install the entire package/release in one atomic action which also includes all of the dependent services like databases and message brokers I use.

Installing Helm

In this blog I want to give a small taste on how nice Helm is. So how do we get started? Well in order to get started with Helm you should first follow the installation instructions at this page:

In case you are using OSX (like me) its relatively simple if you are using homebrew, simply run the following cask:

brew cask install helm

Once helm is installed it should also be installed in your cluster. In my case I will be testing against a minikube installation as described in my previous post:

On the command line I have a kubernetes command line client (kubectl) with my configuration pointing towards my minikube cluster. The only thing I have to do is the following to install Helm in my cluster:

helm init

This will install a container named tiller in my cluster, this container will understand how to deploy the Helm packages (charts) into my cluster. This is in essence the main endpoint the helm client will use to interrogate the cluster for package deployments and package changes.

Creating the package

Next we need to start creating something which is called a Chart, this is the unit of packaging in Helm. For this post I will reduce the set of services I have used in previous posts and only deploy the core services Cassandra, MQTT and ActiveMQ. The first thing to define is the *Chart.yaml** which is the package manifest:

The manifest looks pretty simple, most important is the version number, the rest is mainly metadata for indexing:

name: robotics
version: 0.1
description: Robotic automation stack
- robotics
- application
- name: Renze de Vries
engine: gotpl

The second I am going to define is the deployment objects I want to deploy. For this we create a ‘Charts’ subdirectory which contains these dependent services. In this case I am going to deploy MQTT, ActiveMQ and Cassandra which are required for my project. For each of these services I create a templates folder which contains the Kubernetes Deployment.yaml descriptor and Kubernetes service descriptor file and have their own Charts.yaml file as well.

When you have this all ready it look as following:

I am not going to write out all the files in this blog, if you want to have a look at the full source have a look at the github repository here that contains the full Helm chart structure describe in this post:

Packaging a release

Now that the Chart source files have been created the last thing to do is to create the actual package. For this we have to do nothing else than simply run the following command:

helm package .

This will create a file called robotics-0.1.tgz that we can use further to deploy our release. In a future blog post I will talk a bit about Helm repositories and how you can distribute these packages, but for now we keep them on the local file system.

Installing a release

Once we have defined the packages the only thing thats remaining is to simply install a release into the cluster. This will install all the services that are packaged in the Chart.

In order to install the package we have created above we just have to run the following command:

helm install robotics-0.1.tgz
NAME: washing-tuatar
LAST DEPLOYED: Sun Nov  6 20:42:30 2016
NAMESPACE: default

==> v1/Service
amq   <nodes>   61616/TCP   1s
mqtt   <nodes>   1883/TCP   1s
cassandra-svc   <nodes>   9042/TCP,9160/TCP   1s

==> extensions/Deployment
mqtt      1         1         1            0           1s
amq       1         1         1         0         1s
cassandra   1         1         1         0         1s

We can ask Helm which packages are installed in the cluster by simply asking a list of installed packages as following:

helm list
NAME          	REVISION	UPDATED                 	STATUS  	CHART       
washing-tuatar	1       	Sun Nov  6 20:42:30 2016	DEPLOYED	robotics-0.1

Please note that the name for the installation is a random generated name, in case you want a well known name you can install using the ‘-name’ switch and specify the name yourself.

In order to delete all the deployed objects I can simply ask Helm to uninstall the release as following:

helm delete washing-tuatar


I have found that Helm has a big potential, it allows me to very quickly define a full software solution composed out of many individual deployments. In a future blog post I will talk a bit more about the templating capabilities of Helm and the packaging and distributing of your packages. In the end I hope this blog shows everyone that with Helm you can make all of your Kubernetes work even easier than it already is today 🙂

Using Kubernetes Minikube for Local test deployments

One of the many challenges I face with my Robotics Cloud development is the need to test locally and constantly re-create the stack from scratch. Now I have a lot of automation to deploy against AWS using Jenkins as seen in previous posts. However setting up a local development environment is the thing I do the most and that is costing a lot of time because the tooling always was painful to use.

Now in the last few months there have been a lot of innovations happening in the Kubernetes field. In particular in this blog post I want to talk about using Minikube.


One of the big pains was always to setup a Kubernetes cluster on your local machine. Before there were some solutions, the simplest one was to use vagrant or the kube-up script that would create some vm’s in virtualbox. However my experience was that they were error prone and did not always complete succesfully. For local machine development setups there is now a new solution called minikube. In essence using minikube you can create a single machine kubernetes test cluster to get you quickly up and running.

The simplest way to get started is to install minikube first using the latest release instructions, in my case for OSX on the 0.12.2 release I install it using this command:

curl -Lo minikube &amp;&amp; chmod +x minikube &amp;&amp; sudo mv minikube /usr/local/bin/

Please visit this page for the latest release of minikube:

In essence the above command downloads the minikube binary and moves it to the local usr bin directory so its available on the path. After this we can start creating the minikube machine, in my case I will use virtualbox as the provider which is automatically detected if its installed. In my case all i have to do is the following:
minikube start --memory=8196

The above will start a single node kubernetes cluster which acts both as master and worker in virtualbox with 8GB of memory. Also it will ensure my local kubernetes (kubectl) client configuration is set to point to the cluster master. This will take a few minutes to get up and running but the cluster should be available after this and you can check if its ready by doing this:

kubectl get nodes
minikube   Ready     1h

The minikube setup has created a virtual box setup that exposes all its services via the virtualbox ip. The minikube binary provides a shortcut to get that ip using below command:

minikube ip

This ip can be used to directly access all services that are exposed on the kubernetes cluster.

Now the cluster is available you can start deploying to your hearts content, but you might want to use the kubernetes dashboard for this which is handy for the overview. In order to quickly get to the dashboard you can run this minikube command:

minikube dashboard

If you want to stop the cluster you can simply type the following command:

minikube stop

The next time you start the cluster it will resume the state it was in previously. So all previously running containers will also be started once the cluster comes back up which is quite handy in case of development.


I hope this post helps people who are struggling setting up their own Kubernetes cluster and getting them quickly started. I am sure there is a lot more to come from the Kubernetes folks, its really getting easier and easier 🙂

Versioning and deploying Docker containers using Kubernetes and Jenkins

In the last few posts the main comment that keeps coming back is ‘you should not use latest’. I totally agree on that and in this blog I will finally do something about it 🙂 I mainly used the setup for development purpose, however in production I need something more reliable, meaning versioned deployments.

To do this there are of course two parts, first I have to actually create a build job that can version the containers and second I will need a build job that rolls out the update deployed container.

Versioning and Releasing

Using the below Jenkins pipeline I have a build job that uses a Jenkins input parameter that defines the release version. In order to use this create a Jenkins pipeline job that has one input parameter called ‘RELEASE_VERSION’. The default value I keep to ‘dev-latest’ for development purposes, but when releasing we obviously need to use a sensible number.

node {
    stage 'build'
    build 'home projects/command-svc/master'

    stage 'test-deploy'
    sh "\$(aws ecr get-login)"
    sh "docker tag home-core/command-svc:latest"
    sh "docker push"    

    stage 'qa'
    build 'home projects/command-svc-tests/master'

    stage 'Publish containers'
    sh "docker tag home-core/command-svc:latest'$RELEASE_VERSION'"
    sh "docker push'$RELEASE_VERSION'"

Now when I trigger the build Job, Jenkins will ask me to input the version for releasing that container. So let’s in this article use the version ‘0.0.1’. When running the build job, Jenkins will go through a few stages.
1. Building the container
2. Pushing a dev-latest version
3. Running tests which deploy a container against the test cluster
4. Release the container using a fixed version.

In the latest stage I release the container using the input parameter specified on the job triggering. The build job does not actually deploy to production, that is for the time being still a manual action.

Deploying to Kubernetes

For deploying to production I have done an initial deployment of the service using below deployment descriptor. I started out deploying version ‘0.0.1’ which I have built above.

apiVersion: extensions/v1beta1
kind: Deployment
  name: command-svc
  replicas: 1
        app: command-svc
      - name: command-svc
        - containerPort: 8080
        - name: amq_host
          value: amq
          value: production

Doing a rolling update

This works fine for an initial deployment, however if i want to do an upgrade of my production containers I need something more. In production in essence I just want to upgrade the container image version. This is a relatively simple operation with Kubernetes. Let’s assume i have released a newer version of the container with version ‘0.0.2’.

In order to update the container in Kubernetes I can simply do a rolling update by changing the image of the Deployment object in Kubernetes as following:

kubectl set image deployment/command-svc

I am currently not yet integrating this into my build pipeline as I want a production upgrade to be a conscious decision still. But once all the quality gates are in place, there should be no reason to not automate the above step as well. More on this in some future blog posts.

Having fun with Robots and Model trains

Last few blog posts have all been about heavy docker and Kubernetes stuff. I thought it was time for something more light and today I want to blog about my hobby robotics project again. Before I had robots to tinker with actually I used to play around a lot with trying to automate a model train setup. So recently I had the idea why can’t I combine this and let one of the robots have some fun by playing with a model train 🙂

In this blog post I will use MQTT and my Robot Cloud SDK I have developed to hook up our Nao Robot to MQTT together with the model train.

Needed materials

In order to build a automated train layout I needed a model train setup, I have a already existing H0 based Roco/Fleischmann based model train setup. All the trains on this setup are digitised using decoders that are based on DCC. If you do not know what this means, you can read a about digital train systems here:

Hooking up the train

The train system I have is controlled using an Ecos controller which has a well defined TCP network protocol I can use for controlling it. I have written a small library that hooks the controller to my IoT/robot cloud that I have described in previous blogposts. The commands for moving the train are sent to MQTT which are then translated to a TCP command the controller can understand.

I will have a MQTT broker available somewhere in the Cloud (AWS/Kubernetes) where also my robots can connect to so this will be the glue connecting the robot and trains.

I don’t really want to bother people to much with the technicals of the train and code behind it, but if you are interested in the code I have put it on Github:

Hooking up the robots

Hooking up the robots is actually quite simple, I have done this before and am using the same setup before. The details of this are all available in this blog post:

In this case I will be using our Nao Robot and hook this up to the MQTT bridge. The framework have developed contains a standard message protocol on top of MQTT. This means the messages are always defined the same way and all parties adhering to this can give states and commands to each other. In this case both the train and robot use the same message protocol via MQTT, hence why we can hook them up.

In order to make this a bit more entertaining I want to run a small scenario:
1. Nao walks a bit towards the train and the controller
2. Sits down and says something
3. Starts the train
4. Reacts when the train is running

I always like to put a bit of code in a post, so this code is used to create this scenario:

    private static void runScenario(Robot robot) {
        //Step1: Walk to the train controller (1.2 meters)
        robot.getMotionEngine().walk(WalkDirection.FORWARD, 1.2f);

        //Step2: Let's say something and sit down
        robot.getCapability(SpeechEngine.class).say("Oh is that a model train, let me sit and play with it", "english");
        //Step3: Let's start the train
        sleepUninterruptibly(1, TimeUnit.SECONDS);
        startTrain(robot.getRemoteDriver(), "1005", "forward");
        //Step4: Nao is having lots of fun
        sleepUninterruptibly(5, TimeUnit.SECONDS);
        robot.getCapability(SpeechEngine.class).say("I am having so much fun playing with the train", "english");

    private static void startTrain(RemoteDriver remoteDriver, String trainId, String direction) {
                .property("trainId", trainId).build());
                .property("trainId", trainId)
                .property("state", "on").build());
                .property("trainId", trainId)
                .property("direction", direction).build());
                .property("trainId", trainId)
                .property("speed", "127").build());

What happens here is that in the Robot SDK there is a bit of code that can translate Java objects into MQTT messages. Those MQTT messages are then received by the train controller from the MQTT bridge which translates this again into TCP messages.

For people that are interested in also this piece of code on how I create the scenario’s around the Nao robot it’s also available on github:

End result

So how does this end result look like, well video’s say more than a thousand words (actually ±750 for this post 🙂 )

This is just to show that you can have a bit of fun integrating very different devices. Using protocols like MQTT could really empower robot and other appliances to be tightly integrated very easily. The glue that I am adding is to make sure there is a standard message on top of MQTT for the different appliances and hooking them up to MQTT. Stay tuned for some more posts about my Robotics and hobby projects.

Deploying Docker containers to Kubernetes with Jenkins

After all my recent posts about deploying a Kubernetes cluster to AWS the one step I still wanted to talk about is how you can deploy the Docker containers to a Kubernetes cluster using a bit of automation. I will try to explain here how you can relatively simply do this by using Jenkins pipelines and some groovy scripting 🙂

* Working Kubernetes cluster (see here:
* Jenkins slave/master setup
* Kubectl tool installed and configured on the Jenkins master/slave and desktop
* Publicly accessible Docker images (AWS ECR for example see:

What are we deploying
In order to deploy containers against kubernetes there are two things that are needed. First I need to deploy the services that will ensure that we have ingress traffic via AWS ELB’s and this also ensures we have an internal DNS lookup capability for service to service communication. Second I need to deploy the actual containers using Kubernetes Deployments.

In this post I will focus on mainly one service which is called ‘command-service’. If you want to read a bit more about the services that I deploy you can find that here:

Creating the services

The first task I do is to create the actual kubernetes service for the command-service. The service descriptors are relatively simple in my case, the command-service needs to be publicly load balanced so I want kubernetes to create an AWS ELB for me. I will deploy this service by first checking out my git repository where I contain the service descriptors using Kubernetes yaml files. I will then use a Jenkins pipeline with some groovy scripting to deploy it.

The service descriptor for the public loadbalanced command-svc looks like this. This is a load balancer that is backed by all pods that have a label ‘app’ with value ‘command-svc’ and then attached to the AWS ELB backing this service.

apiVersion: v1
kind: Service
  name: command-svc
    app: command-svc
  type: LoadBalancer
  - port: 8080
    targetPort: 8080
    protocol: TCP
    name: command-svc
    app: command-svc

In order to actually create this services I use the below Jenkins pipeline code. In this code I use the apply command because the services are not very likely to change and this way it works both in clean and already existing environments. Because I constantly create new environments and sometimes update existing ones, I want all my scripts to be runnable multiple times regardless of current cluster/deployment state.

import groovy.json.*

node {
    stage 'prepare'

    git credentialsId: 'bb420c66-8efb-43e5-b5f6-583b5448e984', url: ''
    sh "wget http://localhost:8080/job/kube-deploy/lastSuccessfulBuild/artifact/*zip*/"
    sh "unzip"
    sh "mv archive/* ."

    stage "deploy services"
    sh "kubectl apply -f command-svc.yml --kubeconfig=kubeconfig"

Waiting for creation
One of the challenges I faced tho is that I have a number of containers that I want to deploy that depend on these service definitions. However it takes a bit of time to deploy these services and for the ELB’s to be fully created. So I have created a bit of small waiting code in Groovy that checks if the services are up and running. This is being called using the ‘waitForServices()’ method in the pipeline, you can see the code for this below:

def waitForServices() {
  sh "kubectl get svc -o json > services.json --kubeconfig=kubeconfig"

  while(!toServiceMap(readFile('services.json')).containsKey('command-svc')) {
        echo "Services are not yet ready, waiting 10 seconds"
        sh "kubectl get svc -o json > services.json --kubeconfig=kubeconfig"
  echo "Services are ready, continuing"

Map toServiceMap(servicesJson) {
  def json = new JsonSlurper().parseText(servicesJson)

  def serviceMap = [:]
  json.items.each { i ->
    def serviceName =
    def ingress = i.status.loadBalancer.ingress
    if(ingress != null) {
      def serviceUrl = ingress[0].hostname
      serviceMap.put(serviceName, serviceUrl)

  return serviceMap

This should not complete until at least all the services are ready for usage, in this case my command-svc with its ELB backing.

Creating the containers

The next step is actually the most important, deploying the actual container. In this example I will be using the deployments objects that are there since Kubernetes 1.2.x.

Let’s take a look again at the command-svc container that I want to deploy. I use again the yaml file syntax for describing the deployment object:

apiVersion: extensions/v1beta1
kind: Deployment
  name: command-svc
  replicas: 1
        app: command-svc
      - name: command-svc
        - containerPort: 8080
        - name: amq_host
          value: amq
          value: production

Let’s put all that together for the rest of my deployments for the other containers. In this case I have one additional container that I deploy the edge-service. Using Jenkins pipelines this looks relatively simple:

    stage "deploy"
    sh "kubectl apply -f kubernetes/command-deployment.yml --kubeconfig=kubeconfig"

I currently do not have any active health checking at the end of the deployment, i am still planning on it. For now I just check that the pods and deployments are properly deployed, you can also do this by simply running these commands:
kubectl get deployments

This will yield something like below:

command-svc   1         1         1            1           1m

If you check the running pods kubectl get po you can see the deployment has scheduled a single pod:

NAME                          READY     STATUS    RESTARTS   AGE
command-svc-533647621-e85yo   1/1       Running   0          2m


I hope in this article I have taken away a bit of the difficulty on how to deploy your containers against Kubernetes. It can be done relatively simple, of course its not production grade but it shows on a very basic level how with any basic scripting (groovy) you can accomplish this task by just using Jenkins.

In this particular article I have not zoomed into the act of upgrading a cluster or the containers running on them. I will discuss this in a future blog post where I will zoom in on the particulars of doing rolling-updates on your containers and eventually will address the upgrade of the cluster itself on AWS.