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ca# Hands-on Kubernetes-06 : Deploying Microservices and Service Level Autoscaling

Purpose of the this hands-on training is to give students the knowledge of Autoscaling and Microservices

Learning Outcomes

At the end of the this hands-on training, students will be able to;

  • Understand deployment and management of microservices

  • Explain the Kubernetes Autoscaling

  • Explain Horizontal Pod Autoscaler Business Logic

  • Understand the Need for Metric Server

Outline

  • Part 1 - Setting up the Kubernetes Cluster

  • Part 2 - Outline of the Hands-on Setup

  • Part 3 - Microservices

  • Part 4 - Autoscaling in Kubernetes

Part 1 - Setting up the Kubernetes Cluster

Note: If you have problem with kubernetes cluster, you can use this link for lesson. https://killercoda.com/playgrounds

  • Check if Kubernetes is running and nodes are ready.
kubectl cluster-info
kubectl get no

Part 2 - Outline of the Hands-on Setup

  • First one is simple web&database application to hold the to-do-lists. This sub-application uses MongoDB to store to-do lists created through the web application. For the front-end web application layer, Node.JS is used. Thus, this sub-aplication has 2 microservices.

  • Create a microservices directory and to-do directory in the microservices directory and change directory.

mkdir microservices
cd microservices
mkdir to-do
cd to-do

Steps of execution:

  1. We will deploy the to-do app first and look at some key points.
  2. The Autoscaling in Kubernetes will be demonstrated as a last step.

Part 3 - Microservices

  • The MongoDB application will use a static volume provisioning with the help of persistent volume (PV) and persistent volume claim (PVC).

  • Create a db-pv.yaml file.

apiVersion: v1
kind: PersistentVolume
metadata:
  name: db-pv-vol
  labels:
    type: local
spec:
  storageClassName: manual
  capacity:
    storage: 5Gi
  accessModes:
    - ReadWriteOnce
  hostPath:
    path: "/home/ubuntu/pv-data"
  • Create a db-pvc.yaml file.
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: database-persistent-volume-claim
spec:
  accessModes:
    - ReadWriteOnce
  storageClassName: manual
  resources:
    requests:
      storage: 1Gi

  • It will provision storage from hostpath.

  • Let's create the MongoDB deployment yaml file (name it db-deployment.yaml) to see how the PVC is used.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: db-deployment
  labels:
    app: todoapp
spec:
  replicas: 1
  selector:
    matchLabels:
      name: mongo
  template:
    metadata:
      labels:
        name: mongo
        app: todoapp
    spec:
      containers:
      - image: mongo:5.0
        name: mongo
        ports:
        - containerPort: 27017
        volumeMounts:
          - name: mongo-storage
            mountPath: /data/db
      volumes:
        #- name: mongo-storage
        #  hostPath:
        #    path: /home/ubuntu/pv-data
        - name: mongo-storage
          persistentVolumeClaim:
            claimName: database-persistent-volume-claim

  • The commented part directly uses the local hostpath for storage. Students can try it on their own later.

  • Let's create the MongoDB service and name it db-service.yaml.

apiVersion: v1
kind: Service
metadata:
  name: db-service
  labels:
    name: mongo
    app: todoapp
spec:
  selector:
    name: mongo
  type: ClusterIP
  ports:
    - name: db
      port: 27017
      targetPort: 27017

  • Note that a database has no direct exposure the outside world, so it's type is ClusterIP.

  • Now, create the web-deployment.yaml for web application.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: web-deployment
  labels:
    app: todoapp
spec:
  replicas: 1
  selector:
    matchLabels:
      name: web
  template:
    metadata:
      labels:
        name: web
        app: todoapp
    spec:
      containers: 
        - image: techprodevops348/todo
          imagePullPolicy: Always
          name: myweb
          ports: 
            - containerPort: 3000
          env:
            - name: "DBHOST"
              value: db-service
          resources:
            limits:
              memory: 500Mi
              cpu: 100m
            requests:
              memory: 250Mi
              cpu: 80m

  • Note that this web app is connnected to MongoDB host/service via the DBHOST environment variable. What does db-service:27017 mean here. How is the IP resolution handled?

  • When should we use imagePullPolicy: Always. Explain the image pull policy shortly.

  • This time, we create the web-service.yaml for front-end web application service.

apiVersion: v1
kind: Service
metadata:
  name: web-service
  labels:
    name: web
    app: todoapp
spec:
  selector:
    name: web 
  type: NodePort
  ports:
   - name: http
     port: 3000
     targetPort: 3000
     nodePort: 30001
     protocol: TCP

  • What should be the type of the service? ClusterIP, NodePort or LoadBalancer?

  • Let's deploy the to-do application.

cd ..
kubectl apply -f to-do
deployment.apps/db-deployment created
persistentvolume/db-pv-vol created
persistentvolumeclaim/database-persistent-volume-claim created
service/db-service created
deployment.apps/web-deployment created
service/web-service created

Note that we can use directory with kubectl apply -f command.

Check the persistent-volume and persistent-volume-claim.

$ kubectl get pv
NAME        CAPACITY   ACCESS MODES   RECLAIM POLICY   STATUS   CLAIM                                      STORAGECLASS   REASON   AGE
db-pv-vol   5Gi        RWO            Retain           Bound    default/database-persistent-volume-claim   manual                  23s

$ kubectl get pvc
NAME                               STATUS   VOLUME      CAPACITY   ACCESS MODES   STORAGECLASS   AGE
database-persistent-volume-claim   Bound    db-pv-vol   5Gi        RWO            manual         56s

Check the pods.

$ kubectl get pods
NAME                              READY   STATUS    RESTARTS   AGE
db-deployment-8597967796-q7x5s    1/1     Running   0          4m30s
web-deployment-658cc55dc8-2h2zc   1/1     Running   2          4m30s

Check the services.

$ kubectl get svc
NAME          TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
db-service    ClusterIP   10.105.0.75     <none>        27017/TCP        4m39s
kubernetes    ClusterIP   10.96.0.1       <none>        443/TCP          2d8h
web-service   NodePort    10.107.136.54   <none>        3000:30001/TCP   4m38s

  • Note the PORT(S) difference between db-service and web-service. Why?

  • We can visit http://: and access the application. Note: Do not forget to open the Port in the security group of your node instance.

  • We see the home page. You can add to-do's.

Part 4 - Autoscaling in Kubernetes

Benefits of Autoscaling

To understand better where autoscaling would provide the most value, let’s start with an example. Imagine you have a 24/7 production service with a load that is variable in time, where it is very busy during the day in the US, and relatively low at night. Ideally, we would want the number of nodes in the cluster and the number of pods in deployment to dynamically adjust to the load to meet end user demand. The new Cluster Autoscaling feature together with Horizontal Pod Autoscaler can handle this for you automatically.

Create Horizontal Pod Autoscaler

Now activate the HPAs;

kubectl autoscale deployment web-deployment --cpu-percent=50 --min=3 --max=5

or we can use yaml files.

$ cat << EOF > hpa-web.yaml

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: web-deployment
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: web-deployment
  minReplicas: 3
  maxReplicas: 5
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

EOF
$ kubectl apply -f hpa-web.yaml

Let's look at the status:

$ watch -n3 kubectl get service,hpa,pod -o wide 

or

$ kubectl get service,hpa,pod -o wide -w

Every 3,0s: kubectl get service,hpa,pod -o wide                                                                       ubuntu: Sat Sep 12 17:48:18 2020

NAME                         TYPE        CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE     SELECTOR
service/db-service           ClusterIP   10.105.0.75     <none>        27017/TCP        105m    name=mongo
service/kubernetes           ClusterIP   10.96.0.1       <none>        443/TCP          2d10h   <none>
service/web-service          NodePort    10.107.136.54   <none>        3000:30001/TCP   105m    name=web

NAME                                             REFERENCE                   TARGETS         MINPODS   MAXPODS   REPLICAS   AGE
horizontalpodautoscaler.autoscaling/web          Deployment/web-deployment   <unknown>/50%   3         5         3          76s

NAME                                  READY   STATUS    RESTARTS   AGE    IP           NODE       NOMINATED NODE   READINESS GATES
pod/db-deployment-8597967796-q7x5s    1/1     Running   0          105m   172.18.0.5   minikube   <none>           <none>
pod/web-deployment-658cc55dc8-2h2zc   1/1     Running   2          105m   172.18.0.4   minikube   <none>           <none>
pod/web-deployment-658cc55dc8-88nxz   1/1     Running   0          61s    172.18.0.8   minikube   <none>           <none>
pod/web-deployment-658cc55dc8-c7hdl   1/1     Running   0          61s    172.18.0.9   minikube   <none>           <none>
  • web-deployment Pod number increased to 3, minimum number.
  • The HPA line under TARGETS shows <unknown>/50%. The unknown means the HPA can't idendify the current use of CPU.

We may check the current status of autoscaler by running:

$ kubectl get hpa
NAME         REFERENCE                   TARGETS         MINPODS   MAXPODS   REPLICAS   AGE
web          Deployment/web-deployment   <unknown>/50%   3         5         3          117s
$ kubectl describe hpa
....
Reference:                                             Deployment/web-deployment
Metrics:                                               ( current / target )
  resource cpu on pods  (as a percentage of request):  <unknown> / 50%
....
Conditions:
  Type           Status  Reason                   Message
  ----           ------  ------                   -------
  AbleToScale    True    SucceededGetScale        the HPA controller was able to get the target's current scale
  ScalingActive  False   FailedGetResourceMetric  the HPA was unable to compute the replica count: unable to get metrics for resource cpu: unable to fetch metrics from resource metrics API: the server could not find the requested resource (get pods.metrics.k8s.io)
.....
  • The metrics can't be calculated. So, the metrics server should be uploaded to the cluster.

Install Metric Server

  • First Delete the existing Metric Server if any.
$ kubectl delete -n kube-system deployments.apps metrics-server
  • Get the Metric Server form GitHub.
$ wget https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.6.3/components.yaml
  • Edit the file components.yaml. You will select the Deployment part in the file. Add the below line to containers.args field under the deployment object.
        - --kubelet-insecure-tls

(We have already done for this lesson)

apiVersion: apps/v1
kind: Deployment
......
      containers:
      - args:
        - --cert-dir=/tmp
        - --secure-port=4443
        - --kubelet-insecure-tls
        - --kubelet-preferred-address-types=InternalIP,ExternalIP,Hostname
        - --kubelet-use-node-status-port
        - --metric-resolution=15s
......
  • Add metrics-server to your Kubernetes instance.
$ kubectl apply -f components.yaml

  • Wait 1-2 minute or so.

  • Verify the existace of metrics-server run by below command

$ kubectl -n kube-system get pods
  • Verify metrics-server can access resources of the pods and nodes.
$ kubectl top pods
NAME                              CPU(cores)   MEMORY(bytes)   
db-deployment-8597967796-8lwzr    6m           140Mi                     
web-deployment-6d8d8c777b-2fr9h   1m           22Mi            
web-deployment-6d8d8c777b-z5xd2   1m           24Mi              
$ kubectl top nodes
NAME     CPU(cores)   CPU%   MEMORY(bytes)   MEMORY%
master   188m         9%     1245Mi          32%
node1    108m         5%     1035Mi          27% 
$ kubectl get hpa
NAME         REFERENCE                   TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
web          Deployment/web-deployment   2%/50%    3         5         3          15m

  • Look at the the values under TARGETS. The values are changed from <unknown>/50% to 1%/50% & 2%/50%, means the HPA can now idendify the current use of CPU.

  • If it is still <unknown>/50%, check the spec.template.spec.containers.resources.request field of deployment.yaml files. It is required to define this field. Otherwise, the autoscaler will not take any action for that metric.

For per-pod resource metrics (like CPU), the controller fetches the metrics from the resource metrics API for each Pod targeted by the HorizontalPodAutoscaler. Then, if a target utilization value is set, the controller calculates the utilization value as a percentage of the equivalent resource request on the containers in each Pod.

Please note that if some of the Pod's containers do not have the relevant resource request set, CPU utilization for the Pod will not be defined and the autoscaler will not take any action for that metric.

Increase load

  • Now, we will see how the autoscaler reacts to increased load. We will start a container, and send an infinite loop of queries to the php-apache service (please run it in a different terminal):

  • First look at the services.

$ kubectl get svc
NAME                 TYPE           CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE
db-service           ClusterIP      10.97.2.64      <none>        27017/TCP        23m
kubernetes           ClusterIP      10.96.0.1       <none>        443/TCP          18d
web-service          NodePort       10.96.115.134   <none>        3000:30001/TCP   23m
$ kubectl run -it --rm load-generator --image=busybox /bin/sh  
/ # while true; do wget -q -O- http://<puplic ip>:<port number of web-service> > /dev/null; done

Watch table

$ watch -n3 kubectl get service,hpa,pod -o wide

Every 3.0s: kubectl get service,hpa,pod -o wide                                                                     master: Thu Sep 17 11:29:19 2020

NAME                         TYPE           CLUSTER-IP      EXTERNAL-IP   PORT(S)          AGE   SELECTOR
service/db-service           ClusterIP      10.97.2.64      <none>        27017/TCP        48m   name=mongo
service/kubernetes           ClusterIP      10.96.0.1       <none>        443/TCP          18d   <none>
service/web-service          NodePort       10.96.115.134   <none>        3000:32040/TCP   48m   name=web

NAME                                             REFERENCE                   TARGETS   MINPODS   MAXPODS   REPLICAS   AGE
horizontalpodautoscaler.autoscaling/web          Deployment/web-deployment   62%/50%   3         5         3          36m

NAME                                  READY   STATUS    RESTARTS   AGE     IP               NODE    NOMINATED NODE   READINESS GATES
pod/db-deployment-8597967796-h952d    1/1     Running   0          48m     172.16.166.160   node1   <none>           <none>
pod/load-generator                    1/1     Running   0          4m19s   172.16.166.129   node1   <none>           <none>
pod/web-deployment-6d8d8c777b-2nf9x   1/1     Running   0          8s      172.16.166.188   node1   <none>           <none>
pod/web-deployment-6d8d8c777b-hh2t4   1/1     Running   0          36m     172.16.166.157   node1   <none>           <none>
pod/web-deployment-6d8d8c777b-q9c4t   1/1     Running   0          36m     172.16.166.172   node1   <none>           <none>
pod/web-deployment-6d8d8c777b-tgkzc   1/1     Running   0          48m     172.16.166.159   node1   <none>           <none>

Stop load

  • We will finish our example by stopping the user load.

  • In the terminal where we created the container with busybox image, terminate the load generation by typing Ctrl + C. Close the load introducing terminals grafecully and observe the behaviour at the watch board.

  • Then we will verify the result state (after a minute or so):

$ kubectl get hpa 
$ kubectl get deployment

References:

https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale-walkthrough/

https://www.digitalocean.com/community/tutorials/webinar-series-deploying-and-scaling-microservices-in-kubernetes

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