Kubernetes Guide for Application Developers

Kubernetes is a highly configurable and complex open-source container orchestration engine. Therefore, it is very easy to feel completely overwhelmed when learning it. The goal of this article is to present the very basic concepts at the core of it while keeping the focus on the development side.

Concepts

First, let’s start with some concepts we will play with in this article.

Kubernetes is running and orchestrating containers. I assume here that you are already familiar with containers, if not, have a look at containers first.

Workloads

A workload is an application running on Kubernetes. Workloads are run inside a Pod.

Pod

Pods are the smallest deployable units of computing that you can create and manage in Kubernetes.

A Pod (as in a pod of whales or pea pod) is a group of one or more containers, with shared storage and network resources, and a specification for how to run the containers. A Pod’s contents are always co-located and co-scheduled, and run in a shared context. A Pod models an application-specific “logical host”: it contains one or more application containers which are relatively tightly coupled. In non-cloud contexts, applications executed on the same physical or virtual machine are analogous to cloud applications executed on the same logical host.

Workload Resources

Kubernetes provides several built-in workload resources. We will focus here on two of those, the Deployment and the ReplicaSet.

  • Deployment. A Deployment provides declarative updates for Pods and ReplicaSets. You describe a desired state in a Deployment, and the Deployment Controller changes the actual state to the desired state at a controlled rate. You can define Deployments to create new ReplicaSets or to remove existing Deployments and adopt all their resources with new Deployments.
  • ReplicaSet. A ReplicaSet’s purpose is to maintain a stable set of replica Pods running at any given time. As such, it is often used to guarantee the availability of a specified number of identical Pods. How a ReplicaSet works

Networking

In order for applications to work together, they need to communicate and be accessible to other applications and the outside world. This is managed through networking.

We will focus here on the Service and Ingress notes.

Service

An abstract way to expose an application running on a set of Pods as a network service. With Kubernetes you don’t need to modify your application to use an unfamiliar service discovery mechanism. Kubernetes gives Pods their own IP addresses and a single DNS name for a set of Pods, and can load-balance across them.

Ingress

In order to redirect public traffic into the cluster, we need to define an Ingress.

An API object that manages external access to the services in a cluster, typically HTTP. Ingress may provide load balancing, SSL termination and name-based virtual hosting.

Wrapping It Up

If we had to summarize those concepts in a few words, we would have this:

Pod

A group of one or more containers, sharing storage and network resources with their run instructions that are together.

Deployment

A declarative way of defining the desired state for Pods and how to deploy and roll it out.

ReplicaSet

Guarantees the availability of a specified number of identical Pods.

Service

A single access point (IP/port) with load balancing to a set of Pods.

Ingress

A gateway to the cluster with routing to services.

Interacting With a Kubernetes Cluster

The kubectl The command-line tool is the best way to interact with a Kubernetes cluster.

In order to understand the example application that will follow, let’s introduce the main concepts and patterns behind it.

kubectl

A general pattern for the kubectl command is the kubectl <verb> <resource> one.

Where:

  • <verb> is (but not limited to): create get, describe, patch, delete, expose

  • <resource> is a Kubernetes resource: pod, deployment, service

Note

You can add an s to a resource name when doing a get, it feels more natural or you can use short versions. For example, the following commands will all return the list of services:

  • kubectl get service

  • kubectl get services

  • kubectl get svc

kubectl apply

A typical usage is to apply a Kubernetes resource file. Such a file (usually yaml or JSON) contains the definition of one or more resources that will be applied to the cluster.

For example: kubectl apply -f https://uri.to/resources.yaml

A Sample Application

Prerequisites

It is required to have a Kubernetes cluster available with:

In case you do not already have this, follow instructions in the appendix.

Deploy It

Let’s use a sample echoserver application as an example. This application will echo the HTTP request information received (URI, method, headers, body, …) in the response it will return. It is provided as a container image named k8s.gcr.io/echoserver:1.4.

We can create a deployment for this application with the following command:

❯ kubectl create deployment echoserver --image=k8s.gcr.io/echoserver:1.4
deployment.apps/echoserver created

We can validate our deployment has correctly been created

❯ kubectl get all
NAME                              READY   STATUS    RESTARTS   AGE
pod/echoserver-75d4885d54-cbvhh   1/1     Running   0          11m

NAME                 TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)    AGE
service/kubernetes   ClusterIP   10.96.0.1      <none>        443/TCP    2d22h

NAME                         READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/echoserver   1/1     1            1           11m

NAME                                    DESIRED   CURRENT   READY   AGE
replicaset.apps/echoserver-75d4885d54   1         1         1       11m

We do see our deployment deployment.apps/echoserver has been created. It instantiated a ReplicaSet replicaset.apps/echoserver-75d4885d54 which in turn created pod pod/echoserver-75d4885d54-cbvhh which runs our container.

In the dashboard, the Workloads overview will look like this.

workloads overview

Figure 1. Workloads overview in the Kubernetes dashboard

and our deployment:

kubernetes dashboard

Figure 2. Deployments in the Kubernetes dashboard

Make It Accessible

Now that the application is available, we need to make it accessible. In order to do so, we need to create a service pointing to our pods and expose it externally via an ingress.

Let’s create a service that will listen on port 80 and forward the traffic to one of the pod in the deployment on its port 8080.

❯ kubectl expose deployment echoserver --port 80 --target-port 8080
service/echoserver exposed

Now, let’s expose the service externally and route traffic matching for which the HTTP host is echoserver.localdev.me.

❯ kubectl create ingress echoserver --class=nginx --rule="echoserver.localdev.me/*=echoserver:80"
ingress.networking.k8s.io/echoserver created

Note

The ingress controller will in our example read the host field of incoming HTTP requests and perform routing to internal services based on its value. However, a prerequisite is that the request reaches the ingress controller and this implies the DNS echoserver.localdev.me has to be routed to the Kubernetes cluster which here is localhost. So how does this echoserver.localdev.me work?

localdev.me is a service that defines its own DNS servers and will answer with the loopback IP address (127.0.0.1) to any subdomains of localedev.me DNS requests. You can have a look at their DNS records. This is handy as it allows to use subdomains in local to perform ingress routing without the need for manual /etc/hosts modifications.

We can see the service and ingress.

❯ kubectl get all,ingress
NAME                              READY   STATUS    RESTARTS   AGE
pod/echoserver-75d4885d54-cbvhh   1/1     Running   0          21m

NAME                 TYPE        CLUSTER-IP     EXTERNAL-IP   PORT(S)    AGE
service/echoserver   ClusterIP   10.99.239.96   <none>        8080/TCP   2m16s
service/kubernetes   ClusterIP   10.96.0.1      <none>        443/TCP    2d22h

NAME                         READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/echoserver   1/1     1            1           21m

NAME                                    DESIRED   CURRENT   READY   AGE
replicaset.apps/echoserver-75d4885d54   1         1         1       21m

NAME                                   CLASS   HOSTS                    ADDRESS     PORTS   AGE
ingress.networking.k8s.io/echoserver   nginx   echoserver.localdev.me   localhost   80      66m

In the dashboard, our service will appear as below.

dashboard service

Figure 3. Services in the Kubernetes dashboard

Now that everything is in place, we can target http://echoserver.localdev.me with curl:

❯ curl echoserver.localdev.me
CLIENT VALUES:
client_address=10.1.0.34
command=GET
real path=/
query=nil
request_version=1.1
request_uri=http://echoserver.localdev.me:8080/

SERVER VALUES:
server_version=nginx: 1.10.0 - lua: 10001

HEADERS RECEIVED:
accept=*/*
host=echoserver.localdev.me
user-agent=curl/7.64.1
x-forwarded-for=192.168.65.3
x-forwarded-host=echoserver.localdev.me
x-forwarded-port=80
x-forwarded-proto=http
x-forwarded-scheme=http
x-real-ip=192.168.65.3
x-request-id=c447aa00579e6df16f6b1b854867de12
x-scheme=http
BODY:
-no body in request-

Scale Scale Scale

One interesting feature of Kubernetes is its ability to understand application requirements in terms of resources like CPU and/or memory. Thanks to a metrics server, resource consumption is measured and the cluster can react to it.

But in order for the cluster to react, it first needs to know the application requirements. Kubernetes defines two levels: requests and limits.

  • Requests are the minimum resources that will be allocated. If it is not possible to allocate those resources, the pod or container creation will fail.

  • Limits are the maximum resources that will be assigned. A higher limit than the request value allows for the application to burst for short periods of time. This is especially useful if a warmup phase is necessary or for unpredictable workloads that can’t wait for additional pods to be scheduled and ready to serve traffic.

Let’s patch our deployment to define CPU requests and limits. Here we will define very low limits in order to play with autoscaling. Let’s say a request of 1 milliCPU and a limit of 4 milliCPU. A milliCPU is an abstract CPU unit that aims to be consistent across the cluster. 1000 milliCPU usually boils down to 1 hyperthread on hyperthreaded processors.

❯ kubectl patch deployment echoserver --patch '{"spec": {"template": {"spec": {"containers": [{"name": "echoserver", "resources": {"requests": {"cpu": "1m"}, "limits": {"cpu": "4m"}}}]}}}}'
deployment.apps/echoserver patched

We can now enable Horizontal Pod Autoscaling (HPA) for our deployment from 1 to 4 instances with a target CPU threshold of 50 percent.

❯ kubectl autoscale deployment echoserver --cpu-percent=50 --min=1 --max=4
horizontalpodautoscaler.autoscaling/echoserver autoscaled

We can then generate load with JMeter and see the number of pod replicas increasing until the maximum of 4 is defined in our configuration.

❯ kubectl get hpa echoserver --watch
NAME         REFERENCE               TARGETS         MINPODS   MAXPODS   REPLICAS   AGE
echoserver   Deployment/echoserver   <unknown>/50%   1         4         0          5s
echoserver   Deployment/echoserver   1000%/50%       1         4         1          60s
echoserver   Deployment/echoserver   700%/50%        1         4         4          2m

In the beginning, we had 0 replicas in the echoserver hpa as the hpa was just started. However a pod from the previous deployment was handling the traffic, but that pod wasn’t part of the hpa listed below.

Meanwhile, in the dashboard, we can see the load of each pod replica.

replicaset dashboard

Figure 4. ReplicaSet autoscaling in the Kubernetes dashboard

Declarative Configuration

Until now we use the kubectl tool in an imperative way. It is an easy way to start, but not really the best way to deploy an application in a reproducible way.

To this end we can define our Kubernetes resources (deployments, services, ingress) as JSON or yaml files and apply them to the cluster using the kubectl apply -f <file> pattern.

First, let’s delete our application

❯ kubectl delete deployment,svc,ingress,hpa echoserver
deployment.apps "echoserver" deleted
service "echoserver" deleted
ingress.networking.k8s.io "echoserver" deleted
horizontalpodautoscaler.autoscaling "echoserver" deleted

Now let’s apply the following Kubernetes resources configuration.

❯ kubectl apply -f https://loicrouchon.fr/posts/kubernetes-introductions-for-developers/echoserver.yml
service/echoserver created
ingress.networking.k8s.io/echoserver created
deployment.apps/echoserver created
horizontalpodautoscaler.autoscaling/echoserver created

Here is the content of the resources yaml file. It contains the following resources:

  • The echoserver Deployment with CPU requests and limits.

  • The echoserver Service pointing to the echoserver Deployment.

  • The echoserver Ingress exposed on echoserver.localdev.me and targeting the echoserver Service.

  • the echoserver Horizontal Pod Autoscaler.

---
apiVersion: v1
kind: Service
metadata:
  labels:
    app.kubernetes.io/name: echoserver
    app.kubernetes.io/part-of: echoserver-app
    app.kubernetes.io/version: 1.0.0-SNAPSHOT
  name: echoserver
spec:
  ports:
    - name: http
      port: 80
      targetPort: 8080
  selector:
    app.kubernetes.io/name: echoserver
    app.kubernetes.io/part-of: echoserver-app
    app.kubernetes.io/version: 1.0.0-SNAPSHOT
  type: ClusterIP
---
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  labels:
    app.kubernetes.io/name: echoserver
    app.kubernetes.io/part-of: echoserver-app
    app.kubernetes.io/version: 1.0.0-SNAPSHOT
  name: echoserver
spec:
  ingressClassName: nginx
  rules:
  - host: echoserver.localdev.me
    http:
      paths:
      - backend:
          service:
            name: echoserver
            port:
              number: 80
        path: /
        pathType: Prefix
status:
  loadBalancer:
    ingress:
    - hostname: localhost
---
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app.kubernetes.io/part-of: echoserver-app
    app.kubernetes.io/version: 1.0.0-SNAPSHOT
    app.kubernetes.io/name: echoserver
  name: echoserver
spec:
  replicas: 1
  selector:
    matchLabels:
      app.kubernetes.io/part-of: echoserver-app
      app.kubernetes.io/version: 1.0.0-SNAPSHOT
      app.kubernetes.io/name: echoserver
  template:
    metadata:
      labels:
        app.kubernetes.io/part-of: echoserver-app
        app.kubernetes.io/version: 1.0.0-SNAPSHOT
        app.kubernetes.io/name: echoserver
    spec:
      containers:
        - env:
            - name: KUBERNETES_NAMESPACE
              valueFrom:
                fieldRef:
                  fieldPath: metadata.namespace
          image: k8s.gcr.io/echoserver:1.5
          imagePullPolicy: Always
          name: echoserver
          ports:
            - containerPort: 8080
              name: http
              protocol: TCP
          resources:
            limits:
              cpu: 10m
              memory: 20Mi
            requests:
              cpu: 5m
              memory: 5Mi
          readinessProbe:
            httpGet:
              path: /health
              port: 8080
            initialDelaySeconds: 2
            periodSeconds: 2
          livenessProbe:
            httpGet:
              path: /health
              port: 8080
            initialDelaySeconds: 30
            periodSeconds: 10
---
apiVersion: autoscaling/v1
kind: HorizontalPodAutoscaler
metadata:
  labels:
    app.kubernetes.io/part-of: echoserver-app
    app.kubernetes.io/version: 1.0.0-SNAPSHOT
    app.kubernetes.io/name: echoserver
  name: echoserver
spec:
  maxReplicas: 4
  minReplicas: 1
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: echoserver
  targetCPUUtilizationPercentage: 50

You can find out more about this declarative approach by reading:

Rolling Out a New Version of an Application

Let’s say we want to roll out a new version of our application. Instead of k8s.gcr.io/echoserver:1.4 we want to use the new k8s.gcr.io/echoserver:1.5 container image.

To do that we can edit the echoserver.yml we saw above and replace the image: k8s.gcr.io/echoserver:1.4 in the containers specification of the Deployment resource with image: k8s.gcr.io/echoserver:1.5.

Now let’s apply the configuration again:

❯ kubectl apply -f https://loicrouchon.fr/posts/kubernetes-introductions-for-developers/echoserver.yml
service/echoserver unchanged (1)
ingress.networking.k8s.io/echoserver unchanged (1)
deployment.apps/echoserver configured (2)
horizontalpodautoscaler.autoscaling/echoserver unchanged (1)
  1. Unchanged resources, will not be updated

  2. Updated resource, the deployment will be retriggered

In the console, we can see our deployment being updated with the new image. A new ReplicaSet is started and a new pod is created.

Deployment rollout in the Kubernetes dashboard

Figure 5. Deployment rollout in the Kubernetes dashboard

If in parallel you were shooting requests on a regular basis with a similar command

watch -n 0.5 curl -s echoserver.localdev.me

You would have seen the output being updated when the traffic was routed to the new version of the application. This is without downtime as the Deployment configuration contains information to help Kubernetes understand if the application is ready and live. Those are called Liveness, Readiness, and Startup Probes and are a must for ensuring traffic is only routed to healthy instances of your application.

Going Further

Today we covered some of the basic concepts of Kubernetes. How to deploy an application, expose it outside the cluster, scale it and perform rollouts. But there’s much more to cover.

Appendix A: Installing a Kubernetes Cluster Locally

The easiest ways to install a local Kubernetes cluster are through one of:

For example with Colima:

brew install colima
docker context use colima
colima start --cpu 4 --memory 8 --with-kubernetes

Start the Kubernetes Proxy on Port 8001

In a background terminal start kubectl proxy. It will start a proxy that listens on http://localhost:8001/

Installing an Ingress Controller

In our case, we will use nginx as ingress controller, but any other ingress controller is perfectly fine.

You can install it as below:

kubectl apply -f https://raw.githubusercontent.com/kubernetes/ingress-nginx/controller-v1.1.1/deploy/static/provider/cloud/deploy.yaml
kubectl wait pod --namespace ingress-nginx --for=condition=ready --selector="app.kubernetes.io/component=controller" --timeout=120s
kubectl port-forward --namespace=ingress-nginx service/ingress-nginx-controller 8080:80

Additional installation options are available in the ingress-nginx documentation.

Installing Metrics

Installation of metrics allows supporting autoscaling based on pods CPU consumption.

kubectl apply -f https://github.com/kubernetes-sigs/metrics-server/releases/download/v0.6.1/components.yaml
kubectl patch deployments.app/metrics-server -n kube-system --type=json --patch '[{"op": "add", "path": "/spec/template/spec/containers/0/args/-", "value": "--kubelet-insecure-tls" }]'

Install the Kubernetes Dashboard

Install the dashboard and the metrics server with:

kubectl apply -f https://raw.githubusercontent.com/kubernetes/dashboard/v2.5.1/aio/deploy/recommended.yaml

The dashboard is accessible at URL http://localhost:8001/api/v1/namespaces/kubernetes-dashboard/services/https:kubernetes-dashboard:/proxy

To log in to the dashboard, it is necessary to use a token. You can obtain a token using the following command:

kubectl -n kube-system describe secret (kubectl -n kube-system get secret | awk '/^namespace-controller-token-/{print $1}') | awk '$1=="token:"{print $2}'

.

Leave a Comment