Kubernetes is an open-source container orchestration platform that automates the deployment, management, scaling, and networking of containers.

It was developed by Google using the Go Programming Language, and this amazing technology has been open-source since 2014.

According to the Stack Overflow Developer Survey - 2020, Kubernetes is the #3 most loved platform and #3 most wanted platform.

Apart from being very powerful, Kubernetes is known as quite hard to get started with. I won't say it's easy, but if you are equipped with the prerequisites and go through this guide attentively and with patience, you should be able to:

  • Get a solid understanding of the fundamentals.
  • Create and manage Kubernetes clusters.
  • Deploy (almost) any application to a Kubernetes cluster.

Prerequisites

  • Familiarity with JavaScript
  • Familiarity with the Linux Terminal
  • Familiarity with Docker (suggested read: The Docker Handbook)

Project Code

Code for the example projects can be found in the following repository:

fhsinchy/kubernetes-handbook-projects
Project codes used in “The Kubernetes Handbook” :notebook: - fhsinchy/kubernetes-handbook-projects
Spare a ⭐ to keep me motivated

You can find the complete code in the k8s branch.

Table of Contents

Introduction to Container Orchestration and Kubernetes

According to Red Hat

"Container orchestration is the process of automating the deployment, management, scaling, and networking tasks of containers.

It can be used in any environment where you use containers and can help you deploy the same application across different environments without requiring any redesigning".

Let me show you an example. Assume that you have developed an amazing application that suggests to people what they should eat depending on the time of day.

Now assume that you've containerized the application using Docker and deployed it on AWS.

If the application goes down for any reason, the users lose access to your service immediately.

To solve this issue, you can make multiple copies or replicas of the same application and make it highly available.

Even if one of the instances goes down, the other two will be available to the users.

Now assume that your application has become wildly popular among the night owls and your servers are being flooded with requests at night, while you're sleeping.

What if all the instances go down due to overload? Who's going to do the scaling? Even if you scale up and make 50 replicas of your application, who's going to check on their health? How are going to set-up the networking so that requests hit the right endpoint? Load balancing is going to be a big concern as well, isn't it?

Kubernetes can make things much easier for these kinds of situations. It's a container orchestration platform that consists of several components and it works tirelessly to keep your servers in the state that you desire.

Assume that you want to have 50 replicas of your application running continuously. Even if there is a sudden rise in the user count, the server needs to be scaled up automatically.

You just tell your needs to Kubernetes and it will do the rest of the heavy lifting for you.

Kubernetes will not only implement the state, it'll also maintain it. It will make additional replicas if any of the old ones dies, manage the networking and storage, rollout or rollback updates, or even upscale the server if ever necessary.

Installing Kubernetes

Running Kubernetes in your local machine is actually a lot different than running Kubernetes on the cloud. To get Kubernetes up and running, you need two programs.

  • minikube - it runs a single-node Kubernetes cluster inside a Virtual Machine (VM) on your local computer.
  • kubectl - The Kubernetes command-line tool, which allows you to run commands against Kubernetes clusters.

Apart from these two programs, you'll also need a hypervisor and a containerization platform. Docker is the obvious choice for the containerization platform. Recommended hypervisors are as follows:

Hyper-V comes built into Windows 10 (Pro, Enterprise, and Education) as an optional feature and can be turned on from the control panel.

HyperKit comes bundled with Docker Desktop for Mac as a core component.

And on Linux, you can bypass the entire hypervisor layer by using Docker directly. It's much faster than using any hypervisor and is the recommended way to run Kubernetes on Linux.

You may go ahead and install any of the above mentioned hypervisors. Or if you want to keep things simple, just get VirtualBox.

For the rest of the article, I'll assume that you're using VirtualBox. Don't worry though, even if you're using something else, there shouldn't be that much of a difference.

I'll be using minikube with the Docker driver on a Ubuntu machine throughout the entire article.

Once you have installed the hypervisor and the containerization platform, it's time to install the minikube and kubectl programs.

kubectl usually comes bundled with Docker Desktop on Mac and Windows. Installation instructions for Linux can be found here.

minikube, on the other hand, has to be installed on all three of the systems. You can use Homebrew on Mac, and Chocolatey on Windows to install minikube. Installation instructions for Linux can be found here.

Once you've installed them, you can test out both programs by executing the following commands:

minikube version

# minikube version: v1.12.1
# commit: 5664228288552de9f3a446ea4f51c6f29bbdd0e0

kubectl version

# Client Version: version.Info{Major:"1", Minor:"18", GitVersion:"v1.18.6", GitCommit:"dff82dc0de47299ab66c83c626e08b245ab19037", GitTreeState:"clean", BuildDate:"2020-07-16T00:04:31Z", GoVersion:"go1.14.4", Compiler:"gc", Platform:"darwin/amd64"}
# Server Version: version.Info{Major:"1", Minor:"18", GitVersion:"v1.18.3", GitCommit:"2e7996e3e2712684bc73f0dec0200d64eec7fe40", GitTreeState:"clean", BuildDate:"2020-05-20T12:43:34Z", GoVersion:"go1.13.9", Compiler:"gc", Platform:"linux/amd64"}

If you've downloaded the right versions for your operating system and have set up the paths properly, you should be ready to go.

As I've already mentioned, minikube runs a single-node Kubernetes cluster inside a Virtual Machine (VM) on your local computer. I'll explain clusters and nodes in greater details in an upcoming section.

For now, understand that minikube creates a regular VM using your hypervisor of choice and treats that as a Kubernetes cluster.

If you face any problems in this section please have a look at the Troubleshooting section at the end of this article.

Before you start minikube, you have to set the correct hypervisor driver for it to use. To set VirtualBox as the default driver, execute the following command:

minikube config set driver virtualbox

# ❗ These changes will take effect upon a minikube delete and then a minikube start

You can replace virtualbox with hyperv, hyperkit, or docker as per your preference. This command is necessary for the first time only.

To start minikube, execute the following command:

minikube start

# ? minikube v1.12.1 on Ubuntu 20.04
# ✨ Using the virtualbox driver based on existing profile
# ? Starting control plane node minikube in cluster minikube
# ? Updating the running virtualbox "minikube" VM ...
# ? Preparing Kubernetes v1.18.3 on Docker 19.03.12 ...
# ? Verifying Kubernetes components...
# ? Enabled addons: default-storageclass, storage-provisioner
# ? Done! kubectl is now configured to use "minikube"

You can stop minikube by executing the minikube stop command.

Hello World in Kubernetes

Now that you have Kubernetes on your local system, it's time to get your hands dirty. In this example you'll be deploying a very simple application to your local cluster and getting familiar with the fundamentals.

There will be terminologies like pod, service, load balancer, and so on in this section. Don't stress if you don't understand them right away. I'll go into great details explaining each of them in The Full Picture sub-section.

If you've started minikube in the previous section then you're ready to go. Otherwise you'll have to start it now. Once minikube has started, execute the following command in your terminal:

kubectl run hello-kube --image=fhsinchy/hello-kube --port=80

# pod/hello-kube created

You'll see the pod/hello-kube created message almost immediately. The run command runs the given container image inside a pod.

Pods are like a box that encapsulates a container. To make sure the pod has been created and is running, execute the following command:

kubectl get pod

# NAME         READY   STATUS    RESTARTS   AGE
# hello-kube   1/1     Running   0          3m3s

You should see Running in the STATUS column. If you see something like ContainerCreating, wait for a minute or two and check again.

Pods by default are inaccessible from outside the cluster. To make them accessible, you have to expose them using a service. So, once the pod is up and running, execute the following command to expose the pod:

kubectl expose pod hello-kube --type=LoadBalancer --port=80

# service/hello-kube exposed

To make sure the load balancer service has been created successfully, execute the following command:

kubectl get service

# NAME         TYPE           CLUSTER-IP     EXTERNAL-IP   PORT(S)        AGE
# hello-kube   LoadBalancer   10.109.60.75   <pending>     80:30848/TCP   119s
# kubernetes   ClusterIP      10.96.0.1      <none>        443/TCP        7h47m

Make sure you see the hello-kube service in the list. Now that you have a pod running that is exposed, you can go ahead and access that. Execute the following command to do so:

minikube service hello-kube

# |-----------|------------|-------------|-----------------------------|
# | NAMESPACE |    NAME    | TARGET PORT |             URL             |
# |-----------|------------|-------------|-----------------------------|
# | default   | hello-kube |          80 | http://192.168.99.101:30848 |
# |-----------|------------|-------------|-----------------------------|
# ? Opening service default/hello-kube in default browser...

Your default web browser should open automatically and you should see something like this:

This is a very simple JavaScript application that I've put together using vite and a little bit of CSS. To understand what you just did, you have to gain a good understanding of the Kubernetes architecture.

Kubernetes Architecture

In the world of Kubernetes, a node can be either a physical or a virtual machine with a given role. A collection of such machines or servers using a shared network to communicate between each other is called a cluster.

In your local setup, minikube is a single node Kubernetes cluster. So instead of having multiple servers like in the diagram above, minikube has only one that acts as both the main server and the node.

Each server in a Kubernetes cluster gets a role. There are two possible roles:

  • control-plane — Makes most of the necessary decisions and acts as sort of the brains of the entire cluster. This can be a single server or a group of server in larger projects.
  • node — Responsible for running workloads. These servers are usually micro managed by the control plane and carries out various tasks following supplied instructions.

Every server in you cluster will have a selected set of components. The number and type of those components can vary depending on the role a server has in your cluster. That means the nodes do not have all the components that the control plane has.

In the upcoming subsections, you'll have a more detailed look into the individual components that make up a Kubernetes cluster.

Control Plane Components

The control plane in a Kubernetes cluster consists of five components. These are as follows:

  1. kube-api-server: This acts as the entrance to the Kubernetes control plane, responsible for validating and processing requests delivered using client libraries like the kubectl program.
  2. etcd: This is a distributed key-value store which acts as the single source of truth about your cluster. It holds configuration data and information about the state of the cluster. etcd is an open-source project and is developed by the folks behind Red Hat. The source code of the project is hosted on the etcd-io/etcd GitHub repo.
  3. kube-controller-manager: The controllers in Kubernetes are responsible for controlling the state of the cluster. When you let Kubernetes know what you want in your cluster, the controllers make sure that your request is fulfilled. The kube-controller-manager is all the controller processes grouped into a single process.
  4. kube-scheduler: Assigning task to a certain node considering its available resources and the requirements of the task is known as scheduling. The kube-scheduler component does the task of scheduling in Kubernetes making sure none of the servers in the cluster is overloaded.
  5. cloud-controller-manager: In a real world cloud environment, this component lets you wire-up your cluster with your cloud provider's (GKE/EKS) API. This way, the components that interact with that cloud platform stays isolated from components that just interact with your cluster. In a local cluster like minikube, this component doesn't exist.

Node Components

Compared to the control plane, nodes have a very small number of components. These components are as follows:

  1. kubelet: This service acts as the gateway between the control plain and each of the nodes in a cluster. Every instructions from the control plain towards the nodes, passes through this service. It also interacts with the etcd store to keep the state information updated.
  2. kube-proxy: This small service runs on each node server and maintains network rules on them. Any network request that reaches a service inside your cluster, passes through this service.
  3. Container Runtime: Kubernetes is a container orchestration tool hence it runs applications in containers. This means that every node needs to have a container runtime like Docker or rkt or cri-o.

Kubernetes Objects

According to the Kubernetes documentation

"Objects are persistent entities in the Kubernetes system. Kubernetes uses these entities to represent the state of your cluster.  Specifically, they can describe what containerized applications are running, the resources available to them, and the policies around their behaviour."

When you create a Kubernetes object, you're effectively telling the Kubernetes system that you want this object to exist no matter what and the Kubernetes system will constantly work to keep the object running.

Pods

According to the Kubernetes documentation

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

A pod usually encapsulates one or more containers that are closely related sharing a life cycle and consumable resources.

Although a pod can house more than one container, you shouldn't just put containers in a pod willy nilly. Containers in a pod must be so closely related, that they can be treated as a single application.

As an example, your back-end API may depend on the database but that doesn't mean you'll put both of them in the same pod. Throughout this entire article, you won't see any pod that has more than one container running.

Usually, you should not manage a pod directly. Instead, you should work with higher level objects that can provide you much better manageability. You'll learn about these higher level objects in later sections.

Services

According to the Kubernetes documentation

"A service in Kubernetes is an abstract way to expose an application running on a set of pods as a network service".

Kubernetes pods are ephemeral in nature. They get created and after some time when they get destroyed, they do not get recycled.

Instead new identical pods take the places of the old ones. Some higher level Kubernetes objects are even capable of creating and destroying pods dynamically.

A new IP address is assigned to each pod at the time of their creation. But in case of a high level object that can create, destroy, and group together a number of pods, the set of pods running in one moment in time could be different from the set of pods running that application a moment later.

This leads to a problem: if some set of pods in your cluster depends on another set of pods within your cluster, how do they find out and keep track of each other's IP addresses?

The Kubernetes documentation says —

"a Service is an abstraction which defines a logical set of Pods and a policy by which to access them".

Which essentially means that a Service groups together a number of pods that perform the same function and presents them as a single entity.

This way, the confusion of keeping track of multiple pods goes out of the window as that single Service now acts as a sort of communicator for all of them.

In the hello-kube example, you created a LoadBalancer type of service which allows requests from outside the cluster connect to pods running inside the cluster.

Any time you need to give access to one or more pods to another application or to something outside of the cluster, you should create a service.

For instance, if you have a set of pods running web servers that should be accessible from the internet, a service will provide the necessary abstraction.

The Full Picture

Now that you have a proper understanding of the individual Kubernetes components, here is a visual representation of how they work together behind the scenes:

https://kubernetes.io/docs/concepts/overview/components/

Before I get into explaining the individual details, have a look at what the Kubernetes documentation has to say —

"To work with Kubernetes objects – whether to create, modify, or delete them – you'll need to use the Kubernetes API. When you use the kubectl command-line interface, the CLI makes the necessary Kubernetes API calls for you."

The first command that you ran was the run command. It was as follows:

kubectl run hello-kube --image=fhsinchy/hello-kube --port=80

The run command is responsible for creating a new pod that runs the given image. Once you've issued this command, following sets of events occur inside the Kubernetes cluster:

  • The kube-api-server component receives the request, validates it and processes it.
  • The kube-api-server then communicates with the kubelet component on the node and provides the instructions necessary for creating the pod.
  • The kubelet component then starts working on making the pod up and running and also keeps the state information updated in the etcd store.

Generic syntax for the run command is as follows:

kubectl run <pod name> --image=<image name> --port=<port to expose>

You can run any valid container image inside a pod. The fhsinchy/hello-kube Docker image contains a very simple JavaScript application that runs on port 80 inside the container. The --port=80 option allows the pod to expose port 80 from inside the container.

The newly created pod runs inside the minikube cluster and is inaccessible from the outside. To expose the pod and make it accessible, the second command that you issued was as follows:

kubectl expose pod hello-kube --type=LoadBalancer --port=80

The expose command is responsible for creating a Kubernetes service of type LoadBalancer that allows users to access the application running inside the pod.

Just like the run command, the expose command execution goes through same sort of steps inside the cluster. But instead of a pod, the kube-api-server provides instructions necessary for creating a service in this case to the kubelet component.

Generic syntax for the expose command is as follows:

kubectl expose <resource kind to expose> <resource name> --type=<type of service to create> --port=<port to expose>

The object type can be any valid Kubernetes object type. The name has to match up with the object name you're trying to expose.

--type indicates the type of service you want. There are four different types of services available for internal or external networking.

Lastly, the --port is the port number you want to expose from the running container.

Once the service has been created, the last piece of the puzzle was to access the application running inside the pod. To do that, the command you executed was as follows:

minikube service hello-kube

Unlike the previous ones, this last command doesn't go to the kube-api-server. Rather it communicates with the local cluster using the minikube program. The service command for minikube returns a full URL for a given service.

When you created the hello-kube pod with the --port=80 option, you instructed Kubernetes to let the pod expose port 80 from inside the container but it wasn't accessible from outside the cluster.

Then when you created the LoadBalancer service with the --port=80 option, it mapped port 80 from that container to an arbitrary port in the local system making it accessible from outside the cluster.

On my system, the service command returns 192.168.99.101:30848 URL for the pod. The IP in this URL is actually the IP of the minikube virtual machine. You can verify this by executing the following command:

minikube ip

# 192.168.99.101

To verify that the 30848 port points to port 80 inside the pod, you can execute the following command:

kubectl get service hello-kube

# NAME         TYPE           CLUSTER-IP     EXTERNAL-IP   PORT(S)        AGE
# hello-kube   LoadBalancer   10.109.60.75   <pending>     80:30848/TCP   119s

On the PORT(S) column, you can see that port 80 indeed maps to port 30484 on the local system. So instead of running the service command you can just inspect the IP and port and then put it into your browser manually to access the hello-kube application.

Now, the final state of the cluster can be visualized as follows:

If you're coming from Docker, then the significance of using a service in order to expose a pod may seem a bit too verbose to you at the moment.

But as you go into the examples that deal with more than one pod, you'll start to appreciate everything that Kubernetes has to offer.

Getting Rid of Kubernetes Resources

Now that you know how to create Kubernetes resources like pods and Services, you need to know how to get rid of them. The only way to get rid of a Kubernetes resource is to delete it.

You can do that by using the delete command for kubectl. Generic syntax of the command is as follows:

kubectl delete <resource type> <resource name>

To delete a pod named hello-kube the command will be as follows:

kubectl delete pod hello-kube

# pod "hello-kube" deleted

And to delete a service named hello-kube the command will be as follows:

kubectl delete service hello-kube

# service "hello-kube" deleted

Or if you're in a destructive mood, you can delete all objects of a kind in one go using the --all option for the delete command. Generic syntax for the option is as follows:

kubectl delete <object type> --all

So to delete all pods and services you have to execute kubectl delete pod --all and kubectl delete service --all respectively.

Declarative Deployment Approach

To be honest, the hello-kube example you just saw in the previous section is not an ideal way of performing deployment with Kubernetes.

The approach that you took in that section is an imperative approach which means you had to execute every command one after the another manually. Taking an imperative approach defies the entire point of Kubernetes.

An ideal approach to deployment with Kubernetes is the declarative approach. In it you, as a developer, let Kubernetes know the state you desire your servers to be in and Kubernetes figures out a way to implement that.

In this section you'll be deploying the same hello-kube application in a declarative approach.

If you haven't already cloned the code repository linked above, then go ahead and grab that now.

Once you have that, go inside the hello-kube directory. This directory contains the code for the hello-kube application as well as the Dockerfile for building the image.

├── Dockerfile
├── index.html
├── package.json
├── public
└── src

2 directories, 3 files

The JavaScript code lives inside the src folder but that's not of interest to you. The file you should be looking at is the Dockerfile because it can give you insight into how you should plan your deployment. The contents of the Dockerfile are as follows:

FROM node as builder

WORKDIR /usr/app

COPY ./package.json ./
RUN npm install
COPY . .
RUN npm run build

EXPOSE 80

FROM nginx
COPY --from=builder /usr/app/dist /usr/share/nginx/html

As you can see, this is a multi-staged build process.

  • The first stage uses node as the base image and compiles the JavaScript application into a bunch of production ready files.
  • The second stage copies the files built during the first stage, and pastes them inside the default NGINX document root. Given that the base image for the second phase is nginx, the resulting image will be an nginx image serving the files built during the first phase on port 80 (default port for nginx).

Now to deploy this application on Kubernetes, you'll have to find a way to run the image as a container and make port 80 accessible from the outside world.

Writing Your First Set of Configurations

In the declarative approach, instead of issuing individual commands in the terminal, you instead write down the necessary configuration in a YAML file and feed that to Kubernetes.

In the hello-kube project directory, create another directory named k8s. k8s is short for k(ubernete = 8 character)s.

You don't need to name the folder this way, you can name it whatever you want.

It's not even necessary to keep it within the project directory. These configuration files can live anywhere in your computer, as they have no relation to the project source code.

Now inside that k8s directory, create a new file named hello-kube-pod.yaml. I will go ahead and write the code for the file first and then I'll go line by line and explain it to you. The content for this file is as follows:

apiVersion: v1
kind: Pod
metadata:
  name: hello-kube-pod
  labels:
    component: web
spec:
  containers:
    - name: hello-kube
      image: fhsinchy/hello-kube
      ports:
        - containerPort: 80

Every valid Kubernetes configuration file has four required fields. They are as follows:

  • apiVersion: Which version of the Kubernetes API you're using to create this object. This value may change depending on the kind of object you are creating. For creating a Pod the required version is v1.
  • kind: What kind of object you want to create. Objects in Kubernetes can be of many kinds. As you go through the article, you'll learn about a lot of them, but for now, just understand that you're creating a Pod object.
  • metadata: Data that helps uniquely identify the object. Under this field you can have information like name, labels, annotation etc. The metadata.name string will show up on the terminal and will be used in kubectl commands. The key value pair under the metadata.labels field doesn't have to be components: web. You can give it any label like app: hello-kube. This value will be used as the selector when creating the LoadBalancer service very soon.
  • spec: contains the state you desire for the object. The spec.containers sub-field contains information about the containers that will run inside this Pod. The spec.containers.name value is what the container runtime inside the node will assign to the newly created container. The spec.containers.image is the container image to be used for creating this container. And the spec.containers.ports field holds configuration regarding various ports configuration. containerPort: 80 indicates that you want to expose port 80 from the container.

Now to feed this configuration file to Kubernetes, you'll use the apply command. Generic syntax for the command is as follows:

kubectl apply -f <configuration file>

To feed a configuration file named hello-kube-pod.yaml, the command will be as follows:

kubectl apply -f hello-kube-pod.yaml

# pod/hello-kube-pod created

To make sure that the Pod is up and running, execute the following command:

kubectl get pod

# NAME         READY   STATUS    RESTARTS   AGE
# hello-kube   1/1     Running   0          3m3s

You should see Running in the STATUS column. If you see something like ContainerCreating wait for a minute or two and check again.

Once the Pod is up and running, it's time for you to write the configuration file for the LoadBalancer service.

Create another file inside the k8s directory called hello-kube-load-balancer-service.yaml and put following code in it:

apiVersion: v1
kind: Service
metadata:
  name: hello-kube-load-balancer-service
spec:
  type: LoadBalancer
  ports:
    - port: 80
      targetPort: 80
  selector:
    component: web

Like the previous configuration file, apiVersion, kind, and metadata fields serve the same purpose here. As you can see there are no labels field inside metadata here. That's because a service selects other objects using labels, other objects don't select a service.

Remember, services set-up an access policy for other objects, other objects don't set-up an access policy for a service.

Inside the spec field you can see a new set of values. Unlike a Pod, services have four types. These are ClusterIP, NodePort, LoadBalancer, and ExternalName.

In this example, you're using the type LoadBalancer, which is the standard way for exposing a service outside the cluster. This service will give you an IP address that you can then use to connect to the applications running inside your cluster.

The LoadBalancer type requires two port values to work properly. Under the ports field, the port value is for accessing the pod itself and its value can be anything you want.

The targetPort value is the one from inside the container and has to match up with the port that you want to expose from inside the container.

I've already said that the hello-kube application runs on port 80 inside the container . You've even exposed this port in the Pod configuration file, so the targetPort will be 80.

The selector field is used to identify the objects that will be connected to this service. The component: web key-value pair has to match up with the key-value pair under the labels field in the Pod configuration file. If you've used some other key value pair like app: hello-kube in that configuration file, use that instead.

To feed this file to Kubernetes you will again use the apply command. The command for feeding a file named hello-kube-load-balancer-service.yaml will be as follows:

kubectl apply -f hello-kube-load-balancer-service.yaml

# service/hello-kube-load-balancer-service created

To make sure the load balancer has been created successfully execute the following command:

kubectl get service

# NAME                               TYPE           CLUSTER-IP       EXTERNAL-IP   PORT(S)        AGE
# hello-kube-load-balancer-service   LoadBalancer   10.107.231.120   <pending>     80:30848/TCP   7s
# kubernetes                         ClusterIP      10.96.0.1        <none>        443/TCP        21h

Make sure you see the hello-kube-load-balancer-service name in the list. Now that you have a pod running that is exposed, you can go ahead and access that. Execute the following command to do so:

minikube service hello-kube-load-balancer-service

# |-----------|----------------------------------|-------------|-----------------------------|
# | NAMESPACE |           NAME                   | TARGET PORT |             URL             |
# |-----------|----------------------------------|-------------|-----------------------------|
# | default   | hello-kube-load-balancer-service |          80 | http://192.168.99.101:30848 |
# |-----------|----------------------------------|-------------|-----------------------------|
# ?  Opening service default/hello-kube-load-balancer in default browser...

Your default web browser should open automatically and you should see something like this:

You can also feed both files together instead of feeding them individually. To do that you can replace the file name with the directory name as follows:

kubectl apply -f k8s

# service/hello-kube-load-balancer-service created
# pod/hello-kube-pod created

In this case make sure your terminal is on the parent directory of the k8s directory.

If you're inside the k8s directory, you can use a dot (.) to refer to the current directory. When mass applying configurations, it can be a good idea to get rid of resources created previously. That way the possibility of conflicts becomes much lower.

The declarative approach is the ideal one when working with Kubernetes. Except for some special cases, that you'll see near the end of the article.

The Kubernetes Dashboard

In a previous section, you used the delete command to get rid of a Kubernetes object.

In this section, however, I thought introducing the dashboard would be great idea. The Kubernetes Dashboard is a graphical UI that you can use to manage your workloads, services, and more.

To launch the Kubernetes Dashboard, execute the following command in your terminal:

minikube dashboard

# ? Verifying dashboard health ...
# ? Launching proxy ...
# ? Verifying proxy health ...
# ? Opening http://127.0.0.1:52393/api/v1/namespaces/kubernetes-dashboard/services/http:kubernetes-dashboard:/proxy/ in your default browser...

The dashboard should open automatically in your default browser:

The UI is pretty user-friendly and you are free to roam around here. Although it's completely possible to create, manage, and delete objects from this UI, I'll be using the CLI for the rest of this article.

Here in the Pods list, you can use the three dots menu on the right side to Delete the Pod. You can do the same with the LoadBalancer service as well. In fact the Services list is conveniently placed right after the Pods list.

You can close the dashboard by hitting the Ctrl + C key combination or closing the terminal window.

Working with Multi-Container Applications

So far you've worked with applications that run within a single container.

In this section, you'll be working with an application consisting of two containers. You'll also get familiar with Deployment, ClusterIP, PersistentVolume, PersistentVolumeClaim and some debugging techniques.

The application you'll be working with is a simple express notes API with full CRUD functionality. The application uses PostgreSQL as its database system. So you're not only going to deploy the application but also set-up internal networking between the application and the database.

The code for the application is inside the notes-api directory inside the project repo.

.
├── api
├── docker-compose.yaml
└── postgres

2 directories, 1 file

The application source code resides inside the api directory and the postgres directory contains a Dockerfile for creating the custom postgres image. The docker-compose.yaml file contains the necessary configuration for running the application using docker-compose.

Just like with the previous project, you can look into the individual Dockerfile for each service to get a sense of how the application runs inside the container.

Or you can just inspect the docker-compose.yaml and plan your Kubernetes deployment using that.

version: "3.8"

services: 
    db:
        build:
            context: ./postgres
            dockerfile: Dockerfile.dev
        environment:
            POSTGRES_PASSWORD: 63eaQB9wtLqmNBpg
            POSTGRES_DB: notesdb
    api:
        build: 
            context: ./api
            dockerfile: Dockerfile.dev
        ports: 
            - 3000:3000
        volumes: 
            - /usr/app/node_modules
            - ./api:/usr/app
        environment: 
            DB_CONNECTION: pg
            DB_HOST: db
            DB_PORT: 5432
            DB_USER: postgres
            DB_DATABASE: notesdb
            DB_PASSWORD: 63eaQB9wtLqmNBpg

Looking at the api service definition, you can see that the application runs on port 3000 inside the container. It also requires a bunch of environment variables to function properly.

The volumes can be ignored as they were necessary for development purposes only and the build configuration is Docker-specific. So the two sets of information that you can carry over to your Kubernetes configuration files almost unchanged are as follows:

  • Port mappings – because you'll have to expose the same port from the container.
  • Environment variables – because these variables are going to be the same across all environments (the values are going to change, though).

The db service is even simpler. All it has is bunch of environment variables. You can even use the official postgres image instead of a custom one.

But the only reason for a custom image is if you want the database instance to come with the notes table pre-created.

This table is necessary for the application. If you look inside the postgres/docker-entrypoint-initdb.d directory, you'll see a file named notes.sql which is used for creating the database during initialization.

Deployment Plan

Unlike the previous project you deployed, this project is going to be a bit more complicated.

In this project, you'll create not one but three instances of the notes API. These three instances will be exposed outside of the cluster using a LoadBalancer service.

Apart from these three instances, there will be another instance of the PostgreSQL database system. All three instances of the notes API application will communicate with this database instance using a ClusterIP service.

ClusterIP service is another type of Kubernetes service that exposes an application within your cluster. That means no outside traffic can reach the application using a ClusterIP service.

In this project, the database has to be accessed by the notes API only, so exposing the database service within the cluster is an ideal choice.

I've already mentioned in a previous section that you shouldn't create pods directly. So in this project, you'll be using a Deployment instead of a Pod.

Replication Controllers, Replica Sets, and Deployments

According to the Kubernetes documentation -

"In Kubernetes, controllers are control loops that watch the state of your cluster, then make or request changes where needed. Each controller tries to move the current cluster state closer to the desired state. A control loop is a non-terminating loop that regulates the state of a system."

A ReplicationController, as the name suggests allows you to easily create multiple replicas very easily. Once the desired number of replicas are created, the controller will make sure that the state stays that way.

If after some time you decide to lower the number of replicas, then the ReplicationController will take actions immediately and get rid of the extra pods.

Otherwise if the number of replicas becomes lower than what you wanted (maybe some of the pods have crashed) the ReplicationController will create new ones to match the desired state.

As useful as they may sound to you, the ReplicationController is not the recommended way of creating replicas nowadays. A newer API called a ReplicaSet has taken the place.  

Apart from the fact that a ReplicaSet can provide you with a wider range of selection option, both ReplicationController and ReplicaSet are more or less the same thing.

Having a wider range of selector options is good but what's even better is having more flexibility in terms of rolling out and rolling back updates. This is where another Kubernetes API called a Deployment comes in.

A Deployment is like an extension to the already nice ReplicaSet API. Deployment not only allows you to create replicas in no time, but also allows you to release updates or go back to a previous function with just one or two kubectl commands.

ReplicationController ReplicaSet Deployment
Allows the creation of multiple pods easily Allows the creation of multiple pods easily Allows the creation of multiple pods easily
The original method of replication in Kubernetes Has more flexible selectors Extends ReplicaSets with easy update roll-out and roll-back

In this project, you'll be using a Deployment to maintain the application instances.

Creating Your First Deployment

Let's begin by writing the configuration file for the notes API deployment. Create a k8s directory inside the notes-api project directory.

Inside that directory, create a file named api-deployment.yaml and put following content in it:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: api-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      component: api
  template:
    metadata:
      labels:
        component: api
    spec:
      containers:
        - name: api
          image: fhsinchy/notes-api
          ports:
            - containerPort: 3000

In this file, the apiVersion, kind, metadata and spec fields serve the same purpose as the previous project. Notable changes in this file from the last one are as follows:

  • For creating a Pod, the required apiVersion was v1. But for creating a Deployment, the required version is apps/v1. Kubernetes API versions can be a bit confusing at times, but as you keep working with Kubernetes you'll get the hang of them. Also you can consult the official docs for example YAML files to refer to. The kind is Deployment which is pretty self-explanatory.
  • spec.replicas defines the number of running replicas. Setting this value to 3 means you let Kubernetes know that you want three instances of your application running at all times.
  • spec.selector is where you let the Deployment know which pods to control. I've already mentioned that a Deployment is an extension to ReplicaSet and can control a set of Kubernetes objects. Setting selector.matchLabels to component: api means this Deployment will control the pods that have a label of component: api. This line is letting Kubernetes know that you want this Deployment to control all the pods having the component: api label.
  • spec.template is the template for configuring the pods. It's almost the same as the previous configuration file.

Now to see this configuration in action, apply the file just like in the previous project:

kubectl apply -f api-deployment.yaml

# deployment.apps/api-deployment created

To make sure the Deployment has been created, execute the following command:

kubectl get deployment

# NAME             READY   UP-TO-DATE   AVAILABLE   AGE
# api-deployment   0/3     3            0           2m7s

If you look at the READY column, you'll see 0/3. This means the pods have not been created yet. Wait a few minutes and try once again.

kubectl get deployment

# NAME             READY   UP-TO-DATE   AVAILABLE   AGE
# api-deployment   0/3     3            0           28m

As you can see, I have waited nearly half an hour and still none of the pods are ready. The API itself is only a few hundred kilobytes. A deployment of this size shouldn't have taken this long. Which means there is a problem and we have to fix that.

Inspecting Kubernetes Resources

Before you can solve a problem, you have to first find out the origin. A good starting point is the get command.

You already know the get command that prints a table containing important information about one or more Kubernetes resources. Generic syntax of the command is as follows:

kubectl get <resource type> <resource name>

To run the get command on your api-deployment, execute the following line of code in your terminal:

kubectl get deployment api-deployment

# NAME             READY   UP-TO-DATE   AVAILABLE   AGE
# api-deployment   0/3     3            0           15m

You can omit the api-deployment name to get a list of all available deployments. You can also run the get command on a configuration file.

If you would like to get information about the deployments described in the api-deployment.yaml file, the command should be as follows:

kubectl get -f api-deployment

# NAME             READY   UP-TO-DATE   AVAILABLE   AGE
# api-deployment   0/3     3            0           18m

By default, the get command shows a very small amount of information. You can get more out of it by using the -o option.

The -o option sets the output format for the get command. You can use the wide output format to see more details.

kubectl get -f api-deployment.yaml

# NAME             READY   UP-TO-DATE   AVAILABLE   AGE   CONTAINERS   IMAGES               SELECTOR
# api-deployment   0/3     3            0           19m   api          fhsinchy/notes-api   component=api

As you can see, now the list contains more information than before. You can learn about the options for the get command from the official docs.

Running get on the Deployment doesn't spit out anything interesting, to be honest. In such cases, you have to get down to the lower level resources.

Have a look at the pods list and see if you can find something interesting there:

kubectl get pod

# NAME                             READY   STATUS             RESTARTS   AGE
# api-deployment-d59f9c884-88j45   0/1     CrashLoopBackOff   10         30m
# api-deployment-d59f9c884-96hfr   0/1     CrashLoopBackOff   10         30m
# api-deployment-d59f9c884-pzdxg   0/1     CrashLoopBackOff   10         30m

Now this is interesting. All the pods have a STATUS of CrashLoopBackOff which is new. Previously you've only seen ContainerCreating and Running statuses. You may see Error in place of CrashLoopBackOff as well.

Looking at the RESTARTS column you can see that the pods have been restarted 10 times already. This means for some reason the pods are failing to startup.

Now to get a more detailed look at one of the pods, you can use another command called describe. It's a lot like the get command. Generic syntax of the command is as follows:

kubectl get <resource type> <resource name>

To get details of the api-deployment-d59f9c884-88j45 pod, you can execute the following command:

kubectl describe pod api-deployment-d59f9c884-88j45

# Name:         api-deployment-d59f9c884-88j45
# Namespace:    default
# Priority:     0
# Node:         minikube/172.28.80.217
# Start Time:   Sun, 09 Aug 2020 16:01:28 +0600
# Labels:       component=api
#               pod-template-hash=d59f9c884
# Annotations:  <none>
# Status:       Running
# IP:           172.17.0.4
# IPs:
#   IP:           172.17.0.4
# Controlled By:  ReplicaSet/api-deployment-d59f9c884
# Containers:
#  api:
#     Container ID:   docker://d2bc15bda9bf4e6d08f7ca8ff5d3c8593655f5f398cf8bdd18b71da8807930c1
#     Image:          fhsinchy/notes-api
#     Image ID:       docker-pullable://fhsinchy/notes-api@sha256:4c715c7ce3ad3693c002fad5e7e7b70d5c20794a15dbfa27945376af3f3bb78c
#     Port:           3000/TCP
#     Host Port:      0/TCP
#     State:          Waiting
#       Reason:       CrashLoopBackOff
#     Last State:     Terminated
#       Reason:       Error
#       Exit Code:    1
#       Started:      Sun, 09 Aug 2020 16:13:12 +0600
#       Finished:     Sun, 09 Aug 2020 16:13:12 +0600
#     Ready:          False
#     Restart Count:  10
#     Environment:    <none>
#     Mounts:
      /var/run/secrets/kubernetes.io/serviceaccount from default-token-gqfr4 (ro)
# Conditions:
#   Type              Status
#   Initialized       True
#   Ready             False
#   ContainersReady   False
#   PodScheduled      True
# Volumes:
#   default-token-gqfr4:
#     Type:        Secret (a volume populated by a Secret)
#     SecretName:  default-token-gqfr4
#     Optional:    false
# QoS Class:       BestEffort
# Node-Selectors:  <none>
# Tolerations:     node.kubernetes.io/not-ready:NoExecute for 300s
#                  node.kubernetes.io/unreachable:NoExecute for 300s
# Events:
#   Type     Reason     Age                         From               Message
#   ----     ------     ----                        ----               -------
#   Normal   Scheduled  <unknown>                   default-scheduler  Successfully assigned default/api-deployment-d59f9c884-88j45 to minikube
#   Normal   Pulled     2m40s (x4 over 3m47s)       kubelet, minikube  Successfully pulled image "fhsinchy/notes-api"
#   Normal   Created    2m40s (x4 over 3m47s)       kubelet, minikube  Created container api
#   Normal   Started    2m40s (x4 over 3m47s)       kubelet, minikube  Started container api
#   Normal   Pulling    107s (x5 over 3m56s)        kubelet, minikube  Pulling image "fhsinchy/notes-api"
#   Warning  BackOff    <invalid> (x44 over 3m32s)  kubelet, minikube  Back-off restarting failed container

The most interesting part in this entire wall of text is the Events section. Have a closer look:

Events:
  Type     Reason     Age                         From               Message
  ----     ------     ----                        ----               -------
  Normal   Scheduled  <unknown>                   default-scheduler  Successfully assigned default/api-deployment-d59f9c884-88j45 to minikube
  Normal   Pulled     2m40s (x4 over 3m47s)       kubelet, minikube  Successfully pulled image "fhsinchy/notes-api"
  Normal   Created    2m40s (x4 over 3m47s)       kubelet, minikube  Created container api
  Normal   Started    2m40s (x4 over 3m47s)       kubelet, minikube  Started container api
  Normal   Pulling    107s (x5 over 3m56s)        kubelet, minikube  Pulling image "fhsinchy/notes-api"
  Warning  BackOff    <invalid> (x44 over 3m32s)  kubelet, minikube  Back-off restarting failed container

From these events, you can see that the container image was pulled succesfully. The container was created as well, but it's evident from the Back-off restarting failed container that the container failed to startup.

The describe command is very similar to the get command and has the same sort of options.

You can omit the api-deployment-d59f9c884-88j45 name to get information about all available pods. Or you can also use the -f option to pass a configuration file to the command. Visit the official docs to learn more.

Now that you know that there is something wrong with the container, you have to go down to the container level and see what's going on there.

Getting Container Logs from Pods

There is another kubectl command called logs that can help you to get the container logs from inside a pod. Generic syntax for the command is as follows:

kubectl logs <pod>

To view the logs inside the api-deployment-d59f9c884-88j45 pod, the command should be as follows:

kubectl logs api-deployment-d59f9c884-88j45

# > api@1.0.0 start /usr/app
# > cross-env NODE_ENV=production node bin/www

# /usr/app/node_modules/knex/lib/client.js:55
#     throw new Error(`knex: Required configuration option 'client' is missing.`);
    ^

# Error: knex: Required configuration option 'client' is missing.
#     at new Client (/usr/app/node_modules/knex/lib/client.js:55:11)
#     at Knex (/usr/app/node_modules/knex/lib/knex.js:53:28)
#     at Object.<anonymous> (/usr/app/services/knex.js:5:18)
#     at Module._compile (internal/modules/cjs/loader.js:1138:30)
#     at Object.Module._extensions..js (internal/modules/cjs/loader.js:1158:10)
#     at Module.load (internal/modules/cjs/loader.js:986:32)
#     at Function.Module._load (internal/modules/cjs/loader.js:879:14)
#     at Module.require (internal/modules/cjs/loader.js:1026:19)
#     at require (internal/modules/cjs/helpers.js:72:18)
#     at Object.<anonymous> (/usr/app/services/index.js:1:14)
# npm ERR! code ELIFECYCLE
# npm ERR! errno 1
# npm ERR! api@1.0.0 start: `cross-env NODE_ENV=production node bin/www`
# npm ERR! Exit status 1
# npm ERR!
# npm ERR! Failed at the api@1.0.0 start script.
# npm ERR! This is probably not a problem with npm. There is likely additional logging output above.

# npm ERR! A complete log of this run can be found in:
# npm ERR!     /root/.npm/_logs/2020-08-09T10_28_52_779Z-debug.log

Now this is what you need to debug the problem. Looks like the knex.js library is missing a required value, which is preventing the application from starting. You can learn more about the logs command from the official docs.

This is happening because you're missing some required environment variables in the deployment definition.

If you take another look at the api service definition inside the docker-compose.yaml file, you should see something like this:

    api:
        build: 
            context: ./api
            dockerfile: Dockerfile.dev
        ports: 
            - 3000:3000
        volumes: 
            - /usr/app/node_modules
            - ./api:/usr/app
        environment: 
            DB_CONNECTION: pg
            DB_HOST: db
            DB_PORT: 5432
            DB_USER: postgres
            DB_DATABASE: notesdb
            DB_PASSWORD: 63eaQB9wtLqmNBpg

These environment variables are required for the application to communicate with the database. So adding these to the deployment configuration should fix the issue.

Environment Variables

Adding environment variables to a Kubernetes configuration file is very straightforward. Open up the api-deployment.yaml file and update its content to look like this:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: api-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      component: api
  template:
    metadata:
      labels:
        component: api
    spec:
      containers:
        - name: api
          image: fhsinchy/notes-api
          ports:
            - containerPort: 3000
          
          # these are the environment variables
          env:
            - name: DB_CONNECTION
              value: pg

The containers.env field contains all the environment variables. If you look closely, you'll see that I haven't added all the environment variables from the docker-compose.yaml file. I have added only one.

The DB_CONNECTION indicates that the application is using a PostgreSQL database. Adding this single variable should fix the problem.

Now apply the configuration file again by executing the following command:

kubectl apply -f api-deployment.yaml

# deployment.apps/api-deployment configured

Unlike the other times, the output here says that a resource has been configured. This is the beauty of Kubernetes. You can just fix issues and re-apply the same configuration file immediately.

Now use the get command once more to make sure everything is running properly.

kubectl get deployment

# NAME             READY   UP-TO-DATE   AVAILABLE   AGE
# api-deployment   3/3     3            3           68m

kubectl get pod

# NAME                              READY   STATUS    RESTARTS   AGE
# api-deployment-66cdd98546-l9x8q   1/1     Running   0          7m26s
# api-deployment-66cdd98546-mbfw9   1/1     Running   0          7m31s
# api-deployment-66cdd98546-pntxv   1/1     Running   0          7m21s

All three pods are running and the Deployment is running fine as well.

Creating the Database Deployment

Now that the API is up and running, it's time to write the configuration for the database instance.

Create another file called postgres-deployment.yaml inside the k8s directory and put the following content in it:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: postgres-deployment
spec:
  replicas: 1
  selector:
    matchLabels:
      component: postgres
  template:
    metadata:
      labels:
        component: postgres
    spec:
      containers:
        - name: postgres
          image: fhsinchy/notes-postgres
          ports:
            - containerPort: 5432
          env:
            - name: POSTGRES_PASSWORD
              value: 63eaQB9wtLqmNBpg
            - name: POSTGRES_DB
              value: notesdb

The configuration itself is very similar to the previous one. I am not going to explain everything in this file – hopefully you understand it by yourself with the knowledge you've gained from this article so far.

PostgreSQL runs on port 5432 by default, and the POSTGRES_PASSWORD variable is required for running the postgres container. This password will also be used for connecting to this database by the API.

The POSTGRES_DB variable is optional. But because of the way this project has been structured, it's necessary here – otherwise the initialization will fail.

You can learn more about the official postgres Docker image from their Docker Hub page. For the sake of simplicity, I'm keeping the replica count to 1 in this project.

To apply this file, execute the following command:

kubectl apply -f postgres-deployment.yaml

# deployment.apps/postgres-deployment created

Use the get command to ensure that the deployment and the pods are running properly:

kubectl get deployment

# NAME                  READY   UP-TO-DATE   AVAILABLE   AGE
# postgres-deployment   1/1     1            1           13m

kubectl get pod

# NAME                                   READY   STATUS    RESTARTS   AGE
# postgres-deployment-76fcc75998-mwnb7   1/1     Running   0          13m

Although the deployment and the pods are running properly, there is a big issue with the database deployment.

If you've worked with any database system before, you may already know that databases store data in the filesystem. Right now the database deployment looks like this:

The postgres container is encapsulated by a pod. Whatever data is saved stays within the internal filesystem of the container.

Now, if for some reason, the container crashes or the pod encapsulating the container goes down, all data persisted inside the filesystem will be lost.

Upon crashing, Kubernetes will create a new pod to maintain the desired state, but there is no data carry over mechanism between the two pods whatsoever.

To solve this issue, you can store the data in a separate space outside the pod within the cluster.

Managing such storage is a distinct problem from managing compute instances. The PersistentVolume subsystem in Kubernetes provides an API for users and administrators that abstracts details of how storage is provided from how it is consumed.

Persistent Volumes and Persistent Volume Claims

According to the Kubernetes documentation

"A PersistentVolume (PV) is a piece of storage in the cluster that has been provisioned by an administrator or dynamically provisioned using a StorageClass. It is a resource in the cluster just like a node is a cluster resource."

Which essentially means that a PersistentVolume is a way to take a slice from your storage space and reserve that for a certain pod. Volumes are always consumed by pods and not some high level object like a deployment.

If you want to use a volume with a deployment that has multiple pods, you'll have to go through some additional steps.

Create a new file called database-persistent-volume.yaml inside the k8s directory and put following content in that file:

apiVersion: v1
kind: PersistentVolume
metadata:
  name: database-persistent-volume
spec:
  storageClassName: manual
  capacity:
    storage: 5Gi
  accessModes:
    - ReadWriteOnce
  hostPath:
    path: "/mnt/data"

The apiVersion, kind, and metadata serve the same purpose as any other configuration file. The spec field, however, contains some new fields.

  • spec.storageClassName indicates the class name for this volume. Assume that a cloud provider has three kinds of storage available. These can be slow, fast, and very fast. The kind of storage you get from the provider will depend on the amount of money you're paying. If you ask for a very fast storage, you'll have to pay more. These different types of storage are the classes. I am using manual as an example here. You can use whatever you like in your local cluster.
  • spec.capacity.storage is the amount of storage this volume will have. I am giving it 5 gigabytes of storage in this project.
  • spec.accessModes sets the access mode for the volume. There are three possible access modes. ReadWriteOnce means the volume can be mounted as read-write by a single node. ReadWriteMany on the other hand means the volume can be mounted as read-write by many nodes. ReadOnlyMany means the volume can be mounted read-only by many nodes.
  • spec.hostPath is something development specific. It indicates the directory in your local single node cluster that'll be treated as persistent volume. /mnt/data means that the data saved in this persistent volume will live inside the /mnt/data directory in the cluster.

To apply this file, execute the following command:

kubectl apply -f database-persistent-volume.yaml

# persistentvolume/database-persistent-volume created

Now use the get command to verify that the volume has been created:

kubectl get persistentvolume

# NAME                         CAPACITY   ACCESS MODES   RECLAIM POLICY   STATUS      CLAIM   STORAGECLASS   REASON   AGE
# database-persistent-volume   5Gi        RWO            Retain           Available           manual                  58s

Now that the persistent volume has been created, you need a way to let the postgres pod access it. This is where a PersistentVolumeClaim (PVC) comes in.

A persistent volume claim is a request for storage by a pod. Assume that in a cluster, you have quite a lot of volumes. This claim will define the characteristics that a volume must meet to be able to satisfy a pods' necessities.

A real-life example can be you buying an SSD from a store. You go to the store and the salesperson shows you the following models:

Model 1 Model 2 Model 3
128GB 256GB 512GB
SATA NVME SATA

Now, you claim for a model that has at least 200GB of storage capacity and is an NVME drive.

The first one has less than 200GB and is SATA, so it doesn't match your claim. The third one has more than 200GB, but is not NVME. The second one however has more than 200GB and is also an NVME. So that's the one you get.

The SSD models that the salesperson showed you are equivalent to persistent volumes and your requirements are equivalent to persistent volume claims.

Create another new file called database-persistent-volume-claim.yaml inside the k8s directory and put the following content in that file:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: database-persistent-volume-claim
spec:
  storageClassName: manual
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 2Gi

Again, the apiVersion, kind, and metadata serve the same purpose as any other configuration file.

  • spec.storageClass in a claim configuration file indicates the type of storage this claim wants. That means any PersistentVolume that has spec.storageClass set to manual is suitable to be consumed by this claim. If you have multiple volumes with the manual class, the claim will get any one of them and if you have no volume with manual class – a volume will be provisioned dynamically.
  • spec.accessModes again sets the access mode here. This indicates that this claim wants a storage that has an accessMode of ReadWriteOnce. Assume that you have two volumes with class set to manual. One of them has its accessModes set to ReadWriteOnce and the other one to ReadWriteMany. This claim will get the one with ReadWriteOnce.
  • resources.requests.storage is the amount of storage this claim wants. 2Gi doesn't mean the given volume must have exactly 2 gigabytes of storage capacity. It means that it must have at least 2 gigabytes. I hope you remember that you set the capacity of the persistent volume to be 5 gigabytes, which is more than 2 gigabytes.

To apply this file, execute the following command:

kubectl apply -f database-persistent-volume-claim.yaml

# persistentvolumeclaim/database-persistent-volume-claim created

Now use the get command to verify that the volume has been created:

kubectl get persistentvolumeclaim

# NAME                               STATUS   VOLUME                       CAPACITY   ACCESS MODES   STORAGECLASS   AGE
# database-persistent-volume-claim   Bound    database-persistent-volume   5Gi        RWO            manual         37s

Look at the VOLUME column. This claim is bound to the database-persistent-volume persistent volume that you created earlier. Also look at the CAPACITY. It's 5Gi, because the claim requested a volume with at least 2 gigabytes of storage capacity.

Dynamic Provisioning of Persistent Volumes

In the previous sub-section, you've made a persistent volume and then created a claim. But, what if there isn't any persistent volume previously provisioned?

In such cases, a persistent volume compatible with the claim will be provisioned automatically.

To begin this demonstration, remove the previously created persistent volume and the persistent volume claim with the following commands:

kubectl delete persistentvolumeclaim --all

# persistentvolumeclaim "database-persistent-volume-claim" deleted

kubectl delete persistentvolumeclaim --all

# persistentvolume "database-persistent-volume" deleted

Open up the database-persistent-volume-claim.yaml file and update its content to be as follows:

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: database-persistent-volume-claim
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 2Gi

I've removed the spec.storageClass field from the file. Now re-apply the database-persistent-volume-claim.yaml file without applying the database-persistent-volume.yaml file:

kubectl apply -f database-persistent-volume-claim.yaml

# persistentvolumeclaim/database-persistent-volume-claim created

Now use the get command to look at the claim information:

kubectl get persistentvolumeclaim

# NAME                               STATUS   VOLUME                                     CAPACITY   ACCESS MODES   STORAGECLASS   AGE
# database-persistent-volume-claim   Bound    pvc-525ae8af-00d3-4cc7-ae47-866aa13dffd5   2Gi        RWO            standard       2s

As you can see, a volume with pvc-525ae8af-00d3-4cc7-ae47-866aa13dffd5 name and storage capacity of 2Gi has been provisioned and bound to the claim dynamically.

You can either use a static or dynamically provisioned persistent volume for the rest of this project. I'll be using a dynamically provisioned one.

Connecting Volumes with Pods

Now that you have created a persistent volume and a claim, it's time to let the database pod use this volume.

You do this by connecting the pod to the persistent volume claim you made in the previous sub-section. Open up the postgres-deployment.yaml file and update its content to be as follows:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: postgres-deployment
spec:
  replicas: 1
  selector:
    matchLabels:
      component: postgres
  template:
    metadata:
      labels:
        component: postgres
    spec:
      # volume configuration for the pod
      volumes:
        - name: postgres-storage
          persistentVolumeClaim:
            claimName: database-persistent-volume-claim
      containers:
        - name: postgres
          image: fhsinchy/notes-postgres
          ports:
            - containerPort: 5432
          # volume mounting configuration for the container
          volumeMounts:
            - name: postgres-storage
              mountPath: /var/lib/postgresql/data
              subPath: postgres
          env:
            - name: POSTGRES_PASSWORD
              value: 63eaQB9wtLqmNBpg
            - name: POSTGRES_DB
              value: notesdb

I've added two new fields in this configuration file.

  • spec.volumes field contains the necessary information for the pod to find the persistent volume claim. spec.volumes.name can be anything you want. spec.volumes.persistentVolumeClaim.claimName has to match the metadata.name value from the database-persistent-volume-claim.yaml file.
  • containers.volumeMounts contains information necessary for mounting the volume inside the container. containers.volumeMounts.name has to match the value from spec.volumes.name. containers.volumeMounts.mountPath indicates the directory where this volume will be mounted. /var/lib/postgresql/data is the default data directory for PostgreSQL. containers.volumeMounts.subPath indicates a directory that will be created inside the volume. Assume that you're using the same volume with other pods as well. In that case you can put pod-specific data inside another directory inside that volume. All data saved inside the /var/lib/postgresql/data directory will go inside a postgres directory within the volume.

Now re-apply the postgres-deployment.yaml file by executing the following command:

kubectl apply -f postgres-deployment.yaml

# deployment.apps/postgres-deployment configured

Now you have a proper database deployment with a much smaller risk of data loss.

One thing that I would like to mention here is that the database deployment in this project has only one replica. If there were more than one replica, things would have been different.

Multiple pods accessing the same volume without them knowing about each others' existence can bring catastrophic results. In such cases creating sub directories for the pods inside that volume can be a good idea.

Wiring Everything Up

Now that you have both the API and database running, it's time to finish some unfinished business and set-up the networking.

You've already learned in previous sections that to set up networking in Kubernetes, you use services. Before you start writing the services, have a look at the networking plan that I have for this project.

  • The database will only be exposed within the cluster using a ClusterIP service. No external traffic will be allowed.
  • The API deployment, however, will be exposed to the outside world. Users will communicate with the API and the API will communicate with the database.

You've previously worked with a LoadBalancer service that exposes an application to the outside world. The ClusterIP on the other hand exposes an application within the cluster and allows no outside traffic.

Given that the database service should be available only within the cluster, a ClusterIP service is the perfect fit for this scenario.

Create a new file called postgres-cluster-ip-service.yaml inside the k8s directory and put following content in it:

apiVersion: v1
kind: Service
metadata:
  name: postgres-cluster-ip-service
spec:
  type: ClusterIP
  selector:
    component: postgres
  ports:
    - port: 5432
      targetPort: 5432

As you can see, the configuration file for a ClusterIP is identical to one for a LoadBalancer. The only thing that differs is the spec.type value.

You should be able to interpret this file without any trouble by now. 5432 is the default port that PostgreSQL runs on. That's why that port has to be exposed.

The next configuration file is for the LoadBalancer service, responsible for exposing the API to the outside world. Create another file called api-load-balancer-service.yaml and put the following content in it:

apiVersion: v1
kind: Service
metadata:
  name: api-load-balancer-service
spec:
  type: LoadBalancer
  ports:
    - port: 3000
      targetPort: 3000
  selector:
    component: api

This configuration is identical to the one you've written in a previous section. The API runs in port 3000 inside the container and that's why that port has to be exposed.

The last thing to do is to add the rest of the environment variables to the API deployment. So open up the api-deployment.yaml file and update its content like this:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: api-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      component: api
  template:
    metadata:
      labels:
        component: api
    spec:
      containers:
        - name: api
          image: fhsinchy/notes-api
          ports:
            - containerPort: 3000
          env:
            - name: DB_CONNECTION
              value: pg
            - name: DB_HOST
              value: postgres-cluster-ip-service
            - name: DB_PORT
              value: '5432'
            - name: DB_USER
              value: postgres
            - name: DB_DATABASE
              value: notesdb
            - name: DB_PASSWORD
              value: 63eaQB9wtLqmNBpg

Previously there was just the DB_CONNECTION variable under spec.containers.env. The new variables are as follows:

  • DB_HOST indicates the host address for the database service. In a non-containerized environment the value is usually 127.0.0.1. But in a Kubernetes environment, you don't know the IP address of the database pod. Hence you just use the service name that exposes the database instead.
  • DB_PORT is the port exposed from the database service, which is 5432.
  • DB_USER is the user for connecting to the database. postgres is the default username.
  • DB_DATABASE is the database that the API will connect to. This has to match with the spec.containers.env.DB_DATABASE value from the postgres-deployment.yaml file.
  • DB_PASSWORD is the password for connecting to the database. This has to match with the spec.containers.env.DB_PASSWORD value from the postgres-deployment.yaml file.

With that done, now you're ready to test out the API. Before you do that, I'll suggest applying all the configuration files once again by executing the following command:

kubectl apply -f k8s

# deployment.apps/api-deployment created
# service/api-load-balancer-service created
# persistentvolumeclaim/database-persistent-volume-claim created
# service/postgres-cluster-ip-service created
# deployment.apps/postgres-deployment created

If you face any errors, just delete all resources and re-apply the files. The services, the persistent volumes, and the persistent volume claims should be created instantly.

Use the get command to make sure the deployments are all up and running:

kubectl get deployment

# NAME                  READY   UP-TO-DATE   AVAILABLE   AGE
# api-deployment        3/3     3            3           106s
# postgres-deployment   1/1     1            1           106s

As you can see from the READY column, all the pods are up and running. To access the API, use the service command for minikube.

minikube service api-load-balancer-service

# |-----------|---------------------------|-------------|-----------------------------|
# | NAMESPACE |           NAME            | TARGET PORT |             URL             |
# |-----------|---------------------------|-------------|-----------------------------|
# | default   | api-load-balancer-service |        3000 | http://172.19.186.112:31546 |
# |-----------|---------------------------|-------------|-----------------------------|
# * Opening service default/api-load-balancer-service in default browser...

The API should open automatically in your default browser:

This is the default response for the API. You can also use http://172.19.186.112:31546/ with some API testing tool like Insomnia or Postman to test out the API. The API has full CRUD functionality.

You can see the tests that come with the API source code as documentation. Just open up the api/tests/e2e/api/routes/notes.test.js file. You should be able to understand the file without much hassle if you have experience with JavaScript and express.

Working with Ingress Controllers

So far in this article, you've used ClusterIP to expose an application within the cluster and LoadBalancer to expose an application outside the cluster.

Although I've cited LoadBalancer as the standard service kind for exposing an application outside the cluster, it has some cons.

When using LoadBalancer services to expose applications in cloud environment, you'll have to pay for each exposed services individually which can be expensive in case of huge projects.

There is another kind of service called NodePort that can be used as an alternative to the LoadBalancer kind of services.

NodePort opens a specific port on all the nodes in your cluster, and handles any traffic that comes through that open port.

As you already know, services group together a number of pods, and control the way they can be accessed. So any request that reaches the service through the exposed port will end up in the correct pod.

An example configuration file for creating a NodePort can be as follows:

apiVersion: v1
kind: Service
metadata:
  name: hello-kube-node-port
spec:
  type: NodePort
  ports:
    - port: 8080
      targetPort: 8080
      nodePort: 31515
  selector:
    component: web

The spec.ports.nodePort field here must have a value between 30000 and 32767. This range is out of the well-known ports usually used by various services but is also unusual. I mean how many times do you see a port with so many digits?

You can try to replace the LoadBalancer services you created in the previous sections with a NodePort service. This shouldn't be tough and can be treated as a test for what you've learned so far.

To solve the issues I've mentioned the Ingress API was created. To be very clear, Ingress is actually not a type of service. Instead, it sits in front of multiple services and acts as a router of sorts.

An IngressController is required to work with Ingress resources in your cluster. A list of avalable ingress controllers can be found in the Kubernetes documentation.

Setting up NGINX Ingress Controller

In this example, you'll extend the notes API by adding a front end to it. And instead of using a service like LoadBalancer or NodePort, you'll use Ingress to expose the application.

The controller you'll be using is the NGINX Ingress Controller because NGINX will be used for routing requests to different services here. The NGINX Ingress Controller makes it very easy to work with NGINX configurations in a Kubernetes cluster.

The code for the project lives inside the fullstack-notes-application directory.

.
├── api
├── client
├── docker-compose.yaml
├── k8s
│   ├── api-deployment.yaml
│   ├── database-persistent-volume-claim.yaml
│   ├── postgres-cluster-ip-service.yaml
│   └── postgres-deployment.yaml
├── nginx
└── postgres

5 directories, 1 file

You'll see a k8s directory in there. It contains all the configuration files you wrote in the last sub-section, except the api-load-balancer-service.yaml file.

The reason for that is, in this project, the old LoadBalancer service will be replaced with an Ingress. Also, instead of exposing the API, you'll expose the front-end application to the world.

Before you start writing the new configuration files, have a look at how things are going to work behind the scenes.

A user visits the front-end application and submits the necessary data. The front-end application then forwards the submitted data to the back-end API.

The API then persists the data in the database and also sends it back to the front-end application. Then routing of the requests is achieved using NGINX.

You can have a look at the nginx/production.conf file to understand how this routing has been set-up.

Now the necessary networking required to make this happen is as follows:

This diagram can be explained as follows:

  • The Ingress will act as the entry-point and router for this application. This is an NGINX type Ingress so the port will be the default nginx port which is 80.
  • Every request that comes to / will be routed towards the front-end application (the service on the left). So if the URL for this application is https://kube-notes.test, then any request coming to https://kube-notes.test/foo or https://kube-notes.test/bar will be handled by the front-end application.
  • Every request that comes to /api will be routed towards the back-end API (the service on the right). So if the URL again is https://kube-notes.test, then any request coming to https://kube-notes.test/api/foo or https://kube-notes.test/api/bar will be handled by the back-end API.

It was totally possible to configure the Ingress service to work with sub-domains instead of paths like this, but I chose the path-based approach because that's how my application is designed.

In this sub-section, you'll have to write four new configuration files.

  • ClusterIP configuration for the API deployment.
  • Deployment configuration for the front-end application.
  • ClusterIP configuration for the front-end application.
  • Ingress configuration for the routing.

I'll go through the first three files very quickly without spending much time explaining them.

The first one is the api-cluster-ip-service.yaml configuration and the contents of the file are as follows:

apiVersion: v1
kind: Service
metadata:
  name: api-cluster-ip-service
spec:
  type: ClusterIP
  selector:
    component: api
  ports:
    - port: 3000
      targetPort: 3000

Although in the previous sub-section you exposed the API directly to the outside world, in this one, you'll let the Ingress do the heavy lifting while exposing the API internally using a good old ClusterIP service.

The configuration itself should be pretty self-explanatory at this point, so I won't be spending any time explaining it.

Next, create a file named client-deployment.yaml responsible for running the front-end application. Contents of the file are as follows:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: client-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      component: client
  template:
    metadata:
      labels:
        component: client
    spec:
      containers:
        - name: client
          image: fhsinchy/notes-client
          ports:
            - containerPort: 8080
          env:
            - name: VUE_APP_API_URL
              value: /api

It's almost identical to the the api-deployment.yaml file and I'm assuming that you'll be able to interpret this configuration file by yourself.

The VUE_APP_API_URL environment variable here indicates the path to which the API requests should be forwarded. These forwarded requests will be in turn handled by the Ingress.

To expose this client application internally another ClusterIP service is necessary. Create a new file called client-cluster-ip-service.yaml and put the following content in it:

apiVersion: v1
kind: Service
metadata:
  name: client-cluster-ip-service
spec:
  type: ClusterIP
  selector:
    component: client
  ports:
    - port: 8080
      targetPort: 8080

All this does is expose port 8080 within the cluster on which the front end application runs by default.

Now that the boring old configurations are done, the next configuration is the ingress-service.yaml file and the content of the file is as follows:

apiVersion: extensions/v1beta1
kind: Ingress
metadata:
  name: ingress-service
  annotations:
    kubernetes.io/ingress.class: nginx
    nginx.ingress.kubernetes.io/rewrite-target: /$1
spec:
  rules:
    - http:
        paths:
          - path: /?(.*)
            backend:
              serviceName: client-cluster-ip-service
              servicePort: 8080
          - path: /api/?(.*)
            backend:
              serviceName: api-cluster-ip-service
              servicePort: 3000

This configuration file may look quite a bit unfamiliar to you but it's actually pretty straightforward.

  • The Ingress API is still in beta phase thus the apiVersion is extensions/v1beta1. Although in beta, the API is very stable and usable in production environments.
  • The kind and metadata.name fields serve the same purpose as any of the configurations you wrote earlier.
  • metadata.annotations can contain information regarding the Ingress configuration. The kubernetes.io/ingress.class: nginx indicates that the Ingress object should be controlled by the ingress-nginx controller. nginx.ingress.kubernetes.io/rewrite-target indicates that you want to rewrite the URL target in places.
  • spec.rules.http.paths contains configuration regarding the individual path routings you previously saw inside the nginx/production.conf file. The paths.path field indicates the path that should be routed. backend.serviceName is the service that the aforementioned path should be routed towards and backend.servicePort is the target port inside that service.
  • /?(.*) and /api/?(.*) are simple regex which means that ?(.*) part will be routed towards the designated services.

The way you configure rewrites can change from time to time, so checking out the official docs would be good idea.

Before you apply the new configurations, you'll have to activate the ingress addon for minikube using the addons command. The generic syntax is as follows:

minikube addons <option> <addon name>

To activate the ingress addon, execute the following command:

minikube addons enable ingress

# ? Verifying ingress addon...
# ? The 'ingress' addon is enabled

You can use the disable option for the addon command to disable any addon. You can learn more about the addon command in the official docs.

Once the addon has been activated, you may apply the configuration files. I would suggest deleting all resources (services, deployments, and persistent volume claims) before applying the new ones.

kubectl delete ingress --all

# ingress.extensions "ingress-service" deleted

kubectl delete service --all

# service "api-cluster-ip-service" deleted
# service "client-cluster-ip-service" deleted
# service "kubernetes" deleted
# service "postgres-cluster-ip-service" deleted

kubectl delete deployment --all

# deployment.apps "api-deployment" deleted
# deployment.apps "client-deployment" deleted
# deployment.apps "postgres-deployment" deleted

kubectl delete persistentvolumeclaim --all

# persistentvolumeclaim "database-persistent-volume-claim" deleted

kubectl apply -f k8s

# service/api-cluster-ip-service created
# deployment.apps/api-deployment created
# service/client-cluster-ip-service created
# deployment.apps/client-deployment created
# persistentvolumeclaim/database-persistent-volume-claim created
# ingress.extensions/ingress-service created
# service/postgres-cluster-ip-service created
# deployment.apps/postgres-deployment created

Wait until all the resources have been created. You can utilize the get command to ensure that. Once all of them are running, you can access the application at the IP address of the minikube cluster. To get the IP, you can execute the following command:

minikube ip

# 172.17.0.2

You can also get this IP address by running inspecting the Ingress:

kubectl get ingress

# NAME              CLASS    HOSTS   ADDRESS      PORTS   AGE
# ingress-service   <none>   *       172.17.0.2   80      2m33s

As you can see, the IP and port is visible under the ADDRESS and PORTS columns. By accessing 127.17.0.2:80, you should land directly on the notes application.

You can perform simple CRUD operations in this application. Port 80 is the default port for NGINX, so you don't need to write the port number in the URL.

You can do a lot with this ingress controller if you know how to configure NGINX. After all, that's what this controller is used for – storing NGINX configurations on a Kubernetes ConfigMap, which you'll be learning about in the next sub-section.

Secrets and Config Maps in Kubernetes

So far in your deployments, you've stored sensitive information such as POSTGRES_PASSWORD in plain text, which is not a very good idea.

To store such values in your cluster you can use a Secret which is a much more secure way of storing passwords, tokens, and so on.

The next step may not work the same in the Windows command line. You can use git bash or cmder for the task.

To store information in a Secret you have to first pass your data through base64. If the plain text password is 63eaQB9wtLqmNBpg then execute following command to get a base64 encoded version:

echo -n "63eaQB9wtLqmNBpg" | base64

# NjNlYVFCOXd0THFtTkJwZw==

This step is not optional, you have to run the plain text string through base64. Now create a file named postgres-secret.yaml inside the k8s directory and put following content in there:

apiVersion: v1
kind: Secret
metadata:
  name: postgres-secret
data:
  password: NjNlYVFCOXd0THFtTkJwZw==

The apiVersion, kind, and metadata fields are pretty self-explanatory. The data field holds the actual secret.

As you can see, I've created a key-value pair where the key is password and the value is NjNlYVFCOXd0THFtTkJwZw==. You'll be using the metadata.name value to identify this Secret in other configuration files and the key to access the password value.

Now to use this secret inside your the database configuration, update the postgres-deployment.yaml file as follows:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: postgres-deployment
spec:
  replicas: 1
  selector:
    matchLabels:
      component: postgres
  template:
    metadata:
      labels:
        component: postgres
    spec:
      volumes:
        - name: postgres-storage
          persistentVolumeClaim:
            claimName: database-persistent-volume-claim
      containers:
        - name: postgres
          image: fhsinchy/notes-postgres
          ports:
            - containerPort: 5432
          volumeMounts:
            - name: postgres-storage
              mountPath: /var/lib/postgresql/data
              subPath: postgres
          env:
          	# not putting the password directly anymore
            - name: POSTGRES_PASSWORD
              valueFrom:
                secretKeyRef:
                  name: postgres-secret
                  key: password
            - name: POSTGRES_DB
              value: notesdb

As you can see, the entire file is the same except the spec.template.spec.continers.env field.

The name environment variable used to store the password value was in plain text before. But now there is a new valueFrom.secretKeyRef field.

The name field here refers to the name of the Secret you created moments ago, and the key value refers to the key from the key-value pair in that Secret configuration file. The encoded value will be decoded to plain text internally by Kubernetes.

Apart from the database configuration, you'll also have to update the api-deployment.yaml file as follows:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: api-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      component: api
  template:
    metadata:
      labels:
        component: api
    spec:
      containers:
        - name: api
          image: fhsinchy/notes-api
          ports:
            - containerPort: 3000
          env:
            - name: DB_CONNECTION
              value: pg
            - name: DB_HOST
              value: postgres-cluster-ip-service
            - name: DB_PORT
              value: '5432'
            - name: DB_USER
              value: postgres
            - name: DB_DATABASE
              value: notesdb
              # not putting the password directly anymore
            - name: DB_PASSWORD
              valueFrom:
                secretKeyRef:
                  name: postgres-secret
                  key: password

Now apply all these new configurations by executing the following command:

kubectl apply -f k8s

# service/api-cluster-ip-service created
# deployment.apps/api-deployment created
# service/client-cluster-ip-service created
# deployment.apps/client-deployment created
# persistentvolumeclaim/database-persistent-volume-claim created
# secret/postgres-secret created
# ingress.extensions/ingress-service created
# service/postgres-cluster-ip-service created
# deployment.apps/postgres-deployment created

Depending on the state of your cluster, you may see a different set of output.

In case you're having any issue, delete all Kubernetes resources and create them again by applying the configs.

Use the get command to inspect and make sure all the pods are up and running.

Now to test out the new configuration, access the notes application using the minikube IP and try creating new notes. To get the IP, you can execute the following command:

minikube ip

# 172.17.0.2

By accessing 127.17.0.2:80, you should land directly on the notes application.

There is another way to create secrets without any configuration file. To create the same Secret using kubectl, execute the following command:

kubectl create secret generic postgres-secret --from-literal=password=63eaQB9wtLqmNBpg

# secret/postgres-secret created

This is a more convenient approach as you can skip the whole base64 encoding step. The secret in this case will be encoded automatically.

A ConfigMap is similar to a Secret but is meant to be used with non sensitive information.

To put all the other environment variables in the API deployment inside a ConfigMap, create a new file called api-config-map.yaml inside the k8s directory and put following content in it:

apiVersion: v1 
kind: ConfigMap 
metadata:
  name: api-config-map 
data:
  DB_CONNECTION: pg
  DB_HOST: postgres-cluster-ip-service
  DB_PORT: '5432'
  DB_USER: postgres
  DB_DATABASE: notesdb

apiVersion, kind and metadata are again self-explanatory. The data field can hold the environment variables as key-value pairs.

Unlike the Secret, the keys here have to match the exact key required by the API. Thus, I have sort of copied the variables from api-deployment.yaml file and pasted them here with a slight modification in the syntax.

To make use of this secret in the API deployment, open up the api-deployment.yaml file and update its content as follows:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: api-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      component: api
  template:
    metadata:
      labels:
        component: api
    spec:
      containers:
        - name: api
          image: fhsinchy/notes-api
          ports:
            - containerPort: 3000
          # not putting environment variables directly
          envFrom:
            - configMapRef:
                name: api-config-map
          env:
            - name: DB_PASSWORD
              valueFrom:
                secretKeyRef:
                  name: postgres-secret
                  key: password
          

The entire file is almost unchanged except the spec.template.spec.containers.env field.

I have moved the environment variables to the ConfigMap. spec.template.spec.containers.envFrom is used to get data from a ConfigMap. configMapRef.name here indicates the ConfigMap from where the environment variables will be pulled.

Now apply all these new configurations by executing the following command:

kubectl apply -f k8s

# service/api-cluster-ip-service created
# configmap/api-config-map created
# deployment.apps/api-deployment created
# service/client-cluster-ip-service created
# deployment.apps/client-deployment created
# persistentvolumeclaim/database-persistent-volume-claim created
# ingress.extensions/ingress-service configured
# service/postgres-cluster-ip-service created
# deployment.apps/postgres-deployment created
# secret/postgres-secret created

Depending on the state of your cluster, you may see a different set of output.

In case you're having any issue, delete all Kubernetes resources and create them again by applying the configs.

Upon making sure that the pods are up and running using the get command, access the notes application using the minikube IP and try creating new notes.

To get the IP, you can execute the following command:

minikube ip

# 172.17.0.2

By accessing 127.17.0.2:80, you should land directly on the notes application.

Secret and ConfigMap have a few more tricks up their sleeves that I'm not going to get into right now. But if you're curious, you may check out the official docs.

Performing Update Rollouts in Kubernetes

Now that you've successfully deployed an application consisting of multiple containers on Kubernetes, it's time to learn about performing updates.

As magical as Kubernetes may seem to you, updating a container to a newer image version is a bit of a pain. There are multiple approaches that people often take to update a container, but I am not going to go through all of them.

Instead, I'll jump right into the approach that I mostly take in updating my containers. If you open up the client-deployment.yaml file and look into the spec.template.spec.containers field, you'll find something that looks like this:

containers:
    - name: client
      image: fhsinchy/notes-client

As you can see, in the image field I haven't used any image tag. Now if you think that adding :latest at the end of the image name will ensure that the deployment always pulls the latest available image, you'd be dead wrong.

The approach that I usually take is an imperative one. I've already mentioned in a previous section that, in a few cases, using an imperative approach instead of a declarative one is a good idea. Creating a Secret or updating a container is such a case.

The command you can use to perform the update is the set command, and the generic syntax is as follows:

kubectl set image <resource type>/<resource name> <container name>=<image name with tag>

The resource type is deployment and resource name is client-deployment. The container name can be found under the containers field inside the client-deployment.yaml file, which is client in this case.

I have already build a version of the fhsinchy/notes-client image with a tag of edge that I'll be using to update this deployment.

So the final command should be as follows:

kubectl set image deployment/client-deployment client=fhsinchy/notes-client:edge

# deployment.apps/client-deployment image updated

The update process may take a while, as Kubernetes will recreate all the pods. You can run the get command to know if all the pods are up and running again.

Once they've all been recreated, access the notes application using the minikube IP and try creating new notes. To get the IP, you can execute the following command:

minikube ip

# 172.17.0.2

By accessing 127.17.0.2:80, you should land directly on the notes application.

Given that I haven't made any actual changes to the application code, everything will remain the same. You can ensure that the pods are using the new image using the describe command.

kubectl describe pod client-deployment-849bc58bcc-gz26b | grep 'Image'

# Image:          fhsinchy/notes-client:edge
# Image ID:       docker-pullable://fhsinchy/notes-client@sha256:58bce38c16376df0f6d1320554a56df772e30a568d251b007506fd3b5eb8d7c2

The grep command is available on Mac and Linux. If you're on Windows, use git bash instead of the windows command line.

Although the imperative update process is a bit tedious, but it can be made much easier by using a good CI/CD workflow.

Combining Configurations

As you've already seen, the number of configuration files in this project is pretty huge despite only having three containers in it.

You can actually combine configuration files as follows:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: client-deployment
spec:
  replicas: 3
  selector:
    matchLabels:
      component: client
  template:
    metadata:
      labels:
        component: client
    spec:
      containers:
        - name: client
          image: fhsinchy/notes-client
          ports:
            - containerPort: 8080
          env:
            - name: VUE_APP_API_URL
              value: /api
              
---

apiVersion: v1
kind: Service
metadata:
  name: client-cluster-ip-service
spec:
  type: ClusterIP
  selector:
    component: client
  ports:
    - port: 8080
      targetPort: 8080

As you can see, I've combined the contents of the client-deployment.yaml and client-cluster-ip-service.yaml file using a delimiter (---). Although it's possible and can help in projects where the number of containers is very high, I recommend keeping them separate, clean, and concise.

Troubleshooting

In this section, I'll be listing some common issues that you may face during your time with Kubernetes.

  • If you're on Windows or Mac and using the Docker driver for minikube, the Ingress plugin will not work.
  • If you have Laravel Valet running on Mac and are using the HyperKit driver for minikube, it'll fail to connect to the internet. Turning off the dnsmasq service will resolve the issue.
  • If you have a Ryzen (mine is R5 1600) PC and are running Windows 10, the VirtualBox driver may fail to start due to the lack of support for nested virtualization. You'll have to use the Hyper-V driver on Windows 10 (Pro, Enterprise, and Education). For the Home edition users, sadly there is no safe option on that hardware.
  • If you're running Windows 10 (Pro, Enterprise, and Education) with the Hyper-V driver for minikube, the VM may fail to start with a message regarding insufficient memory. Don't panic, and execute the minikube start command once again to start the VM properly.
  • If you see some of the commands executed in this article missing or misbehaving in the Windows command line, use git bash or cmder instead.

I would suggest installing a good Linux distribution on your system and using the Docker driver for minikube. This is by far the fastest and most reliable set-up.

Conclusion

I would like to thank you from the bottom of my heart for the time you've spent reading this article. I hope you've enjoyed your time and have learned all the essentials of Kubernetes.

You can follow me on Twitter @frhnhsin or connect with me on on LinkedIn at /in/farhanhasin if you prefer that.