Mauricio Salatino
Mauricio Salatino

Developing and deploying Spring Boot microservices on Kubernetes

Updated in March 2022


Developing and deploying Spring Boot microservices on Kubernetes

TL;DR: In this guide you will launch a local Kubernetes cluster, develop an app using the Spring Boot framework and deploy it as a container in Kubernetes.

Learning how to design and architect applications that leverage Kubernetes is the most valuable skill that you could learn to be successful in deploying and scaling in the cloud.

In this guide, you will develop a small application for note-taking similar to Evernote and Google Keep.

The app lets you:

  1. record notes and
  2. attach images to your notes

The notes are persisted, which gives us a good reason to explore some basic storage options.

Here is how the app looks like:

Adding images and notes in Knote

Bootstrapping the app

You will build the app with Spring Boot.

If at any time you're stuck, you can find the final code of the app in this repository.

Let's get started.

First, you need to go to https://start.spring.io to generate the skeleton of the project:

Setting up the project

You should enter the Group and Name for your application:

Setting up the dependencies

Next, go to the dependencies section and choose:

Then click Generate the project to download a zip file containing the skeleton of your app.

Unzip the file and start a terminal session in that directory.

You will do the front-end stuff first.

Within knote-java application, there are two files in charge of rendering the Front End:

You can find the Freemarker template in this repository.

Apache FreeMarker™ is a template engine: a Java library to generate text output (HTML web pages, e-mails, configuration files, source code, etc.) based on templates and changing data.

With the front-end stuff out of the way, let's turn to code.

You can open our application in our favourite IDE and import it as a Maven Project.

Now, open src/main/java/io/learnk8s/knote-java/KnoteJavaApplication.java:

KnoteJavaApplication.java

package io.learnk8s.knote;

import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

@SpringBootApplication
public class KnoteJavaApplication {

    public static void main(String[] args) {
        SpringApplication.run(KnoteJavaApplication.class, args);
    }

}

This is not much more than a standard Spring Boot Application.

It doesn't yet do anything useful.

But you will change this now by connecting it to a database.

Connecting a database

The database will store the notes.

What database should you use? MySQL? Redis? Oracle?

MongoDB is well-suited for your note-taking application because it's easy to set up and doesn't introduce the overhead of a relational database.

Because you had included the Spring Data MongoDB support, there is not much that you need to do to connect to the database.

You should open the src/main/resources/application.properties file and enter the URL for the database.

application.properties

spring.data.mongodb.uri=mongodb://localhost:27017/dev

You have to consider something important here.

When the app starts, it shouldn't crash because the database isn't ready too.

Instead, the app should keep retrying to connect to the database until it succeeds.

Kubernetes expects that application components can be started in any order.

If you make this code change, you can deploy your apps to Kubernetes in any order.

Luckily for you, Spring Data automatically reconnects to the database until the connection is successful.

The next step is to use the database.

Saving and retrieving notes

When the main page of your app loads, two things happen:

Let's address the displaying of existing notes first.

First, you should create a Note class that holds the note's details.

The same note is also stored in the "notes" MongoDB collection.

KnoteJavaApplication

@SpringBootApplication
public class KnoteJavaApplication {
    public static void main(String[] args) {
        SpringApplication.run(KnoteJavaApplication.class, args);
    }
}

@Document(collection = "notes")
@Setter
@Getter
@NoArgsConstructor
@AllArgsConstructor
class Note {
    @Id
    private String id;
    private String description;

    @Override
    public String toString() {
        return description;
    }
}

Next, you should leverage Spring Template to create a new repository to store the notes.

KnoteJavaApplication.java

@Document(collection = "notes")
@Setter
@Getter
@NoArgsConstructor
@AllArgsConstructor
class Note {
    @Id
    private String id;
    private String description;

    @Override
    public String toString() {
        return description;
    }
}

interface NotesRepository extends MongoRepository<Note, String> {

}

As you can see, you define an interface and Spring Data MongoDB generates the implementation.

Also, notice how the notes are persisted in the database:

You should notice the two types in the interface signature MongoRepository<Note, String>.

You can access the repository by autowiring it.

You should create a new class with the @Controller annotation to select the views in your application.

KnoteJavaApplication.java

...

@Controller
class KNoteController {

    @Autowired
    private NotesRepository notesRepository;

}

When a user accesses the / route, they should see all notes.

You should add a @GetMapping endpoint to return the FreeMarker template index.ftl.

Please notice how we dropped the .ftl extension from the filename to refer to it.

KnoteJavaApplication.java

@Controller
class KNoteController {

    @Autowired
    private NotesRepository notesRepository;

    @GetMapping("/")
    public String index(Model model) {
        getAllNotes(model);
        return "index";
    }

    private void getAllNotes(Model model) {
        List<Note> notes = notesRepository.findAll();
        Collections.reverse(notes);
        model.addAttribute("notes", notes);
    }
    ...
}

The getAllNotes(Model model) method is in charge of

  1. retrieving all the notes stored in MongoDB
  2. reversing the order of the notes (to show the last one first) and
  3. updating the model consumed by the view

Next, let's address the creation of new notes.

You should add a method to save a single note in the database:

KnoteJavaApplication.java

private void saveNote(String description, Model model) {
  if (description != null && !description.trim().isEmpty()) {
    notesRepository.save(new Note(null, description.trim()));
    //After publish you need to clean up the textarea
    model.addAttribute("description", "");
  }
}

The form for creating notes is defined in the index.ftl template.

Note that the form handles both the creation of notes and the uploading of pictures.

The form submits to the /note route, so you need to another endpoint to your @Controller:

KnoteJavaApplication.java

@PostMapping("/note")
public String saveNotes(@RequestParam("image") MultipartFile file,
                        @RequestParam String description,
                        @RequestParam(required = false) String publish,
                        @RequestParam(required = false) String upload,
                        Model model) throws IOException {

  if (publish != null && publish.equals("Publish")) {
    saveNote(description, model);
    getAllNotes(model);
    return "redirect:/";
  }
  // After save fetch all notes again
  return "index";
}

The above endpoint calls the saveNote method with the content of the text box, which causes the note to be saved in the database.

It then redirects to the main page ("index"), so that the newly created note appears immediately on the screen.

Your app is functional now (although not yet complete)!

You can already run your app at this stage.

But to do so, you need to run MongoDB as well.

You can install MongoDB following the instructions in the official MongoDB documentation.

Once MongoDB is installed, start a MongoDB server with:

bash

mongod

Now run your app with:

bash

mvn clean install spring-boot:run

The app should connect to MongoDB and then listen for requests.

You can access your app on http://localhost:8080.

You should see the main page of the app.

Try to create a note — you should see it being displayed on the main page.

Your app seems to works.

But it's not yet complete.

The following requirements are missing:

Let's fix those next.

Rendering Markdown to HTML

The Markdown notes should be rendered to HTML so that you can read them properly formatted.

You will use commonmark-java from Atlassian to parse the notes and render HTML.

But first you should add a dependency to your pom.xml file:

pom.xml

<dependencies>
  ...
  <dependency>
    <groupId>com.atlassian.commonmark</groupId>
    <artifactId>commonmark</artifactId>
    <version>0.12.1</version>
  </dependency>
  ...
</dependencies>

Then, change the saveNote method as follows (changed lines are highlighted):

KnoteJavaApplication.java

private void saveNote(String description, Model model) {
  if (description != null && !description.trim().isEmpty()) {
    //You need to translate markup to HTML
    Node document = parser.parse(description.trim());
    String html = renderer.render(document);
    notesRepository.save(new Note(null, html));
    //After publish you need to clean up the textarea
    model.addAttribute("description", "");
  }
}

You also need to add to the @Controller itself:

KnoteJavaApplication.java

@Controller
class KNoteController {

    @Autowired
    private NotesRepository notesRepository;
    private Parser parser = Parser.builder().build();
    private HtmlRenderer renderer = HtmlRenderer.builder().build();

The new code converts all the notes from Markdown to HTML before storing them into the database.

Kill the app with CTRL + C and then start it again:

bash

mvn clean install spring-boot:run

Access it on http://localhost:8080.

Now you can add a note with the following text:

snippet.md

Hello World! **Kubernetes Rocks!**

And you should see Kubernetes Rocks! in bold fonts.

All your notes should now be nicely formatted.

Let's tackle uploading files.

Uploading pictures

When a user uploads a picture, the file should be saved on disk, and a link should be inserted in the text box.

This is similar to how adding pictures on StackOverflow works.

Note that for this to work, the picture upload endpoint must have access to the text box — this is the reason that picture uploading and note creation are combined in the same form.

For now, the pictures will be stored on the local file system.

Change the endpoint for the POST /note inside the @Controller (changed lines are highlighted):

KnoteJavaApplication.java

@PostMapping("/note")
public String saveNotes(@RequestParam("image") MultipartFile file,
                        @RequestParam String description,
                        @RequestParam(required = false) String publish,
                        @RequestParam(required = false) String upload,
                        Model model) throws IOException {
  if (publish != null && publish.equals("Publish")) {
    saveNote(description, model);
    getAllNotes(model);
    return "redirect:/";
  }
  if (upload != null && upload.equals("Upload")) {
    if (file != null && file.getOriginalFilename() != null
          && !file.getOriginalFilename().isEmpty()) {
      uploadImage(file, description, model);
    }
    getAllNotes(model);
    return "index";
  }
  return "index";
}

private void uploadImage(MultipartFile file, String description, Model model) throws Exception {
  File uploadsDir = new File(properties.getUploadDir());
  if (!uploadsDir.exists()) {
    uploadsDir.mkdir();
  }
  String fileId = UUID.randomUUID().toString() + "."
                    + file.getOriginalFilename().split("\\.")[1];
  file.transferTo(new File(properties.getUploadDir() + fileId));
  model.addAttribute("description", description + " ![](/uploads/" + fileId + ")");
}

As you can see from the uploadImage() method, you are using Spring Boot configuration properties to inject application configurations.

These properties can be defined in the application.properties file or as environmental variables.

But you should define the @ConfigurationProperties class to retrieve those values.

Outside of the Controller class, you should define the KnoteProperties class annotated with @ConfigurationProperties(prefix = "knote"):

KnoteJavaApplication.java

@ConfigurationProperties(prefix = "knote")
class KnoteProperties {
    @Value("${uploadDir:/tmp/uploads/}")
    private String uploadDir;

    public String getUploadDir() {
        return uploadDir;
    }
}

By default, the uploadImage method uses the /tmp/uploads/ directory to store the images.

Notice that the uploadImage method checks if the directory exists and creates it if it doesn't.

If you decide to change the path, make sure that the application has write access to that folder.

One last code change is required for the webserver (embedded in the spring boot application) to host files outside of the JVM classpath:

KnoteJavaApplication.java

...
@Configuration
@EnableConfigurationProperties(KnoteProperties.class)
class KnoteConfig implements WebMvcConfigurer {

    @Autowired
    private KnoteProperties properties;

    @Override
    public void addResourceHandlers(ResourceHandlerRegistry registry) {
        registry
                .addResourceHandler("/uploads/**")
                .addResourceLocations("file:" + properties.getUploadDir())
                .setCachePeriod(3600)
                .resourceChain(true)
                .addResolver(new PathResourceResolver());
    }

}
...

The class annotated with the @Configuration annotation maps the path /uploads/ to the files located inside the file:/tmp/uploads/ directory.

The class annotated with @EnableConfigurationProperties(KnoteProperties.class) allows Spring Boot to read and autowire the application properties.

You can override those properties in application.properties file or with environment variables.

Kill the app with CTRL + C and then start the application again:

bash

mvn clean install spring-boot:run

Access it on http://localhost:8080.

Try to upload a picture — you should see a link is inserted in the text box.

And when you publish the note, the picture should be displayed in the rendered note.

Your app is feature complete now.

Note that you can find the complete code for the app in in this repository.

The next step is to containerise your app.

Containerising the app

First of all, you have to install the Docker Community Edition (CE).

You can follow the instructions in the official Docker documentation.

If you're on Windows, you can follow our handy guide on how to install Docker on Windows.

You can verify that Docker is installed correctly with the following command:

bash

docker run hello-world

Hello from Docker!
This message shows that your installation appears to be working correctly.

You're now ready to build Docker containers.

Docker containers are built from Dockerfiles.

A Dockerfile is like a recipe — it defines what goes in a container.

A Dockerfile consists of a sequence of commands.

You can find the full list of commands in the Dockerfile reference.

Here is a Dockerfile that packages your app into a container image:

Dockerfile

FROM adoptopenjdk/openjdk11:jdk-11.0.2.9-slim
WORKDIR /opt
ENV PORT 8080
EXPOSE 8080
COPY target/*.jar /opt/app.jar
ENTRYPOINT exec java $JAVA_OPTS -jar app.jar

Go on and save this as Dockerfile in the root directory of your app.

The above Dockerfile includes the following commands:

You can now build a container image from your app with the following command:

bash

docker build -t knote-java .

Note the following about this command:

The command executes the steps outlined in the Dockerfile, one by one:

The output is a Docker image.

What is a Docker image?

A Docker image is an archive containing all the files that go in a container.

You can create many Docker containers from the same Docker image:

Don't believe that Docker images are archives? Save the image locally with docker save knote-java > knote-java.tar and inspect it.

You can list all the images on your system with the following command:

bash

docker images
REPOSITORY    TAG         IMAGE ID         CREATED            SIZE
knote-java                  latest              b9dfdd2b85ca        About a minute ago   385MB
adoptopenjdk/openjdk11      jdk-11.0.2.9-slim   9a223081d1a1        2 months ago         358MB

You should see the knote-java image that you just built.

You should also see the adoptopenjdk/openjdk11 which is the base layer of your knote-java image — it is just an ordinary image as well, and the docker run command downloaded it automatically from Docker Hub.

Docker Hub is a container registry — a place to distribute and share container images.

You packaged your app as a Docker image — let's run it as a container.

Running the container

Remember that your app requires a MongoDB database.

In the previous section, you installed MongoDB on your machine and ran it with the mongod command.

You could do the same now.

But guess what: you can run MongoDB as a container too.

MongoDB is provided as a Docker image named mongo on Docker Hub.

You can run MongoDB without actually "installing" it on your machine.

You can run it with docker run mongo.

But before you do that, you need to connect the containers.

The knote and mongo cointainers should communicate with each other, but they can do so only if they are on the same Docker network.

So, create a new Docker network as follows:

bash

docker network create knote

Now you can run MongoDB with:

bash

docker run \
  --name=mongo \
  --rm \
  --network=knote mongo

Note the following about this command:

Note that the docker run command automatically downloads the mongo image from Docker Hub if it's not yet present on your machine.

MongoDB is now running.

Now you can run your app as follows:

bash

docker run \
  --name=knote-java \
  --rm \
  --network=knote \
  -p 8080:8080 \
  -e MONGO_URL=mongodb://mongo:27017/dev \
  knote-java

Note the following about this command:

Regarding the last point, remember that your app reads the URL of the MongoDB server to connect to from the MONGO_URL environment variable.

If you look closely at the value of MONGO_URL, you see that the hostname is mongo.

Why is it mongo and not an IP address?

mongo is precisely the name that you gave to the MongoDB container with the --name=mongo flag.

If you named your MongoDB container foo, then you would need to change the value of MONGO_URL to mongodb://foo:27017.

Containers in the same Docker network can talk to each other by their names.

This is made possible by a built-in DNS mechanism.

You should now have two containers running on your machine, knote-java and mongo.

You can display all running containers with the following command:

bash

docker ps
CONTAINER ID    IMAGE       COMMAND                 PORTS                    NAMES
9b908ee0798a    knote-java  "/bin/sh -c 'exec ja…"  0.0.0.0:8080->8080/tcp   knote-java
1fb37b278231    mongo       "docker-entrypoint.s…"  27017/tcp                mongo

Great!

It's time to test your application!

Since you published port 8080 of your container to port 8080 of your local machine, your app is accessible on http://localhost:8080.

Go on and open the URL in your web browser.

You should see your app!

Verify that everything works as expected by creating some notes with pictures.

When you're done experimenting, stop and remove the containers as follows:

bash

docker stop mongo knote-java
docker rm mongo knote-java

Uploading the container image to a container registry

Imagine you want to share your app with a friend — how would you go about sharing your container image?

Sure, you could save the image to disk and send it to your friend.

But there is a better way.

When you ran the MongoDB container, you specified its Docker Hub ID (mongo), and Docker automatically downloaded the image.

You could create your images and upload them to DockerHub.

If your friend doesn't have the image locally, Docker automatically pulls the image from DockerHub.

There exist other public container registries, such as Quay — however, Docker Hub is the default registry used by Docker.

To use Docker Hub, you first have to create a Docker ID.

A Docker ID is your Docker Hub username.

Once you have your Docker ID, you have to authorise Docker to connect to the Docker Hub account:

bash

docker login

Before you can upload your image, there is one last thing to do.

Images uploaded to Docker Hub must have a name of the form username/image:tag:

To rename your image according to this format, run the following command:

bash

docker tag knote-java <username>/knote-java:1.0.0

Please replace <username> with your Docker ID.

Now you can upload your image to Docker Hub:

bash

docker push username/knote-java:1.0.0

Your image is now publicly available as <username>/knote-java:1.0.0 on Docker Hub and everybody can download and run it.

To verify this, you can re-run your app, but this time using the new image name.

Please notice that the command below runs the learnk8s/knote-java:1.0.0 image. If you wish to use yours, replace learnk8s with your Docker ID.

bash

docker run \
  --name=mongo \
  --rm \
  --network=knote \
  mongo
docker run \
  --name=knote-java \
  --rm \
  --network=knote \
  -p 8080:8080 \
  -e MONGO_URL=mongodb://mongo:27017/dev \
  learnk8s/knote-java:1.0.0

Everything should work exactly as before.

Note that now everybody in the world can run your application by executing the above two commands.

And the app will run on their machine precisely as it runs on yours — without installing any dependencies.

This is the power of containerisation!

Once you're done testing your app, you can stop and remove the containers with:

bash

docker stop mongo knote-java
docker rm mongo knote-java

So far, you have written a Java application and packaged it as a Docker image so that it can be run as a container.

The next step is to run this containerised app on a container orchestrator.

Kubernetes — the container orchestrator

Container orchestrators are designed to run complex applications with large numbers of scalable components.

They work by inspecting the underlying infrastructure and determining the best server to run each container.

They can scale to thousands of computers and tens of thousands of containers and still work efficiently and reliably.

You can imagine a container orchestrator as a highly-skilled Tetris player.

Containers are the blocks, servers are the boards, and the container orchestrator is the player.

A few key points to remember about Kubernetes. It's:

  1. Open-source: you can download and use it without paying any fee. You're also encouraged to contribute to the official project with bug fixes and new features
  2. Battle-tested: there're plenty of examples of companies running it in production. There's even a website where you can learn from the mistake of others.
  3. Well-looked-after: Redhat, Google, Microsoft, IBM, Cisco are only a few of the companies that have heavily invested in the future of Kubernetes by creating managed services, contributing to upstream development and offering training and consulting.

Kubernetes is an excellent choice to deploy your containerised application.

But how do you do that?

It all starts by creating a Kubernetes cluster.

Creating a local Kubernetes cluster

There are several ways to create a Kubernetes cluster:

In this section, you are going to use Minikube.

Minikube creates a single-node Kubernetes cluster running in a virtual machine.

A Minikube cluster is only intended for testing purposes, not for production. Later in this course, you will create an Amazon EKS cluster, which is suited for production.

Before you install Minikube, you have to install kubectl.

kubectl is the primary Kubernetes CLI — you use it for all interactions with a Kubernetes cluster, no matter how the cluster was created.

Once kubectl is installed, go on and install Minikube according to the official documentation.

If you're on Windows, you can follow our handy guide on how to install Minikube on Windows.

With Minikube installed, you can create a cluster as follows:

bash

minikube start

The command creates a virtual machine and installs Kubernetes.

Starting the virtual machine and cluster may take a couple of minutes, so please be patient!

When the command completes, you can verify that the cluster is created with:

bash

kubectl cluster-info

You have a fully-functioning Kubernetes cluster on your machine now.

Time to learn about some fundamental Kubernetes concepts.

Kubernetes resources

Kubernetes has a declarative interface.

In other words, you describe how you want the deployment of your application to look like, and Kubernetes figures out the necessary steps to reach this state.

The "language" that you use to communciate with Kubernetes consists of so-called Kubernetes resources.

There are many different Kubernetes resources — each is responsible for a specific aspect of your application.

You can find the full list of Kubernetes resources in the Kubernetes API reference.

Kubernetes resources are defined in YAML files and submitted to the cluster through the Kubernetes HTTP API.

Kubernetes resource definitions are also sometimes called "resource manifests" or "resource configurations".

As soon as Kubernetes receives your resource definitions, it takes the necessary steps to reach the target state.

Similarly, to query the state of your applications, you retrieve Kubernetes resources through the Kubernetes HTTP API.

In practice, you do all these interactions with kubectl - your primary client for the Kubernetes API.

In the remainder of this section, you will define a set of Kubernetes resources that describe your Knote application, and in the end, you will submit them to your Kubernets cluster.

The resources that you will use are the Deployment and the Service.

Let's start with the Deployment.

Defining a Deployment

First of all, create a folder named kube in your application directory:

bash

mkdir kube

The purpose of this folder is to hold all the Kubernetes YAML files that you will create.

It's a best practice to group all resource definitions for an application in the same folder because this allows to submit them to the cluster with a single command.

The first Kubernetes resource is a Deployment.

A Deployment creates and runs containers and keeps them alive.

Here is the definition of a Deployment for your Knote app:

kube/knote.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: knote
spec:
  replicas: 1
  selector:
    matchLabels:
      app: knote
  template:
    metadata:
      labels:
        app: knote
    spec:
      containers:
        - name: app
          image: learnk8s/knote-java:1.0.0
          ports:
            - containerPort: 8080
          env:
            - name: MONGO_URL
              value: mongodb://mongo:27017/dev
          imagePullPolicy: Always

That looks complicated, but we will break it down and explain it in detail.

For now, save the above content in a file named knote.yaml in the kube folder.

Please replace learnk8s with your Docker ID (username) in the container's image value. If you didn't upload your image to Docker Hub, you can use the learnk8s/knote-java:1.0.0 image provided by Learnk8s on Docker Hub.

You must be wondering how you can you find out about the structure of a Kubernetes resource.

The answer is, in the Kubernetes API reference.

The Kubernetes API reference contains the specification for every Kubernetes resource, including all the available fields, their data types, default values, required fields, and so on.

Here is the specification of the Deployment resource.

If you prefer to work in the command-line, there's an even better way.

The kubectl explain command can print the specification of every Kubernetes resource directly in your terminal:

bash

kubectl explain deployment

The command outputs exactly the same information as the web-based API reference.

To drill down to a specific field use:

bash

kubectl explain deployment.spec.replicas

Now that you know how to look up the documentation of Kubernetes resources, let's turn back to the Deployment.

The first four lines define the type of resource (Deployment), the version of this resource type (apps/v1), and the name of this specific resource (knote):

kube/knote.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: knote
spec:
  replicas: 1
  selector:
    matchLabels:
      app: knote
  template:
    metadata:
      labels:
        app: knote
    spec:
      containers:
        - name: app
          image: learnk8s/knote-java:1.0.0
          ports:
            - containerPort: 8080
          env:
            - name: MONGO_URL
              value: mongodb://mongo:27017/dev
          imagePullPolicy: Always

Next, you have the desired number of replicas of your container:

kube/knote.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: knote
spec:
  replicas: 1
  selector:
    matchLabels:
      app: knote
  template:
    metadata:
      labels:
        app: knote
    spec:
      containers:
        - name: app
          image: learnk8s/knote-java:1.0.0
          ports:
            - containerPort: 8080
          env:
            - name: MONGO_URL
              value: mongodb://mongo:27017/dev
          imagePullPolicy: Always

You don't usually talk about containers in Kubernetes.

Instead, you talk about Pods.

What is a Pod?

A Pod is a wrapper around one or more containers.

Most often, a Pod contains only a single container — however, for advanced use cases, a Pod may contain multiple containers.

If a Pod contains multiple containers, they are treated by Kubernetes as a unit — for example, they are started and stopped together and executed on the same node.

A Pod is the smallest unit of deployment in Kubernetes — you never work with containers directly, but with Pods that wrap containers.

Technically, a Pod is a Kubernetes resource, like a Deployment or Service.

Let's turn back to the Deployment resource.

The next part ties together the Deployment resource with the Pod replicas:

kube/knote.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: knote
spec:
  replicas: 1
  selector:
    matchLabels:
      app: knote
  template:
    metadata:
      labels:
        app: knote
    spec:
      containers:
        - name: app
          image: learnk8s/knote-java:1.0.0
          ports:
            - containerPort: 8080
          env:
            - name: MONGO_URL
              value: mongodb://mongo:27017/dev
          imagePullPolicy: Always

The template.metadata.labels field defines a label for the Pods that wrap your Knote container (app: knote).

The selector.matchLabels field selects those Pods with a app: knote label to belong to this Deployment resource.

Note that there must be at least one shared label between these two fields.

The next part in the Deployment defines the actual container that you want to run:

kube/knote.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: knote
spec:
  replicas: 1
  selector:
    matchLabels:
      app: knote
  template:
    metadata:
      labels:
        app: knote
    spec:
      containers:
        - name: app
          image: learnk8s/knote-java:1.0.0
          ports:
            - containerPort: 8080
          env:
            - name: MONGO_URL
              value: mongodb://mongo:27017/dev
          imagePullPolicy: Always

It defines the following things:

The above arguments should look familiar to you: you used similar ones when you ran your app with docker run previously.

That's not a coincidence.

When you submit a Deployment resource to the cluster, you can imagine Kubernetes executing docker run and launching your container in one of the computers.

The container specification also defines an imagePullPolicy of Always — the instruction forces the Docker image to be downloaded, even if it was already downloaded.

A Deployment defines how to run an app in the cluster, but it doesn't make it available to other apps.

To expose your app, you need a Service.

Defining a Service

A Service resource makes Pods accessible to other Pods or users outside the cluster.

Without a Service, a Pod cannot be accessed at all.

A Service forwards requests to a set of Pods:

Services in Kubernetes

In this regard, a Service is akin to a load balancer.

Here is the definition of a Service that makes your Knote Pod accessible from outside the cluster:

kube/knote.yaml

apiVersion: v1
kind: Service
metadata:
  name: knote
spec:
  selector:
    app: knote
  ports:
    - port: 80
      targetPort: 8080
  type: LoadBalancer

Again, to find out about the available fields of a Service, look it up in the API reference, or, even better, use kubectl explain service.

Where should you save the above definition?

It is a best-practice to save resource definitions that belong to the same application in the same YAML file.

To do so, paste the above content at the beginning of your existing knote.yaml file, and separate the Service and Deployment resources with three dashes like this:

kube/knote.yaml

# ... Deployment YAML definition
---
# ... Service YAML definition

You can find the final YAML files for this section in this repository.

Let's break down the Service resource.

It consists of three crucial parts.

The first part is the selector:

kube/knote.yaml

apiVersion: v1
kind: Service
metadata:
  name: knote
spec:
  selector:
    app: knote
  ports:
    - port: 80
      targetPort: 8080
  type: LoadBalancer

It selects the Pods to expose according to their labels.

In this case, all Pods that have a label of app: knote will be exposed by the Service.

Note how this label corresponds exactly to what you specified for the Pods in the Deployment resource:

kube/knote.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: knote
spec:
  # ...
  template:
    metadata:
      labels:
        app: knote
    # ...

It is this label that ties your Service to your Deployment resource.

The next important part is the port:

kube/knote.yaml

apiVersion: v1
kind: Service
metadata:
  name: knote
spec:
  selector:
    app: knote
  ports:
    - port: 80
      targetPort: 8080
  type: LoadBalancer

In this case, the Service listens for requests on port 80 and forwards them to port 8080 of the target Pods:

Service and ports

The last important part is the type of the Service:

kube/knote.yaml

apiVersion: v1
kind: Service
metadata:
  name: knote
spec:
  selector:
    app: knote
  ports:
    - port: 80
      targetPort: 8080
  type: LoadBalancer

In this case, the type is LoadBalancer, which makes the exposed Pods accessible from outside the cluster.

The default Service type is ClusterIP, which makes the exposed Pods only accessible from within the cluster.

Pro tip: find out about all available Service types with kubectl explain service.spec.type.

Beyond exposing your containers, a Service also ensures continuous availability for your app.

If one of the Pod crashes and is restarted, the Service makes sure not to route traffic to this container until it is ready again.

Also, when the Pod is restarted, and a new IP address is assigned, the Service automatically handles the update too.

Furthermore, if you decide to scale your Deployment to 2, 3, 4, or 100 replicas, the Service keeps track of all of these Pods.

This completes the description of your app — a Deployment and Service is all you need.

You need to do the same thing for the database component now.

Defining the database tier

In principle, a MongoDB Pod can be deployed similarly as your app — that is, by defining a Deployment and Service resource.

However, deploying MongoDB needs some additional configuration.

MongoDB requires a persistent storage.

This storage must not be affected by whatever happens to the MongoDB Pod.

If the MongoDB Pod is deleted, the storage must persist — if the MongoDB Pod is moved to another node, the storage must persist.

There exists a Kubernetes resource that allows obtaining persistent storage volume: the PersistentVolumeClaim.

Consequently, the description of your database component should consist of three resource definitions:

Here's the complete configuration:

kube/mongo.yaml

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: mongo-pvc
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 256Mi
---
apiVersion: v1
kind: Service
metadata:
  name: mongo
spec:
  selector:
    app: mongo
  ports:
    - port: 27017
      targetPort: 27017
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mongo
spec:
  selector:
    matchLabels:
      app: mongo
  template:
    metadata:
      labels:
        app: mongo
    spec:
      containers:
        - name: mongo
          image: mongo
          ports:
            - containerPort: 27017
          volumeMounts:
            - name: storage
              mountPath: /data/db
      volumes:
        - name: storage
          persistentVolumeClaim:
            claimName: mongo-pvc

Please save this YAML definition in a file named mongo.yaml in the kube directory.

Let's look at each of the three parts of the definition.

PersistentVolumeClaim

The PersistentVolumeClaim requests a persistent storage volume of 256 MB.

This volume is made available to the MongoDB container to save its data.

Service

The Service is similar to the Service you defined for the app component.

However, note that it does not have a type field.

If a Service does not have a type field, Kubernetes assigns it the default type ClusterIP.

ClusterIP makes the Pod accessible from within the cluster, but not from outside — this is fine because the only entity that has to access the MongoDB Pod is your app.

Deployment

The Deployment has a similar structure to the other Deployment.

However, it contains an additional field that you haven't seen yet: volumes.

The volumes field defines a storage volume named storage, which references the PersistentVolumeClaim.

Furthermore, the volume is referenced from the volumeMounts field in the definition of the MongoDB container.

The volumeMount field mounts the referenced volume at the specified path in the container, which in this case is /data/db.

And /data/db is where MongoDB saves its data.

In other words, the MongoDB database data is stored in a persistent storage volume that has a lifecycle independent of the MongoDB container.

Deploying stateful applications to Kubernetes is a complex but essential topic. You can learn more about it in Managing State module of the Learnk8s Academy.

There's one more important thing to note.

Do you remember the value of the MONGO_URL environment variable in the Knote Deployment?

It is mongodb://mongo:27017/dev.

The hostname is mongo.

Why is it mongo?

Because the name of the MongoDB Service is mongo.

If you named your MongoDB service foo, then you would need to change the value of the MONGO_URL variable to monogdb://foo:27017/dev.

Service discovery is a critical Kubernetes concept.

Pods within a cluster can talk to each other through the names of the Services exposing them.

Kubernetes has an internal DNS system that keeps track of domain names and IP addresses.

Similarly to how Docker provides DNS resolution for containers, Kubernetes provides DNS resolution for Services.

All components of your app are described by Kubernetes resources now — let's deploy them to the cluster.

Deploying the application

So far, you created a few YAML files with resource definitions.

You didn't yet touch the cluster.

But now comes the big moment!

You are going to submit your resource definitions to Kubernetes.

And Kubernetes will bring your application to life.

First of all, make sure that you have a knote.yaml and mongo.yaml file inside the kube directory:

bash

tree .
kube/
├── knote.yaml
└── mongo.yaml

You can find these files also in this repository.

Also, make sure that your Minikube cluster is running:

bash

minikube status

Then submit your resource definitions to Kubernetes with the following command:

bash

kubectl apply -f kube

This command submits all the YAML files in the kube directory to Kubernetes.

The -f flag accepts either a single filename or a directory. In the latter case, all YAML files in the directory are submitted.

As soon as Kubernetes receives your resources, it creates the Pods.

You can watch your Pods coming alive with:

bash

kubectl get pods --watch

You should see two Pods transitioning from Pending to ContainerCreating to Running.

These Pods correspond to the Knote and MongoDB containers.

As soon as both Pods are in the Running state, your application is ready.

You can now access your application through the knote Service.

In Minikube, a Service can be accessed with the following command:

bash

minikube service knote --url

The command should print the URL of the knote Service.

You can open the URL in a web browser.

You should see your application.

Verify that your app works as expected by creating some notes with pictures.

The app should work as it did when you ran it locally with Docker.

But now it's running on Kubernetes.

Scaling your app

Kubernetes makes it very easy to increase the number of replicas to 2 or more:

bash

kubectl scale --replicas=2 deployment/knote

You can watch how a new Pod is created with:

bash

kubectl get pods -l app=knote --watch

The -l flag restricts the output to only those Pods with a app=knote label.

There are now two replicas of the Knote Pod running.

Did it work?

Reaccess your app:

bash

minikube service knote --url

And create a note with a picture.

Now try to reload your app a couple of times (i.e. hit your browser's reload button).

Did you notice any glitch?

The picture that you added to your note is not displayed on every reload.

If you pay attention, the picture is only displayed on every second reload, on average.

Why is that?

Remember that your application saves uploaded pictures in the local file system.

If your app runs in a container, then pictures are saved within the container's file system.

When you had only a single Pod, this was fine.

But since you have two replicas, there's an issue.

The picture that you previously uploaded is saved in only one of the two Pods.

When you access your app, the knote Service selects one of the available Pods.

When it selects the Pod that has the picture in its file system, the image is displayed.

But when it selects the other Pod, the picture isn't displayed, because the container doesn't have it.

Your application is stateful.

The pictures in the local filesystem constitute a state that is local to each container.

To be scalable, applications must be stateless.

Stateless means that an instance can be killed restarted or duplicated at any time without any data loss or inconsistent behaviour.

You must make your app stateless before you can scale it.

In this section, you will refactor your app to make it stateless.

But before you proceed, remove your current application from the cluster:

bash

kubectl delete -f kube

The command ensures that the old data in the database's persistent volume does not stick around.

Making the app stateless

How can you make your application stateless?

The challenge is that uploaded pictures are saved in the container's file system where they can be accessed only by the current Pod.

However, you could save the pictures in a central place where all Pods can access them.

An object storage is an ideal system to store files centrally.

You could use a cloud solution, such as Amazon S3.

But to hone your Kubernetes skills, you could deploy an object storage service yourself.

MinIO is an open-source object storage service that can be installed on your infrastructure.

And you can install MinIO in your Kubernetes cluster.

Let's start by changing the app to use MinIO.

First of all, add the MinIO SDK for Java and Commons IO dependencies to our project pom.xml file:

pom.xml

<dependencies>
...
  <dependency>
    <groupId>io.minio</groupId>
    <artifactId>minio</artifactId>
    <version>6.0.8</version>
  </dependency>
  <dependency>
    <groupId>commons-io</groupId>
    <artifactId>commons-io</artifactId>
    <version>2.6</version>
  </dependency>
...
</dependencies>

Next, you should connect to the MinIO server.

As for MongoDB, your app should keep trying to connect to MinIO until it succeeds.

You should gracefully handle the case when the MinIO Pod is started with a delay.

Also, you have to create a so-called bucket on MinIO, which can be seen as the "folder" where your pictures are saved.

The above tasks are summarised as code in the following method:

KnoteJavaApplication.java

private void initMinio() throws InterruptedException {
  boolean success = false;
  while (!success) {
    try {
      minioClient = new MinioClient("http://" + properties.getMinioHost() + ":9000" ,
                                    properties.getMinioAccessKey(),
                                    properties.getMinioSecretKey(),
                                    false);
      // Check if the bucket already exists.
      boolean isExist = minioClient.bucketExists(properties.getMinioBucket());
      if (isExist) {
        System.out.println("> Bucket already exists.");
      } else {
        minioClient.makeBucket(properties.getMinioBucket());
      }
      success = true;
    } catch (Exception e) {
      e.printStackTrace();
      System.out.println("> Minio Reconnect: " + properties.isMinioReconnectEnabled());
      if (properties.isMinioReconnectEnabled()) {
        try {
          Thread.sleep(5000);
        } catch (InterruptedException ex) {
          ex.printStackTrace();
        }
      } else {
        success = true;
      }
    }
  }
  System.out.println("> Minio initialized!");
}

Notice that you are still using @ConfigurationProperties so you should add all the MinIO properties:

KnoteJavaApplication.java

@ConfigurationProperties(prefix = "knote")
class KnoteProperties {

    @Value("${minio.host:localhost}")
    private String minioHost;

    @Value("${minio.bucket:image-storage}")
    private String minioBucket;

    @Value("${minio.access.key:}")
    private String minioAccessKey;

    @Value("${minio.secret.key:}")
    private String minioSecretKey;

    @Value("${minio.useSSL:false}")
    private boolean minioUseSSL;

    @Value("${minio.reconnect.enabled:true}")
    private boolean minioReconnectEnabled;

    public String getMinioHost() {
        return minioHost;
    }

    public String getMinioBucket() {
        return minioBucket;
    }

    public String getMinioAccessKey() {
        return minioAccessKey;
    }

    public String getMinioSecretKey() {
        return minioSecretKey;
    }

    public boolean isMinioUseSSL() {
        return minioUseSSL;
    }

    public boolean isMinioReconnectEnabled() {
        return minioReconnectEnabled;
    }
}

You should call this initialization code after creating our @Controller:

KnoteJavaApplication.java

@Controller
@EnableConfigurationProperties(KnoteProperties.class)
class KNoteController {
  ...
  private MinioClient minioClient;

  @PostConstruct
  public void init() throws InterruptedException {
    initMinio();
  }
  ...

Now modify the handler of the uploadImage() method to save the pictures to MinIO, rather than to the local file system (changed lines are highlighted):

KnoteJavaApplication.java

private void uploadImage(MultipartFile file, String description, Model model) throws Exception {
  String fileId = UUID.randomUUID().toString() + "." + file.getOriginalFilename().split("\\.")[1];
  minioClient.putObject(properties.getMinioBucket(), fileId, file.getInputStream(),
                                file.getSize(), null, null, file.getContentType());
  model.addAttribute("description",
                description + " ![](/img/" + fileId + ")");
}

You're almost done.

There's one last thing to do.

The pictures are served to the clients via the /img route of your app.

So, you should create an additional route:

KnoteJavaApplication.java

@GetMapping(value = "/img/{name}", produces = MediaType.IMAGE_PNG_VALUE)
public @ResponseBody byte[] getImageByName(@PathVariable String name) throws Exception {
  InputStream imageStream = minioClient.getObject(properties.getMinioBucket(), name);
  return IOUtils.toByteArray(imageStream);
}

The /img route retrieves a picture by its name from MinIO and serves it to the client.

Also, we can remove the following code that is not longer needed:

KnoteJavaApplication.java

class KnoteConfig implements WebMvcConfigurer{
  ...
}

Now you're done.

Note that you can find the final source code file in this repository.

But can you be sure that you didn't introduce any bug into the code?

Please test your code right now before you build a new Docker image and deploy it to Kubernetes.

To do so, you can run your changed app locally.

Testing the app

Your app has two dependencies now: MongoDB and MinIO.

To run your app locally, you must run its dependencies too.

You can run both MongoDB and MinIO as Docker containers.

MinIO is provided as minio/minio on Docker Hub.

You can start a MinIO container like this:

bash

docker run \
  --name=minio \
  --rm \
  -p 9000:9000 \
  -e MINIO_ACCESS_KEY=mykey \
  -e MINIO_SECRET_KEY=mysecret \
  minio/minio server /data

Note that mykey and mysecret are the MinIO credentials and you can choose them yourself.

And you can start a MongoDB container like this:

bash

docker run \
  --name=mongo \
  --rm \
  -p 27017:27017 \
  mongo

Note that both of these commands expose a container port to the local machine with the -p flag.

You will use those containers as dependent components while your app is connected to localhost.

You can start the app like this:

bash

mvn -Dminio.access.key=mykey -Dminio.secret.key=mysecret spring-boot:run

Note that it's necessary to set the -Dminio.access.key=mykey and -Dminio.secret.key=mysecret variables every time you run your app now.

Furthermore, the values of these variables must match the same credentials defined earlier for MinIO.

You should never hard-code credentials in the application code, that's why these environment variables don't have any default values in your java applications.

If you visit http://localhost:8080, you should see your app.

Test if everything works correctly by creating some notes with pictures.

Your app should behave just like it did before.

But the crucial difference is that now uploaded pictures are saved in the MinIO object storage rather than on the local file system.

When you're convinced that your app works correctly, terminate it with Ctrl-C and stop MongoDB and MinIO with:

bash

docker stop mongo minio
docker rm mongo minio

You created a new version of your app.

It's time to package it as a new Docker image.

Containerising the new app

To build a new Docker image of your app, run the following command:

bash

docker build -t <username>/knote-java:2.0.0 .

You should be familiar with that command.

It is precisely the command that you used to build the first version of the app in the "Containerisation" section.

Only the tag is different, though.

You should also upload your new image Docker Hub:

bash

docker push <username>/knote-java:2.0.0

Please replace <username> with your Docker ID.

The two versions of the image will live side-by-side in your Docker Hub repository.

You can use the second version by specifying <username>/knote-java:2.0.0, but you can still use the first version by specifying <username>/knote-java:1.0.0.

You have now three Docker images, all available on Docker Hub.

Let's do a little exercise.

You will rerun your application, but this time with all three components as Docker containers.

This is how you ran the application in the "Containerisation" section, but there you had only two containers, now you have three.

First of all, make sure that you still have the knote Docker network:

bash

docker network ls

Next, run the MongoDB container as follows:

bash

docker run \
  --name=mongo \
  --rm \
  --network=knote \
  mongo

Then, run the MinIO container as follows:

bash

docker run \
  --name=minio \
  --rm \
  --network=knote \
  -e MINIO_ACCESS_KEY=mykey \
  -e MINIO_SECRET_KEY=mysecret \
  minio/minio server /data

Note the following about these two docker run commands:

  1. They don't publish any ports to the host machine with the -p flag, because they don't need to be accessed from the host machine. The only entity that has to access them is the Knote container
  2. Both containers are started in the knote Docker network to enable them to communicate with the Knote container

Now, run the Knote container as follows:

bash

docker run \
  --name=knote \
  --rm \
  --network=knote \
  -p 3000:3000 \
  -e MONGO_URL=mongodb://mongo:27017/dev \
  -e MINIO_ACCESS_KEY=mykey \
  -e MINIO_SECRET_KEY=mysecret \
  -e MINIO_HOST=minio \
  learnk8s/knote-java:2.0.0

You can change learnk8s to your Docker ID to use your own built image.

Note the following about this command:

These latter two environment variables are particularly important.

The hostname of the MONGO_URL variable is mongo — this corresponds to the name of the MongoDB container.

The MINIO_HOST variable is set to minio — this corresponds to the name fo the MinIO container.

These values are what allows the Knote container to talk to the MongoDB and MinIO container in the Docker network.

Remember from the "Containerisation" section that containers in the same Docker network can talk to each other by their names.

You should now be able to access your app on http://localhost:3000.

Verify that your app still works as expected.

But since you didn't do any further changes to your app, everything should still work correctly.

When you're done, stop and tidy up with:

bash

docker stop knote minio mongo
docker rm knote minio mongo

After this digression to Docker, let's return to Kubernetes.

Updating the Knote configuration

If you want to deploy the new version of your app to Kubernetes, you have to do a few changes to the YAML resources.

In particular, you have to update the Docker image name and add the additional environment variables in the Deployment resource.

You should change the Deployment resource in your knote.yaml file as follows (changed lines are highlighted):

kube/knote.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: knote
spec:
  replicas: 1
  selector:
    matchLabels:
      app: knote
  template:
    metadata:
      labels:
        app: knote
    spec:
      containers:
        - name: knote
          image: learnk8s/knote-java:2.0.0
          ports:
            - containerPort: 3000
          env:
            - name: MONGO_URL
              value: mongodb://mongo:27017/dev
            - name: MINIO_ACCESS_KEY
              value: mykey
            - name: MINIO_SECRET_KEY
              value: mysecret
            - name: MINIO_HOST
              value: minio
          imagePullPolicy: Always

Don't forget to replace learnk8s with your Docker ID in the image name. If you didn't push your image to Docker Hub, you could use the learnk8s/knote-java:2.0.0 image which is provided by Learnk8s.

The Deployment is ready to be submitted to Kubernetes.

However, something else is missing.

You should create a Kubernetes description for the new MinIO component.

So, let's do it.

Defining the MinIO component

The task at hand is to deploy MinIO to a Kubernetes cluster.

You should be able to guess what the Kubernetes description for MinIO looks like.

It should look like the MongoDB description that you defined in the "Deploying to Kubernetes" section.

As for MongoDB, MinIO requires persistent storage to save its state.

Also like MongoDB, MinIO must be exposed with a Service for Pods inside the cluster.

Here's the complete MinIO configuration:

kube/minio.yaml

apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: minio-pvc
spec:
  accessModes:
    - ReadWriteOnce
  resources:
    requests:
      storage: 256Mi
---
apiVersion: v1
kind: Service
metadata:
  name: minio
spec:
  selector:
    app: minio
  ports:
    - port: 9000
      targetPort: 9000
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: minio
spec:
  strategy:
    type: Recreate
  selector:
    matchLabels:
      app: minio
  template:
    metadata:
      labels:
        app: minio
    spec:
      containers:
        - name: minio
          image: minio/minio:RELEASE.2020-03-14T02-21-58Z
          args:
            - server
            - /storage
          env:
            - name: MINIO_ACCESS_KEY
              value: mykey
            - name: MINIO_SECRET_KEY
              value: mysecret
          ports:
            - containerPort: 9000
          volumeMounts:
            - name: storage
              mountPath: /storage
      volumes:
        - name: storage
          persistentVolumeClaim:
            claimName: minio-pvc

Go on and save the content in a file named minio.yaml in the kube directory.

The YAML file has a Deployment, Service and PersistenVolumeClaim definition.

So, if you recall the MongoDB resource definition, you should have no troubles understanding the MinIO configuration.

If you want to learn more about deploying stateful applications to Kubernetes, check out the Managing state module in the Learnk8s Academy.

You just defined the last of the three components of your application.

Your Kubernetes configuration is now complete.

It's time to deploy the application!

Deploying the new app

Now comes the big moment!

You will deploy the new version of your app to Kubernetes.

First of all, make sure that you have the following three YAML files in the kube directory:

bash

tree .
kube/
├── knote.yaml
├── minio.yaml
└── mongo.yaml

Also, make sure that you deleted the previous version of the app from the cluster:

bash

kubectl get pods

The command shouldn't output any resources.

Deleting the previous version of the app makes sure that the old data in the MongoDB database is removed.

Now deploy your app to Kubernetes:

bash

kubectl apply -f kube

Watch your Pods being created:

bash

kubectl get pods --watch

You should see three Pods.

Once all three Pods are Running, your application is ready.

Access your app with:

bash

minikube service knote --url

The command should open your app in a web browser.

Verify that it works correctly by creating some notes with pictures.

It should work as expected!

As a summary, here is what your application looks like now:

Knote application architecture

It consists of three components:

  1. Knote as the primary application for creating notes
  2. MongoDB for storing the text of the notes, and
  3. MinIO for storing the pictures of the notes.

Only the Knote component is accessible from outside the cluster — the MongoDB and MinIO components are hidden inside.

At the moment, you're running a single Knote container.

But the topic of this section is "scaling".

And you completed some major refactoring to your app to make it stateless and scalable.

So, let's scale it!

Scaling the app

Your app is now stateless because it saves the uploaded pictures on a MinIO server instead of the Pod's file system.

In other words, when you scale your app, all pictures should appear on every request.

It's the moment of truth.

Scale the Knote container to 10 replicas:

bash

kubectl scale --replicas=10 deployment/knote

There should be nine additional Knote Pods being created.

You can watch them come online with:

bash

kubectl get pods -l app=knote --watch

After a short moment, the new Pods should all be Running.

Go back to your app in the web browser and reload the page a couple of times.

Are the pictures always displayed?

Yes they are!

Thanks to statelessness, your Pods can now be scaled to any number of replicas without any data loss or inconsistent behaviour.

You just created a scalable app!

When you're done playing with your app, delete it from the cluster with:

bash

kubectl delete -f kube

And stop Minikube with:

bash

minikube stop

You don't need Minikube anymore.

In the next section, you will create a new Kubernetes cluster in the cloud and deploy your app there!

Deploying to the cloud

At this point, you have defined a Kubernetes application consisting of three components and deployed them to a local Minikube cluster.

The app worked perfectly on Minikube.

Does it works when you deploy it to a "real" Kubernetes cluster in the cloud?

In other words, can you submit the same three YAML files to a cluster in the cloud and expect it to work?

It's time to find out.

In this section, you will create a production-grade Kubernetes cluster on AWS using Amazon Elastic Kubernetes Service (EKS), and you will deploy your application to it.

Creating and setting up an AWS account

To use AWS services, you need an AWS account.

If you already have an AWS account, you can jump to the next section →

The first step is signing up for an AWS account.

Note that to create any resources on AWS, you have to provide valid credit card details.

Next, you should create an AWS access key — this is necessary to access AWS services from the command line.

To do so, follow these instructions:

  • You should see your AWS console once you're logged in.
    2/13

    You should see your AWS console once you're logged in.

  • Click on your user name at the top right of the page.
    3/13

    Click on your user name at the top right of the page.

  • In the drop down there's an item for "My Security Credentials".
    4/13

    In the drop down there's an item for "My Security Credentials".

  • Click on "My Security Credentials".
    5/13

    Click on "My Security Credentials".

  • You should land on Your Security Credentials page.
    6/13

    You should land on Your Security Credentials page.

  • Click on Access Keys.
    7/13

    Click on Access Keys.

  • The accordion unfolds the list of active keys (if any) and a button to create a new access key.
    8/13

    The accordion unfolds the list of active keys (if any) and a button to create a new access key.

  • Click on "Create New Access Key".
    9/13

    Click on "Create New Access Key".

  • A modal window appears suggesting that the key was created successfully.
    10/13

    A modal window appears suggesting that the key was created successfully.

  • Click on "Show Access Key" to reveal the access key.
    11/13

    Click on "Show Access Key" to reveal the access key.

  • You should see your access and secret key.
    12/13

    You should see your access and secret key.

  • Please make a note of your keys as you will need those values in the next step.
    13/13

    Please make a note of your keys as you will need those values in the next step.

You should save the access key ID and secret access key in a file named ~/.aws/credentials as follows:

$HOME/.aws/credentials

[default]
aws_access_key_id=[access-key-id]
aws_secret_access_key=[secret-access-key]

That's it! You're an AWS user now.

Creating a Kubernetes cluster on AWS

You will use Amazon Elastic Kubernetes Service (EKS) for creating a Kubernetes cluster on AWS.

Amazon EKS is the managed Kubernetes service of AWS — it is comparable to Azure Kubernetes Service (AKS) or Google Kubernetes Engine (GKE).

Caution: AWS isn't free, and the resources that you will create in this section will produce very reasonable charges on your credit card. In practice, the total costs will be around USD 0.40 per hour. In other words, using the cluster for 5 hours will set you back around 2 dollars.

You will create the cluster with a tool called eksctl — a third-party command-line tool that allows creating an EKS cluster with a single command.

You can install eksctl according to the instructions in the official project page.

With eksctl installed, it's time to create an Amazon EKS cluster.

Run the following eksctl command:

bash

eksctl create cluster --region=eu-west-2 --name=knote

The command creates an Amazon EKS Kubernetes cluster with the following properties:

You can use any other AWS region where Amazon EKS is available. Just avoid the us-east-1 region, because issues have been observed in this region.

Please be patient! Creating an EKS cluster usually takes around 15 minutes.

While you wait for the cluster being created, you have some time to think about Amazon EKS.

Amazon EKS is a managed Kubernetes service, in the sense that AWS runs the Kubernetes control plane for you.

That means, AWS runs the master nodes, and you run the worker nodes.

AWS runs three master nodes in three availability zones in your selected region.

  • Amazon Elastic Kubernetes Service (EKS) is the managed Kubernetes offering of AWS. It allows you to create a resilient Kubernetes cluster running on the AWS infrastructure.
    1/5

    Amazon Elastic Kubernetes Service (EKS) is the managed Kubernetes offering of AWS. It allows you to create a resilient Kubernetes cluster running on the AWS infrastructure.

  • AWS creates three master nodes. The three master nodes are deployed in three different availability zones.
    2/5

    AWS creates three master nodes. The three master nodes are deployed in three different availability zones.

  • An Application Load Balancer (ALB) distributes the traffic to the three API servers on the master nodes.
    3/5

    An Application Load Balancer (ALB) distributes the traffic to the three API servers on the master nodes.

  • There are also three etcd instances. They sync between themselves. Even if one availability zone is lost, the cluster can still operate correctly.
    4/5

    There are also three etcd instances. They sync between themselves. Even if one availability zone is lost, the cluster can still operate correctly.

  • There are also three etcd instances. They sync between themselves. Even if one availability zone is lost, the cluster can still operate correctly.
    5/5

    There are also three etcd instances. They sync between themselves. Even if one availability zone is lost, the cluster can still operate correctly.

You have no access to the master nodes.

But you have full control over the worker nodes.

The worker nodes are ordinary Amazon EC2 instances in your AWS account.

Once the eksctl command completes Amazon EKS cluster should be ready!

You can list the two worker nodes of your cluster with:

bash

kubectl get nodes
NAME                                           STATUS   ROLES    AGE   VERSION
ip-192-168-25-57.eu-west-2.compute.internal    Ready    <none>   23m   v1.12.7
ip-192-168-68-152.eu-west-2.compute.internal   Ready    <none>   23m   v1.12.7

Note that you can't list or inspect the master nodes in any way with Amazon EKS. AWS fully manages the master nodes, and you don't need to be concerned about them.

Since the worker nodes are regular Amazon EC2 instances in your AWS account, you can inspect them in the AWS EC2 Console.

You can also inspect the Amazon EKS resource itself in your AWS account in the AWS EKS Console.

As you can see, your Amazon EKS cluster has further related resources — they handle all the aspects that are required for a production-grade cluster such as networking, access control, security, and logging.

Those resources are created by eksctl.

eksctl creates a CloudFormation stack with all resources belonging to your Amazon EKS cluster.

You have a production-grade Kubernetes cluster on AWS now.

It's time to deploy the application.

Deploying the app

Now comes the moment of truth.

Does the app work on Amazon EKS without any changes?

It's time to find out.

First of all, make sure that you still have the three YAML files in the kube directory:

bash

tree .
kube/
├── knote.yaml
├── minio.yaml
└── mongo.yaml

Note that these are precisely the same YAML files that you deployed to the Minikube cluster.

Next, submit your configuration to the new Amazon EKS cluster:

bash

kubectl apply -f kube

Watch the Pods being created:

bash

kubectl get pods --watch

The app seems to be running now.

To access the app, you need the public address of the knote Service.

You can get it with this command:

bash

kubectl get service knote
NAME  TYPE         CLUSTER-IP    EXTERNAL-IP                        PORT(S)
knote LoadBalancer 10.100.188.43 <xxx.eu-west-2.elb.amazonaws.com>  80:31373/TCP

The EXTERNAL-IP column should contain a fully-qualified domain name.

The address allows you to access the app from anywhere.

Open the domain name in your web browser.

Note that it may take a couple of minutes until AWS DNS resolution is set up. If you get an ERR_NAME_NOT_RESOLVED error, try again in a few minutes.

You should see your app!

Verify that the app works as expected by creating some notes with pictures.

Everything should work correctly.

So, your hopes came true — you can deploy the same configuration to Amazon EKS!

And everything should work the same if you want to deploy it to Azure Kubernetes Service or Google Kubernetes Engine.

But what about scaling?

Go ahead and scale your Knote container to 10 replicas:

bash

kubectl scale --replicas=10 deployment knote

And check if everything still works by creating some more notes with pictures and reloading the page a couple of times.

Everything should indeed work as it did before.

You just discovered the beauty of Kubernetes: you can define an application once and run it on any Kubernetes cluster.

Taking into account resource limits

There is something else that you should know about scaling Pods.

The number of Pods running in a given cluster is not unlimited.

In particular, there are limits on the maximum number of Pods that can run on a node.

On Amazon EKS, these limits depend on the EC2 instance type that you select.

Larger instances can host more Pods than smaller instances.

The details are described in the eni-max-pods.txt document from AWS.

This document defines the maximum number of Pods that can be run on each Amazon EC2 instance type.

The m5.large instance type that you are using for your worker nodes can host up to 29 Pods.

You have two worker nodes in your cluster — that means you can run up to 58 Pods in your cluster.

So, what happens when you scale your Pods to 100 replicas?

The replicas that exceed the limit of 58 Pods should be stuck in the Pending state and never run.

Because no node can run them.

Let's test this.

First, check how many Pods are running in your cluster right now:

bash

kubectl get pods --all-namespaces

You should see 9 Pods — the 3 Pods of your application and six system Pods.

Kubernetes runs some system Pods on your worker nodes in the kube-system namespace. These Pods count against the limit too. The --all-namespaces flag outputs the Pods from all namespaces of the cluster.

So, how many replicas of the Knote Pod can you run in the cluster?

8 Pods are not Knote Pods, and 58 is the maximum number of Pods that can run in the cluster.

Hence, there can be up to 50 replicas of the Knote Pod.

Let's exceed this limit on purpose to observe what happens.

Scale your Knote app to 60 replicas:

bash

kubectl scale --replicas=60 deployment/knote

Wait for the Pods to start up.

If your calculations were correct, only 50 of these 60 replicas should be running in the cluster — the remaining ten should be stuck in the Pending state.

Let's start by counting the Knote Pods that are Running:

bash

kubectl get pods \
  -l app=knote \
  --field-selector='status.phase=Running' \
  --no-headers | wc -l

The command should output 50.

And now the Knote Pods that are Pending:

bash

kubectl get pods \
  -l app=knote \
  --field-selector='status.phase=Pending' \
  --no-headers | wc -l

The command should output 10.

So your calculations were correct.

50 of the 60 replicas are Running — the remaining 10 are Pending.

The ten pending replicas can't run because the maximum number of 58 running Pods in the cluster has been reached.

You can verify that there are indeed 58 running Pods in the cluster with:

bash

kubectl get pods \
  --all-namespaces \
  --field-selector='status.phase=Running' \
  --no-headers | wc -l

The command should output 58.

To fix the issue, you can scale down the number of replicas to 50:

bash

kubectl scale --replicas=50 deployment/knote

After a while, you should see no Pending Pods anymore — all the replicas should be running.

But what if you want a higher number of replicas at all costs?

You could add more nodes to your cluster.

Nothing prevents you from creating a cluster with 10, 20, 30, or 1000 worker nodes.

Kubernetes is tested to run reliably with up to several thousands of nodes and tens of thousands of Pods.

Or you could use larger EC2 instances that can host more Pods (see AWS limits).

Nothing prevents you from using m5.24xlarge EC2 instances for your worker nodes and have 737 Pods on each of them.

Whatever your scaling requirements are, Kubernetes can accommodate them — you just have to design your cluster accordingly.

Cleaning up

Before you leave, you should remember something important:

Running an Amazon EKS cluster is not free.

Running the cluster alone (without the worker nodes) costs USD 0.20 per hour.

The price stays the same, no matter how many Pods you run on the cluster.

And running the two m5.large worker node costs USD 0.096 per hour for each one.

The total amount is around USD 0.40 per hour for running your cluster.

While it might not seem a lot, if you forget to delete your cluster, it could add up quickly.

That's why you should always delete your Amazon EKS cluster when you don't need it anymore.

You can do this conveniently with eksctl:

bash

eksctl delete cluster --region=eu-west-2 --name=knote

The command deletes all AWS resources that belong to your Amazon EKS cluster.

After the command completes, you can double-check that the AWS resources have been deleted in the AWS Console:

When you access the AWS Console, always double-check that you selected the correct region in the top-right corner (e.g. London). If you are in the wrong region, you can't see the resources from another region.

If you want to work with your cluster again at a later time, you can repeat the same steps:

  1. Create a new cluster with eksctl
  2. Deploy your app
  3. Delete the cluster with eksctl

Conclusion

You've reached the end of this crash course on Kubernetes.

Let's recap what you achieved:

In the course of this, you learnt about many topics, including:

Where to go from here?

If you want to keep experimenting, you can create a Kubernetes cluster with a different provider, such as Google Kubernetes Engine (GKE) or Azure Kubernetes Service (AKS) and deploy your application there.

Since a Kubernetes deployment configuration, once defined, can be deployed to every Kubernetes cluster, you should have no troubles with that.

During this course, you covered several topics, but you didn't have the time to study them in-depth.

An obvious place to learn more about Kubernetes is in the official documentation where you can find more about fundamental concepts, everyday tasks, or even learn how to install Kubernetes from scratch.

Finally, the Learnk8s Academy offers a broad range of Kubernetes courses, similar to the one you completed.

These courses treat various topics in much more depth than this introductory course could provide.

The Learnk8s Academy courses can also prepare you for the Certified Kubernetes Administrator (CKA) and Certified Kubernetes Application Developer (CKAD) exams.

Happy navigating with Kubernetes!

That's all folks!

If you enjoyed this article, you might find the following articles interesting:

Be the first to be notified when a new article or Kubernetes experiment is published.

*We'll never share your email address, and you can opt-out at any time.

There are more articles like this!

Be notified every time we publish articles, insights and new research on Kubernetes!

You are in!

We're also maintain an active Telegram, Slack & Twitter community!