Edit on GitHub
Report issue
Page history

Author(s): @{[ username ]}  Published: {[ TutorialCtrl.tutorial.date_published | date:'mediumDate' ]}

title: Run a Kotlin Spring Boot application on Google Kubernetes Engine description: Learn how to deploy a Kotlin Spring Boot application in containers using Kubernetes Engine. author: hhariri tags: Kubernetes, Container Engine, Kotlin, Spring Boot, Docker date_published: 2018-01-05


This tutorial helps you get started deploying your Kotlin app using the Spring Boot Framework to Google Kubernetes Engine, Google's hosting solution for containerized applications. Kubernetes Engine, earlier known as Google Container Engine, is based on the popular open-source Kubernetes system, and leverages Google's deep expertise with container-based deployments.

You will create a new Spring Boot application, and then you will learn how to:

  • Create a Docker image file that will be used to build and run your app
  • Deploy your app on Kubernetes Engine
  • Scale and update your app using Kubernetes

While the tutorial uses Kotlin 1.2 and Spring Boot 2 M7, other releases of Kotlin and Spring Boot should work without any modifications (other than version numbers in Gradle files). This tutorial does assume you're familiar with Spring Boot and creating web applications. For simplicity the tutorial responds with JSON to a specific HTTP request, but can be built-on to connect to other Google services and/or databases.

Before you begin

Before running this tutorial, you must set up a Google Cloud Platform project, and you need to have Docker and the Google Cloud SDK installed.

Create a project that will host your Spring Boot application. You can also reuse an existing project.

  1. Use the Google Cloud Platform Console to create a new Cloud Platform project. Remember the project ID; you will need it later. Later commands in this tutorial will use ${PROJECT_ID} as a substitution, so you might consider setting the PROJECT_ID environment variable in your shell.

  2. Enable billing for your project.

  3. Go to the API Library in the Cloud Console. Use it to enable the following APIs:

    • Google Cloud Container Builder API
    • Google Container Engine API

Perform the installations:

  1. Install Docker 17.05 or later if you do not already have it. Find instructions on the Docker website.

  2. Install the Google Cloud SDK if you do not already have it. Make sure you initialize the SDK and set the default project to the new project you created.

  3. Install the Kubernetes component of the Google Cloud SDK:

    gcloud components install kubectl
    
  4. Install JDK 8 or higher if you do not already have it.

Creating a new app and running it locally

In this section, you will create a new Spring Boot app and make sure it runs. If you already have an app to deploy, you can use it instead.

  1. Use start.spring.io to generate a Spring Boot application using Kotlin as the language, Gradle as the build system. Alternatively, you can download the sample application.

  2. Download the generated project and save it to a local folder.

  3. Open the resulting project in your favourite IDE or editor and create a new source file named MessageController.kt with the following contents:

    package com.jetbrains.demo
    
    import org.springframework.web.bind.annotation.*
    
    data class Message(val text: String, val priority: String)
    
    @RestController
    class MessageController {
        @RequestMapping("/message")
        fun message(): Message {
            return Message("Hello from Google Cloud", "High")
        }
    }
    

The package should match that of your group and artifact name.

  1. Make sure you have the right dependencies in your Gradle file to import RestController:

    compile("org.springframework.boot:spring-boot-starter-web")
    
  2. Run the application from the command line using Gradle:

    gradle bootRun
    

    Note: The gradle bootRun is a quick way to build and run the application. Later on when creating the Docker image, you'll need to first build the app using the Gradle build task and then run it.

  3. Open the browser and make sure your get a valid JSON response when accessing http://localhost:8080/message. The result should be:

    {
        "text": "Hello from Google Cloud",
        "priority": "High"
    }
    

Dockerizing your application

The next step is to produce a Docker image that builds and runs your application in a Docker container. You will define this image using a Dockerfile.

Creating a Dockerfile

Various considerations go into designing a good Docker image. The Dockerfile used by this tutorial builds a release and runs it with Alpine Linux. If you are experienced with Docker, you can customize your image.

  1. Create a file called Dockerfile in your project directory and copy the following content into it. Alternately, you can download the Dockerfile to study and customize.

    FROM openjdk:8-jdk-alpine
    VOLUME /tmp
    RUN mkdir /work
    COPY . /work
    WORKDIR /work
    RUN /work/gradlew build
    RUN mv /work/build/libs/*.jar /work/app.jar
    ENTRYPOINT ["java","-Djava.security.egd=file:/dev/./urandom","-jar","/work/app.jar"]
    
  2. Create a file called .dockerignore in your project directory and copy the following content into it. Alternately, you can download the Dockerfile file to study and customize.

    .gradle
    build
    out
    

The Dockerfile performs a series of steps.

  1. It creates a work folder where sources will be copied to and built from.
  2. It then copies the sources from the local file system (which should be your project folder) to the container.
  3. It sets the working directory to the work folder created and then runs the Gradle wrapper command (gradlew) to build the project. This wrapper automatically downloads Gradle and then builds the project.
  4. Once built, it then copies the output of the jar to the work folder ready for execution when invoking docker run.

Test the Dockerfile

Build the image locally and test running your application from the image:

docker build --no-cache -t demo .
docker run -it --rm -p 8080:8080 demo

The period at the end of the docker build command is required. It denotes the root directory of the application you are building.

Visit http://localhost:8080/message to see the application respond running locally from your Docker image.

Deploying your application

Now you're ready to deploy your application to Kubernetes Engine!

Build the production image

To deploy the app, you will use Google Cloud Container Build service to build your Docker image in the cloud and store the resulting Docker image in your project in Google Cloud Container Registry.

Execute the following command to run the build:

gcloud container builds submit --tag=gcr.io/${PROJECT_ID}/demo:v1 .

Replace ${PROJECT_ID} with the ID of your Google Cloud Platform project. The period at the end is required.

After the build finishes, the image gcr.io/${PROJECT_ID}/demo:v1 is available. You can list the images you have built in your project using:

gcloud container images list

You can even push and pull the image directly from your registry. See the Container Registry how-to guides for more details.

Create a cluster

Kubernetes Engine lets you create Kubernetes clusters to host your application. These are clusters of VMs in the cloud, managed by a Kubernetes server.

  1. Choose a cluster name. For the rest of these instructions, I'll assume that name is "demo-cluster".

  2. Create the cluster.

    gcloud container clusters create demo-cluster --num-nodes=2
    

    This command creates a cluster of two machines. You can choose a different size, but two is a good starting point.

    It might take several minutes for the cluster to be created. You can check the cloud console at http://cloud.google.com/console, under the Kubernetes Engine section, to see that your cluster is running. You will also be able to see the individual running VMs under the Compute Engine section. Note that once the cluster is running, you will be charged for the VM usage.

  3. Configure the gcloud command-line tool to use your cluster by default, so you don't have to specify it every time for the remaining gcloud commands.

    gcloud config set container/cluster demo-cluster
    

    Replace the name if you named your cluster differently.

Deploy to the cluster

A production deployment comprises two parts: your Docker container, and a front-end load balancer (which also provides a public IP address.)

We'll assume that you built the image to gcr.io/${PROJECT_ID}/demo:v1 and you've created the Kubernetes cluster as described above.

  1. Create a deployment:

    kubectl run demo --image=gcr.io/${PROJECT_ID}/demo:v1 --port 8080
    

    This runs your image on a Kubernetes pod, which is the deployable unit in Kubernetes. The pod opens port 8080, which is the port your Spring Boot application is listening on.

    You can view the running pods using:

    kubectl get pods
    
  2. Expose the application by creating a load balancer pointing at your pod:

    kubectl expose deployment demo --type=LoadBalancer --port 80 --target-port 8080
    

    This creates a service resource pointing at your running pod. It listens on the standard HTTP port 80, and proxies back to your pod on port 8080.

  3. Obtain the IP address of the service by running:

    kubectl get service
    

    Initially, the external IP field will be pending while Kubernetes Engine procures an IP address for you. If you rerun the kubectl get service command repeatedly, eventually the IP address will appear. You can then point your browser at that URL to view the running application.

Congratulations! Your application is now up and running!

Scaling and updating your application

You'll now explore a few of the basic features of Kubernetes for managing your running app.

Set the replica count

Initially your deployment runs a single instance of your application. You can add more replicas using the kubectl scale command. For example, to add two additional replicas (for a total of three), run:

kubectl scale deployment demo --replicas=3

Once the additional replicas are running, you can see the list of three pods by running:

kubectl get pods

Kubernetes automatically allocates your running pods on the virtual machines in your cluster. You can configure pods in your deployment with specific resource requirements such as memory and CPU. See the Kubernetes documentation for more details.

Update your application

After you make a change to your app, redeploying is just a matter of building a new image and pointing your deployment to it.

  1. Make a change to the app. (For example, modify the front page template.)

  2. Perform a new build with a new version tag "v2":

    gcloud container builds submit --tag=gcr.io/${PROJECT_ID}/demo:v2 .
    

    Now you have two builds stored in your project, demo:v1 and demo:v2. In general it's good practice to set the image tag for each build to a unique build number. This will let you identify and deploy any build, making updates and rollbacks easy.

  3. Set the deployment to use the new image:

    kubectl set image deployment/demo demo=gcr.io/${PROJECT_ID}/demo:v2
    

    This performs a rolling update of all the running pods.

  4. You can roll back to the earlier build by calling kubectl set image again, specifying the earlier build tag.

    kubectl set image deployment/demo demo=gcr.io/${PROJECT_ID}/demo:v1
    

Note: If a deployment gets stuck because an error in the image prevents it from starting successfuly, you can recover by undoing the rollout. See the Kubernetes deployment documentation for more info.

Clean up

After you've finished this tutorial, clean up the resources you created on Google Cloud Platform so you won't be billed for them going forward. To clean, either delete your Kubernetes Engine resources, or delete the entire project.

Deleting Kubernetes Engine resources

To delete your app from Kubernetes Engine, you must remove both the load balancer and the Kubernetes Engine cluster.

  1. Delete the service, which deallocates the load balancer:

    kubectl delete service demo
    
  2. The load balancer will be deleted asynchronously. Wait for that process to complete by monitoring the output of:

    gcloud compute forwarding-rules list
    

    The forwarding rule will disappear when the load balancer is deleted.

  3. Delete the cluster, which deletes the resources used by the cluster, including virtual machines, disks, and network resources:

    gcloud container clusters delete demo-cluster
    

Deleting the project

Alternately, you can delete the project in its entirety. To do so using the gcloud tool, run:

gcloud projects delete ${PROJECT_ID}

where ${PROJECT_ID} is your Google Cloud Platform project ID.

Warning: Deleting a project has the following consequences:

If you used an existing project, you'll also delete any other work you've done in the project. You can't reuse the project ID of a deleted project. If you created a custom project ID that you plan to use in the future, you should delete the resources inside the project instead. This ensures that URLs that use the project ID, such as an appspot.com URL, remain available.

Next steps

If you want to procure a static IP address and connect your domain name, you might find this tutorial helpful.

See the Kubernetes Engine documentation for more information on managing Kubernetes Engine clusters.

See the Kubernetes documentation for more information on managing your application deployment using Kubernetes.

See more by @{[ username ]} and more tagged {[ tag ]}{[ $last ? '' : ', ' ]}

Submit a Tutorial

Share step-by-step guides

SUBMIT A TUTORIAL

Request a Tutorial

Ask for community help

SUBMIT A REQUEST

GCP Tutorials

Tutorials published by GCP

VIEW TUTORIALS

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see our Site Policies. Java is a registered trademark of Oracle and/or its affiliates.