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      How to Use a Remote Docker Server to Speed Up Your Workflow


      Introduction

      Building CPU-intensive images and binaries is a very slow and time-consuming process that can turn your laptop into a space heater at times. Pushing Docker images on a slow connection takes a long time, too. Luckily, there’s an easy fix for these issues. Docker lets you offload all those tasks to a remote server so your local machine doesn’t have to do that hard work.

      This feature was introduced in Docker 18.09. It brings support for connecting to a Docker host remotely via SSH. It requires very little configuration on the client, and only needs a regular Docker server without any special config running on a remote machine. Prior to Docker 18.09, you had to use Docker Machine to create a remote Docker server and then configure the local Docker environment to use it. This new method removes that additional complexity.

      In this tutorial, you’ll create a Droplet to host the remote Docker server and configure the docker command on your local machine to use it.

      Prerequisites

      To follow this tutorial, you’ll need:

      • A DigitalOcean account. You can create an account if you don’t have one already.
      • Docker installed on your local machine or development server. If you are working with Ubuntu 18.04, follow Steps 1 and 2 of How To Install and Use Docker on Ubuntu 18.04; otherwise, follow the official documentation for information about installing on other operating systems. Be sure to add your non-root user to the docker group, as described in Step 2 of the linked tutorial.

      Step 1 – Creating the Docker Host

      To get started, spin up a Droplet with a decent amount of processing power. The CPU Optimized plans are perfect for this purpose, but Standard ones work just as well. If you will be compiling resource-intensive programs, the CPU Optimized plans provide dedicated CPU cores which allow for faster builds. Otherwise, the Standard plans offer a more balanced CPU to RAM ratio.

      The Docker One-click image takes care of all of the setup for us. Follow this link to create a 16GB/8vCPU CPU-Optimized Droplet with Docker from the control panel.

      Alternatively, you can use doctl to create the Droplet from your local command line. To install it, follow the instructions in the doctl README file on GitHub.

      The following command creates a new 16GB/8vCPU CPU-Optimized Droplet in the FRA1 region based on the Docker One-click image:

      • doctl compute droplet create docker-host
      • --image docker-18-04
      • --region fra1
      • --size c-8
      • --wait
      • --ssh-keys $(doctl compute ssh-key list --format ID --no-header | sed 's/$/,/' | tr -d 'n' | sed 's/,$//')

      The doctl command uses the ssh-keys value to specify which SSH keys it should apply to your new Droplet. We use a subshell to call doctl compute ssh-key-list to retrieve the SSH keys associated with your DigitalOcean account, and then parse the results using the sed and tr commands to format the data in the correct format. This command includes all of your account’s SSH keys, but you can replace the highlighted subcommand with the fingerprint of any key you have in your account.

      Once the Droplet is created you’ll see its IP address among other details:

      Output

      ID Name Public IPv4 Private IPv4 Public IPv6 Memory VCPUs Disk Region Image Status Tags Features Volumes 148681562 docker-host your_server_ip 16384 8 100 fra1 Ubuntu Docker 5:18.09.6~3 on 18.04 active

      You can learn more about using the doctl command in the tutorial How To Use doctl, the Official DigitalOcean Command-Line Client.

      When the Droplet is created, you’ll have a ready to use Docker server. For security purposes, create a Linux user to use instead of root.

      First, connect to the Droplet with SSH as the root user:

      Once connected, add a new user. This command adds one named sammy:

      Then add the user to the docker group to give it permission to run commands on the Docker host.

      • sudo usermod -aG docker sammy

      Finally, exit from the remote server by typing exit.

      Now that the server is ready, let's configure the local docker command to use it.

      Step 2 – Configuring Docker to Use the Remote Host

      To use the remote host as your Docker host instead of your local machine, set the DOCKER_HOST environment variable to point to the remote host. This variable will instruct the Docker CLI client to connect to the remote server.

      • export DOCKER_HOST=ssh://sammy@your_server_ip

      Now any Docker command you run will be run on the Droplet. For example, if you start a web server container and expose a port, it will be run on the Droplet and will be accessible through the port you exposed on the Droplet's IP address.

      To verify that you're accessing the Droplet as the Docker host, run docker info.

      You will see your Droplet's hostname listed in the Name field like so:

      Output

      … Name: docker-host

      One thing to keep in mind is that when you run a docker build command, the build context (all files and folders accessible from the Dockerfile) will be sent to the host and then the build process will run. Depending on the size of the build context and the amount of files, it may take a longer time compared to building the image on a local machine. One solution would be to create a new directory dedicated to the Docker image and copy or link only the files that will be used in the image so that no unneeded files will be uploaded inadvertently.

      Conclusion

      You've created a remote Docker host and connected to it locally. The next time your laptop's battery is running low or you need to build a heavy Docker image, use your shiny remote Docker server instead of your local machine.

      You might also be interested in learning how to optimize Docker images for production, or how to optimize them specifically for Kubernetes.



      Source link

      How To Migrate a Docker Compose Workflow to Kubernetes


      Introduction

      When building modern, stateless applications, containerizing your application’s components is the first step in deploying and scaling on distributed platforms. If you have used Docker Compose in development, you will have modernized and containerized your application by:

      • Extracting necessary configuration information from your code.
      • Offloading your application’s state.
      • Packaging your application for repeated use.

      You will also have written service definitions that specify how your container images should run.

      To run your services on a distributed platform like Kubernetes, you will need to translate your Compose service definitions to Kubernetes objects. This will allow you to scale your application with resiliency. One tool that can speed up the translation process to Kubernetes is kompose, a conversion tool that helps developers move Compose workflows to container orchestrators like Kubernetes or OpenShift.

      In this tutorial, you will translate Compose services to Kubernetes objects using kompose. You will use the object definitions that kompose provides as a starting point and make adjustments to ensure that your setup will use Secrets, Services, and PersistentVolumeClaims in the way that Kubernetes expects. By the end of the tutorial, you will have a single-instance Node.js application with a MongoDB database running on a Kubernetes cluster. This setup will mirror the functionality of the code described in Containerizing a Node.js Application with Docker Compose and will be a good starting point to build out a production-ready solution that will scale with your needs.

      Prerequisites

      Step 1 — Installing kompose

      To begin using kompose, navigate to the project’s GitHub Releases page, and copy the link to the current release (version 1.18.0 as of this writing). Paste this link into the following curl command to download the latest version of kompose:

      • curl -L https://github.com/kubernetes/kompose/releases/download/v1.18.0/kompose-linux-amd64 -o kompose

      For details about installing on non-Linux systems, please refer to the installation instructions.

      Make the binary executable:

      Move it to your PATH:

      • sudo mv ./kompose /usr/local/bin/kompose

      To verify that it has been installed properly, you can do a version check:

      If the installation was successful, you will see output like the following:

      Output

      1.18.0 (06a2e56)

      With kompose installed and ready to use, you can now clone the Node.js project code that you will be translating to Kubernetes.

      Step 2 — Cloning and Packaging the Application

      To use our application with Kubernetes, we will need to clone the project code and package the application so that the kubelet service can pull the image.

      Our first step will be to clone the node-mongo-docker-dev repository from the DigitalOcean Community GitHub account. This repository includes the code from the setup described in Containerizing a Node.js Application for Development With Docker Compose, which uses a demo Node.js application to demonstrate how to set up a development environment using Docker Compose. You can find more information about the application itself in the series From Containers to Kubernetes with Node.js.

      Clone the repository into a directory called node_project:

      • git clone https://github.com/do-community/node-mongo-docker-dev.git node_project

      Navigate to the node_project directory:

      The node_project directory contains files and directories for a shark information application that works with user input. It has been modernized to work with containers: sensitive and specific configuration information has been removed from the application code and refactored to be injected at runtime, and the application's state has been offloaded to a MongoDB database.

      For more information about designing modern, stateless applications, please see Architecting Applications for Kubernetes and Modernizing Applications for Kubernetes.

      The project directory includes a Dockerfile with instructions for building the application image. Let's build the image now so that you can push it to your Docker Hub account and use it in your Kubernetes setup.

      Using the docker build command, build the image with the -t flag, which allows you to tag it with a memorable name. In this case, tag the image with your Docker Hub username and name it node-kubernetes or a name of your own choosing:

      • docker build -t your_dockerhub_username/node-kubernetes .

      The . in the command specifies that the build context is the current directory.

      It will take a minute or two to build the image. Once it is complete, check your images:

      You will see the following output:

      Output

      REPOSITORY TAG IMAGE ID CREATED SIZE your_dockerhub_username/node-kubernetes latest 9c6f897e1fbc 3 seconds ago 90MB node 10-alpine 94f3c8956482 12 days ago 71MB

      Next, log in to the Docker Hub account you created in the prerequisites:

      • docker login -u your_dockerhub_username

      When prompted, enter your Docker Hub account password. Logging in this way will create a ~/.docker/config.json file in your user's home directory with your Docker Hub credentials.

      Push the application image to Docker Hub with the docker push command. Remember to replace your_dockerhub_username with your own Docker Hub username:

      • docker push your_dockerhub_username/node-kubernetes

      You now have an application image that you can pull to run your application with Kubernetes. The next step will be to translate your application service definitions to Kubernetes objects.

      Step 3 — Translating Compose Services to Kubernetes Objects with kompose

      Our Docker Compose file, here called docker-compose.yaml, lays out the definitions that will run our services with Compose. A service in Compose is a running container, and service definitions contain information about how each container image will run. In this step, we will translate these definitions to Kubernetes objects by using kompose to create yaml files. These files will contain specs for the Kubernetes objects that describe their desired state.

      We will use these files to create different types of objects: Services, which will ensure that the Pods running our containers remain accessible; Deployments, which will contain information about the desired state of our Pods; a PersistentVolumeClaim to provision storage for our database data; a ConfigMap for environment variables injected at runtime; and a Secret for our application's database user and password. Some of these definitions will be in the files kompose will create for us, and others we will need to create ourselves.

      First, we will need to modify some of the definitions in our docker-compose.yaml file to work with Kubernetes. We will include a reference to our newly-built application image in our nodejs service definition and remove the bind mounts, volumes, and additional commands that we used to run the application container in development with Compose. Additionally, we'll redefine both containers' restart policies to be in line with the behavior Kubernetes expects.

      Open the file with nano or your favorite editor:

      The current definition for the nodejs application service looks like this:

      ~/node_project/docker-compose.yaml

      ...
      services:
        nodejs:
          build:
            context: .
            dockerfile: Dockerfile
          image: nodejs
          container_name: nodejs
          restart: unless-stopped
          env_file: .env
          environment:
            - MONGO_USERNAME=$MONGO_USERNAME
            - MONGO_PASSWORD=$MONGO_PASSWORD
            - MONGO_HOSTNAME=db
            - MONGO_PORT=$MONGO_PORT
            - MONGO_DB=$MONGO_DB 
          ports:
            - "80:8080"
          volumes:
            - .:/home/node/app
            - node_modules:/home/node/app/node_modules
          networks:
            - app-network
          command: ./wait-for.sh db:27017 -- /home/node/app/node_modules/.bin/nodemon app.js
      ...
      

      Make the following edits to your service definition:

      • Use your node-kubernetes image instead of the local Dockerfile.
      • Change the container restart policy from unless-stopped to always.
      • Remove the volumes list and the command instruction.

      The finished service definition will now look like this:

      ~/node_project/docker-compose.yaml

      ...
      services:
        nodejs:
          image: your_dockerhub_username/node-kubernetes
          container_name: nodejs
          restart: always
          env_file: .env
          environment:
            - MONGO_USERNAME=$MONGO_USERNAME
            - MONGO_PASSWORD=$MONGO_PASSWORD
            - MONGO_HOSTNAME=db
            - MONGO_PORT=$MONGO_PORT
            - MONGO_DB=$MONGO_DB 
          ports:
            - "80:8080"
          networks:
            - app-network
      ...
      

      Next, scroll down to the db service definition. Here, change the restart policy for the service to always and remove the .env file. Instead of using values from the .env file, we will pass the values for our MONGO_INITDB_ROOT_USERNAME and MONGO_INITDB_ROOT_PASSWORD to the database container using the Secret we will create in Step 4.

      The db service definition will now look like this:

      ~/node_project/docker-compose.yaml

      ...
        db:
          image: mongo:4.1.8-xenial
          container_name: db
          restart: always
          environment:
            - MONGO_INITDB_ROOT_USERNAME=$MONGO_USERNAME
            - MONGO_INITDB_ROOT_PASSWORD=$MONGO_PASSWORD
          volumes:  
            - dbdata:/data/db   
          networks:
            - app-network
      ...  
      

      Finally, at the bottom of the file, remove the node_modules volumes from the top-level volumes key. The key will now look like this:

      ~/node_project/docker-compose.yaml

      ...
      volumes:
        dbdata:
      

      Save and close the file when you are finished editing.

      Before translating our service definitions, we will need to write the .env file that kompose will use to create the ConfigMap with our non-sensitive information. Please see Step 2 of Containerizing a Node.js Application for Development With Docker Compose for a longer explanation of this file.

      In that tutorial, we added .env to our .gitignore file to ensure that it would not copy to version control. This means that it did not copy over when we cloned the node-mongo-docker-dev repository in Step 2 of this tutorial. We will therefore need to recreate it now.

      Create the file:

      kompose will use this file to create a ConfigMap for our application. However, instead of assigning all of the variables from the nodejs service definition in our Compose file, we will add only the MONGO_DB database name and the MONGO_PORT. We will assign the database username and password separately when we manually create a Secret object in Step 4.

      Add the following port and database name information to the .env file. Feel free to rename your database if you would like:

      ~/node_project/.env

      MONGO_PORT=27017
      MONGO_DB=sharkinfo
      

      Save and close the file when you are finished editing.

      You are now ready to create the files with your object specs. kompose offers multiple options for translating your resources. You can:

      • Create yaml files based on the service definitions in your docker-compose.yaml file with kompose convert.
      • Create Kubernetes objects directly with kompose up.
      • Create a Helm chart with kompose convert -c.

      For now, we will convert our service definitions to yaml files and then add to and revise the files kompose creates.

      Convert your service definitions to yaml files with the following command:

      You can also name specific or multiple Compose files using the -f flag.

      After you run this command, kompose will output information about the files it has created:

      Output

      INFO Kubernetes file "nodejs-service.yaml" created INFO Kubernetes file "db-deployment.yaml" created INFO Kubernetes file "dbdata-persistentvolumeclaim.yaml" created INFO Kubernetes file "nodejs-deployment.yaml" created INFO Kubernetes file "nodejs-env-configmap.yaml" created

      These include yaml files with specs for the Node application Service, Deployment, and ConfigMap, as well as for the dbdata PersistentVolumeClaim and MongoDB database Deployment.

      These files are a good starting point, but in order for our application's functionality to match the setup described in Containerizing a Node.js Application for Development With Docker Compose we will need to make a few additions and changes to the files kompose has generated.

      Step 4 — Creating Kubernetes Secrets

      In order for our application to function in the way we expect, we will need to make a few modifications to the files that kompose has created. The first of these changes will be generating a Secret for our database user and password and adding it to our application and database Deployments. Kubernetes offers two ways of working with environment variables: ConfigMaps and Secrets. kompose has already created a ConfigMap with the non-confidential information we included in our .env file, so we will now create a Secret with our confidential information: our database username and password.

      The first step in manually creating a Secret will be to convert your username and password to base64, an encoding scheme that allows you to uniformly transmit data, including binary data.

      Convert your database username:

      • echo -n 'your_database_username' | base64

      Note down the value you see in the output.

      Next, convert your password:

      • echo -n 'your_database_password' | base64

      Take note of the value in the output here as well.

      Open a file for the Secret:

      Note: Kubernetes objects are typically defined using YAML, which strictly forbids tabs and requires two spaces for indentation. If you would like to check the formatting of any of your yaml files, you can use a linter or test the validity of your syntax using kubectl create with the --dry-run and --validate flags:

      • kubectl create -f your_yaml_file.yaml --dry-run --validate=true

      In general, it is a good idea to validate your syntax before creating resources with kubectl.

      Add the following code to the file to create a Secret that will define your MONGO_USERNAME and MONGO_PASSWORD using the encoded values you just created. Be sure to replace the dummy values here with your encoded username and password:

      ~/node_project/secret.yaml

      apiVersion: v1
      kind: Secret
      metadata:
        name: mongo-secret
      data:
        MONGO_USERNAME: your_encoded_username
        MONGO_PASSWORD: your_encoded_password
      

      We have named the Secret object mongo-secret, but you are free to name it anything you would like.

      Save and close this file when you are finished editing. As you did with your .env file, be sure to add secret.yaml to your .gitignore file to keep it out of version control.

      With secret.yaml written, our next step will be to ensure that our application and database Pods both use the values we added to the file. Let's start by adding references to the Secret to our application Deployment.

      Open the file called nodejs-deployment.yaml:

      • nano nodejs-deployment.yaml

      The file's container specifications include the following environment variables defined under the env key:

      ~/node_project/nodejs-deployment.yaml

      apiVersion: extensions/v1beta1
      kind: Deployment
      ...
          spec:
            containers:
            - env:
              - name: MONGO_DB
                valueFrom:
                  configMapKeyRef:
                    key: MONGO_DB
                    name: nodejs-env
              - name: MONGO_HOSTNAME
                value: db
              - name: MONGO_PASSWORD
              - name: MONGO_PORT
                valueFrom:
                  configMapKeyRef:
                    key: MONGO_PORT
                    name: nodejs-env
              - name: MONGO_USERNAME
      

      We will need to add references to our Secret to the MONGO_USERNAME and MONGO_PASSWORD variables listed here, so that our application will have access to those values. Instead of including a configMapKeyRef key to point to our nodejs-env ConfigMap, as is the case with the values for MONGO_DB and MONGO_PORT, we'll include a secretKeyRef key to point to the values in our mongo-secret secret.

      Add the following Secret references to the MONGO_USERNAME and MONGO_PASSWORD variables:

      ~/node_project/nodejs-deployment.yaml

      apiVersion: extensions/v1beta1
      kind: Deployment
      ...
          spec:
            containers:
            - env:
              - name: MONGO_DB
                valueFrom:
                  configMapKeyRef:
                    key: MONGO_DB
                    name: nodejs-env
              - name: MONGO_HOSTNAME
                value: db
              - name: MONGO_PASSWORD
                valueFrom:
                  secretKeyRef:
                    name: mongo-secret
                    key: MONGO_PASSWORD
              - name: MONGO_PORT
                valueFrom:
                  configMapKeyRef:
                    key: MONGO_PORT
                    name: nodejs-env
              - name: MONGO_USERNAME
                valueFrom:
                  secretKeyRef:
                    name: mongo-secret
                    key: MONGO_USERNAME
      

      Save and close the file when you are finished editing.

      Next, we'll add the same values to the db-deployment.yaml file.

      Open the file for editing:

      In this file, we will add references to our Secret for following variable keys: MONGO_INITDB_ROOT_USERNAME and MONGO_INITDB_ROOT_PASSWORD. The mongo image makes these variables available so that you can modify the initialization of your database instance. MONGO_INITDB_ROOT_USERNAME and MONGO_INITDB_ROOT_PASSWORD together create a root user in the admin authentication database and ensure that authentication is enabled when the database container starts.

      Using the values we set in our Secret ensures that we will have an application user with root privileges on the database instance, with access to all of the administrative and operational privileges of that role. When working in production, you will want to create a dedicated application user with appropriately scoped privileges.

      Under the MONGO_INITDB_ROOT_USERNAME and MONGO_INITDB_ROOT_PASSWORD variables, add references to the Secret values:

      ~/node_project/db-deployment.yaml

      apiVersion: extensions/v1beta1
      kind: Deployment
      ...
          spec:
            containers:
            - env:
              - name: MONGO_INITDB_ROOT_PASSWORD
                valueFrom:
                  secretKeyRef:
                    name: mongo-secret
                    key: MONGO_PASSWORD        
              - name: MONGO_INITDB_ROOT_USERNAME
                valueFrom:
                  secretKeyRef:
                    name: mongo-secret
                    key: MONGO_USERNAME
              image: mongo:4.1.8-xenial
      ...
      

      Save and close the file when you are finished editing.

      With your Secret in place, you can move on to creating your database Service and ensuring that your application container only attempts to connect to the database once it is fully set up and initialized.

      Step 5 — Creating the Database Service and an Application Init Container

      Now that we have our Secret, we can move on to creating our database Service and an Init Container that will poll this Service to ensure that our application only attempts to connect to the database once the database startup tasks, including creating the MONGO_INITDB user and password, are complete.

      For a discussion of how to implement this functionality in Compose, please see Step 4 of Containerizing a Node.js Application for Development with Docker Compose.

      Open a file to define the specs for the database Service:

      Add the following code to the file to define the Service:

      ~/node_project/db-service.yaml

      apiVersion: v1
      kind: Service
      metadata:
        annotations: 
          kompose.cmd: kompose convert
          kompose.version: 1.18.0 (06a2e56)
        creationTimestamp: null
        labels:
          io.kompose.service: db
        name: db
      spec:
        ports:
        - port: 27017
          targetPort: 27017
        selector:
          io.kompose.service: db
      status:
        loadBalancer: {}
      

      The selector that we have included here will match this Service object with our database Pods, which have been defined with the label io.kompose.service: db by kompose in the db-deployment.yaml file. We've also named this service db.

      Save and close the file when you are finished editing.

      Next, let's add an Init Container field to the containers array in nodejs-deployment.yaml. This will create an Init Container that we can use to delay our application container from starting until the db Service has been created with a Pod that is reachable. This is one of the possible uses for Init Containers; to learn more about other use cases, please see the official documentation.

      Open the nodejs-deployment.yaml file:

      • nano nodejs-deployment.yaml

      Within the Pod spec and alongside the containers array, we are going to add an initContainers field with a container that will poll the db Service.

      Add the following code below the ports and resources fields and above the restartPolicy in the nodejs containers array:

      ~/node_project/nodejs-deployment.yaml

      apiVersion: extensions/v1beta1
      kind: Deployment
      ...
          spec:
            containers:
            ...
              name: nodejs
              ports:
              - containerPort: 8080
              resources: {}
            initContainers:
            - name: init-db
              image: busybox
              command: ['sh', '-c', 'until nc -z db:27017; do echo waiting for db; sleep 2; done;']
            restartPolicy: Always
      ...               
      

      This Init Container uses the BusyBox image, a lightweight image that includes many UNIX utilities. In this case, we'll use the netcat utility to poll whether or not the Pod associated with the db Service is accepting TCP connections on port 27017.

      This container command replicates the functionality of the wait-for script that we removed from our docker-compose.yaml file in Step 3. For a longer discussion of how and why our application used the wait-for script when working with Compose, please see Step 4 of Containerizing a Node.js Application for Development with Docker Compose.

      Init Containers run to completion; in our case, this means that our Node application container will not start until the database container is running and accepting connections on port 27017. The db Service definition allows us to guarantee this functionality regardless of the exact location of the database container, which is mutable.

      Save and close the file when you are finished editing.

      With your database Service created and your Init Container in place to control the startup order of your containers, you can move on to checking the storage requirements in your PersistentVolumeClaim and exposing your application service using a LoadBalancer.

      Step 6 — Modifying the PersistentVolumeClaim and Exposing the Application Frontend

      Before running our application, we will make two final changes to ensure that our database storage will be provisioned properly and that we can expose our application frontend using a LoadBalancer.

      First, let's modify the storage resource defined in the PersistentVolumeClaim that kompose created for us. This Claim allows us to dynamically provision storage to manage our application's state.

      To work with PersistentVolumeClaims, you must have a StorageClass created and configured to provision storage resources. In our case, because we are working with DigitalOcean Kubernetes, our default StorageClass provisioner is set to dobs.csi.digitalocean.com — DigitalOcean Block Storage.

      We can check this by typing:

      If you are working with a DigitalOcean cluster, you will see the following output:

      Output

      NAME PROVISIONER AGE do-block-storage (default) dobs.csi.digitalocean.com 76m

      If you are not working with a DigitalOcean cluster, you will need to create a StorageClass and configure a provisioner of your choice. For details about how to do this, please see the official documentation.

      When kompose created dbdata-persistentvolumeclaim.yaml, it set the storage resource to a size that does not meet the minimum size requirements of our provisioner. We will therefore need to modify our PersistentVolumeClaim to use the minimum viable DigitalOcean Block Storage unit: 1GB. Please feel free to modify this to meet your storage requirements.

      Open dbdata-persistentvolumeclaim.yaml:

      • nano dbdata-persistentvolumeclaim.yaml

      Replace the storage value with 1Gi:

      ~/node_project/dbdata-persistentvolumeclaim.yaml

      apiVersion: v1
      kind: PersistentVolumeClaim
      metadata:
        creationTimestamp: null
        labels:
          io.kompose.service: dbdata
        name: dbdata
      spec:
        accessModes:
        - ReadWriteOnce
        resources:
          requests:
            storage: 1Gi
      status: {}
      

      Also note the accessMode: ReadWriteOnce means that the volume provisioned as a result of this Claim will be read-write only by a single node. Please see the documentation for more information about different access modes.

      Save and close the file when you are finished.

      Next, open nodejs-service.yaml:

      We are going to expose this Service externally using a DigitalOcean Load Balancer. If you are not using a DigitalOcean cluster, please consult the relevant documentation from your cloud provider for information about their load balancers. Alternatively, you can follow the official Kubernetes documentation on setting up a highly available cluster with kubeadm, but in this case you will not be able to use PersistentVolumeClaims to provision storage.

      Within the Service spec, specify LoadBalancer as the Service type:

      ~/node_project/nodejs-service.yaml

      apiVersion: v1
      kind: Service
      ...
      spec:
        type: LoadBalancer
        ports:
      ...
      

      When we create the nodejs Service, a load balancer will be automatically created, providing us with an external IP where we can access our application.

      Save and close the file when you are finished editing.

      With all of our files in place, we are ready to start and test the application.

      Step 7 — Starting and Accessing the Application

      It's time to create our Kubernetes objects and test that our application is working as expected.

      To create the objects we've defined, we'll use kubectl create with the -f flag, which will allow us to specify the files that kompose created for us, along with the files we wrote. Run the following command to create the Node application and MongoDB database Services and Deployments, along with your Secret, ConfigMap, and PersistentVolumeClaim:

      • kubectl create -f nodejs-service.yaml,nodejs-deployment.yaml,nodejs-env-configmap.yaml,db-service.yaml,db-deployment.yaml,dbdata-persistentvolumeclaim.yaml,secret.yaml

      You will see the following output indicating that the objects have been created:

      Output

      service/nodejs created deployment.extensions/nodejs created configmap/nodejs-env created service/db created deployment.extensions/db created persistentvolumeclaim/dbdata created secret/mongo-secret created

      To check that your Pods are running, type:

      You don't need to specify a Namespace here, since we have created our objects in the default Namespace. If you are working with multiple Namespaces, be sure to include the -n flag when running this command, along with the name of your Namespace.

      You will see the following output while your db container is starting and your application Init Container is running:

      Output

      NAME READY STATUS RESTARTS AGE db-679d658576-kfpsl 0/1 ContainerCreating 0 10s nodejs-6b9585dc8b-pnsws 0/1 Init:0/1 0 10s

      Once that container has run and your application and database containers have started, you will see this output:

      Output

      NAME READY STATUS RESTARTS AGE db-679d658576-kfpsl 1/1 Running 0 54s nodejs-6b9585dc8b-pnsws 1/1 Running 0 54s

      The Running STATUS indicates that your Pods are bound to nodes and that the containers associated with those Pods are running. READY indicates how many containers in a Pod are running. For more information, please consult the documentation on Pod lifecycles.

      Note:
      If you see unexpected phases in the STATUS column, remember that you can troubleshoot your Pods with the following commands:

      • kubectl describe pods your_pod
      • kubectl logs your_pod

      With your containers running, you can now access the application. To get the IP for the LoadBalancer, type:

      You will see the following output:

      Output

      NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE db ClusterIP 10.245.189.250 <none> 27017/TCP 93s kubernetes ClusterIP 10.245.0.1 <none> 443/TCP 25m12s nodejs LoadBalancer 10.245.15.56 your_lb_ip 80:30729/TCP 93s

      The EXTERNAL_IP associated with the nodejs service is the IP address where you can access the application. If you see a <pending> status in the EXTERNAL_IP column, this means that your load balancer is still being created.

      Once you see an IP in that column, navigate to it in your browser: http://your_lb_ip.

      You should see the following landing page:

      Application Landing Page

      Click on the Get Shark Info button. You will see a page with an entry form where you can enter a shark name and a description of that shark's general character:

      Shark Info Form

      In the form, add a shark of your choosing. To demonstrate, we will add Megalodon Shark to the Shark Name field, and Ancient to the Shark Character field:

      Filled Shark Form

      Click on the Submit button. You will see a page with this shark information displayed back to you:

      Shark Output

      You now have a single instance setup of a Node.js application with a MongoDB database running on a Kubernetes cluster.

      Conclusion

      The files you have created in this tutorial are a good starting point to build from as you move toward production. As you develop your application, you can work on implementing the following:



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