# gcloud and kubectl for Google Kubernetes Engine - GSP821 ## Overview [Google Kubernetes Engine](https://cloud.google.com/kubernetes-engine) (GKE) provides a managed environment for deploying, managing, and scaling your containerized applications using Google infrastructure. The GKE environment consists of multiple machines (specifically [Compute Engine](https://cloud.google.com/compute) instances) grouped to form a [container cluster](https://cloud.google.com/kubernetes-engine/docs/concepts/cluster-architecture). In this lab, you get hands-on practice with container creation and application deployment with GKE. ### What you'll learn to do In this lab you will learn how to: * Deploy an application to the GKE cluster #### Cluster orchestration with Google Kubernetes Engine Google Kubernetes Engine (GKE) clusters are powered by the [Kubernetes](https://kubernetes.io/) open source cluster management system. Kubernetes provides the mechanisms through which you interact with your container cluster. You use Kubernetes commands and resources to deploy and manage your applications, perform administrative tasks, set policies, and monitor the health of your deployed workloads. Kubernetes draws on the same design principles that run popular Google services and provides the same benefits: automatic management, monitoring and liveness probes for application containers, automatic scaling, rolling updates, and more. When you run your applications on a container cluster, you're using technology based on Google's 10+ years of experience with running production workloads in containers. #### Kubernetes on Google Cloud When you run a GKE cluster, you also gain the benefit of advanced cluster management features that Google Cloud provides. These include: * [Load balancing](https://cloud.google.com/compute/docs/load-balancing-and-autoscaling) for Compute Engine instances * [Node pools](https://cloud.google.com/kubernetes-engine/docs/node-pools) to designate subsets of nodes within a cluster for additional flexibility * [Automatic scaling](https://cloud.google.com/kubernetes-engine/docs/cluster-autoscaler) of your cluster's node instance count * [Automatic upgrades](https://cloud.google.com/kubernetes-engine/docs/node-auto-upgrade) for your cluster's node software * [Node auto-repair](https://cloud.google.com/kubernetes-engine/docs/how-to/node-auto-repair) to maintain node health and availability * [Logging and Monitoring](https://cloud.google.com/kubernetes-engine/docs/how-to/logging) with Cloud Monitoring for visibility into your cluster Now that you have a basic understanding of Kubernetes, you will learn how to deploy a containerized application with GKE in less than 15 minutes. Follow the steps below to set up your lab environment. ## Setup and requirements * Labs are timed and cannot be paused. The timer starts when you click **Start Lab**. * The included cloud terminal is preconfigured with the gcloud SDK. * Use the terminal to execute commands and then click **Check my progress** to verify your work. **Pre-configured resource:** * You have a pre-created GKE cluster for this lab named **lab-cluster**. ## Task 1. Set a default compute zone Your [compute zone](https://cloud.google.com/compute/docs/regions-zones/#available) is an approximate regional location in which your clusters and their resources live. For example, `us-central1-a` is a zone in the `us-central1` region. In your cloud terminal, run the following commands. 1. Set the default compute region: ```apache gcloud config set compute/region "REGION" ``` **Expected output**: ```apache Updated property [compute/region]. ``` 2. Set the default compute zone: ```apache gcloud config set compute/zone "ZONE" ``` **Expected output**: ```apache Updated property [compute/zone]. ``` ## Task 2. Get authentication credentials for the GKE cluster You need authentication credentials to interact with your cluster. * Use the following `gcloud` command to retrieve the cluster credentials: ```apache gcloud container clusters get-credentials lab-cluster ``` **Expected output**: ```apache Fetching cluster endpoint and auth data. kubeconfig entry generated for lab-cluster. ``` This command is used to authenticate your `kubectl` client to a specific Google Kubernetes Engine (GKE) cluster so that you can interact with the cluster. This command fetches the cluster's credentials and updates your `kubectl` configuration with the necessary information. ## Task 3. Deploy an application to the GKE cluster You can now deploy a containerized application to the cluster. For this lab, you'll deploy `hello-app` on your cluster. GKE uses Kubernetes objects to create and manage your cluster's resources. Kubernetes provides the [Deployment](https://kubernetes.io/docs/concepts/workloads/controllers/deployment/) object for deploying stateless applications like web servers. [Service](https://kubernetes.io/docs/concepts/services-networking/service/) objects define rules and load balancing for accessing your application from the internet. 1. To create a new Deployment named `hello-server` from the `hello-app` container image, run the following `kubectl create` command: ```apache kubectl create deployment hello-server --image=gcr.io/google-samples/hello-app:1.0 ``` **Expected output**: ```apache deployment.apps/hello-server created ``` This Kubernetes command creates a deployment object that represents `hello-server`. In this case, `--image` specifies a container image to deploy. The command pulls the example image from a [Container Registry](https://cloud.google.com/container-registry/docs) bucket. `gcr.io/google-samples/hello-app:1.0` indicates the specific image version to pull. If a version is not specified, the latest version is used. Click **Check my progress** to verify the objective. Create a new Deployment: hello-server 2. To create a Kubernetes Service, which is a Kubernetes resource that lets you expose your application to external traffic, run the following `kubectl expose` command: ```apache kubectl expose deployment hello-server --type=LoadBalancer --port 8080 ``` In this command: * `--port` specifies the port that the container exposes. * `type="LoadBalancer"` creates a Compute Engine load balancer for your container. **Expected output**: ```apache service/hello-server exposed ``` 3. To inspect the `hello-server` Service, run `kubectl get`: ```apache kubectl get service ``` **Expected output**: ```apache NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hello-server loadBalancer 10.39.244.36 35.202.234.26 8080:31991/TCP 65s kubernetes ClusterIP 10.39.240.1 433/TCP 5m13s ``` **Note:** It might take a minute for an external IP address to be generated. Run the previous command again if the `EXTERNAL-IP` column status is **pending**. 4. To view the application from your web browser, open a new tab and enter the following address, replacing `[EXTERNAL IP]` with the `EXTERNAL-IP` for `hello-server`. ```apache http://[EXTERNAL-IP]:8080 ``` **Expected output**: The browser tab displays the message **Hello, world!** as well as the version and hostname. Click **Check my progress** to verify the objective. Create a Kubernetes Service --- ## Solution of Lab ### Quick ```apache curl -LO raw.githubusercontent.com/ePlus-DEV/storage/refs/heads/main/labs/GSP821/lab.sh source lab.sh ``` --- ### Manual %[https://youtu.be/ADauF_R5sl0]