Demo: Getting Started with Kubernetes Engine

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From the course by Google Cloud

Google Cloud Platform Fundamentals: Core Infrastructure

13055 ratings

Google Cloud

Google Cloud Platform Fundamentals: Core Infrastructure

13055 ratings

Course 1 of 4 in the Specialization Architecting with Google Kubernetes Engine

This course introduces you to important concepts and terminology for working with Google Cloud Platform (GCP). You learn about, and compare, many of the computing and storage services available in Google Cloud Platform, including Google App Engine, Google Compute Engine, Google Kubernetes Engine, Google Cloud Storage, Google Cloud SQL, and BigQuery. You learn about important resource and policy management tools, such as the Google Cloud Resource Manager hierarchy and Google Cloud Identity and Access Management. Hands-on labs give you foundational skills for working with GCP. Note: •Google services are currently unavailable in China.

From the lesson

Containers in the Cloud

Containers are simple and interoperable, and they enable seamless, fine-grained scaling. Kubernetes is an orchestration layer for containers. Kubernetes Engine is Kubernetes as a service, a scalable managed offering that runs on Google’s infrastructure. You direct the creation of a cluster, and Kubernetes Engine schedules your containers into the cluster and manages them automatically, based on requirements you define. This module explains how Kubernetes Engine works and how it helps deploy applications in containers.

Demo and Lab Introduction0:28

Demo: Getting Started with Kubernetes Engine6:07

Meet the Instructors

Google Cloud Training

In this demonstration, I'll create

a Kubernetes Engine cluster and I'll deploy a load balance service to it.

I'll scale the service and we'll see what happens.

First, let's use the GCP console to confirm that the APIs we need are enabled.

In the Products and Services menu,

we scroll down to APIs and Services.

We're looking for the Kubernetes Engine API and the Container Registry API.

There's the Kubernetes Engine API, it's enabled.

There's Container Registry.

It's also enabled.

Now, we're ready to start a cluster.

For this activity, we'll use the command line in the Cloud Shell.

For convenience, I'm going to define an environment

variable that contains my preferred GCP zone.

Now, I'll launch a Kubernetes cluster in that zone.

The cluster will have two nodes.

Now, the cluster is ready.

Let's confirm what version of Kubernetes the cluster is running.

It's version 1.8.

When you launch a Kubernetes cluster,

you may see a newer version.

Remember that Kubernetes cluster nodes are Compute Engine virtual machines.

Let's go back to the GCP console and view them.

In the Products and Services menu,

scroll to Compute Engine and click on VM instances.

There are cluster nodes.

We can also view the cluster in the Kubernetes Engine console.

The console reports the cluster name,

its location and its size.

Now, let's return to Cloud Shell.

Let's run a web server in our cluster.

We've created a Kubernetes deployment called nginx.

The deployment consists of a single pod.

Let's confirm that it's running.

There's our pod. Now, let's

expose the deployment we created so that clients from outside Kubernetes can access it.

Now, let's view the new service.

It takes a moment for an external IP address to be assigned.

Now, an external IP address has been assigned.

Let's attempt to visit this IP address using a web browser.

We see the nginx home page.

Our web server running inside of a Kubernetes deployment is accessible from the Internet.

Now, let's scale up our deployment.

We would do this if load were rising.

We named the deployment and specified the new number of replicas.

Now, let's look at the new number of pods.

There are additional pods.

Now, they're in the running state.

Let's also confirm that the external IP address did not change.

The external IP address is the same.

Let's refresh the home page.

We've confirmed that the web server deployment continues to work.

In this lab, I created a Kubernetes Engine cluster.

I deployed a load balance service to it and we tried the scaling operation,

and we saw how seamlessly it worked.

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