GKE - Google Kubernetes Engine

Introduction

Learning Objectives and Prerequisites

Before we explore Google Kubernetes Engine (GKE), let’s outline what you’ll achieve in this lesson and what foundation you need to get started. By the end of this guide, you’ll understand how to:

  • Architect and provision GKE clusters
  • Configure networking and integrate with Cloud services
  • Deploy applications, manage secrets, and optimize resource usage
  • Monitor, log, and perform cluster upgrades
  • Implement autoscaling, load balancing, and high availability

The image outlines learning objectives related to Google Kubernetes Engine (GKE), including creating a GKE cluster, configuring networking, and integrating with services. It features a diagram with a central "Objectives" label and a GKE icon.

Key Topics

  1. GKE Architecture & Cluster Creation
    Examine the control plane versus data plane components, and create clusters via the gcloud CLI or Cloud Console.

  2. Networking & Service Integration
    Configure VPCs, subnets, and firewall rules; integrate with Cloud Load Balancing, Cloud DNS, and other managed services.

  3. Application Deployment & Management
    Explore rolling updates, canary releases, Secrets, ConfigMaps, and best practices for resource requests and limits.

  4. Administration & Maintenance
    Use Cloud Monitoring for resource metrics, Cloud Logging for logs aggregation, and automate cluster upgrades.

The image is a diagram outlining learning objectives related to scaling and load balancing, specifically mentioning GKE's capabilities. It includes icons and a dotted path connecting different elements.

Scaling & Load Balancing

GKE provides multiple strategies to handle varying workloads and traffic patterns:

FeaturePurposeExample Command
Horizontal Pod Autoscaler (HPA)Scales pods based on CPU, memory, or custom metricskubectl autoscale deployment webapp --cpu-percent=75 --min=2 --max=10
Cluster AutoscalerAdjusts node pool size according to pending pod demandsgcloud container clusters update my-cluster --enable-autoscaling --min-nodes=1 --max-nodes=5 --node-pool default-pool
Cloud Load BalancingDistributes traffic globally or regionally across podsConfigure an HTTP(S) load balancer in the Console or with gcloud compute url-maps and backend services

You’ll learn how to tune HPA thresholds, set up Cluster Autoscaler policies, and integrate with Google Cloud’s global load balancing for optimal performance and cost efficiency.

The image is a slide titled "Delivery Framework" featuring a section on "Learning" with a brain icon and references to official GKE documentation, community forums, and GKE blogs.

Delivery Framework

This lesson is divided into modules, each covering a core GKE topic. To deepen your understanding, refer to:

Note

Hands-on practice is crucial. We recommend following along in a Google Cloud project with billing enabled and the Google Cloud SDK installed.

The image lists prerequisites for learning, including DevOps practices, Google Cloud Platform fundamentals, Kubernetes fundamentals, Unix CLI familiarity, and SRE practices.

Prerequisites

Ensure you have the following before starting:

  • Strong grasp of DevOps principles (CI/CD, containers, deployments)
  • Experience with Google Cloud Platform core services (Compute Engine, Networking)
  • Familiarity with Kubernetes basics (Pods, Deployments, Services)
  • Comfort using the Unix/Linux CLI and shell scripting
  • Understanding of SRE concepts such as SLIs, SLOs, and error budgets

Warning

Skipping these prerequisites may slow your progress when tackling advanced GKE features.

The image outlines key outcomes related to Google Kubernetes Engine (GKE), including creating and managing clusters, deploying applications, using features like autoscaling, and integrating with Google Cloud services.

Expected Outcomes

After completing this lesson, you will be able to:

  • Provision and manage GKE clusters via CLI and Console
  • Deploy, update, and observe containerized workloads
  • Configure autoscaling, load balancing, and centralized logging
  • Integrate applications with Google Cloud services like Pub/Sub and Cloud SQL
  • Design a secure, highly available, and cost-effective GKE environment

Armed with these skills, you’ll be ready to architect, deploy, and maintain production-grade Kubernetes clusters on Google Cloud. Let’s get started!

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