Advanced Kubernetes Techniques: Monitoring, Logging, Auto-Scaling

Course Overview

The world of DevOps has exploded with the help of technologies like Kubernetes and Docker. In this course, you will learn about more advanced Kubernetes features such as monitoring, alerting, logging, and scaling - crucial concepts for any DevOps professional. You will learn how to monitor clusters, how to send alerts when potential issues arise, and how to query the whole system’s met... See more

How You'll Learn

Faster than videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

Progress you can show

Built in assessments let you test your skills. Completion certificates let you show them off.

Faster than videos

Videos are holding you back. The average video tutorial is spoken at 150 words per minute, while you can read at 250. That‘s why our courses are text-based.

Progress you can show

Built in assessments let you test your skills. Completion certificates let you show them off.

Course Contents

1. Before Getting Started

Introduction

Intended Audience and Course Requirements

2. Autoscaling Deployments and StatefulSets

Introduction

Create a Cluster

Install Metrics Server

Observe Metrics Server Data

Get Started with Auto-Scaling Pods

Auto-Scale Pods Based on Resource Utilization

Specify Replicas in Deployments or Statefulsets?

Revise and Test the Concepts

3. Auto-Scaling Nodes Of A Kubernetes Cluster

Introduction

Create a Cluster

Set up Cluster Autoscaler

Scale up the Cluster

Scale down the Cluster

Can We Scale up Too Much or De-scale to Zero Nodes?

Cluster Autoscaler Compared in GKE, EKS, and AKS

Revise and Test the Concepts

4. Collecting and Querying Metrics and Sending Alerts

Introduction

Create a Cluster

Choose the Tools for Storing and Querying Metrics and Alerting

Defining Alert Rules

Seeing Green Alerts in Action

Seeing Red Alerts in Action

Which Metric Types Should We Use?

Alerting on Latency-related Issues

Alerting on Traffic-related Issues

Alerting on Error-related Issues

Alerting on Saturation-related Issues

Saturation-related Issues: Measure Memory Usage

Alerting on Unschedulable or Failed Pods

Upgrading Old Pods

Measuring Containers Memory and CPU Usage

Comparing Actual Resource Usage with Defined Requests

Comparing Actual Resource Usage with Defined Limits

Revise and Test the Concepts

5. Debugging Issues Discovered Through Metrics and Alerts

6. Extending HorizontalPodAutoscaler With Custom Metrics

7. Visualizing Metrics And Alerts

8. Collecting And Querying Logs

9. Conclusion