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 m... See More

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Faster than videos

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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

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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

Introduction

Create a Cluster

Face a Disaster

Use Instrumentation to Provide More Detailed Metrics

Using Internal Metrics to Debug Potential Issues

Revise and Test the Concepts

6. Extending HorizontalPodAutoscaler With Custom Metrics

Introduction

Creating a Cluster

Using HorizontalPodAutoscaler Without Metrics Adapter

Exploring Prometheus Adapter

Create HPA with Custom Metrics pulled through Exporters

Create HPA with Custom Metrics pulled through Instrumented metric

Combine Metric Server Data with Custom Metrics

The Complete HorizontalPodAutoscaler Flow of Events

Reaching Nirvana

Revise and Test the Concepts

7. Visualizing Metrics And Alerts

Introduction

Create a Cluster

Which Tools Should We Use for Dashboards?

Installing And Setting up Grafana

Importing and Customizing Pre-Made Dashboards

Creating Custom Dashboards

Creating Semaphore Dashboards

A Better Dashboard for Big Screens

Prometheus and Graph Alerts, Grafana Notifications, Semaphores

Revise and Test the Concepts

8. Collecting And Querying Logs

Introduction

Create a Cluster

Exploring Logs Through kubectl

Choosing a Centralized Logging Solution

Exploring Logs Collection and Shipping

Explore Centralized Logging Through Papertrail

Combine GCP StackDriver with a GKE Cluster

Combine AWS CloudWatch with an EKS Cluster

Combine Azure Log Analytics with an AKS Cluster

Explore Centralized Logging

Switching to Elasticsearch for Storing Metrics

What Should We Expect from Centralized Logging?

Revise and Test the Concepts

9. Conclusion

What did we do?