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Mastering Elastic Kubernetes Service on AWS

Mastering Elastic Kubernetes Service on AWS

By : Malcolm Orr, Yang-Xin Cao, Yang-Xin Cao (Eason)
4.9 (23)
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Mastering Elastic Kubernetes Service on AWS

Mastering Elastic Kubernetes Service on AWS

4.9 (23)
By: Malcolm Orr, Yang-Xin Cao, Yang-Xin Cao (Eason)

Overview of this book

Kubernetes has emerged as the de facto standard for container orchestration, with recent developments making it easy to deploy and handle a Kubernetes cluster. However, a few challenges such as networking, load balancing, monitoring, and security remain. To address these issues, Amazon EKS offers a managed Kubernetes service to improve the performance, scalability, reliability, and availability of AWS infrastructure and integrate with AWS networking and security services with ease. You’ll begin by exploring the fundamentals of Docker, Kubernetes, Amazon EKS, and its architecture along with different ways to set up EKS. Next, you’ll find out how to manage Amazon EKS, encompassing security, cluster authentication, networking, and cluster version upgrades. As you advance, you’ll discover best practices and learn to deploy applications on Amazon EKS through different use cases, including pushing images to ECR and setting up storage and load balancing. With the help of several actionable practices and scenarios, you’ll gain the know-how to resolve scaling and monitoring issues. Finally, you will overcome the challenges in EKS by developing the right skill set to troubleshoot common issues with the right logic. By the end of this Kubernetes book, you’ll be able to effectively manage your own Kubernetes clusters and other components on AWS.
Table of Contents (28 chapters)
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1
Part 1: Getting Started with Amazon EKS
7
Part 2: Deep Dive into EKS
13
Part 3: Deploying an Application on EKS
20
Part 4: Advanced EKS Service Mesh and Scaling
24
Part 5: Overcoming Common EKS Challenges

Autoscaling applications with custom metrics

In order for you to use custom metrics, the following must be fulfilled:

  1. Your application needs to be instrumented to produce metrics.
  2. These metrics need to be exposed through the custom.metrics.k8s.io endpoint.

The application developer or dev team is responsible for point 1, and we will install and use Prometheus and the Prometheus adapter to satisfy point 2. The following diagram illustrates the high-level flow of this solution.

Figure 18.8 – HPA custom metrics high-level flow

Figure 18.8 – HPA custom metrics high-level flow

Let’s look at the flow in brief.

  1. The Prometheus server installed in your cluster will “scrape” custom metrics from your pods.
  2. HPA will do the following:
    1. Read metrics from the custom.metrics.k8s.io custom endpoint, hosted on the Prometheus adapter.
    2. The Prometheus adapter will pull data from the Prometheus server.
  3. HPA assesses these metrics against the desired state of the autoscaling...

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