Book conventions

The upcoming sections will introduce various new concepts, accompanied by hands-on examples that need active engagement with a Unix shell environment. To ensure clarity in the practical demonstrations, the book will adhere to the following conventions:

• Every practical section will start with the "Hands-On" prefix.
• Commands preceded by a $ symbol indicate actions you need to perform.
• For shell commands or outputs that exceed the width of a single page, we'll use the \ character to break the line. The continuation of the command or output will appear on the subsequent line.

These guidelines will help you to interpret long commands or outputs more easily as we progress through the book.

Technical Requirements

To practice what you'll be learning throughout this book, you need to have access to a Kubernetes cluster. In this chapter, I'm offering two options for cluster creation: a local setup and a cloud-based solution in AWS. If you're just starting out or you want to focus solely on workload scaling in the upcoming chapters, the local option using Kind (Kubernetes in Docker) is a good choice. Alternatively, you could use Docker Desktop and turn on Kubernetes or use a Kubernetes playground like Killercoda or Play with Kubernetes.However, if you're eager to dive into the full cloud experience from the start, I'll also provide instructions for setting up an Amazon EKS cluster using Terraform. This option will give you a more realistic environment, closely mimicking production scenarios. Just remember, if you choose this route, to delete all resources after your study sessions to avoid unnecessary charges.To make the process as smooth as possible...

Scalability Foundations

Before I dive into how autoscaling works in Kubernetes, let me first talk about what scaling means in the context of computing systems.Scaling is the ability of a system to handle increasing amounts of work by adding resources to manage the load efficiently. It's a fundamental concept in computing that has become increasingly crucial in the era of cloud-native applications and microservices architectures. The true power of scaling lies in its automation - the ability to adjust resources dynamically without human intervention, which is essential for managing the rapid and unpredictable demand. The following diagram illustrates the pitfalls of static resource allocation:

As you can see in Figure 1.1, Overprovisioning wastes resources and increases costs, while Underprovisioning risks poor performance and potential system failures. Autoscaling offers a solution to both these problems. Consider...

Kubernetes Architecture

Before I dive into the specifics about autoscaling in Kubernetes, let's have a quick overview of the Kubernetes architecture and its components, especially those involved in the autoscaling aspect of Kubernetes.Assuming you're already familiar with Kubernetes, you know that each cluster is composed of several nodes where one or more components run (e.g., kube-apiserver, kube-scheduler, or kubelet). A node is essentially a server, machine, or instance. Depending on where you're hosting your Kubernetes cluster, you'll encounter different terminology. In this book, I'll stick with node. As shown in Figure 1.4, a very simplified version of the Kubernetes architecture, Kubernetes cluster nodes are grouped into two: 1) the Control Plane, and 2) the Data Plane.

In Figure 1.4, you can see the control plane nodes on the left side. TheseThe Control Plane nodes contain all...

Efficient Kubernetes Data Planes

Understanding how to optimize your Kubernetes data plane is crucial for building cost-effective, high-performing clusters that can adapt to changing workloads. To do so, we'll explore what efficiency means in the context of Kubernetes, why it's important, and the key factors that contribute to an efficient data plane. Then, I'll cover the challenges of achieving and maintaining efficiency, as well as strategies for optimizing resource utilization.

What do I mean by efficiency?

To answer this question, let's first talk about what efficiency means in a broader sense. Efficiency, in general terms, is the ability to accomplish a task with minimal waste of time and effort. It's about achieving the desired outcome while using the least number of resources possible. In other words, no wasting of resources. In a formula, as shown in Figure 1.5, it will be like this:

...

Kubernetes Autoscaling Categories

When working with Kubernetes, there are two primary categories of scaling that require attention, as alluded to earlier. The first category pertains to the application workloads, represented by pods, while the second category involves the underlying infrastructure of the data plane, specifically the nodes. This distinction is crucial because each category requires different approaches and tools for effective autoscaling.

Hands-On: Creating a Kubernetes Cluster

Enough theory - it's time to get our hands dirty! Grab your computer, open your terminal, and prepare to dive into the practical side of Kubernetes autoscaling. In this section, I'll guide you through creating your very own Kubernetes cluster, providing you with a sandbox environment to experiment with the concepts we've discussed.If you already have a Kubernetes cluster that you could use to practice the workload autoscaling chapters, skip this section and go to the summary of the chapter.