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Learn Helm
Learn Helm

Learn Helm: Improve productivity, reduce complexity, and speed up cloud-native adoption with Helm for Kubernetes

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Profile Icon Andrew Block Profile Icon Austin Dewey
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Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (4 Ratings)
Paperback Jun 2020 344 pages 1st Edition
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Profile Icon Andrew Block Profile Icon Austin Dewey
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Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (4 Ratings)
Paperback Jun 2020 344 pages 1st Edition
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eBook
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Learn Helm

From monoliths to modern microservices

Software applications are a foundational component of most modern technology. Whether they take the form of a word processor, web browser, or media player, they enable user interaction to complete one or more tasks. Applications have a long and storied history, from the days of ENIAC—the first general-purpose computer—to taking man to the moon in the Apollo space missions, to the rise of the World Wide Web, social media, and online retail.

These applications can operate on a wide range of platforms and system. We said in most cases they run on virtual or physical resources, but aren't these technically the only options? Depending on their purpose and resource requirements, entire machines may be dedicated to serving the compute and/or storage needs of an application. Fortunately, thanks in part to the realization of Moore's law, the power and performance of microprocessors initially increased with each passing year, along with the overall cost associated with the physical resources. This trend has subsided in recent years, but the advent of this trend and its persistence for the first 30 years of the existence of processors was instrumental to the advances in technology.

Software developers took full advantage of this opportunity and bundled more features and components in their applications. As a result, a single application could consist of several smaller components, each of which, on their own, could be written as their own individual services. Initially, bundling components together yielded several benefits, including a simplified deployment process. However, as industry trends began to change and businesses focused more on the ability to deliver features more rapidly, the design of a single deployable application brought with it a number of challenges. Whenever a change was required, the entire application and all of its underlying components needed to be validated once again to ensure the change had no adverse features. This process potentially required coordination from multiple teams, which slowed the overall delivery of the feature.

Delivering features more rapidly, especially across traditional divisions within organizations, was also something that organizations wanted. This concept of rapid delivery is fundamental to a practice called DevOps, whose rise in popularity occurred around the year 2010. DevOps encouraged more iterative changes to applications over time, instead of extensive planning prior to development. In order to be sustainable in this new model, architectures evolved from being a single large application to instead favoring several smaller applications that can be delivered faster. Because of this change in thinking, the more traditional application design was labeled as monolithic. This new approach of breaking components down into separate applications coined the name for these components as microservices. The traits that were inherent in microservice applications brought with them several desirable features, including the ability to develop and deploy services concurrently from one another as well as to scale (increase the number of instances) them independently.

The change in software architecture from monolithic to microservices also resulted in re-evaluating how applications are packaged and deployed at runtime. Traditionally, entire machines were dedicated to either one or two applications. Now, as microservices resulted in the overall reduction of resources required for a single application, dedicating an entire machine to one or two microservices was no longer viable.

Fortunately, a technology called containers was introduced and gained popularity for filling in the gaps for many missing features needed to create a microservices runtime environment. Red Hat defines a container as 'a set of one or more processes that are isolated from the rest of the system and includes all of the files necessary to run' (https://www.redhat.com/en/topics/containers/whats-a-linux-container). Containerized technology has a long history in computing, dating back to the 1970s. Many of the foundational container technologies, including chroot (the ability to change the root directory of a process and any of its children to a new location on the filesystem) and jails, are still in use today.

The combination of a simple and portable packaging model, along with the ability to create many isolated sandboxes on each physical or virtual machine, led to the rapid adoption of containers in the microservices space. This rise in container popularity in the mid-2010s can also be attributed to Docker, which brought containers to the masses through simplified packaging and a runtime that could be utilized on Linux, macOS, and Windows. The ability to distribute container images with ease led to the increase in popularity of container technologies. This was because first-time users did not need to know how to create images but instead could make use of existing images that were created by others.

Containers and microservices became a match made in heaven. Applications had a packaging and distribution mechanism, along with the ability to share the same compute footprint while taking advantage of being isolated from one another. However, as more and more containerized microservices were deployed, the overall management became a concern. How do you ensure the health of each running container? What do you do if a container fails? What happens if your 0my underlying machine does not have the compute capacity required? Enter Kubernetes, which helped answer this need for container orchestration.

In the next section, we will discuss how Kubernetes works and provides value to an enterprise.

What is Kubernetes?

Kubernetes, often abbreviated as k8s (pronounced as kaytes), is an open source container orchestration platform. Originating from Google's proprietary orchestration tool, Borg, the project was open sourced in 2015 and was renamed Kubernetes. Following the v1.0 release on July 21, 2015, Google and the Linux Foundation partnered to form the Cloud Native Computing Foundation (CNCF), which acts as the current maintainer of the Kubernetes project.

The word Kubernetes is a Greek word meaning 'helmsman' or 'pilot'. A helmsman is the person who is in charge of steering a ship and works closely with the ship's officer to ensure a safe and steady course, along with the overall safety of the crew. Kubernetes has similar responsibilities with regards to containers and microservices. Kubernetes is in charge of the orchestration and scheduling of containers. It is in charge of 'steering' those containers to proper worker nodes that can handle their workloads. Kubernetes will also help ensure the safety of those microservices by providing high availability and health checks.

Let's review some of the ways Kubernetes helps simplify the management of containerized workloads.

Container Orchestration

The most prominent feature of Kubernetes is container orchestration. This is a fairly loaded term, so we'll break it down into different pieces.

Container orchestration is about placing containers on certain machines from a pool of compute resources based on their requirements. The simplest use case for container orchestration is for deploying containers on machines that can handle their resource requirements. In the following diagram, there is an application that requests 2 Gi of memory (Kubernetes resource requests typically use their 'power of two' values, which in this case is roughly equivalent to 2 GB) and one CPU core. This means that the container will be allocated 2 Gi of memory and 1 CPU core from the underlying machine that it is scheduled on. It is up to Kubernetes to track which machines, which in this case are called nodes, have the required resources available and to place an incoming container on that machine. If a node does not have enough resources to satisfy the request, the container will not be scheduled on that node. If all of the nodes in a cluster do not have enough resources to run the workload, the container will not be deployed. Once a node has enough resources free, the container will be deployed on the node with sufficient resources:

Figure 1.1: Kubernetes orchestration and scheduling

Figure 1.1 - Kubernetes orchestration and scheduling

Container orchestration relieves you of putting in the effort to track the available resources on machines at all times. Kubernetes and other monitoring tools provide insight into these metrics. So, a day-to-day developer does not need to worry about available resources. A developer can simply declare the amount of resources they expect a container to use and Kubernetes will take care of the rest on the backend.

High availability

Another benefit of Kubernetes is that it provides features that help take care of redundancy and high availability. High availability is a characteristic that prevents application downtime. It's performed by a load balancer, which splits incoming traffic across multiple instances of an application. The premise of high availability is that if one instance of an application goes down, other instances are still available to accept incoming traffic. In this regard, downtime is avoided and the end user, whether a human or another microservice, remains completely unaware that there was a failed instance of the application. Kubernetes provides a networking mechanism, called a Service, that allows applications to be load balanced. We will talk about Services in greater detail later on in the Deploying a Kubernetes application section of this chapter.

Scalability

Given the lightweight nature of containers and microservices, developers can use Kubernetes to rapidly scale their workloads, both horizontally and vertically.

Horizontal scaling is the act of deploying more container instances. If a team running their workloads on Kubernetes were expecting increased load, they could simply tell Kubernetes to deploy more instances of their application. Since Kubernetes is a container orchestrator, developers would not need to worry about the physical infrastructure that those applications would be deployed on. It would simply locate a node within the cluster with the available resources and deploy the additional instances there. Each extra instance would be added to a load-balancing pool, which would allow the application to continue to be highly available.

Vertical scaling is the act of allocating additional memory and CPU to an application. Developers can modify the resource requirements of their applications while they are running. This will prompt Kubernetes to redeploy the running instances and reschedule them on nodes that can support the new resource requirements. Depending on how this is configured, Kubernetes can redeploy each instance in a way that prevents downtime while the new instances are being deployed.

Active community

The Kubernetes community is an incredibly active open source community. As a result, Kubernetes frequently receives patches and new features. The community has also made many contributions to documentation, both to the official Kubernetes documentation as well as to professional or hobbyist blog websites. In addition to documentation, the community is highly involved in planning and attending meetups and conferences around the world, which helps increase education and innovation of the platform.

Another benefit of Kubernetes's large community is the number of different tools built to augment the abilities that are provided. Helm is one of those tools. As we'll see later in this chapter and throughout this book, Helm—a tool built by members of the Kubernetes community—vastly improves a developer's experience by simplifying application deployments and life cycle management.

With an understanding of the benefits Kubernetes brings to managing containerized workloads, let's now discuss how an application can be deployed in Kubernetes.

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Key benefits

  • Effectively manage applications deployed in Kubernetes using Helm
  • Learn to install, upgrade, share, and manage applications deployed in Kubernetes
  • Get up and running with a package manager for Kubernetes

Description

Containerization is currently known to be one of the best ways to implement DevOps. While Docker introduced containers and changed the DevOps era, Google developed an extensive container orchestration system, Kubernetes, which is now considered the frontrunner in container orchestration. With the help of this book, you’ll explore the efficiency of managing applications running on Kubernetes using Helm. Starting with a short introduction to Helm and how it can benefit the entire container environment, you’ll then delve into the architectural aspects, in addition to learning about Helm charts and its use cases. You’ll understand how to write Helm charts in order to automate application deployment on Kubernetes. Focused on providing enterprise-ready patterns relating to Helm and automation, the book covers best practices for application development, delivery, and lifecycle management with Helm. By the end of this Kubernetes book, you will have learned how to leverage Helm to develop an enterprise pattern for application delivery.

Who is this book for?

This book is for Kubernetes developers or administrators who are interested in learning Helm to provide automation for application development on Kubernetes. Although no prior knowledge of Helm is required, basic knowledge of Kubernetes application development will be useful.

What you will learn

  • Develop an enterprise automation strategy on Kubernetes using Helm
  • Create easily consumable and configurable Helm charts
  • Use Helm in orchestration tooling and Kubernetes operators
  • Explore best practices for application delivery and life cycle management
  • Leverage Helm in a secure and stable manner that is fit for your enterprise
  • Discover the ins and outs of automation with Helm

Product Details

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Publication date : Jun 10, 2020
Length: 344 pages
Edition : 1st
Language : English
ISBN-13 : 9781839214295
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Length: 344 pages
Edition : 1st
Language : English
ISBN-13 : 9781839214295
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Table of Contents

14 Chapters
Section 1: Introduction and Setup Chevron down icon Chevron up icon
Chapter 1: Understanding Kubernetes and Helm Chevron down icon Chevron up icon
Chapter 2: Preparing a Kubernetes and Helm Environment Chevron down icon Chevron up icon
Chapter 3: Installing your First Helm Chart Chevron down icon Chevron up icon
Section 2: Helm Chart Development Chevron down icon Chevron up icon
Chapter 4: Understanding Helm Charts Chevron down icon Chevron up icon
Chapter 5: Building Your First Helm Chart Chevron down icon Chevron up icon
Chapter 6: Testing Helm Charts Chevron down icon Chevron up icon
Section 3: Adanced Deployment Patterns Chevron down icon Chevron up icon
Chapter 7: Automating Helm Processes Using CI/CD and GitOps Chevron down icon Chevron up icon
Chapter 8: Using Helm with the Operator Framework Chevron down icon Chevron up icon
Chapter 9: Helm Security Considerations Chevron down icon Chevron up icon
ASSESSMENTS Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(4 Ratings)
5 star 25%
4 star 25%
3 star 0%
2 star 25%
1 star 25%
Balazs Szeti Nov 02, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Despite Helm 3 getting more and more attention around Kubernetes application management there are still not many related books around. This book is a great choice to start with if you want to learn Helm basics. Obviously you need a basic understanding of containers and Kubernetes - that you probably have if you heard about Helm - but otherwise the book is an easy read for beginners.There a detailed step by step guide how to setup an environment (CLI tools and Minikube) on your own laptop so you don't need a "real" Kubernetes cluster. The chapters are built upon each other, in some way the book feels like workshop or a training guide, you can also find some "knowledge check" questions at the end of each chapter. It's great to have the output of each command included, so even if you don't want to try them yourself you can easily follow what would happen after each step.As being a relatively short read, this book is ideal for starters. It doesn't get lost in the details of edge cases or deployment "theories" nor tries to be a full reference for Helm - we have the documentation for that already.
Amazon Verified review Amazon
Nicolas Gabriel Schenker Oct 15, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
The authors have written a comprehensive introduction to Helm. The book is easy to read and all instructions are provided as simple and easy to follow step by step guidelines.The first chapter of the book, Understanding Kubernetes and Helm, is easy to read yet very thoughtful introduction into the topic, starting from applications to containers and Kubernetes and finally to Helm, the subject of the book. In the next chapter, Preparing a Kubernetes and Helm Environment, the authors go to great length to describe in detail how to prepare a reader's system for use with helm. I particularly like the fact that they give clear and comprehensive instructions for all 3 major operating systems, namely Windows, MacOS and Linux. The subsequent chapters lead the reader through the details of what a Helm chart is and how one can be authored, debugged and tested. It the touches on more advanced topics such as integrating Helm in your CI/CD pipeline, using Helm with the Operator Framework and last but least provides some useful thoughts about security and Helm.The book targets developers and DevOps engineers with no prior knowledge of Helm and a basic understanding of containers and Kubernetes.I can highly recommend this book.
Amazon Verified review Amazon
Chris F Feb 12, 2021
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
I just now received this book and have been reading it for 2 hours, so far so good. I am in Chapter 3, ad doing the exercises, and wanted to just be able to copy/paste the commands, so I went onto the Packt website to download the PDF or other eBook formats - NONE provided for this book! What? I see the code download link, but that does NOT have everything. Come on guys - provide a PDF to be able to cut/paste some of the long commands.Hence 3 stars instead of 5, which the book probably deserves on the content.EDIT: Downgraded from 3 to 2 starsI get to Chapter 7 to learn CI/CD & GitOps. When installing the jenkins helm chart, the Jenkins pod does NOT start because liveliness and readiness probes fail, and just keeps restarting the pod. Where is the code for the Jenkins pipeline? This is probably the MOST important chapter in the book.
Amazon Verified review Amazon
G-Slave Dec 29, 2020
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
You get past chapter 4 and it becomes incomprehensible. Rather than teaching you anything, the book just has you copy/paste snippets of YAML configs while poorly explaining it, if at all.
Amazon Verified review Amazon
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