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.
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:
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.
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.
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.
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.
Deploying a Kubernetes application
Deploying an application on Kubernetes is fundamentally similar to deploying an application outside of Kubernetes. All applications, whether containerized or not, must have configuration details around topics that include the following:
- Persistent storage and file mounts
- Availability and redundancy
- Application configuration
The Kubernetes API serves as a set of endpoints that can be interacted with to view, modify, or delete different Kubernetes resources, many of which are used to configure different details of an application.
The first Kubernetes resource we will explore is called a Deployment. Deployments determine the basic details required to deploy an application on Kubernetes. One of these basic details consists of the container image that Kubernetes should deploy. Container images can be built on local workstations using tools such as
jib but images can also be built right on Kubernetes using
kaniko. Because Kubernetes does not expose a native API endpoint for building container images, we will not go into detail about how a container image is built prior to configuring a Deployment resource.
In addition to specifying the container image, Deployments also specify the number of replicas, or instances, of an application to deploy. When a Deployment is created, it spawns an intermediate resource, called a ReplicaSet. The ReplicaSet deploys as many instances of the application as determined by the
replicas field on the Deployment. The application is deployed inside a container, which itself is deployed inside a construct called a Pod. A Pod is the smallest unit in Kubernetes and encapsulates at least one container.
While Deployments are used to deploy an application to Kubernetes, they do not configure the networking components that allow an application to be communicated with Kubernetes exposes a separate API endpoint used to define the networking layer, called a Service. Services allow users and other applications to talk to each other by allocating a static IP address to a Service endpoint. The Service endpoint can then be configured to route traffic to one or more application instances. This kind of configuration provides load balancing and high availability.
An example architecture using a Service is described in the following diagram. Notice that the Service sits in between the client and the Pods to provide load balancing and high availability:
Microservice-style applications embrace being self-sufficient by maintaining their state in an ephemeral manner. However, there are numerous use cases where data must live beyond the life span of a single container. Kubernetes addresses this issue by providing a subsystem for abstracting the underlying details of how storage is provided and how it is consumed. To allocate persistent storage for their application, users can create a
PersistentVolumeClaim endpoint, which specifies the type and amount of storage that is desired. Kubernetes administrators are responsible for either statically allocating storage, expressed as
PersistentVolume, or dynamically provisioning storage using
StorageClass, which allocates
PersistentVolume in response to a
PersistentVolume captures all of the necessary storage details, including the type (such as network file system [NFS], internet small computer systems interface [iSCSI], or from a cloud provider), along with the size of the storage. From a user's perspective, regardless of which method of the
PersistentVolume allocation method or storage backend that is used within the cluster, they do not need to manage the underlying details of managing storage. The ability to leverage persistent storage within Kubernetes increases the number of potential applications that can be deployed on the platform.
An example of persistent storage being provisioned is depicted in the following diagram. The diagram assumes that an administrator has configured dynamic provisioning via
We will explore this question further in the next section.
Approaches in resource management
In order to deploy an application on Kubernetes, we need to interact with the Kubernetes API to create resources.
kubectl is the tool we use to talk to the Kubernetes API.
kubectl is a command-line interface (CLI) tool used to abstract the complexity of the Kubernetes API from end users, allowing them to more efficiently work on the platform.
Imperative and declarative configuration
kubectl commands follow a common format:
The verb refers to one of the
kubectl subcommands and the noun refers to a particular Kubernetes resource. For example, the following command can be run to create a Deployment:
You could use
kubectl to get more information on the Deployment that was created by using the
kubectl describe deployment my-deployment
If a change to the Deployment was desired, a developer could use the
edit subcommand to modify it in place:
When it comes to deleting the resource, the user can run the
Kubernetes resources, once created, exist in the cluster as JSON resource files, which can be exported as YAML files for greater human readability. An example resource in YAML format can be seen here:
apiVersion: apps/v1 kind: Deployment metadata: name: busybox spec: replicas: 1 selector: matchLabels: app: busybox template: metadata: labels: app: busybox spec: containers: - name: main image: busybox args: - sleep - infinity
While it may be easier to create resources imperatively using the
kubectl subcommands we have just described, Kubernetes allows you to directly manage the YAML resources in a declarative fashion to gain more control over resource creation. The
kubectl subcommands do not always let you configure all the possible resource options, but creating the YAML files directly allows you to more flexibly create resources and fill in the gaps that the
kubectl subcommands may contain.
When creating resources declaratively, users first write out the resource they want to create in YAML format. Next, they use the
kubectl tool to apply the resource against the Kubernetes API. While in imperative configuration developers use
kubectl subcommands to manage resources, declarative configuration relies primarily on only one subcommand—
Declarative configuration often takes the following form:
kubectl apply -f my-deployment.yaml
This command gives Kubernetes a YAML resource that contains a resource specification, although the JSON format can be used as well. Kubernetes infers the action to perform on resources (create or modify) based on whether or not they exist.
An application may be configured declaratively by following these steps:
- First, the user can create a file called
deployment.yamland provide a YAML-formatted specification for the deployment. We will use the same example as before:
apiVersion: apps/v1 kind: Deployment metadata: name: busybox spec: replicas: 1 selector: matchLabels: app: busybox template: metadata: labels: app: busybox spec: containers: - name: main image: busybox args: - sleep - infinity
- The Deployment can then be created with the following command:
kubectl apply -f deployment.yaml
Upon running this command, Kubernetes will attempt to create the Deployment in the way you specified.
- If you wanted to make a change to the Deployment, say by changing the number of
2, you would first modify the
apiVersion: apps/v1 kind: Deployment metadata: name: busybox spec: replicas: 2 selector: matchLabels: app: busybox template: metadata: labels: app: busybox spec: containers: - name: main image: busybox args: - sleep - infinity
- You would then apply the change with
kubectl apply -f deployment.yaml
After running that command, Kubernetes would apply the provided Deployment declaration over the previously applied
deployment. At this point, the application would scale up from a
- When it comes to deleting an application, the Kubernetes documentation actually recommends doing so in an imperative manner; that is, using the
deletesubcommand instead of
kubectl delete -f deployment.yaml
deletesubcommand can be made more declarative by passing in the
-fflag and a filename. This gives
kubectlthe name of the resource to delete that is declared in a specific file and it allows the developers to continue managing resources with declarative YAML files.
With an understanding of how Kubernetes resources are created, let's now discuss some of the challenges involved in resource configuration.
Resource configuration challenges
In the previous section, we covered how Kubernetes has two different configuration methods—imperative and declarative. One question to consider is what challenges do users need to be aware of when creating Kubernetes resources with imperative and declarative methodologies?
Let's discuss some of the most common challenges.
The many types of Kubernetes resources
First of all, there are many, many different resources in Kubernetes. Here's a short list of resources a developer should be aware of:
Out of the box, deploying an application on Kubernetes is not as simple as pushing a big red button marked
Deploy. Developers need to be able to determine which resources are required to deploy their application and they need to understand those resources at a deep enough level to be able to configure them appropriately. This requires a lot of knowledge of and training on the platform. While understanding and creating resources may already sound like a large hurdle, this is actually just the beginning of many different operational challenges.
Keeping the live and local states in sync
A method of configuring Kubernetes resources that we would encourage is to maintain their configuration in source control for teams to edit and share, which also allows the source control repository to become the source of truth. The configuration defined in source control (referred to as the 'local state') is then created by applying them to the Kubernetes environment and the resources become 'live' or enter what can be called the 'live state.' This sounds simple enough, but what happens when developers need to make changes to their resources? The proper answer would be to modify the local files and apply the changes to synchronize the local state to the live state in an effort to update the source of truth. However, this isn't what usually ends up happening. It is often simpler, in the short term, to modify the live resource in place with
kubectl patch or
kubectl edit and completely skip over modifying the local files. This results in a state inconsistency between local and live states and is an act that makes scaling on Kubernetes difficult.
Application life cycles are hard to manage
Life cycle management is a loaded term, but in this context, we'll refer to it as the concept of installing, upgrading, and rolling back applications. In the Kubernetes world, an installation would create resources to deploy and configure an application. The initial installation would create what we refer to here as
version 1 of an application.
An upgrade, then, can be thought of as an edit or modification to one or many of those Kubernetes resources. Each
batch of edits can be thought of as a single upgrade. A developer could modify a single Service resource, which would bump the version number to
version 2. The developer could then modify a Deployment, a ConfigMap, and a Service, bumping the version count to
As newer versions of an application continue to be rolled out onto Kubernetes, it becomes more difficult to keep track of the changes that have occurred. Kubernetes, in most cases, does not have an inherent way of keeping a history of changes. While this makes upgrades harder to keep track of, it also makes restoring a prior version of an application much more difficult. Say a developer previously made an incorrect edit on a particular resource. How would a team know where to roll back to? The
n-1 case is particularly easy to work out, as that is the most recent version. What happens, however, if the latest stable release was five versions ago? Teams often end up scrambling to resolve issues because they cannot quickly identify the latest stable configuration that worked previously.
Resource files are static
This is a challenge that primarily affects the declarative configuration style of applying YAML resources. Part of the difficulty in following a declarative approach is that Kubernetes resource files are not natively designed to be parameterized. Resource files are largely designed to be written out in full before being applied and the contents remain the source of truth until the file is modified. When dealing with Kubernetes, this can be a frustrating reality. Some API resources can be lengthy, containing many different customizable fields, and it can be quite cumbersome to write and configure YAML resources in full.
Static files lend themselves to becoming boilerplate. Boilerplate represents text or code that remains largely consistent in different but similar contexts. This becomes an issue if developers manage multiple different applications, where they could potentially manage multiple different Deployment resources, multiple different Services, and so on. In comparing the different applications' resource files, you may find large numbers of similar YAML configuration between them.
The following figure depicts an example of two resources with significant boilerplate configuration between them. The blue text denotes lines that are boilerplate, while the red text denotes lines that are unique:
Notice, in this example, that each file is almost exactly the same. When managing files that are as similar as this, boilerplate becomes a major headache for teams managing their applications in a declarative fashion.
Helm to the rescue!
Over time, the Kubernetes community discovered that creating and maintaining Kubernetes resources to deploy applications is difficult. This prompted the development of a simple yet powerful tool that would allow teams to overcome the challenges posed by deploying applications on Kubernetes. The tool that was created is called Helm. Helm is an open source tool used for packaging and deploying applications on Kubernetes. It is often referred to as the Kubernetes Package Manager because of its similarities to any other package manager you would find on your favorite OS. Helm is widely used throughout the Kubernetes community and is a CNCF graduated project.
Understanding package managers
Package managers are used to simplify the process of installing, upgrading, reverting, and removing a system's applications. These applications are defined in units, called packages, which contain metadata around target software and its dependencies.
The process behind package managers is simple. First, the user passes the name of a software package as an argument. The package manager then performs a lookup against a package repository to see whether that package exists. If it is found, the package manager installs the application defined by the package and its dependencies to the specified locations on the system.
Package managers make managing software very easy. As an example, let's imagine you wanted to install
htop, a Linux system monitor, to a Fedora machine. Installing this would be as simple as typing a single command:
dnf, the Fedora package manager since 2015, to find
htop in the Fedora package repository and install it.
dnf also takes care of installing the
htop package's dependencies, so you would not have to worry about installing its requirements beforehand. After
dnf finds the
htop package from the upstream repository, it asks you whether you're sure you want to proceed. The
--assumeyes flag automatically answers
yes to this question and any other prompts that
dnf may potentially ask.
Over time, newer versions of
htop may appear in the upstream repository.
dnf and other package managers allow users to efficiently upgrade to new versions of the software. The subcommand that allows users to upgrade using
dnf is upgrade:
dnf to upgrade
htop to its latest version. It also upgrades its dependencies to the versions specified in the package's metadata.
While moving forward is often better, package managers also allow users to move backward and revert an application back to a prior version if necessary.
dnf does this with the
This is a powerful process because the package manager allows users to quickly roll back if a critical bug or vulnerability is reported.
In this section, we reviewed how the
dnf package manager on Fedora can be used to manage a software package. Helm, as the Kubernetes package manager, is similar to
dnf, both in its purpose and functionality. While
dnf is used to manage applications on Fedora, Helm is used to manage applications on Kubernetes. We will explore this in greater detail next.
The Kubernetes package manager
Given that Helm was designed to provide an experience similar to that of package managers, experienced users of
dnf or similar tools will immediately understand Helm's basic concepts. Things become more complicated, however, when talking about the specific implementation details.
dnf operates on
RPM packages that provide executables, dependency information, and metadata. Helm, on the other hand, works with charts. A Helm chart can be thought of as a Kubernetes package. Charts contain the declarative Kubernetes resource files required to deploy an application. Similar to an
RPM, it can also declare one or more dependencies that the application needs in order to run.
Helm relies on repositories to provide widespread access to charts. Chart developers create declarative YAML files, package them into charts, and publish them to chart repositories. End users then use Helm to search for existing charts to deploy onto Kubernetes, similar to how end users of
dnf will search for
RPM packages to deploy to Fedora.
Let's go through a basic example. Helm could be used to deploy
Redis, an in-memory cache, to Kubernetes by using a chart published to an upstream repository. This could be performed using Helm's
This would install the
redis chart from the bitnami chart repository to a Kubernetes namespace called
redis. This installation would be referred to as the initial revision, or the initial deployment of a Helm chart.
If a new version of the
redis chart becomes available, users can upgrade to a new version using the
This would upgrade
Redis to meet the specification defined by the newer
With operating systems, users should be concerned about rollbacks if a bug or vulnerability is found. The same concern exists with applications on Kubernetes, and Helm provides the rollback command to handle this use case:
This command would roll
Redis back to its first revision.
Finally, Helm provides the ability to remove
Redis altogether with the
Earlier in this chapter, we reviewed how Kubernetes applications are created by managing Kubernetes resources, and we discussed some of the challenges involved. Here are few ways that Helm can overcome these challenges.
The abstracted complexity of Kubernetes resources
Let's assume that a developer has been given the task of deploying a MySQL database onto Kubernetes. The developer would need to create the resources required to configure its containers, network, and storage. The amount of Kubernetes knowledge required to configure such an application from scratch is high and is a big hurdle for new and even intermediate Kubernetes users to clear.
With Helm, a developer tasked with deploying a MySQL database could simply search for MySQL charts in upstream chart repositories. These charts would have already been written by chart developers in the community and would already contain the declarative configuration required to deploy a MySQL database. In this regard, developers with this kind of task would act as simple end users that use Helm in a similar way to any other package manager.
The ongoing history of revisions
Helm has a concept called release history. When a Helm chart is installed for the first time, Helm adds that initial revision to the history. The history is further modified as revisions increase via upgrades, keeping various snapshots of how the application was configured at varying revisions.
The following diagram depicts an ongoing history of revisions. The squares in blue illustrate resources that have been modified from their previous versions:
The process of tracking each revision provides opportunities for rollback. Rollbacks in Helm are very simple. Users simply point Helm to a previous revision and Helm reverts the live state to that of the selected revision. With Helm, gone are the days of the
n-1 backup. Helm allows users to roll back their applications as far back as they desire, even back to the very first installation.
Dynamically configured declarative resources
One of the biggest hassles with creating resources declaratively is that Kubernetes resources are static and cannot be parameterized. As you may recall from earlier, this results in resources becoming boilerplate across applications and similar configurations, making it more difficult for teams to configure their applications as code. Helm alleviates these issues by introducing values and templates.
Values are simply what Helm calls parameters for charts. Templates are dynamically generated files based on a given set of values. These two constructs provide chart developers the ability to write Kubernetes resources that are automatically generated based on values that end users provide. By doing so, applications managed by Helm become more flexible, less boilerplate, and easier to maintain.
Values and templates allow users to do things such as the following:
- Parameterize common fields, such as the image name in a Deployment and the ports in a Service
- Generate long pieces of YAML configuration based on user input, such as volume mounts in a Deployment or the data in a ConfigMap
- Include or exclude resources based on user input
Consistency between the local and live states
Package managers prevent users from having to manage an application and its dependencies manually. All management can be done through the package manager itself. The same idea holds true with Helm. Because a Helm chart contains a flexible configuration of Kubernetes resources, users shouldn't have to make modifications directly to live Kubernetes resources. Users that want to modify their applications can do so by providing new values to a Helm chart or by upgrading their application to a more recent version of the associated chart. This allows the local state (represented by the Helm chart configuration) and the live state to remain consistent across modifications, giving users the ability to provide a source of truth for their Kubernetes resource configurations.
Helm simplifies application deployments by determining the order that Kubernetes resources need to be created. Helm analyzes each of a chart's resources and orders them based on their types. This pre-deterministic order exists to ensure that resources that commonly have resources dependent on them are created first. For example, Secrets and ConfigMaps should be created before Deployments, since a Deployment would likely consume those resources as volumes. Helm performs this ordering without any interaction from the user, so this complexity is abstracted and prevents users from needing to worry about the order that these resources are applied.
Automated life cycle hooks
Similar to other package managers, Helm provides the ability to define life cycle hooks. Life cycle hooks are actions that take place automatically at different stages of an application's life cycle. They can be used to do things such as the following:
- Perform a data backup on an upgrade.
- Restore data on a rollback.
- Validate a Kubernetes environment prior to installation.
Life cycle hooks are valuable because they abstract complexities around tasks that may not be Kubernetes-specific. For example, a Kubernetes user may not be familiar with the best practices behind backing up a database or may not know when such a task should be performed. Life cycle hooks allow experts to write automation that performs those best practices when recommended so that users can continue to be productive without needing to worry about those details.
In this chapter, we began by exploring the change in architectural trends of adopting microservice-based architectures to decompose applications into several smaller applications instead of deploying one large monolith. The creation of applications that are more lightweight and easier to manage has led to utilizing containers as a packaging and runtime format to produce releases more frequently. By adopting containers, additional operational challenges were introduced and solved by using Kubernetes as a container orchestration platform to manage the container life cycle.
Our discussion turned to the various ways that Kubernetes applications can be configured, including Deployments, Services, and PersistentVolumeClaims. These resources can be expressed using two distinct styles of application configuration: imperative and declarative. Each of these configuration styles contributes to a set of challenges involved in deploying Kubernetes applications, including the amount of knowledge required to understand how Kubernetes resources work and the challenge of managing application life cycles.
To better manage each of the assets that comprise an application, Helm was introduced as the package manager for Kubernetes. Through its rich feature set, the full life cycle of applications from install, upgrade, rollback, and removal can be managed with ease.
In the next chapter, we'll walk through the process of configuring a Helm environment. We will also install the tooling required for consuming the Helm ecosystem and following along with the examples provided in this book.
For more information about the Kubernetes resources that make up an application, please see the Understanding Kubernetes Objects page from the Kubernetes documentation at https://kubernetes.io/docs/concepts/overview/working-with-objects/kubernetes-objects/.
To reinforce some of the benefits of Helm discussed in this chapter, please refer to the Using Helm page of the Helm documentation at https://helm.sh/docs/intro/using_helm/. (This page also dives into some basic usage around Helm, which will be discussed throughout this book in greater detail.)
- What is the difference between a monolithic and a microservices application?
- What is Kubernetes? What problems was it designed to solve?
- What are some of the
kubectlcommands commonly used when deploying applications to Kubernetes?
- What challenges are often involved in deploying applications to Kubernetes?
- How does Helm function as a package manager for Kubernetes? How does it address the challenges posed by Kubernetes?
- Imagine you want to roll back an application deployed on Kubernetes. What Helm command allows you to perform this action? How does Helm keep track of your changes to make this rollback possible?
- What are the four primary Helm commands that allow Helm to function as a package manager?