Kubernetes operators

What is a Kubernetes operator?

As a DevOps engineer, the most tedious job that you must be handling would be cluster management. Gladly, now you have Kubernetes Operators by your side.

Call it a method of an application-agnostic controller, it is a way to expand the existing capabilities for k8s and related APIs so that developing, managing, and deploying clusters/ applications becomes easier than before.

At the very basic level, operators are developed using fundamental k8s resource and controller concepts. However, they also feature knowledge related to the domain/app that automates the SDLC it manages. 

Operators are designed to make it easier to manage complex apps on a k8s cluster. They do this by extending the tools and instructions that control how applications are deployed and run on a cluster and are made to manage a few particular types of applications, like databases or messaging systems. Think of them as the ultimate multitaskers – they can handle all the little details, so you don't have to.

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Importance of Kubernetes Operators: What are its Benefits?

You probably know that running applications in k8s clusters can be a real challenge. But don't worry, k8s operators are here to make your life easier (and more fun). So, what makes Kubernetes Operators so important? Here are just a few reasons:

• Ease of use

Managing complex applications on a k8s cluster is simpler with operators. Instead of having to manually control each component and resource for your projects, you can simply define the desired app state and let the Operator handle the rest. This declarative approach can save you a lot of time & effort and allow you to focus on more important tasks (like trying to beat your high score on that annoying phone game). 🙌

• High-level Interface

Kubernetes Operators provide a high-level interface to perform app management operations, which makes it quicker to deploy and operate complex solutions using Kubernetes. Instead of diving into the nitty-gritty details, you can simply use the Operator for higher-level app management/control. This can be especially helpful if you're working with a team, as it allows everyone to understand the overall management for the app more easily.

• Customization

Operators in k8s can be customized. So, you can tailor the Operator to work exactly how you want and make sure that it fits your app’s needs. This level of customization can be extremely helpful, as it allows you to fine-tune the app management cycle to your specific needs and requirements. 🎯

• Reliability and stability 

A k8s operator makes a vital tool for improving the overall reliability and stability of your applications. By automating certain tasks, operators can help reduce the risk of errors and downtime in a reliable manner. This is especially important for mission-critical applications, as it can help ensure that they are always available and performing at their best. 💪

• Time-saving

As mentioned earlier, k8s operators save you a lot of time and effort in managing your applications. Instead of having to manually control each component or resource for your k8s app, you can simply define the preferred state and let the Operator handle the rest. This can free up your time and allow you to focus on more important tasks (like finally getting around to organizing your desk). 🎯

• Team collaboration

K8s Operators can be a valuable tool for improving team collaboration. By providing a high-level interface for managing applications, Operators can help ensure that everyone on the team is on the same page. This can make it easier to work together and get things done more efficiently.

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Managing applications using k8s operators

It is beneficial for multiple reasons, such as - 

• Declarative approach: Instead of having to manually control each component and resource of an application, you can simply define the desired app state and let the Operator handle the rest. 
• ‍Automation: Automating certain tasks throughout the app management cycle adds to your productivity. For example, an Operator could automatically scale an application up or down based on specific criteria or automatically perform routine maintenance tasks. This can save you a lot of time/effort while ensuring that your applications are always running smoothly. 🤖

Kubernetes Operator Framework 

A must-have for DevOps engineers who use k8s, it's a collection of tools and libraries that make it easier to craft, package, and deploy Operators – those special programs that help you manage and implement applications on a cluster.

The Framework consists of several components, including the following:

• An SDK to help developers program the Operators;
• The Registry for storage & distribution purposes;
• Operator Lifecycle Manager (OLM) to take care of the cluster-specific Operators throughout their lives.

But what makes the Framework so great?

For one, it provides best practices and standards for building Operators, which helps improve the reliability and stability of your applications. It also includes a command-line interface (CLI) that makes it easy to create and test Operators and a registry for publishing and distributing Operators to other users.

The OLM takes care of tasks like installing, upgrading, and uninstalling Operators to speed them and reduce your team’s involvement from the process. Plus, the high-level interface provided by the Framework makes it easier for teams to collaborate on Operators, which can help improve efficiency and speed up the development process.

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Operator Technologies

While Operator is a pivotal Kubernetes component, it’s not omnipresent, and only certain technologies are ideal for its implementation. The top 3 technologies are quoted below.

• Ansible 

Ansible is a globally recognized Python-based configuration management tool. It works well only when the concerned machine and controlling nodes are written in Python. It can operate seamlessly without controlling the machine.

As far as operator support is concerned, Ansible is an ideal choice to make by the instructor team as this is a less taxing programming language for operator generators. There are mainly 2 elements of an Ansible Operator.

The first part is a small Golang code chunk that works as an interface for the operator and K8s. The 2nd component is the container, whose job is to receive the events that Golang code generates and process Ansible Playbooks accordingly.

Together, Ansible and Operator SDK make k8s Operator generation quick and easy. This way, developers can focus less on coding and more on managing the overall solution more efficiently. Once generated, the Ansible operator can be used widely for app-related management and can access any Ansible-supportive module.

This ability of the Ansible operator lets developers manage off-cluster application workflow simultaneously. For instance, it’s easy to generate DNS entries, spin the resources outside the cluster, and streamline the load balancing using customized metrics.

• GoLang

Go is a Google-owned programming language. Go is a Kubernetes-friendly programming language, as Go codes have functionalities similar to Kubernetes. In fact, Kubernetes is implemented in a Go-based ecosystem. Go also has vast learning resources to offer to support the operator generation process. Nearly 71% of OperatorHub. co-operators are built using Go only. Hence, it’s an ideal choice to make for generating k8s operators. 

• Helm

Anyone using Kubernetes knows what Helm is as it’s a fully automated Kubernetes ecosystem handling generation, implementation, optimization, and taking care of cluster operations. Helm is an ideal choice to make to generate Operators because it permits developers to converge their focus on application development. 

With Helm charts like MongoDB and MySQL, developers have a chance to work swiftly with the database. It even permits developers to develop customized charts using which they can automate the deployment. 

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An example operator

Seeing the wide range of features and facilities one ought to enjoy with Operator, its usage has increased, and we are now surrounded by many tools of this category. Let's dive into this list of kubernetes operator example! 🚀

HPA k8s Operator

It helps automate the process of scaling applications on a Kubernetes cluster. It works by monitoring metrics such as CPU and memory usage and automatically scales the number of pods in an application up or down based on demand. It confirms if your applications are using their optimal capacity or not in real-time. Plus, the Kubernetes pod operator is super easy to use – just define the desired state of your application and let the HPA Operator do the rest. 🚀

RBAC Manager Operator

This operator takes care of RBAC (Role-Based Access Control) to say the least. It allows you to define roles and permissions for different users/groups and helps ensure that resources are only accessed by authorized users. It’s really helpful if you're working on a team, as it can help prevent unauthorized access to mission-critical resources. 🔒

Istio Operator

Istio is a powerful service mesh that helps microservices of your application intract. The Istio Operator makes it easy to deploy and manage Istio components such as Envoy proxies. Plus, the Istio Operator helps your applications run smoothly and securely, which is always a plus. 💪

Grafana Operator

Anyone seeking an easy way to fully manage and deploy Grafana instances in an optimized manner must try the Grafana Operator. With its help, Grafana instances can be easily incorporated inside a cluster. Mainly, this open-source tool is used to have a fully visualized display of data and metrics in complex applications. With Grafana Operator, developers don’t have to invest time and effort in Grafana development. 📊

Starboard Operator

It is a valuable tool for any DevOps engineer looking to deploy and manage the Starboard observability platform. It has tools for logging, tracing, and tracking app usage. If you're working on a large or complex application, it can help you keep track of key metrics and identify any issues that might arise. 

ECK (Elastic Cloud on k8s) Operator

It is useful for any DevOps engineer as it makes Elastic Stack management on clusters easier than before. Plus, it will ensure that Elastic Stack is up and running quickly.

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Kubernetes operators vs Helm charts

K8s Operators are typically implemented as custom controllers or controllers that extend the Kubernetes API server. They are written in Go and bank upon Kubernetes API for interacting within clusters. Here's a basic example of a k8s Operator written in Go:

package main

import (
	"fmt"
	"time"

	"github.com/operator-framework/operator-sdk/pkg/sdk"
	sdkVersion "github.com/operator-framework/operator-sdk/version"

	"github.com/example-inc/app-operator/pkg/apis"
	"github.com/example-inc/app-operator/pkg/controller"
)

func main() {
	sdk.ExposeMetricsPort()

	resource := "example.com/v1alpha1"
	kind := "AppService"
	namespace, err := sdk.GetWatchNamespace()
	if err != nil {
		namespace = ""
	}
	resyncPeriod := 5 * time.Minute
	logger := log.New().WithField("controller", "app-service")
	sdk.Watch(resource, kind, namespace, resyncPeriod)
	sdk.Handle(controller.NewHandler(logger))
	sdk.Run(context.TODO())
}

On the other hand, Helm charts are typically implemented using YAML templates that define the resources that run a Kubernetes cluster-based application. They use the Go template language to allow for variable substitution and customization, and can also include scripts for custom logic or automation. Here's a basic example of a Helm chart written in YAML:

apiVersion: v1
kind: Service
metadata:
  name: {{ .Release.Name }}-{{ .Values.service.name }}
  labels:
    app: {{ .Release.Name }}
    chart: {{ .Chart.Name }}-{{ .Chart.Version }}
    release: {{ .Release.Name }}
    heritage: {{ .Release.Service }}
spec:
  type: {{ .Values.service.type }}
  ports:
    - port: {{ .Values.service.port }}
      targetPort: {{ .Values.service.targetPort }}
  selector:
    app: {{ .Release.Name }}
    release: {{ .Release.Name }}

As you can see, both k8s Operators and Helm charts have their own unique syntax and code structures. However, both can be powerful tools for managing and deploying applications on a k8s cluster, and can help make the job of a DevOps engineer a little easier. 

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Best Practices To Follow To Develop Kubernetes Operators

No matter how much perfection one tends to achieve during operator generation, errors and flaws tend to take place. Things can go out of hand when developers stick to outdated practices such as using the StatefulSet and Persistent Volumes.

These conventional practices have lost significance in the modern era. Hence, developers must learn about recent and relevant practices as they want to achieve perfection during k8s operator development. We have listed the best approaches.

1. Work on one operator at a time

Seeing the widespread features and facilities of the operator, it’s obvious to get greedy and plan to use manyOperators per application. However, this is an ideal practice. Developers must understand that 1 operator/application is enough. They should also work on one operator at a time. This way, it’s easy to develop Operators with specific intentions in mind and with fewer redo and errors.

2. Try using Kubebuilder

It’s easy to have the aid of a tool that brings every possible resource that is required for developing operators. Kubebuilder is one such tool. This is a full-fledged development kit that enables developers to build and publish Controllers and Kubernetes API.  

One doesn’t have to have extensive domain knowledge to build operators. Kubebuilder has an automated implementation process and allows developers to build Operators over a few clicks.

3. Always try to use declarative APIs

Try to stick to declarative APIs as you plan to build Kubernetes Operators and avoid imperative APIs completely. This is recommended because Kubernetes APIs are also declarative and using declarative APIs for generating operators makes the entire development fully synced.  

Declarative APIs also permits developers to express the required cluster state and use it accordingly.

4. Try asynchronous sync loops

If you wish to stop error spread within an operator, you must try using the asynchronous sync loops as they are capable to block the errors in the early stage. These loops disable the current synced call if an error like failed pod is figured out. 

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Conclusion

In conclusion, Kubernetes Operators provide a high-level interface for the app’s better management and can help automate certain tasks in the app management process. This makes them especially useful for managing complex or mission-critical applications, as they can help ensure that your applications are always running smoothly and reliably.

However, it's important to choose the right tool for the job. While Operators greatly manage complex k8s projects, they might not be the best choice for every situation. For example, if you're looking to quickly deploy a simple application or component, you might be better off using Helm charts. Helm charts provide a simple way to package and deploy/launch solutions, and can be easily shared and reused across different clusters.