Kubernetes has become the gold standard for container orchestration. However, running stateful applications on [Kubernetes can be challenging](https://konghq.com/blog/enterprise/solve-kubernetes-challenges)Kubernetes can be challenging. This is where Kubernetes operators come in. [Kubernetes operators resolve common issues](https://konghq.com/blog/enterprise/how-kubernetes-gets-work-done)Kubernetes operators resolve common issues like automating management of complex applications such as databases, caches, and monitoring systems
In this article, we’ll cover what a Kubernetes operator is, the benefits of using them, and how they compare to other tools like Ingress controllers and Helm. We’ll give you some real-world examples of popular Kubernetes controllers, including the Prometheus Operator for monitoring solutions and the Postgres Operator for managing high-availability PostgreSQL database clusters, and how they compare. And show you how to build and deploy custom operators yourself.
These operators are critical for running production workloads on [Kubernetes](https://konghq.com/blog/learning-center/what-is-kubernetes)Kubernetes. By the end of this guide, you’ll understand the importance of Kubernetes operators and how they make it easier to manage applications on Kubernetes.
An operator is an extension of Kubernetes that uses resources to automatically manage applications and their components. Operators, also known as software extensions, follow Kubernetes principles and are typically built as a set of independent controllers, where each is responsible for its own subset of resources and tasks. They encode specialized knowledge about applications to automate deployment, scaling, upgrades, and more for complex applications running on Kubernetes clusters.
Operators do not require custom resources, although they very often work with those. There are also operators that work with built-in resource types that come standard with Kubernetes out-of-the-box.
Operators add extra functionality to Kubernetes for managing specific applications that have multiple components which need coordination. For example, a Postgres operator helps run PostgreSQL databases on Kubernetes by handling tasks like provisioning, failover, and backups automatically.
Operators follow the "operator pattern" where they constantly watch for certain resources and then perform actions to reach or maintain a desired state. This makes them ideal for deploying and running stateful services like databases, caches and monitoring systems.
By leveraging native Kubernetes approaches like control loops, resource management and health checks, operators simplify running production-grade applications on Kubernetes.
Now that we know what Kubernetes operators are, let's explore some of the major benefits of using operators to manage applications:
Operators encode deep application knowledge to extend Kubernetes' built-in capabilities for provisioning, auto-scaling, upgrades, failover and more. They handle complex tasks like:
By focusing on administrative best practices and implementing them directly into software, operators will simplify lifecycle management.
Operators provide a unified control plane to deploy and manage applications across on-premise, multi-cloud, edge, and hybrid environments. They abstract infrastructure complexity by:
This makes hybrid deployments smoother.
By leveraging native Kubernetes APIs and resources like the control loop, operators minimize how much custom YAML you need to write. They simplify automation by:
Operators enhance Kubernetes, reducing manual operations. These richer advantages of deployment automation, smooth hybrid cloud portability and simplified app management make operators a very useful tool for running production workloads.
Next we'll explore how operators achieve this by working with Kubernetes API
We looked at why operators are useful. Now let's explore how they work to simplify app management on Kubernetes.
Operators integrate with core Kubernetes components to automate application deployment and management. They extend the functionality of Kubernetes including:
Additionally, operators encode intricate knowledge of managing production-grade services like databases, caches, streaming platforms and more. For example, an operator knows how to:
By leveraging Kubernetes and adding app-specific logic, operators simplify deploying and managing complex stateful apps compared to doing it manually.
Next, we’ll compare operators to other Kubernetes tools before looking at real examples.
Kubernetes controllers and operators are both extensions of the control plane that automate tasks, but they differ in scope and complexity.
Kubernetes controllers are control loops that watch the state of the cluster via the API server and make changes to drive the actual state towards the desired state. They handle common tasks like replicating pods, tracking node assignments, handling rolling updates, and maintaining the desired number of nodes.
Controllers use a declarative model where you specify the desired state in API objects, and the controllers do the work to make that state happen. The Kubernetes controller manager runs a set of default controllers like replication controller, endpoints controller, namespace controller, etc.
Operators, on the other hand, are custom Kubernetes controllers that use custom resources to manage applications and their components. They extend the Kubernetes control plane by allowing you to define application-specific controllers for complex stateful applications.
An operator encapsulates human operational knowledge into software, reducing the effort of running complex applications on Kubernetes. It can perform app-specific tasks like:
Operators allow you to define a desired state for your application using custom resources - the operator then continuously takes action to reach and maintain this state. This can involve complex workflows like provisioning infrastructure, setting up monitoring, taking backups, coordinating upgrades, etc.
In essence, operators are an extension of the controller pattern that allows capturing more complex, app-specific workflows beyond the generic functionality Kubernetes controllers provide. By combining general Kubernetes controllers with application-specific operators, you can achieve end-to-end automation of complex applications running on Kubernetes.
Next, we'll contrast operators with another common Kubernetes pattern - Helm charts.
Helm streamlines installing and upgrading applications on Kubernetes through packaging formats like charts, templating, and version management. Helm focuses on the initial deployment of applications.
In contrast, operators focus more on the ongoing lifecycle after applications have been deployed. Operators pick up where Helm charts leave off to automate repetitive management tasks like:
Together Helm and operators provide complementary capabilities for application management lifecycle. Helm handles templated deployments while operators manage complex day-2 operations.
By extending native Kubernetes APIs, encoding domain expertise into specialist controllers, and integrating with Kubernetes resources like jobs, statefulsets and RBAC, operators build on Kubernetes to simplify previously complex application management for stateful services.
Next we'll look at some real-world examples of popular Kubernetes operators used by cloud native organizations globally.
We've understood how operators work. Let's explore some popular real-world operators used by organizations:
**Kong Gateway Operator** - Provides a Kubernetes-native way to deploy and manage Kong Gateway on Kubernetes clusters. It allows users to deploy and configure Kong Gateway using Kubernetes Custom Resource Definitions (CRDs) instead of traditional tools like Helm.
**RBAC Manager Operator** - Simplifies Role-Based Access Control (RBAC) management on Kubernetes clusters by centralizing user permission policies instead of individual namespaces. It automates complex tasks like policy reconciliation, user group management, and audit logging.
**HPA Operator **- The Horizontal Pod Autoscaler (HPA) Operator manages and automates pod auto-scaling across namespaces using Kubernetes-native HorizontalPodAutoscaler resources. It scales pods based on metrics and ensures availability during surges.
**Istio Operator** - This operator simplifies installation, upgrade, and lifecycle management of [Istio's service mesh](https://konghq.com/blog/learning-center/what-is-istio-service-mesh)Istio's service mesh capabilities directly on Kubernetes clusters. It automates tasks like traffic flow control, security policy configuration, certificate management, and more.
**Elastic Cloud on Kubernetes** - This operator deploys and manages complex Elasticsearch clusters on Kubernetes by automating provisioning, scaling, upgrades, retries and failure recovery across various environments like public cloud and on-premise.
**Grafana Operator** - Makes deploying and managing Grafana analytics and monitoring dashboards on Kubernetes simple through automation of Grafana instances, associated datastores, dashboard and datasource configuration along with seamless upgrades.
These demonstrate how operators encapsulate complex application knowledge to automate provisioning, high availability, scalability, upgrades and more - making Kubernetes much easier for running business-critical systems.
Next we will provide a quick overview of building your own custom operators.
Creating and managing Kubernetes operators efficiently requires a solid grasp of the operator pattern and operator framework. These tools allow you to extend Kubernetes functionality with custom controllers tailored to your unique requirements. By using a programming language, you can define custom resources that align with your application's needs and automate complex tasks.
Interacting seamlessly with the Kubernetes API is essential for operators to communicate effectively with the cluster's control plane. Adhering to software development best practices ensures that your operators are highly available and capable of automating deployment processes, leading to smoother operations overall.
For streamlined configuration management and workload handling, consider implementing Helm charts, which simplify the deployment process and enhance scalability. Exploring popular examples on platforms like GitHub can provide valuable insights into how Kubernetes operators can enhance the versatility and functionality of your containerized applications.
While several operators exist, you may also build your own custom operator for specific applications using frameworks like:
**Operator SDK**
This is an open source toolkit to facilitate building Kubernetes native operators in Golang, Ansible, or Helm. It handles boilerplate code and uses controller-runtime libraries to simplify development. The SDK provides high level APIs and abstractions for common operator tasks. It supports multiple languages and frameworks while integrating well with Kubernetes ecosystem tools.
**Kubebuilder**
It is a framework to construct Kubernetes APIs and operators for core resources and external services by extending the Kubernetes control plane. It provides code generation and custom resource validation. Kubebuilder generates CRDs, controllers, admission webhooks etc. It provides a testing harness and tooling while integrating with RBAC, aggregated APIs for easier extension.
Key steps to build an operator involve:
Using operators involves deploying them into Kubernetes clusters through manifests so they can watch over applications via custom resources. Common workflows include:
By bridging intimate application knowledge with Kubernetes automation capabilities, operators greatly simplify app deployment and lifecycle management - making them a key part of the cloud-native ecosystem.
**Q: What is a Kubernetes operator?**
A: A Kubernetes operator is an extension of Kubernetes that uses resources to automatically manage applications and their components. It's a software extension that encodes specialized knowledge about applications to automate deployment, scaling, upgrades, and more for complex applications running on Kubernetes clusters. Operators follow the 'operator pattern' where they constantly watch for certain resources and perform actions to reach or maintain a desired state.
**Q: What's the difference between Kubernetes controller and Kubernetes operator?**
A: Kubernetes controllers are control loops that watch the state of the cluster via the API server and make changes to drive the actual state towards the desired state. They handle common tasks like replicating pods and tracking node assignments. Operators, on the other hand, are custom Kubernetes controllers that use custom resources to manage applications and their components. Operators extend the Kubernetes control plane by allowing you to define application-specific controllers for complex stateful applications, encapsulating human operational knowledge into software.
**Q: What are the benefits of using Kubernetes operators in container orchestration?**
A: The benefits of using Kubernetes operators include extended functionality for tasks like automated provisioning, scaling, upgrades, and failover handling. They enable hybrid environment management across on-premise, multi-cloud, edge, and hybrid environments. Operators simplify Kubernetes automation by handling app lifecycles automatically and encapsulating domain expertise in code. This leads to reduced operational complexity and increased resilience. Additionally, operators simplify the deployment and management of stateful applications on Kubernetes.
**Q: How does a Kubernetes operator differ from traditional deployment methods in containerized environments?**
A: Kubernetes operators differ from traditional deployment methods by providing more automation and application-specific management. While traditional methods might rely on manual interventions or generic scripts, operators encode deep application knowledge to extend Kubernetes' capabilities. They handle complex tasks like automated provisioning, scaling, upgrades, and failovers specific to the application they manage. Operators also provide a unified control plane for hybrid environments and simplify Kubernetes automation, reducing the need for custom YAML and manual operations.
**Q: Are there any challenges or limitations when using Kubernetes operators?**
A: While the document doesn't explicitly mention challenges or limitations, some potential issues can be inferred. These include complexity in creating custom operators, which requires a solid grasp of the operator pattern and Kubernetes APIs. There's also a need for rigorous testing and validation before deployment to ensure reliability. Potential overhead in managing and maintaining multiple operators for different applications may arise. Teams new to Kubernetes might face a learning curve in understanding and effectively using operators. Lastly, there's a need to keep operators updated as the Kubernetes ecosystem evolves.
**Continued Learning & Related Content**[](https://konghq.com/blog/product-releases/kong-gateway-operator-oss)
Kubernetes has become the standard for [container orchestration](https://konghq.com/blog/enterprise/container-orchestration)container orchestration, but running stateful, production-grade services on it remains challenging. This is where Kubernetes operators come in. They automate complex application management by encoding admin logic into software directly.
We covered how operators extend Kubernetes functionality by integrating with its control plane and API server to manage applications via custom resources. Operators contain application domain knowledge to simplify tasks like auto-scaling, upgrades, failovers, and more.
Compared to manual workflows or general Kubernetes configuration, operators provide a simpler and more robust way of deploying and managing stateful applications on Kubernetes. That means reduced operations overhead, increased resilience, and hybrid/multi-cloud portability.
As Kubernetes becomes the runtime across environments from cloud to edge, operators will play a pivotal role in making application management easier. They simplify automation for entire application lifecycles. By leveraging operators, organizations can reduce operational complexity and make the most of Kubernetes.[](https://konghq.com/blog/learning-center/kubernetes-architecture)
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