Kubernetes Operator: A Complete Guide to Operators and the Operator Pattern

As the Kubernetes ecosystem in your organization gains traction, managing applications in production environments becomes even more complex. While Kubernetes is known for its container orchestration, it does not natively handle the full lifecycle of complex applications, such as upgrades, backups, failovers, or others. This is exactly where the Kubernetes Operators come into play. 

Kubernetes operators help extend the functionality by embedding the domain specific knowledge into the cluster and automate tasks that would otherwise need manual involvement. 

In this guide, we shall discuss all important things about the Kubernetes Operators, how the pattern works, and steps to deploy it successfully. 

What is a Kubernetes Operator?

A Kubernetes Operator is a method of packaging, deploying, and managing a Kubernetes application with custom controllers, known as operators. Unlike standard controllers, which would only handle generic Kubernetes resources such as Pods, Deployments, or Services, Operators mainly focus on the logic needed by applications. 

To put in simple words, an operator stands for: 

• It is a Custom Resource Definition (CRD) that helps set new rules/ types of a Kubernetes resource. 
• A custom controller that monitors this resource and takes action when the state changes from the regular configuration. 

For example, a database Operator can handle:

• Creating and configuring a database cluster.
• Managing scaling and failover.
• Performing backups and restores.
• Handling upgrades without downtime.

By combining CRDs with custom controllers, Kubernetes Operators allow Kubernetes to not just run applications, but also understand and manage them throughout their lifecycle.

The Kubernetes Operator Pattern Explained

The Kubernetes Operator Pattern is a design pattern in Kubernetes that has capabilities extended beyond the native capabilities. It allows you to encode domain-specific operational knowledge into Kubernetes, so that the platform can manage complex applications easily.  

At its core, the Operator Pattern follows this principle:

• Custom Resource (CR): Define new types of resources in Kubernetes (e.g., PostgresCluster, FlinkApplication).
• Controller: Continuously keep an eye on the resources and compare the actual state to the required state. 
• Reconciliation Loop: If there is still a difference, the Operator will take the necessary initiative to fix it. 

This pattern just copies the same thing that a manual intervention would be like to fix the state and keep the application in the intended running condition.

How Kubernetes Operators Work

Here is a simplified workflow of how a Kubernetes Operator works: 

1. A user would create a Custom Resource (CR) file that describes what the application should be like. 
2. Then the CR is registered within the Kubernetes using a CRD, which then extends the Kubernetes API to recognize this new resource type. 
3. The Kubernetes Operator then runs as the primary controller inside a pod and watches for changes in the system. 
4. Then it runs a reconciliation loop:  

• The Operator checks the actual state of the application.
• If it doesn’t match the desired state, the Operator takes corrective actions—like provisioning pods, reconfiguring services, or restarting failed instances.

5. The Operator never stops and keeps on running to watch for drift between the desired and actual state. 

For example, a Flink Kubernetes Operator can automatically create, scale, and manage Flink jobs. If a job fails, the Operator restarts it without manual intervention. 

Flink Kubernetes Operator: Managing Flink on Kubernetes:

Apache Flink is a powerful framework but managing it on Kubernetes is super complex. The Flink Kubernetes Operator helps simplify this process using automation for deployment, scaling, and job management. 

Key capabilities include:

• Deploying Flink jobs via YAML manifests instead of manual scripts. 
• Scale the system up/ down based on the Kubernetes workloads and restart paused jobs. 
• Manage TaskManagers and JobManagers as Kubernetes Pods. 
• Simplifies the rolling updates for Flink clusters without downtime. 

This Operator follows the same pattern as others: users define a FlinkApplication custom resource, and the Operator ensures the cluster runs as described.

Common Use Cases of a Kubernetes Operator

A Kubernetes Operator helps in simplifying the stateful and complex workloads. Some of the most common examples include: 

1. Database Management: Automatically provision, scale, create backups, and restore like PostgreSQL, MySQL, and MongoDB to ensure high availability and failure without manual intervention. 
2. Streaming & Big Data Applications: Run distributed data processing frameworks like Apache Flink, Kafka, and others. 
3. Monitoring & Logging Systems: Operators like the Prometheus Operator automate monitoring stack setup, alert rules, and Grafana dashboards. 
4. Custom Applications: Enterprises build operators for proprietary apps to handle deployments, upgrades, or maintenance tasks. 
5. Manage automatic backups for essential applications and ensure quick recovery in case of failures. 

Examples of Popular Kubernetes Operators

Some popular Kubernetes Operators include; 

• Prometheus Operator – For management and setup. 
• Postgres Operator (CrunchyData, Zalando) – for managing PostgreSQL clusters with automated backups, scaling, and failover. 
• Flink Operator – handles Flink job lifecycle. 
• Kafka Operator (Strimzi) – deployment and management for Apache Kafka clusters. 
• ElasticSearch Operator (ECK by Elastic) – manages Elasticsearch clusters on Kubernetes
• MongoDB Operator – MangoDB automation for deployment, scaling, and monitoring. 

A Kubernetes Operator is not only useful for databases, but also analytics, observability, and custom workloads. 

How to Create and Deploy a Kubernetes Operator

Building a Kubernetes Operator involves defining Custom Resources (CRDs) and writing a controller that matches the required state with the current state. Here is how you can create an operator. 

1. Create a YAML manifest file that describes the new resource type (e.g., FlinkApplication, DatabaseCluster).
2. Use tools like Operator SDK, Kubebuilder, or client libraries to create a controller logic. This controller continuously monitors the CRDs and ensures that the workloads match the desired state. 
3. Bundle the container image and other parameters as a package to run inside the cluster. 
4. Apply the CRD and controller manifests to Kubernetes for users to interact with the new resource just like native Kubernetes objects (kubectl apply -f my-resource.yaml).
5. Test, monitor, and adjust according to the requirements. 

Kubernetes Operators vs Helm Charts

Both Operators and Helm Charts simplify application management, but they differ in scope and capabilities:

Aspect	Helm Charts	Kubernetes Operators
Purpose	Templating & packaging apps	Automating complex operations
Complexity	Easy to use, less flexible	More complex, but highly customizable
State Management	No active reconciliation	Actively monitors & reconciles desired vs actual state
Use Cases	Stateless apps, simple deployments	Stateful apps, databases, streaming, upgrades
Programming Needed	None (YAML templates)	Requires coding knowledge (Go, Python, etc.)
Extensibility	Limited	Very high (custom logic & automation)

Challenges and Limitations of Kubernetes Operators

While a Kubernetes Operator is powerful, they come with challenges:

• Writing operators require technical knowledge. 
• Custom operators need long term support. 
• Misconfigured operators can pose a threat to security. 
• Teams must adapt to the CRD for successful programming. 

Conclusion

A Kubernetes Operator can extend the platform by automation. A Kubernetes Operator gaps the space between manual DevOps processes and fully automated, self-healing workspaces.