Kubernetes Operator: How It Works, Examples, and Best Practices

What Is a Kubernetes Operator?

A Kubernetes Operator is a method of packaging, deploying, and managing a Kubernetes application. It extends Kubernetes capabilities to automate the lifecycle of complex stateful applications.

Inspired by a human operator’s role, a Kubernetes Operator uses Custom Resource Definitions (CRDs) to add functionality to Kubernetes environments. This enables users to deploy applications as native Kubernetes objects, maintaining control and automation over deployment and operations.

Operators manage specific application types, ranging from databases to monitoring systems, adapting to diverse workloads. They simplify application administration by encapsulating its lifecycle logic in code, reducing the need for human intervention. The operator pattern was introduced by CoreOS to provide better application management beyond basic container orchestration, offering higher-level control and automation.

The Need for Kubernetes Operators

The complexity of managing stateful applications in Kubernetes environments gave rise to the need for operators. Typical Kubernetes operations involve deploying containers and ensuring their healthy state. However, stateful applications require intricate management, such as configuring replication, failover, and backup policies, which standard Kubernetes doesn’t natively support. Operators bridge this gap by automating tasks traditionally done manually.

As organizations increasingly adopt Kubernetes for critical systems, improving scalability and reliability becomes a priority. Operators ensure that complex applications function efficiently and scale according to demand, without manual intervention. They provide deep domain expertise within applications, handling complex operational tasks like upgrades, failure recovery, and backup automations.

How Kubernetes Operators Work

Kubernetes operators function by leveraging custom resource definitions (CRDs) and controllers to manage the lifecycle of an application. The CRD defines a new Kubernetes resource type, while the controller continuously monitors the cluster state and ensures the desired configuration is maintained.

Custom resource definitions (CRDs): CRDs allow users to define custom Kubernetes resources that represent their application. These resources act as an extension of the Kubernetes API, enabling the cluster to understand and manage new application-specific objects.
Controller logic: The operator includes a controller that observes changes in the custom resource and reconciles the application’s state to match the desired configuration. This logic is responsible for automating application deployment, scaling, and recovery processes.
Reconciliation loop: The reconciliation loop is a core mechanism in operators. It continuously watches the state of the application and takes corrective actions if discrepancies arise. For example, if a database pod crashes, the operator detects the failure and initiates recovery by launching a replacement instance.
Automation of complex tasks: Beyond deployment and scaling, Operators handle tasks such as rolling updates, version upgrades, backup scheduling, and failover orchestration.

Related content: Read our guide to Kubernetes deployment strategy

Benefits of Using Kubernetes Operators

Kubernetes operators provide several advantages, making it easier to manage complex applications in a Kubernetes environment. Key benefits include:

Automation of operational tasks: Operators automate critical processes such as provisioning, scaling, backups, and updates.
Simplified application management: They encapsulate application-specific operational knowledge, making it easier to deploy, configure, and maintain stateful applications.
Improved reliability and consistency: By continuously monitoring and reconciling the application’s state, operators help ensure stability.
Enhanced scalability: Operators enable dynamic scaling based on workload demands, optimizing resource usage.
Seamless upgrades and rollbacks: They handle software updates by managing versioning, dependency management, and automated rollbacks in case of failures.

Custom resource extensibility: Operators extend Kubernetes functionality through custom resources, allowing teams to define and manage domain-specific workloads.

TIPS FROM THE EXPERT

In my experience, here are tips that can help you better design, deploy, and maintain Kubernetes Operators:

Prioritize reconciling small, atomic changes: Ensure the operator reconciles incremental changes instead of applying large updates. Breaking down updates into small, manageable tasks reduces the risk of inconsistencies and failed reconciliation loops, providing more stable automation and faster error recovery.
Leverage Kubernetes-native events for proactive issue detection: Operators can listen to and react to Kubernetes events to detect issues proactively. By integrating with native event streams, the operator can trigger pre-emptive fixes, such as scaling pods before they fail under heavy load or initiating backups when a critical update is scheduled.
Use leader election to avoid duplicate operations: In high-availability environments, run multiple replicas of the operator but ensure that only one is actively making changes through leader election. This prevents conflicting operations and ensures safe, coordinated resource management.
Integrate rollback mechanisms for failed updates: Implement rollback logic within the operator to revert changes when an upgrade or configuration update fails. Monitor success criteria (e.g., pod readiness or custom health checks) to trigger automated rollbacks and maintain system stability.
Optimize reconciliation loops for performance: Avoid continuously polling the cluster with high frequency. Instead, use Kubernetes Informers or efficient watchers to reduce the load on the API server while maintaining near-real-time awareness of changes. This optimization is critical in large clusters.

Kubernetes Operators vs. Controllers vs. Helm

Kubernetes operators, controllers, and Helm charts are all tools used for managing applications in Kubernetes, but they serve different purposes and operate at different levels of automation and abstraction.

Kubernetes Operators

Operators extend Kubernetes by encapsulating application-specific logic into custom controllers. They leverage custom resource definitions (CRDs) and continuously reconcile the application’s desired state with the actual state. Operators automate tasks such as self-healing, scaling, backups, and upgrades, making them useful for managing stateful applications like databases or monitoring systems.

Kubernetes Controllers

Controllers are a core part of Kubernetes that manage built-in resources such as deployments, replicaSets, and nodes. They follow a control loop mechanism, continuously monitoring and adjusting resources to match the desired state. Unlike operators, controllers manage generic Kubernetes objects and do not provide domain-specific automation for complex applications. Operators usually include one or more controllers in their implementation, and may or may not have their own CRDs.

Helm

Helm is a package manager for Kubernetes, simplifying application deployment through reusable configuration templates called charts. While Helm simplifies installation and upgrades, it does not include active monitoring or self-healing capabilities. It is best suited for stateless applications or simple stateful applications where automation beyond deployment is not required.

Key Differences

Feature	Kubernetes Operator	Kubernetes Controller	Helm
Primary Function	Application lifecycle management	Kubernetes resource management	Application packaging and deployment
Automation Level	High (self-healing, upgrades, backups)	Moderate (ensures desired state)	Low (templated deployment)
Uses CRDs?	Yes	Sometimes	No
Best For	Stateful applications with complex logic	Built-in Kubernetes resources	Deploying apps with pre-configured settings
Active Monitoring?	Yes	Yes	No

Examples of Kubernetes Operators

Kubernetes operators are widely used to manage complex applications, particularly stateful workloads. Here are some popular examples:

Prometheus Operator: The Prometheus Operator simplifies the deployment and management of Prometheus monitoring instances. It provides Kubernetes-native configuration management and automates tasks like discovery, scaling, and upgrades.
MySQL Operator: MySQL operators, such as those provided by Oracle or Percona, manage MySQL database instances in Kubernetes. They handle automatic failover, backup scheduling, scaling, and version upgrades.
Elasticsearch Operator: The Elasticsearch Operator, often provided by Elastic or Red Hat, automates the deployment and scaling of Elasticsearch clusters. It ensures cluster health, manages rolling upgrades, and optimizes storage configurations.
Kafka Operator: Kafka operators, like Strimzi, manage Apache Kafka clusters in Kubernetes. They handle broker provisioning, topic configuration, authentication, and automated recovery.

PostgreSQL Operator: Operators like Crunchy Data’s PostgreSQL Operator provide automated management of PostgreSQL databases. They handle backup and restore processes, scaling, failover, and security configurations.

5 Best Practices for Writing Kubernetes Operators

Writing effective Kubernetes operators requires adhering to best practices to maximize benefits and performance.

1. Be Sure You Really Need an Operator

Before creating an operator, assess its necessity. Not all applications require an operator; simpler applications might be well-managed by Kubernetes controllers or Helm charts. Operators are most beneficial for complex, stateful applications requiring intricate management routines and automated processes.

Evaluate the cost-benefit ratio, considering the complexity and overheads introduced by operators. If manual interventions or simple tools suffice, an operator might add unnecessary complexity. For applications relying on deep lifecycle management, however, operators offer significant advantages in automation and operational efficiency.

2. Keep CRDs Simple

Simplicity in CRDs is crucial for effective operator management. Overcomplicated CRDs complicate implementation and maintenance, increasing the potential for errors. By focusing on core functionalities, organizations can maintain clarity and functionality.

Ensure that CRDs only encompass essential details needed for effective automation. Avoid including excessive configurations that a basic operator doesn’t utilize, as this can complicate development and debugging. Keeping CRDs straightforward ensures flexibility and eases adaptation to changing specifications or improvements.

3. One Operator per Requirement

Adopting a one-operator-per-requirement strategy can lead to clearer and more maintainable systems. Each operator should manage a single application or service, avoiding entanglement of responsibilities across multiple operators. This distinct separation simplifies debugging, deployment, and iteration processes.

This approach ensures encapsulation and specialized focus, fulfilling operational needs without interference. Should a different application need management, creating a separate operator maintains system integrity and adaptability. By focusing each operator on individual tasks, organizations mitigate risks and ensure operational efficiency for the application environment.

4. Test Your Operators

Thorough testing is essential when developing Kubernetes operators. Since operators manage critical application operations, untested code can lead to serious production issues. Testing should involve unit tests for the logic and simulated Kubernetes environments for integration assessments.

Automated testing frameworks can help validate operational functionality, offering valuable insights into performance, scalability, and failure scenarios. Conducting extensive testing minimizes risks, ensures robustness, and uncovers potential bugs before real-world deployment.

5. Consider an Operator SDK

Using an operator SDK can simplify the development of Kubernetes operators, providing ready-to-use libraries, tools, and templates. SDKs like the Operator Framework simplify the entire process, handling several intricacies involved in writing and maintaining operators.

An operator SDK reduces boilerplate code, allowing developers to focus on business logic, not the complexities of the Kubernetes API. These SDKs also provide testing and validation tooling, boosting development efficiency and reducing potential errors. By using these specialized frameworks, developers can accelerate deployment.
Read our guide to Kubernetes tools