Deploying applications on Kubernetes and OpenShift platforms is straightforward with built-in resources like pods, deployments, and services. The real complexity emerges when managing these applications in production environments. Tasks such as configuration updates, monitoring, upgrades, and decommissioning—especially for stateful applications like databases and messaging systems—require specialized operational knowledge. Traditionally, teams handle these responsibilities through scattered scripts, manual commands, and tribal knowledge, creating inefficiency and risk. An openshift operator solves this problem by packaging operational expertise directly into the application, automating lifecycle management and eliminating error-prone manual processes. This article examines how operators work and their role in streamlining Day 1 and Day 2 operations.
Understanding Operators and Their Role
Managing applications beyond their initial deployment requires specialized knowledge that traditionally resides with operations teams. Consider deploying Argo CD, a GitOps continuous delivery platform, on a Kubernetes cluster. The standard approach uses manifest files or Helm charts for installation, which handles the basics effectively. Yet this baseline setup falls short of production requirements.
Production environments demand continuous attention: adjusting capacity to meet traffic patterns, applying version updates, creating backups, and tracking system health. Each task requires deep understanding of the application's architecture and behavior. This expertise typically exists in documentation, automation scripts, operational runbooks, or simply in the experience of system administrators. When critical situations arise—system failures, security breaches, or scheduled maintenance—teams must execute these procedures manually, creating pressure and opportunity for mistakes.
The Operator Solution
Operators transform this operational knowledge into executable code embedded within the application package itself. Rather than relying on external processes and human intervention, an operator extends the platform's native capabilities through custom resource definitions. These extensions introduce application-specific controllers that handle lifecycle management autonomously, making intelligent decisions based on encoded expertise.
The Argo CD operator demonstrates this approach by exposing custom APIs that simplify complex management tasks. Through the OpenShift OperatorHub interface, administrators can install the operator and access APIs including Argo CD, Application, ApplicationSet, AppProject, Argo CDExport, and NotificationsConfig. These interfaces abstract the underlying complexity of running Argo CD in production.
Configuration and Deployment
Operators provide flexible deployment options through channels and installation modes. Update channels determine the source for receiving new versions—the Argo CD operator uses an alpha channel for updates. Installation modes define the operator's reach within the cluster: selecting "All namespaces" grants cluster-wide access, while the operator itself resides in the openshift-operators namespace. Update approval can be configured as automatic, allowing seamless version transitions without manual intervention.
This architecture eliminates the fragmentation of operational procedures across different teams and tools. Instead of maintaining separate scripts and documentation, the operator encapsulates best practices and operational logic in a consistent, testable, and repeatable format. The result is reduced operational overhead, fewer human errors, and improved reliability for production workloads.
Operands and Operator Scope
An operator manages specific workloads and applications, which are collectively known as operands. These represent the actual running components that deliver functionality to users. When the Argo CD operator creates a cluster instance, it generates multiple operand resources that host the necessary workloads, including components like argocd-server and argocd-notifications-controller. These operands are the tangible manifestation of the operator's management activities, representing the deployed application infrastructure.
Cluster-Scoped Operators
OpenShift operators operate at two distinct levels: cluster-wide or namespace-specific. Cluster-scoped operators monitor and control resources throughout the entire cluster, across every namespace. This broad reach requires extensive permissions granted through cluster roles and cluster role bindings, enabling the operator to act on any resource regardless of its location.
Certificate management tools like cert-manager exemplify cluster-scoped operators, as do platform operators visible through the command "oc get co". These operators provide centralized control and simplified deployment patterns, managing resources from a single point of administration. However, this expansive reach introduces elevated risk. A security vulnerability in a cluster-scoped operator could compromise the entire platform due to its broad permissions. Similarly, configuration errors or software defects propagate across all projects, potentially affecting every application running on the cluster.
Namespace-Scoped Operators
Namespace-scoped operators take a more focused approach, monitoring and managing resources within designated namespaces or OpenShift projects. Their permissions are constrained through roles and role bindings that apply only to their assigned namespace, creating natural boundaries for access control.
This limited scope delivers significant advantages in isolation, flexibility, and security. When issues occur—whether from upgrades, security incidents, or system failures—the impact remains contained within the namespace boundary. Other projects continue operating normally, unaffected by problems in isolated environments. This separation allows different teams to manage their own operators independently, applying updates and configurations according to their specific schedules and requirements.
The choice between cluster-scoped and namespace-scoped operators depends on the application's requirements and organizational policies. Applications requiring cluster-wide visibility benefit from cluster scope, while those serving specific teams or projects work better with namespace isolation. Understanding these scoping options helps architects design operator deployments that balance operational efficiency with security and risk management.
The Operator Framework Components
Building and managing operators at scale requires specialized tooling that addresses both development and operational concerns. The Operator Framework provides an integrated collection of tools designed to streamline the entire operator lifecycle, from initial creation through production deployment and ongoing management.
Operator SDK for Development
The Operator SDK serves as the foundation for operator development, offering a comprehensive framework that simplifies building, testing, and packaging. Rather than starting from scratch, developers leverage high-level abstractions, automated scaffolding, and code generation utilities that accelerate the initial setup process. This allows developers to concentrate on what matters most: encoding application-specific operational intelligence into custom controllers.
The SDK enables developers to implement upgrade strategies, scaling algorithms, and backup procedures using the controller runtime library, which manages the underlying reconciliation loop. Built-in patterns and established best practices guide developers toward creating sophisticated, automated, production-grade operators. The framework supports multiple development approaches, allowing teams to build operators using Go for maximum flexibility, Helm for packaging existing charts, or Ansible for leveraging automation playbooks.
Operator Lifecycle Manager
While operators automate application management, deploying numerous operators across multiple clusters creates its own operational complexity. Tracking operator versions across different environments, resolving dependencies between operators sharing common components, and maintaining consistent installations become significant challenges at scale. The Operator Lifecycle Manager addresses these issues through a comprehensive management framework.
OLM enables catalog-based discovery, allowing administrators to browse available operators from centralized repositories. It automatically resolves dependencies between operators, ensuring all required components are present before installation. Update channels provide controlled pathways for receiving new versions, while approval workflows give teams control over when updates apply. The framework supports automatic over-the-air updates for both operators and the applications they manage, reducing manual maintenance overhead.
OperatorHub for Discovery
OpenShift includes OperatorHub, an embedded web console that provides a centralized marketplace for discovering and installing operators. This graphical interface eliminates the need for manual operator deployment, offering a curated catalog of certified and community operators. Administrators can browse available operators, review their capabilities, and install them with minimal effort. The integration between OperatorHub and OLM creates a seamless experience from discovery through installation and ongoing lifecycle management, making operator adoption accessible to teams regardless of their Kubernetes expertise.
Conclusion
Managing cloud-native applications in production environments extends far beyond initial deployment. The operational challenges of Day 1 and Day 2 activities—configuration management, monitoring, upgrades, and maintenance—require specialized knowledge that traditionally depends on manual intervention, scattered documentation, and experienced personnel. This approach creates bottlenecks, introduces errors, and fails to scale effectively across growing infrastructure.
Operators fundamentally change this paradigm by embedding operational expertise directly into application packages. Through custom resource definitions and intelligent controllers, operators automate complex lifecycle management tasks that once required human decision-making. They transform tribal knowledge into executable code, making sophisticated operational procedures consistent, repeatable, and reliable.
The Operator Framework provides the essential tooling to realize this vision at scale. The Operator SDK accelerates development by providing scaffolding and best practices for building operators across multiple languages and frameworks. The Operator Lifecycle Manager addresses the meta-challenge of managing operators themselves, offering dependency resolution, update channels, and automated upgrades across distributed environments. OperatorHub completes the ecosystem by providing accessible discovery and installation through an integrated web interface.
Whether choosing cluster-scoped operators for centralized management or namespace-scoped operators for isolation and security, organizations gain powerful capabilities for automating application operations. This automation reduces operational overhead, minimizes human error, and enables teams to manage complex stateful workloads with confidence. As cloud-native adoption accelerates, operators have become essential tools for organizations seeking to operate production applications efficiently and reliably at scale.
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