Managing applications on Red Hat OpenShift gets complicated quickly. Updates break things, scaling requires constant attention, and recovery from failures eats up valuable time. OpenShift Operators eliminate these headaches by automating tasks that normally demand manual work from your team. These Kubernetes-native tools package, deploy, and manage services across your cluster. They act like experienced site reliability engineers, monitoring your environment and making adjustments to keep everything running as configured.
Need to deploy databases, messaging systems, or monitoring tools? An operator handles the setup and manages ongoing operations like backups, upgrades, and failover. This blog explains how OpenShift operators function, their key components, and practical implementation strategies that protect your critical workloads from disruption.
What Are OpenShift Operators?
OpenShift operators extend Kubernetes functionality by encoding operational knowledge into software. Think of them as automated administrators that know exactly how to deploy, configure, and maintain specific applications throughout their entire lifecycle.
The Basics of OpenShift Operator Technology
An OpenShift Operator is a method of packaging, deploying, and managing a Kubernetes application. Red Hat OpenShift Operators use the Kubernetes API to observe your cluster’s current state and make decisions to maintain your desired configuration. For example, when you specify that you want three database replicas running, the OpenShift Operator continuously monitors this requirement and takes action if reality drifts from your specification.
These operators follow a pattern called the reconciliation loop. They watch for changes, compare the actual state against the desired state, and execute corrections when discrepancies appear. If a pod crashes, the OpenShift Operator detects the failure and launches a replacement. If you need to upgrade an application version, the operator handles the rollout sequence, health checks, and rollback procedures if problems emerge.
OpenShift operators run continuous control loops to check current application status against desired state, automatically correcting any deviations.
Red Hat OpenShift operators package everything an application needs: installation procedures, configuration templates, upgrade paths, backup strategies, and recovery workflows. This approach converts specialized knowledge, the kind that typically lives in runbooks and experienced engineers’ heads, into executable code that runs consistently across environments.
How Red Hat OpenShift Operators Differ from Traditional Management
Traditional application management relies on scripts, manual procedures, and human intervention. You write deployment scripts, create monitoring alerts, and respond when things break. According to Densify’s OpenShift Architecture guide, OpenShift operators represent a fundamental shift by handling cluster-wide operations through automated platform operators and add-on operators for specific business needs.
Standard configuration management tools push changes but don’t continuously verify results. Red Hat OpenShift Operators operate differently: They maintain persistent awareness of your application’s health and automatically correct problems without waiting for alerts or ticket escalations. When storage fills up, networking changes, or dependencies fail, OpenShift operators respond based on programmed operational expertise rather than waiting for someone to notice dashboard warnings.
Automated Red Hat OpenShift Data Protection & Intelligent Recovery
Perform secure application-centric backups of containers, VMs, helm & operators
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Scale with fully automated policy-driven backup-and-restore workflows
Core Components of OpenShift Operators
To understand how OpenShift operators work, you need to know the ecosystem that supports them. Red Hat built a framework that handles everything from operator discovery to installation and lifecycle management. These components create a consistent experience for deploying and managing applications across your infrastructure.
The Operator Framework Explained
The Operator Framework provides the foundation for building, testing, and managing OpenShift operators. It includes tools and libraries that help developers create operators without starting from zero. The framework’s centerpiece is the operator SDK, which streamlines development through code scaffolding and handles common patterns you’d otherwise code manually.
You can build operators using Go, Ansible, or Helm, based on your team’s expertise and what your application needs. The SDK manages Kubernetes API interactions, freeing you to concentrate on business logic instead of infrastructure code. This cuts development time significantly and ensures that your operators follow proven patterns that perform reliably across different environments.
The Operator Framework standardizes how operators interact with Kubernetes, creating predictable behavior regardless of which team built the operator or which application it manages.
The framework also includes testing tools that simulate Kubernetes environments, allowing you to validate operator behavior before deployment. This testing capability identifies configuration errors and logic problems early, preventing issues that would otherwise appear in production and cause downtime.
OperatorHub: Your Application Marketplace
OperatorHub functions as a centralized catalog where you discover and install OpenShift operators. It aggregates operators from multiple sources: Red Hat–certified operators, community operators, and custom operators your organization builds internally. The interface lets you browse available operators, read documentation, and install them directly into your cluster with a few clicks.
Each operator listing provides details about functionality, required permissions, supported versions, and installation prerequisites. You can filter by category, search by name, or explore featured operators. Centralization saves you from hunting through repositories or documentation sites to find the right tool.
Red Hat OpenShift operators available through OperatorHub undergo different levels of validation. Red Hat–certified operators receive thorough testing and full support, while community operators offer broader options with community-driven maintenance. This tiered approach balances security with flexibility, letting you choose operators that match your risk tolerance and support requirements.
Operator Lifecycle Manager (OLM)
The Operator Lifecycle Manager handles installation, updates, and dependency management for OpenShift Operators running in your cluster. When you install an operator from OperatorHub, OLM creates the necessary resources, configures permissions, and monitors the operator’s health. It ensures that operators receive updates without manual intervention and manages version compatibility across dependencies.
OLM tracks which operators are installed, their versions, and their relationships to each other. If an operator requires specific custom resource definitions or depends on another operator, OLM verifies that these prerequisites are satisfied before installation proceeds. This dependency resolution prevents installation failures and configuration conflicts that could break your applications.
Upgrade management represents one of OLM’s most valuable capabilities. It can perform automatic updates based on approval policies you define or wait for manual approval before applying changes. This control helps you stay current with security patches and features while maintaining stability during critical business periods.
The Operator Registry
The Operator Registry stores metadata about available OpenShift operators, including their versions, dependencies, and installation requirements. It acts as the backend database that powers OperatorHub, maintaining information about operator catalogs and their contents. Organizations can create private registries to distribute internal operators alongside public ones, keeping proprietary tools secure.
Registry entries use a standardized format that describes what an operator does, what resources it creates, and how it should be configured. The metadata enables OLM to make informed decisions about installations and upgrades. When you search OperatorHub, you’re querying these registry entries behind the scenes.
Here’s how the different operator components compare in terms of their primary functions and how you interact with each one.
Component | Primary Function | User Interaction |
Operator Framework | Development toolkit for building and testing operators | Developers use SDK and testing tools |
OperatorHub | Catalog interface for discovering and selecting operators | Browse, search, and initiate installations |
Operator Lifecycle Manager | Installation, update, and dependency management | Configure update policies and approve changes |
Operator Registry | Metadata storage and catalog management | Minimal direct interaction; powers OperatorHub queries |
These components work together to create a cohesive ecosystem. The framework produces operators, the registry stores their definitions, OperatorHub presents them to users, and OLM manages their lifecycle once installed. This architecture separates concerns while maintaining tight integration between each stage of the operator lifecycle, giving you a streamlined experience from development through production deployment.
How OpenShift Operators Work
Understanding how OpenShift Operators function internally reveals why they excel at managing applications compared to traditional methods. These tools combine Kubernetes resources with custom logic to build self-managing applications that respond to changes automatically with no manual intervention required.
Automating Kubernetes Application Management
OpenShift operators handle tasks that traditionally demand manual execution or custom scripts. They monitor cluster resources, interpret application requirements, and execute the necessary actions to maintain your desired states. When you deploy a database using an OpenShift operator, it goes far beyond creating pods and services; it configures replication, establishes backup schedules, monitors performance metrics, and adjusts resources based on current load.
The automation continues long after initial deployment. Red Hat OpenShift operators manage ongoing operations, including rolling updates, certificate rotation, and capacity adjustments. If your database needs additional storage, the operator expands volumes automatically. When network policies change, it reconfigures connections to keep your application running smoothly. This continuous management reduces operational overhead for your team and eliminates the delay between detecting an issue and resolving it.
OpenShift operators encode operational expertise into software, transforming reactive maintenance tasks into automated responses that execute consistently across every environment.
Custom Resource Definitions (CRDs) in Action
CRDs extend the Kubernetes API with application-specific objects. When you install an OpenShift operator, it typically creates one or more CRDs that represent the components it manages. A database operator, for instance, might define a CRD called “DatabaseCluster” that specifies parameters like version, replica count, and storage size.
You interact with these CRDs exactly like native Kubernetes resources. Create a DatabaseCluster object, and the operator detects it and provisions the infrastructure. Modify the replica count, and the operator scales the cluster accordingly. Delete the object, and the operator cleans up all associated resources. This approach creates a consistent interface regardless of which application the operator manages. Your deployment workflows remain uniform whether you’re deploying databases, message queues, or monitoring systems: Each OpenShift Operator presents its configuration through CRDs that follow the same patterns.
The Control Loop Pattern
Every OpenShift operator runs a control loop that continuously compares actual cluster state against the desired state defined in your CRDs. This loop executes repeatedly, checking conditions, identifying discrepancies, and taking corrective actions. The frequency depends on the operator’s configuration, though most run these checks every few seconds.
Here’s how the control loop operates in practice:
- Observe: The operator queries the Kubernetes API to retrieve current resource states and reads CRD specifications that define what should exist.
- Analyze: It compares actual conditions against desired configurations, identifying any differences in resource counts, versions, configurations, or health status.
- Act: When discrepancies appear, the operator executes operations to reconcile the difference, creating missing resources, updating outdated configurations, or removing excess components.
- Repeat: The loop continues indefinitely, maintaining constant vigilance over the application’s state and responding to changes immediately as they occur.
This pattern makes OpenShift Operators self-healing. If someone accidentally deletes a pod, the operator detects the missing resource during its next loop iteration and recreates it. If configuration drift occurs due to manual changes, the operator reverts the unauthorized modifications to restore the specified configuration. The result is reliable application behavior that maintains consistency even when unexpected events occur in your cluster.
Protecting Your OpenShift Environment
Deploying OpenShift operators solves application management challenges, but it also raises important questions about protecting the applications and data they manage. When operators automate critical workloads like databases, message queues, and storage systems, losing those configurations or their data can bring your entire infrastructure to a standstill.
Why Data Protection Matters for OpenShift Operators
OpenShift operators manage stateful applications that store business-critical information. Unlike stateless containers that you can simply restart, these applications maintain persistent data that your organization depends on. A database operator manages customer records, transaction histories, and application state. A messaging operator handles event streams and communication queues. When failures occur (whether from hardware malfunctions, accidental deletions, ransomware attacks, or configuration errors), you need reliable recovery mechanisms to be in place.
Traditional backup approaches fall short in Kubernetes environments. You can’t simply back up virtual machines or file systems and expect consistent recovery. OpenShift Operators create complex relationships among pods, persistent volumes, ConfigMaps, secrets, and custom resource definitions. A complete backup must capture all these components and their interdependencies to restore applications to working states.
Protecting operator-managed applications requires capturing not just data volumes but the entire application context, including Kubernetes objects, metadata, and configurations that define how components interact.
Compliance requirements add another layer of complexity. Regulations mandate specific retention periods, recovery time objectives, and audit trails for data management operations. When OpenShift operators handle regulated workloads, your backup strategy must demonstrate that you can restore data within specified timeframes and prove recovery capabilities through documented testing.
OpenShift Backup and Recovery Solutions
Effective protection for Red Hat OpenShift operators demands solutions designed specifically for Kubernetes architectures. Traditional namespace-based backup methods fall short because they fail to recognize OLM operators in their native form. These conventional approaches may miss operators and their associated custom resources if they exist in different namespaces, resulting in incomplete restores where applications don’t appear or function as they did before backup.
Trilio’s OpenShift Backup and Recovery stands alone as the only backup vendor that captures OLM operators natively. Beyond just protecting application data, Kubernetes resources, metadata, and configurations, Trilio captures the OLM operator itself, preserving it in its original form. This ensures that entire environments can be restored accurately, maintaining all the relationships between components that operators depend on.
When you restore from a Trilio backup, operators reappear seamlessly on the OpenShift UI exactly as they were before. These operators can immediately resume their lifecycles, receiving upgrades and updates just like operators freshly installed from OperatorHub. The application continues its lifecycle as if no interruption occurred, with no manual reconfiguration, no broken operator state, no visibility gaps in your OpenShift console.
The solution performs application-consistent backups, capturing data at points where applications are in stable states rather than mid-transaction. This consistency prevents corruption during restoration and eliminates the manual work typically required to verify data integrity after recovery. Incremental backup capabilities reduce storage costs and backup windows by only capturing changes since the last backup, rather than duplicating entire datasets repeatedly.
Automation features let you schedule backups across on-premises, hybrid, or cloud environments without manual intervention. The system monitors backup status and provides visibility through reporting dashboards, alerting your team when issues require attention. Role-based access control ensures that only authorized personnel can execute recovery operations or modify backup policies, while retention management helps you comply with data regulations through automatic removal of backups after specified periods.
Understanding the differences between traditional backup methods and Kubernetes-native protection helps clarify why purpose-built solutions matter for OpenShift environments.
Capability | Traditional VM Backups | Kubernetes-Native Protection |
Application Consistency | File-level or volume snapshots without application context | Captures complete application state including Kubernetes objects |
Metadata Protection | Limited to guest OS configurations | Includes CRDs, ConfigMaps, secrets, and operator configurations |
Cross-Cluster Recovery | Requires manual reconfiguration | Restore to any OpenShift cluster with preserved relationships |
Incremental Backups | Block-level changes only | Application-aware incremental backups with change tracking |
Cross-cluster portability matters when disasters affect entire data centers or when you need to migrate workloads between environments. Solutions built for OpenShift operators enable restoration to different clusters while maintaining application functionality, supporting business continuity strategies that span geographic regions and cloud providers. This flexibility protects against regional outages and provides options for disaster recovery testing without impacting production systems.
Protecting operator-managed applications safeguards the automation you’ve built. When you can quickly restore applications to known-good states, you reduce downtime, meet compliance requirements, and maintain the operational efficiency that led you to adopt OpenShift operators in the first place. Schedule a demo to see how purpose-built OpenShift protection handles your specific backup and recovery requirements.
Watch this 1-min video to see how easily you can recover K8s, VMs, and containers
Conclusion
OpenShift operators change how you manage Kubernetes applications. They take manual operational tasks and turn them into automated, self-healing systems. These tools package technical expertise into software that continuously monitors your environment and maintains your desired configurations with minimal intervention. The Operator Framework, OperatorHub, and Operator Lifecycle Manager work together to simplify how you discover, install, and manage these capabilities across your infrastructure.
When you deploy OpenShift operators to handle critical workloads, you need backup solutions designed specifically for Kubernetes architectures. Start by identifying which applications in your environment would benefit most from operator-based management, then evaluate what those workloads need for protection. You might be running databases, messaging systems, or storage platforms. Pairing operator automation with solid data protection keeps your applications resilient against failures while preserving the operational efficiency that brought you to OpenShift.
FAQs
Can I use OpenShift operators with existing applications, or do I need to rebuild them?
You can use OpenShift operators with existing applications without rebuilding them, as operators handle the operational management layer rather than requiring application code changes. Many pre-built operators exist for common software like databases and monitoring tools that work with your current deployments.
What programming languages can I use to build custom operators?
The Operator SDK supports development in Go, Ansible, and Helm, allowing teams to choose based on their existing expertise. Go provides the most flexibility for complex logic, while Ansible and Helm offer faster development for simpler use cases.
How do OpenShift operators handle application upgrades without causing downtime?
Operators execute rolling upgrades by gradually replacing application instances while monitoring health checks, automatically rolling back if issues emerge. This built-in upgrade intelligence eliminates the manual coordination typically required for zero-downtime deployments.
What's the difference between cluster operators and application operators in OpenShift?
Cluster operators manage OpenShift platform components like networking and authentication that affect the entire cluster, while application operators manage specific workloads like databases or message queues within namespaces. Both follow the same operator pattern but serve different infrastructure layers.
Do I need separate backup strategies for each operator I deploy?
A unified Kubernetes-native backup solution can protect all operator-managed applications by capturing their complete state, including data, configurations, and dependencies. This centralized approach is more efficient than creating individual backup strategies for each operator deployment.
