- Documentation
- Red Hat OpenShift Service on AWS
- Operators
- Developing Operators
- About the Operator SDK history bug_report picture_as_pdf
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- About
- What's new
- Introduction to ROSA
- Architecture
- Tutorials
- Tutorials overview
- ROSA with HCP activation and account linking
- ROSA with HCP private offer acceptance and sharing
- Verifying Permissions for a ROSA STS Deployment
- Deploying ROSA with a Custom DNS Resolver
- Using AWS WAF and Amazon CloudFront to protect ROSA workloads
- Using AWS WAF and AWS ALBs to protect ROSA workloads
- Deploying OpenShift API for Data Protection on a ROSA cluster
- AWS Load Balancer Operator on ROSA
- Configuring Microsoft Entra ID (formerly Azure Active Directory) as an identity provider
- Using AWS Secrets Manager CSI on ROSA with STS
- Using AWS Controllers for Kubernetes on ROSA
- Deploying the External DNS Operator on ROSA
- Dynamically issuing certificates using the cert-manager Operator on ROSA
- Assigning consistent egress IP for external traffic
- Updating component routes with custom domains and TLS certificates
- Getting started with ROSA
- Deploying an application
- Getting started
- Prepare your environment
- Install ROSA with HCP clusters
- Creating ROSA with HCP clusters using the default options
- Creating a ROSA cluster using Terraform
- Creating ROSA with HCP clusters using a custom AWS KMS encryption key
- Creating a private cluster on ROSA with HCP
- Creating ROSA with HCP clusters with external authentication
- Creating ROSA with HCP clusters without a CNI plugin
- Deleting a ROSA with HCP cluster
- Install ROSA Classic clusters
- Creating a ROSA cluster with STS using the default options
- Creating a ROSA cluster with STS using customizations
- Creating a ROSA (classic architecture) cluster using Terraform
- Interactive cluster creation mode reference
- Creating an AWS PrivateLink cluster on ROSA
- Configuring a shared virtual private cloud for ROSA clusters
- Accessing a ROSA cluster
- Configuring identity providers using Red Hat OpenShift Cluster Manager
- Revoking access to a ROSA cluster
- Deleting a ROSA cluster
- Deploying ROSA without AWS STS
- AWS prerequisites for ROSA
- Understanding the ROSA deployment workflow
- Required AWS service quotas
- Configuring your AWS account
- Installing the ROSA CLI
- Creating a ROSA cluster without AWS STS
- Configuring a private cluster
- Deleting access to a ROSA cluster
- Deleting a ROSA cluster
- Command quick reference for creating clusters and users
- Support
- Support overview
- Managing your cluster resources
- Approved Access
- Getting support
- Remote health monitoring with connected clusters
- Gathering data about your cluster
- Summarizing cluster specifications
- Troubleshooting
- Troubleshooting ROSA installations
- Troubleshooting networking
- Verifying node health
- Troubleshooting Operator issues
- Investigating pod issues
- Troubleshooting storage issues
- Investigating monitoring issues
- Diagnosing OpenShift CLI (oc) issues
- Troubleshooting expired offline access tokens
- Troubleshooting IAM roles
- Troubleshooting cluster deployments
- Red Hat OpenShift Service on AWS managed resources
- Web console
- CLI tools
- Red Hat OpenShift Cluster Manager
- Cluster administration
- Security and compliance
- Authentication and authorization
- Authentication and authorization overview
- Understanding authentication
- Managing user-owned OAuth access tokens
- Configuring identity providers
- Using RBAC to define and apply permissions
- Understanding and creating service accounts
- Using service accounts in applications
- Using a service account as an OAuth client
- Assuming an AWS IAM role for a service account
- Scoping tokens
- Using bound service account tokens
- Managing security context constraints
- Understanding and managing pod security admission
- Syncing LDAP groups
- Upgrading
- CI/CD
- Images
- Overview of images
- Overview of the Cluster Samples Operator
- Using the Cluster Samples Operator with an alternate registry
- Creating images
- Managing images
- Managing image streams
- Using image streams with Kubernetes resources
- Triggering updates on image stream changes
- Image configuration resources (Classic)
- Image configuration resources (HCP)
- Using templates
- Using Ruby on Rails
- Using images
- Add-on services
- Storage
- Registry
- Operators
- Operators overview
- Understanding Operators
- User tasks
- Administrator tasks
- Developing Operators
- About the Operator SDK
- Installing the Operator SDK CLI
- Go-based Operators
- Ansible-based Operators
- helm-based Operators
- Defining cluster service versions (CSVs)
- Working with bundle images
- Complying with pod security admission
- Validating Operators using the scorecard
- Validating Operator bundles
- High-availability or single-node cluster detection and support
- Configuring built-in monitoring with Prometheus
- Configuring leader election
- Object pruning utility
- Migrating package manifest projects to bundle format
- Operator SDK CLI reference
- Migrating to Operator SDK v0.1.0
- Networking
- Building applications
- Building applications overview
- Projects
- Creating applications
- Viewing application composition using the Topology view
- Working with helm charts
- Deployments
- Quotas
- Using config maps with applications
- Monitoring project and application metrics using the Developer perspective
- Monitoring application health
- Editing applications
- Working with quotas
- Pruning objects to reclaim resources
- Idling applications
- Deleting applications
- Using the Red Hat Marketplace
- Backing up and restoring applications
- Nodes
- Overview of nodes
- Working with pods
- Automatically scaling pods with the Custom Metrics Autoscaler Operator
- Release notes
- Custom Metrics Autoscaler Operator overview
- Installing the custom metrics autoscaler
- Understanding the custom metrics autoscaler triggers
- Understanding the custom metrics autoscaler trigger authentications
- Pausing the custom metrics autoscaler
- Gathering audit logs
- Gathering debugging data
- Viewing Operator metrics
- Understanding how to add custom metrics autoscalers
- Removing the Custom Metrics Autoscaler Operator
- Controlling pod placement onto nodes (scheduling)
- About pod placement using the scheduler
- Placing pods relative to other pods using pod affinity and anti-affinity rules
- Controlling pod placement on nodes using node affinity rules
- Placing pods onto overcommited nodes
- Placing pods on specific nodes using node selectors
- Controlling pod placement using pod topology spread constraints
- Using jobs and daemon sets
- Working with nodes
- Working with containers
- Understanding containers
- Using Init Containers to perform tasks before a pod is deployed
- Using volumes to persist container data
- Mapping volumes using projected volumes
- Allowing containers to consume API objects
- Copying files to or from a container
- Executing remote commands in a container
- Using port forwarding to access applications in a container
- Working with clusters
- Observability
- Observability overview
- Monitoring
- Monitoring overview
- Accessing monitoring for user-defined projects
- Configuring the monitoring stack
- Disabling monitoring for user-defined projects
- Enabling alert routing for user-defined projects
- Managing metrics
- Managing alerts
- Reviewing monitoring dashboards
- Accessing third-party monitoring APIs
- Troubleshooting monitoring issues
- Config map reference for the Cluster Monitoring Operator
- Logging
- Release notes
- Support
- Troubleshooting logging
- About Logging
- Installing Logging
- Updating Logging
- Visualizing logs
- Configuring your Logging deployment
- Log collection and forwarding
- Log storage
- Logging alerts
- Performance and reliability tuning
- Scheduling resources
- Uninstalling Logging
- Exported fields
- API reference
- Glossary
- Service Mesh
- Service Mesh 2.x
- About OpenShift Service Mesh
- Service Mesh 2.x release notes
- Service Mesh architecture
- Service Mesh deployment models
- Service Mesh and Istio differences
- Preparing to install Service Mesh
- Installing the Operators
- Creating the ServiceMeshControlPlane
- Adding workloads to a service mesh
- Enabling sidecar injection
- Upgrading Service Mesh
- Managing users and profiles
- Security
- Traffic management
- Metrics, logs, and traces
- Performance and scalability
- Deploying to production
- Federation
- Extensions
- 3scale WebAssembly for 2.1
- 3scale Istio adapter for 2.0
- Troubleshooting Service Mesh
- Control plane configuration reference
- Kiali configuration reference
- Jaeger configuration reference
- Uninstalling Service Mesh
- Service Mesh 2.x
- Serverless
- Virtualization
- About
- Getting started
- Installing
- Post-installation configuration
- Updating
- Virtual machines
- Creating VMs from Red Hat images
- Creating VMs from custom images
- Connecting to VM consoles
- Configuring SSH access to VMs
- Editing virtual machines
- Editing boot order
- Deleting virtual machines
- Exporting virtual machines
- Managing virtual machine instances
- Controlling virtual machine states
- Using virtual Trusted Platform Module devices
- Managing virtual machines with OpenShift Pipelines
- Advanced virtual machine management
- Working with resource quotas for virtual machines
- Specifying nodes for virtual machines
- Configuring certificate rotation
- Configuring the default CPU model
- UEFI mode for virtual machines
- Configuring PXE booting for virtual machines
- Scheduling virtual machines
- About high availability for virtual machines
- Control plane tuning
- VM disks
- Networking
- Networking configuration overview
- Connecting a VM to the default pod network
- Exposing a VM by using a service
- Connecting a VM to an OVN-Kubernetes secondary network
- Connecting a VM to a service mesh
- Configuring a dedicated network for live migration
- Configuring and viewing IP addresses
- Managing MAC address pools for network interfaces
- Storage
- Storage configuration overview
- Configuring storage profiles
- Managing automatic boot source updates
- Reserving PVC space for file system overhead
- Configuring local storage by using HPP
- Enabling user permissions to clone data volumes across namespaces
- Configuring CDI to override CPU and memory quotas
- Preparing CDI scratch space
- Using preallocation for data volumes
- Managing data volume annotations
- Live migration
- Nodes
- Monitoring
- Support
- Backup and restore
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About the Operator SDK
The Operator Framework is an open source toolkit to manage Kubernetes native applications, called Operators, in an effective, automated, and scalable way. Operators take advantage of Kubernetes extensibility to deliver the automation advantages of cloud services, like provisioning, scaling, and backup and restore, while being able to run anywhere that Kubernetes can run.
Operators make it easy to manage complex, stateful applications on top of Kubernetes. However, writing an Operator today can be difficult because of challenges such as using low-level APIs, writing boilerplate, and a lack of modularity, which leads to duplication.
The Operator SDK, a component of the Operator Framework, provides a command-line interface (CLI) tool that Operator developers can use to build, test, and deploy an Operator.
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The Red Hat-supported version of the Operator SDK CLI tool, including the related scaffolding and testing tools for Operator projects, is deprecated and is planned to be removed in a future release of Red Hat OpenShift Service on AWS. Red Hat will provide bug fixes and support for this feature during the current release lifecycle, but this feature will no longer receive enhancements and will be removed from future Red Hat OpenShift Service on AWS releases. The Red Hat-supported version of the Operator SDK is not recommended for creating new Operator projects. Operator authors with existing Operator projects can use the version of the Operator SDK CLI tool released with Red Hat OpenShift Service on AWS to maintain their projects and create Operator releases targeting newer versions of Red Hat OpenShift Service on AWS. The following related base images for Operator projects are not deprecated. The runtime functionality and configuration APIs for these base images are still supported for bug fixes and for addressing CVEs.
For information about the unsupported, community-maintained, version of the Operator SDK, see Operator SDK (Operator Framework). |
Why use the Operator SDK?
The Operator SDK simplifies this process of building Kubernetes-native applications, which can require deep, application-specific operational knowledge. The Operator SDK not only lowers that barrier, but it also helps reduce the amount of boilerplate code required for many common management capabilities, such as metering or monitoring.
The Operator SDK is a framework that uses the controller-runtime library to make writing Operators easier by providing the following features:
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High-level APIs and abstractions to write the operational logic more intuitively
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Tools for scaffolding and code generation to quickly bootstrap a new project
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Integration with Operator Lifecycle Manager (OLM) to streamline packaging, installing, and running Operators on a cluster
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Extensions to cover common Operator use cases
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Metrics set up automatically in any generated Go-based Operator for use on clusters where the Prometheus Operator is deployed
Operator authors with dedicated-admin access to Red Hat OpenShift Service on AWS can use the Operator SDK CLI to develop their own Operators based on Go, Ansible, Java, or helm. Kubebuilder is embedded into the Operator SDK as the scaffolding solution for Go-based Operators, which means existing Kubebuilder projects can be used as is with the Operator SDK and continue to work.
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Red Hat OpenShift Service on AWS supports Operator SDK 1.36.1. |
What are Operators?
For an overview about basic Operator concepts and terminology, see Understanding Operators.
Development workflow
The Operator SDK provides the following workflow to develop a new Operator:
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Create an Operator project by using the Operator SDK command-line interface (CLI).
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Define new resource APIs by adding custom resource definitions (CRDs).
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Specify resources to watch by using the Operator SDK API.
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Define the Operator reconciling logic in a designated handler and use the Operator SDK API to interact with resources.
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Use the Operator SDK CLI to build and generate the Operator deployment manifests.
Figure 1. Operator SDK workflow
At a high level, an Operator that uses the Operator SDK processes events for watched resources in an Operator author-defined handler and takes actions to reconcile the state of the application.