$(nproc) X 1/2 MiB- Documentation
- OpenShift Dedicated
- Nodes
- Working with containers
- Understanding containers history bug_report picture_as_pdf
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- About
- What's new
- Introduction to OpenShift Dedicated
- Architecture
- Red Hat OpenShift Cluster Manager
- Planning your environment
- Getting started
- Installing, accessing, and deleting OpenShift Dedicated clusters
- Support
- Web console
- CLI tools
- Cluster administration
- Security and compliance
- Authentication and authorization
- Authentication and authorization overview
- Understanding authentication
- Managing user-owned OAuth access tokens
- Configuring identity providers
- Revoking privileges and access to an OpenShift Dedicated cluster
- Managing administration roles and users
- 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
- 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
- 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
- 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
- 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
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Understanding Containers
The basic units of OpenShift Dedicated applications are called containers. Linux container technologies are lightweight mechanisms for isolating running processes so that they are limited to interacting with only their designated resources.
Many application instances can be running in containers on a single host without visibility into each others' processes, files, network, and so on. Typically, each container provides a single service (often called a "micro-service"), such as a web server or a database, though containers can be used for arbitrary workloads.
The Linux kernel has been incorporating capabilities for container technologies for years. OpenShift Dedicated and Kubernetes add the ability to orchestrate containers across multi-host installations.
About containers and RHEL kernel memory
Due to Red Hat Enterprise Linux (RHEL) behavior, a container on a node with high CPU usage might seem to consume more memory than expected. The higher memory consumption could be caused by the kmem_cache in the RHEL kernel. The RHEL kernel creates a kmem_cache for each cgroup. For added performance, the kmem_cache contains a cpu_cache, and a node cache for any NUMA nodes. These caches all consume kernel memory.
The amount of memory stored in those caches is proportional to the number of CPUs that the system uses. As a result, a higher number of CPUs results in a greater amount of kernel memory being held in these caches. Higher amounts of kernel memory in these caches can cause OpenShift Dedicated containers to exceed the configured memory limits, resulting in the container being killed.
To avoid losing containers due to kernel memory issues, ensure that the containers request sufficient memory. You can use the following formula to estimate the amount of memory consumed by the kmem_cache, where nproc is the number of processing units available that are reported by the nproc command. The lower limit of container requests should be this value plus the container memory requirements:
About the container engine and container runtime
A container engine is a piece of software that processes user requests, including command line options and image pulls. The container engine uses a container runtime, also called a lower-level container runtime, to run and manage the components required to deploy and operate containers. You likely will not need to interact with the container engine or container runtime.
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The OpenShift Dedicated documentation uses the term container runtime to refer to the lower-level container runtime. Other documentation can refer to the container engine as the container runtime. |
OpenShift Dedicated uses CRI-O as the container engine and runC or crun as the container runtime. The default container runtime is runC.