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Scalability and performance

  1. Recommended node host practices
  2. Master node sizing
  3. Create a KubeletConfig CRD to edit kubelet parameters
  4. Recommended etcd practices
  5. Use the Node Tuning Operator
  6. Use Cluster Loader
  7. Use CPU Manager
  8. Scaling the Cluster Monitoring Operator
  9. Plan your environment according to object limits
  10. Optimizing storage
  11. Optimize HAProxy router
  12. What huge pages do and how they are consumed by applications

Master node sizing

The master node resource requirements depend on the number of nodes in the cluster. The following master node size recommendations are based on the results of control plane density focused testing.

Number of worker nodes CPU cores Memory (GB)
25 4 16
100 8 32
250 16 64

Because we cannot modify the master node size in a running OpenShift 4.1 cluster, we must estimate your total node count and use the suggested master size during installation.

In OpenShift 4.1, half of a CPU core (500 millicore) is now reserved by the system by default compared to OpenShift 3.11 and previous versions. The sizes are determined taking that into consideration.

Create a KubeletConfig CRD to edit kubelet parameters

The kubelet configuration is currently serialized as an ignition configuration, so it can be directly edited. However, there is also a new kubelet-config-controller added to the Machine Config Controller (MCC). This allows us to create a KubeletConfig custom resource (CR) to edit the kubelet parameters.


  1. Run:

      $ oc get machineconfig

    This provides a list of the available machine configuration objects we can select. By default, the two kubelet-related configs are 01-master-kubelet and 01-worker-kubelet.

  2. To check the current value of max Pods per node, run:

      # oc describe node <node-ip> | grep Allocatable -A6

    Look for value: pods: <value>.

    For example:

      # oc describe node ip-172-31-128-158.us-east-2.compute.internal | grep Allocatable -A6
       attachable-volumes-aws-ebs:  25
       cpu:                         3500m
       hugepages-1Gi:               0
       hugepages-2Mi:               0
       memory:                      15341844Ki
       pods:                        250

  3. To set the max Pods per node on the worker nodes, create a YAML file containing the kubelet configuration. For example, max-worker-pods.yaml:

      apiVersion: machineconfiguration.openshift.io/v1
      kind: KubeletConfig
        name: set-max-pods
        machineConfigSelector: 01-worker-kubelet
          maxPods: 250

  4. Apply the file by running:

  5. Verify the change by running:

      $ oc get kubeletconfigs set-max-pods -o yaml

By default, only one machine is allowed to be unavailable when applying the kubelet-related configuration to the available worker nodes. For a large cluster, it can take a long time for the configuration change to be reflected. At any time, we can adjust the number of machines that are updating to speed up the process.


  1. Run:

      $ oc edit machineconfigpool worker

  2. Set maxUnavailable to the desired value.

        maxUnavailable: <node_count>

When setting the value, consider the number of worker nodes that can be unavailable without affecting the applications running on the cluster.

Use the Node Tuning Operator

Learn about the Node Tuning Operator and how we can use it to manage node-level tuning by orchestrating the tuned daemon.

About the Node Tuning Operator

The Node Tuning Operator helps you manage node-level tuning by orchestrating the tuned daemon. The majority of high-performance applications require some level of kernel tuning. The Node Tuning Operator provides a unified management interface to users of node-level sysctls and more flexibility to add custom tuning, which is currently a Technology Preview feature, specified by user needs. The Operator manages the containerized tuned daemon for OpenShift as a Kubernetes DaemonSet. It ensures the custom tuning specification is passed to all containerized tuned daemons running in the cluster in the format that the daemons understand. The daemons run on all nodes in the cluster, one per node.

The Node Tuning Operator is part of a standard OpenShift installation in version 4.1 and later.

Access an example Node Tuning Operator specification

To access an example Node Tuning Operator specification.

    $ oc get Tuned/default -o yaml -n openshift-cluster-node-tuning-operator

Default profiles set on a cluster

The following are the default profiles set on a cluster.

    apiVersion: tuned.openshift.io/v1alpha1
    kind: Tuned
      name: default
      namespace: openshift-cluster-node-tuning-operator
      - name: "openshift"
        data: |
          summary=Optimize systems running OpenShift (parent profile)
      - name: "openshift-control-plane"
        data: |
          summary=Optimize systems running control plane
          # ktune sysctl settings, maximizing i/o throughput
          # Minimal preemption granularity for CPU-bound tasks:
          # (default: 1 msec#  (1 + ilog(ncpus)), units: nanoseconds)
          # The total time the scheduler will consider a migrated process
          # "cache hot" and thus less likely to be re-migrated
          # (system default is 500000, i.e. 0.5 ms)
          # SCHED_OTHER wake-up granularity.
          # Preemption granularity when tasks wake up.  Lower the value to
          # improve wake-up latency and throughput for latency critical tasks.
      - name: "openshift-node"
        data: |
          summary=Optimize systems running OpenShift nodes
      - name: "openshift-control-plane-es"
        data: |
          summary=Optimize systems running ES on OpenShift control-plane
      - name: "openshift-node-es"
        data: |
          summary=Optimize systems running ES on OpenShift nodes
      - profile: "openshift-control-plane-es"
        priority: 10
        - label: "tuned.openshift.io/elasticsearch"
          type: "pod"
          - label: "node-role.kubernetes.io/master"
          - label: "node-role.kubernetes.io/infra"
      - profile: "openshift-node-es"
        priority: 20
        - label: "tuned.openshift.io/elasticsearch"
          type: "pod"
      - profile: "openshift-control-plane"
        priority: 30
        - label: "node-role.kubernetes.io/master"
        - label: "node-role.kubernetes.io/infra"
      - profile: "openshift-node"
    priority: 40

Custom profiles for custom tuning specification is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

Custom tuning specification

The custom resource (CR) for the operator has two major sections. The first section, profile:, is a list of tuned profiles and their names. The second, recommend:, defines the profile selection logic.

Multiple custom tuning specifications can co-exist as multiple CRs in the operator's namespace. The existence of new CRs or the deletion of old CRs is detected by the Operator. All existing custom tuning specifications are merged and appropriate objects for the containerized tuned daemons are updated.

Profile data

The profile: section lists tuned profiles and their names.

    - name: tuned_profile_1
      data: |
        # Tuned profile specification
        summary=Description of tuned_profile_1 profile
        # ... other sysctl's or other tuned daemon plugins supported by the containerized tuned
    # ...
    - name: tuned_profile_n
      data: |
        # Tuned profile specification
        summary=Description of tuned_profile_n profile
        # tuned_profile_n profile settings

Recommended profiles

The profile: selection logic is defined by the recommend: section of the CR:

    - match:                              # optional; if omitted, profile match is assumed unless a profile with a higher matches first
      <match>                             # an optional array
      priority: <priority>                # profile ordering priority, lower numbers mean higher priority (0 is the highest priority)
      profile: <tuned_profile_name>       # e.g. tuned_profile_1
    # ...
    - match:
      priority: <priority>
      profile: <tuned_profile_name>       # e.g. tuned_profile_n

If <match> is omitted, a profile match (for example, true) is assumed.

<match> is an optional array recursively defined as follows:

    - label: <label_name>     # node or pod label name
      value: <label_value>    # optional node or pod label value; if omitted, the presence of <label_name> is enough to match
      type: <label_type>      # optional node or pod type ("node" or "pod"); if omitted, "node" is assumed
      <match>                 # an optional <match> array

If <match> is not omitted, all nested <match> sections must also evaluate to true. Otherwise, false is assumed and the profile with the respective <match> section will not be applied or recommended. Therefore, the nesting (child <match> sections) works as logical AND operator. Conversely, if any item of the <match> array matches, the entire <match> array evaluates to true. Therefore, the array acts as logical OR operator.


    - match:
      - label: tuned.openshift.io/elasticsearch
        - label: node-role.kubernetes.io/master
        - label: node-role.kubernetes.io/infra
        type: pod
      priority: 10
      profile: openshift-control-plane-es
    - match:
      - label: node-role.kubernetes.io/master
      - label: node-role.kubernetes.io/infra
      priority: 20
      profile: openshift-control-plane
    - priority: 30
      profile: openshift-node

The CR above is translated for the containerized tuned daemon into its recommend.conf file based on the profile priorities. The profile with the highest priority (10) is openshift-control-plane-es and, therefore, it is considered first. The containerized tuned daemon running on a given node looks to see if there is a pod running on the same node with the tuned.openshift.io/elasticsearch label set. If not, the entire <match> section evaluates as false. If there is such a pod with the label, in order for the <match> section to evaluate to true, the node label also needs to be node-role.kubernetes.io/master or node-role.kubernetes.io/infra.

If the labels for the profile with priority 10 matched, openshift-control-plane-es profile is applied and no other profile is considered. If the node/pod label combination did not match, the second highest priority profile (openshift-control-plane) is considered. This profile is applied if the containerized tuned pod runs on a node with labels node-role.kubernetes.io/master or node-role.kubernetes.io/infra.

Finally, the profile openshift-node has the lowest priority of 30. It lacks the <match> section and, therefore, will always match. It acts as a profile catch-all to set openshift-node profile, if no other profile with higher priority matches on a given node. Decision workflow

Supported Tuned daemon plug-ins

Excluding the [main] section, the following Tuned plug-ins are supported when using custom profiles defined in the profile: section of the Tuned CR:

  • audio
  • cpu
  • disk
  • eeepc_she
  • modules
  • mounts
  • net
  • scheduler
  • scsi_host
  • selinux
  • sysctl
  • sysfs
  • usb
  • video
  • vm

There is some dynamic tuning functionality provided by some of these plug-ins that is not supported. The following Tuned plug-ins are currently not supported:

See Available Tuned Plug-ins and Get Started with Tuned for more information.

Use Cluster Loader

Cluster Loader is a tool that deploys large numbers of various objects to a cluster, which creates user-defined cluster objects. Build, configure, and run Cluster Loader to measure performance metrics of the OpenShift deployment at various cluster states.

Install Cluster Loader

Cluster Loader is included in the origin-tests container image.

To pull the origin-tests container image, run:

    $ sudo podman pull quay.io/openshift/origin-tests:4.1

Running Cluster Loader

  1. Execute Cluster Loader using the built-in test configuration, which deploys five template builds and waits for them to complete:

      $ sudo podman run 
          -v ${LOCAL_KUBECONFIG}:/root/.kube/config \
          -i quay.io/openshift/origin-tests:4.1 /bin/bash \
          -c 'export KUBECONFIG=/root/.kube/config && \
          openshift-tests run-test "[Feature:Performance][Serial][Slow] Load cluster should load the \
          cluster [Suite:openshift]"'

    Alternatively, execute Cluster Loader with a user-defined configuration by setting the environment variable for VIPERCONFIG:

      $ sudo podman run \
          -v ${LOCAL_KUBECONFIG}:/root/.kube/config 
          -i quay.io/openshift/origin-tests:4.1 /bin/bash 
          -c 'export KUBECONFIG=/root/.kube/config && \
          export VIPERCONFIG=config/test && \
          openshift-tests run-test "[Feature:Performance][Serial][Slow] Load cluster should load the \
          cluster [Suite:openshift]"'

    In this example, there is a subdirectory called config/ with a configuration file called test.yml. In the command line, exclude the extension of the configuration file, as the tool will automatically determine the file type and extension.

Configure Cluster Loader

The tool creates multiple namespaces (projects), which contain multiple templates or pods.

Locate the configuration files for Cluster Loader in the config/ subdirectory. The pod files and template files referenced in these configuration examples are found in the content/ subdirectory.

Example Cluster Loader configuration file

Cluster Loader's configuration file is a basic YAML file:

    provider: local 1
      cleanup: true
        - num: 1
          basename: clusterloader-cakephp-mysql
          tuning: default
          ifexists: reuse
    - num: 1
      file: ./examples/quickstarts/cakephp-mysql.json
        - num: 1
          basename: clusterloader-dancer-mysql
          tuning: default
          ifexists: reuse
    - num: 1
      file: ./examples/quickstarts/dancer-mysql.json
        - num: 1
          basename: clusterloader-django-postgresql
          tuning: default
          ifexists: reuse
    - num: 1
      file: ./examples/quickstarts/django-postgresql.json
        - num: 1
          basename: clusterloader-nodejs-mongodb
          tuning: default
          ifexists: reuse
    - num: 1
      file: ./examples/quickstarts/nodejs-mongodb.json
        - num: 1
          basename: clusterloader-rails-postgresql
          tuning: default
    - num: 1
      file: ./examples/quickstarts/rails-postgresql.json
      tuningset: 2
        - name: default
    stepping: 3
      stepsize: 5
      pause: 0 s
    rate_limit: 4
      delay: 0 ms

1 Optional setting for end-to-end tests. Set to local to avoid extra log messages.

2 The tuning sets allow rate limiting and stepping, the ability to create several batches of pods while pausing in between sets. Cluster Loader monitors completion of the previous step before continuing.

3 Stepping will pause for M seconds after each N objects are created.

4 Rate limiting will wait M milliseconds between the creation of objects.

Configuration fields

Table 3.1. Top-level Cluster Loader Fields

Field Description
cleanup Set to true or false. One definition per configuration. If set to true, cleanup deletes all namespaces (projects) created by Cluster Loader at the end of the test.
projects A sub-object with one or many definition(s). Under projects, each namespace to create is defined and projects has several mandatory subheadings.
tuningset A sub-object with one definition per configuration. tuningset allows the user to define a tuning set to add configurable timing to project or object creation (pods, templates, and so on).
sync An optional sub-object with one definition per configuration. Adds synchronization possibilities during object creation.

Table 3.2. Fields under projects

Field Description
num An integer. One definition of the count of how many projects to create.
basename A string. One definition of the base name for the project. The count of identical namespaces will be appended to Basename to prevent collisions.
tuning A string. One definition of what tuning set we want to apply to the objects, which you deploy inside this namespace.
ifexists A string containing either reuse or delete. Defines what the tool does if it finds a project or namespace that has the same name of the project or namespace it creates during execution.
configmaps A list of key-value pairs. The key is the ConfigMap name and the value is a path to a file from which created the ConfigMap.
secrets A list of key-value pairs. The key is the secret name and the value is a path to a file from which created the secret.
pods A sub-object with one or many definition(s) of pods to deploy.
templates A sub-object with one or many definition(s) of templates to deploy.

Table 3.3. Fields under pods and templates

Field Description
num An integer. The number of pods or templates to deploy.
image A string. The docker image URL to a repository where it can be pulled.
basename A string. One definition of the base name for the template (or pod) that we want to create.
file A string. The path to a local file, which is either a PodSpec or template to be created.
parameters Key-value pairs. Under parameters, we can specify a list of values to override in the pod or template.

Table 3.4. Fields under tuningset

Field Description
name A string. The name of the tuning set which will match the name specified when defining a tuning in a project.
pods A sub-object identifying the tuningset that will apply to pods.
templates A sub-object identifying the tuningset that will apply to templates.

Table 3.5. Fields under tuningset pods or tuningset templates

Field Description
stepping A sub-object. A stepping configuration used to create an object in a step creation pattern.
rate_limit A sub-object. A rate-limiting tuning set configuration to limit the object creation rate.

Table 3.6. Fields under tuningset pods or tuningset templates, stepping

Field Description
stepsize An integer. How many objects to create before pausing object creation.
pause An integer. How many seconds to pause after creating the number of objects defined in stepsize.
timeout An integer. How many seconds to wait before failure if the object creation is not successful.
delay An integer. How many milliseconds (ms) to wait between creation requests.

Table 3.7. Fields under sync

Field Description
server A sub-object with enabled and port fields. The boolean enabled defines whether to start a HTTP server for pod synchronization. The integer port defines the HTTP server port to listen on (9090 by default).
running A boolean. Wait for pods with labels matching selectors to go into Running state.
succeeded A boolean. Wait for pods with labels matching selectors to go into Completed state.
selectors A list of selectors to match pods in Running or Completed states.
timeout A string. The synchronization timeout period to wait for pods in Running or Completed states. For values that are not 0, use units: [ns|us|ms|s|m|h].

Known issues

If the IDENTIFIER parameter is not defined in user templates, template creation fails with error: unknown parameter name "IDENTIFIER". If you deploy templates, add this parameter to your template to avoid this error:

      "name": "IDENTIFIER",
      "description": "Number to append to the name of resources",
      "value": "1"

If you deploy pods, adding the parameter is unnecessary.

Use CPU Manager

CPU Manager manages groups of CPUs and constrains workloads to specific CPUs.

CPU Manager is useful for workloads that have some of these attributes:

  • Require as much CPU time as possible.
  • Are sensitive to processor cache misses.
  • Are low-latency network applications.
  • Coordinate with other processes and benefit from sharing a single processor cache.

Set up CPU Manager


  1. Optional: Label a node:

      # oc label node perf-node.example.com cpumanager=true

  2. Edit the MachineConfigPool of the nodes where CPU Manager should be enabled. In this example, all workers have CPU Manager enabled:

      # oc edit machineconfigpool worker

  3. Add a label to the worker MachineConfigPool:

        creationTimestamp: 2019-xx-xxx
        generation: 3
          custom-kubelet: cpumanager-enabled

  4. Create a KubeletConfig, cpumanager-kubeletconfig.yaml, custom resource (CR). Refer to the label created in the previous step to have the correct nodes updated with the new KubeletConfig. See the machineConfigPoolSelector section:

      apiVersion: machineconfiguration.openshift.io/v1
      kind: KubeletConfig
        name: cpumanager-enabled
            custom-kubelet: cpumanager-enabled
           cpuManagerPolicy: static
           cpuManagerReconcilePeriod: 5s

  5. Create the dynamic KubeletConfig:

      # oc create -f cpumanager-kubeletconfig.yaml

    This adds the CPU Manager feature to the KubeletConfig and, if needed, the Machine Config Operator (MCO) reboots the node. To enable CPU Manager, a reboot is not needed.

  6. Check for the merged KubeletConfig:

      # oc get machineconfig 99-worker-XXXXXX-XXXXX-XXXX-XXXXX-kubelet -o json | grep ownerReference -A7
             "ownerReferences": [
          "apiVersion": "machineconfiguration.openshift.io/v1",
          "kind": "KubeletConfig",
          "name": "cpumanager-enabled",
          "uid": "7ed5616d-6b72-11e9-aae1-021e1ce18878"

  7. Check the worker for the updated kubelet.conf:

      # oc debug node/perf-node.example.com
      sh-4.4# cat /host/etc/kubernetes/kubelet.conf | grep cpuManager
      cpuManagerPolicy: static        1
      cpuManagerReconcilePeriod: 5s   2

    1 2 These settings were defined when createdd the KubeletConfig CR.

  8. Create a Pod that requests a core or multiple cores. Both limits and requests must have their CPU value set to a whole integer. That is the number of cores that will be dedicated to this Pod:

      # cat cpumanager-pod.yaml
      apiVersion: v1
      kind: Pod
        generateName: cpumanager-
        - name: cpumanager
          image: gcr.io/google_containers/pause-amd64:3.0
      cpu: 1
      memory: "1G"
      cpu: 1
      memory: "1G"
          cpumanager: "true"

  9. Create the Pod:

      # oc create -f cpumanager-pod.yaml

  10. Verify that the Pod is scheduled to the node that you labeled:

      # oc describe pod cpumanager
      Name:               cpumanager-6cqz7
      Namespace:          default
      Priority:           0
      PriorityClassName:  <none>
      Node:  perf-node.example.com/xxx.xx.xx.xxx
            cpu:     1
            memory:  1G
            cpu:        1
            memory:     1G
      QoS Class:       Guaranteed
      Node-Selectors:  cpumanager=true

  11. Verify that the cgroups are set up correctly. Get the process ID (PID) of the pause process:

      # init.scope
      1 /usr/lib/systemd/systemd --switched-root --system --deserialize 17
        32706 /pause

    Pods of quality of service (QoS) tier Guaranteed are placed within the kubepods.slice. Pods of other QoS tiers end up in child cgroups of kubepods:

      # cd /sys/fs/cgroup/cpuset/kubepods.slice/kubepods-pod69c01f8e_6b74_11e9_ac0f_0a2b62178a22.slice/crio-b5437308f1ad1a7db0574c542bdf08563b865c0345c86e9585f8c0b0a655612c.scope
      # for i in `ls cpuset.cpus tasks` ; do echo -n "$i "; cat $i ; done
      cpuset.cpus 1
      tasks 32706

  12. Check the allowed CPU list for the task:

      # grep ^Cpus_allowed_list /proc/32706/status
       Cpus_allowed_list:    1

  13. Verify that another pod (in this case, the pod in the burstable QoS tier) on the system cannot run on the core allocated for the Guaranteed pod:

      # cat /sys/fs/cgroup/cpuset/kubepods.slice/kubepods-besteffort.slice/kubepods-besteffort-podc494a073_6b77_11e9_98c0_06bba5c387ea.slice/crio-c56982f57b75a2420947f0afc6cafe7534c5734efc34157525fa9abbf99e3849.scope/cpuset.cpus

      # oc describe node perf-node.example.com
       attachable-volumes-aws-ebs:  39
       cpu:                         2
       ephemeral-storage:           124768236Ki
       hugepages-1Gi:               0
       hugepages-2Mi:               0
       memory:                      8162900Ki
       pods:                        250
       attachable-volumes-aws-ebs:  39
       cpu:                         1500m
       ephemeral-storage:           124768236Ki
       hugepages-1Gi:               0
       hugepages-2Mi:               0
       memory:                      7548500Ki
       pods:                        250
      -------                               ----                           ------------  ----------  ---------------  -------------  ---
        default                                 cpumanager-6cqz7               1 (66%)       1 (66%)     1G (12%)         1G (12%)       29m
      Allocated resources:
        (Total limits may be over 100 percent, i.e., overcommitted.)
        Resource                    Requests          Limits
        --------                    --------          ------
        cpu                         1440m (96%)       1 (66%)

    This VM has two CPU cores. You set kube-reserved to 500 millicores, meaning half of one core is subtracted from the total capacity of the node to arrive at the Node Allocatable amount. We can see that Allocatable CPU is 1500 millicores. This means we can run one of the CPU Manager pods since each will take one whole core. A whole core is equivalent to 1000 millicores. If you try to schedule a second pod, the system will accept the pod, but it will never be scheduled:

      NAME                    READY   STATUS    RESTARTS   AGE
      cpumanager-6cqz7        1/1     Running   0          33m
      cpumanager-7qc2t        0/1     Pending   0          11s

Scaling the Cluster Monitoring Operator

OpenShift exposes metrics that the Cluster Monitoring Operator collects and stores in the Prometheus-based monitoring stack. As an OpenShift administrator, we can view system resources, containers and components metrics in one dashboard interface, Grafana.

Prometheus database storage requirements

Red Hat performed various tests for different scale sizes.

Table 5.1. Prometheus Database storage requirements based on number of nodes/pods in the cluster

Number of Nodes Number of Pods Prometheus storage growth per day Prometheus storage growth per 15 days RAM Space (per scale size) Network (per tsdb chunk)
50 1800 6.3 GB 94 GB 6 GB 16 MB
100 3600 13 GB 195 GB 10 GB 26 MB
150 5400 19 GB 283 GB 12 GB 36 MB
200 7200 25 GB 375 GB 14 GB 46 MB

Approximately 20 percent of the expected size was added as overhead to ensure that the storage requirements do not exceed the calculated value.

The above calculation is for the default OpenShift Cluster Monitoring Operator.

CPU utilization has minor impact. The ratio is approximately 1 core out of 40 per 50 nodes and 1800 pods.

Lab environment

In a previous release, all experiments were performed in an OpenShift on OpenStack environment:

  • Infra nodes (VMs) - 40 cores, 157 GB RAM.
  • CNS nodes (VMs) - 16 cores, 62 GB RAM, NVMe drives.

Currently, OpenStack environments are not supported for OpenShift 4.1.


  • Use at least three infrastructure (infra) nodes.
  • Use at least three openshift-container-storage nodes with non-volatile memory express (NVMe) drives.

OpenShift Container Storage (OCS) is currently a Technology Preview feature. Technology Preview features are not supported with Red Hat production service level agreements (SLAs), might not be functionally complete, and Red Hat does not recommend to use them for production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

See the Red Hat Technology Preview features support scope for more information.

Configure cluster monitoring

To increase the storage capacity for Prometheus:

  1. Create a YAML configuration file, `cluster-monitoring-config.yml. For example:

      apiVersion: v1
      kind: ConfigMap
        config.yaml: |
            baseImage: quay.io/coreos/prometheus-operator
            prometheusConfigReloaderBaseImage: quay.io/coreos/prometheus-config-reloader
            configReloaderBaseImage: quay.io/coreos/configmap-reload
      node-role.kubernetes.io/infra: ""
            retention: {{PROMETHEUS_RETENTION_PERIOD}} 1
            baseImage: openshift/prometheus
      node-role.kubernetes.io/infra: ""
        storageClassName: gp2
        storage: {{PROMETHEUS_STORAGE_SIZE}} 2
            baseImage: openshift/prometheus-alertmanager
      node-role.kubernetes.io/infra: ""
        storageClassName: gp2
        storage: {{ALERTMANAGER_STORAGE_SIZE}} 3
            baseImage: openshift/prometheus-node-exporter
            baseImage: quay.io/coreos/kube-rbac-proxy
            baseImage: quay.io/coreos/kube-state-metrics
      node-role.kubernetes.io/infra: ""
            baseImage: grafana/grafana
      node-role.kubernetes.io/infra: ""
            baseImage: openshift/oauth-proxy
      node-role.kubernetes.io/infra: ""
        name: cluster-monitoring-config
      namespace: openshift-monitoring

    1 A typical value is PROMETHEUS_RETENTION_PERIOD=15d. Units are measured in time using one of these suffixes: s, m, h, d.

    2 A typical value is PROMETHEUS_STORAGE_SIZE=2000Gi. Storage values can be a plain integer or as a fixed-point integer using one of these suffixes: E, P, T, G, M, K. We can also use the power-of-two equivalents: Ei, Pi, Ti, Gi, Mi, Ki.

    3 A typical value is ALERTMANAGER_STORAGE_SIZE=20Gi. Storage values can be a plain integer or as a fixed-point integer using one of these suffixes: E, P, T, G, M, K. We can also use the power-of-two equivalents: Ei, Pi, Ti, Gi, Mi, Ki.
  2. Set the values like the retention period and storage sizes.

  3. Apply the changes by running:

      $ oc create -f cluster-monitoring-config.yml

Planning your environment according to object limits

Consider the following object limits when you plan the OpenShift cluster.

These limits are based on on the largest possible cluster. For smaller clusters, the limits are proportionally lower. There are many factors that influence the stated thresholds, including the etcd version or storage data format.

In most cases, exceeding these limits results in lower overall performance. It does not necessarily mean that the cluster will fail.

OpenShift cluster limits

Limit type 3.9 limit 3.10 limit 3.11 limit 4.1 limit
Number of nodes [a] 2,000 2,000 2,000 2,000
Number of pods [b] 120,000 150,000 150,000 150,000
Number of pods per node 250 250 250 250
Number of pods per core Default: 10. Max is number of pods per node. No default. Max is number of pods per node. No default. Max is number of pods per node. No default. Max is number of pods per node.
Number of namespaces [c] 10,000 10,000 10,000 10,000
Number of builds: Pipeline Strategy 10,000
Default pod RAM 512 Mi
Default pod RAM 512 Mi
Default pod RAM 512 Mi
Default pod RAM 512 Mi
Number of pods per namespace [d] 3,000 3,000 25,000 25,000
Number of services [e] 10,000 10,000 10,000 10,000
Number of services per namespace N/A 5,000 5,000 5,000
Number of back-ends per service 5,000 5,000 5,000 5,000
Number of deployments per namespace [d] 2,000 2,000 2,000 2,000
[a] Clusters with more than the stated limit are not supported. Consider splitting into multiple clusters. [b] The pod count displayed here is the number of test pods. The actual number of pods depends on the application's memory, CPU, and storage requirements. [c] When there are a large number of active projects, etcd may suffer from poor performance if the keyspace grows excessively large and exceeds the space quota. Periodic maintenance of etcd, including defragmentaion, is highly recommended to free etcd storage. [d] There are a number of control loops in the system that must iterate over all objects in a given namespace as a reaction to some changes in state. Having a large number of objects of a given type in a single namespace can make those loops expensive and slow down processing given state changes. The limit assumes that the system has enough CPU, memory, and disk to satisfy the application requirements. [e] Each service port and each service back-end has a corresponding entry in iptables. The number of back-ends of a given service impact the size of the endpoints objects, which impacts the size of data that is being sent all over the system.

In OpenShift 4.1, half of a CPU core (500 millicore) is now reserved by the system compared to OpenShift 3.11 and previous versions.

In OpenShift 4.1, the tested node limit has been lowered until scale tests can be run at a higher node count.

Plan the environment according to cluster limits

Oversubscribing the physical resources on a node affects resource guarantees the Kubernetes scheduler makes during pod placement. Learn what measures we can take to avoid memory swapping.

Some of the limits are stretched only in a single dimension. They will vary when many objects are running on the cluster.

While planning your environment, determine how many pods are expected to fit per node:

    Maximum Pods per Cluster / Expected Pods per Node = Total Number of Nodes

The number of pods expected to fit on a node is dependent on the application itself. Consider the application's memory, CPU, and storage requirements.

Example scenario

To scope the cluster for 2200 pods per cluster, you would need at least nine nodes, assuming that there are 250 maximum pods per node:

    2200 / 250 = 8.8

If you increase the number of nodes to 20, then the pod distribution changes to 110 pods per node:

    2200 / 20 = 110

Plan the environment according to application requirements

Consider an example application environment:

Pod type Pod quantity Max memory CPU cores Persistent storage
apache 100 500 MB 0.5 1 GB
node.js 200 1 GB 1 1 GB
postgresql 100 1 GB 2 10 GB
JBoss EAP 100 1 GB 1 1 GB

Extrapolated requirements: 550 CPU cores, 450GB RAM, and 1.4TB storage.

Instance size for nodes can be modulated up or down, depending on your preference. Nodes are often resource overcommitted. In this deployment scenario, we can choose to run additional smaller nodes or fewer larger nodes to provide the same amount of resources. Factors such as operational agility and cost-per-instance should be considered.

Node type Quantity CPUs RAM (GB)
Nodes (option 1) 100 4 16
Nodes (option 2) 50 8 32
Nodes (option 3) 25 16 64

Some applications lend themselves well to overcommitted environments, and some do not. Most Java applications and applications that use huge pages are examples of applications that would not allow for overcommitment. That memory can not be used for other applications. In the example above, the environment would be roughly 30 percent overcommitted, a common ratio.

Optimizing storage

Optimizing storage helps to minimize storage use across all resources. By optimizing storage, administrators help ensure that existing storage resources are working in an efficient manner.

Available persistent storage options

Understand your persistent storage options so that we can optimize the OpenShift environment.

Table 7.1. Available storage options

Storage type Description Examples

  • Presented to the operating system (OS) as a block device
  • Suitable for applications that need full control of storage and operate at a low level on files bypassing the file system
  • Also referred to as a Storage Area Network (SAN)
  • Non-shareable, which means that only one client at a time can mount an endpoint of this type
AWS EBS and VMware vSphere support dynamic persistent volume (PV) provisioning natively in OpenShift.

  • Presented to the OS as a file system export to be mounted
  • Also referred to as Network Attached Storage (NAS)
  • Concurrency, latency, file locking mechanisms, and other capabilities vary widely between protocols, implementations, vendors, and scales.
RHEL NFS, NetApp NFS [a], and Vendor NFS

  • Accessible through a REST API endpoint
  • Configurable for use in the OpenShift Registry
  • Applications must build their drivers into the application and/or container.
[a] NetApp NFS supports dynamic PV provisioning when using the Trident plug-in.

Currently, CNS is not supported in OpenShift 4.1.

Optimize HAProxy router

The HAProxy router scales to optimize performance.

Baseline router performance

The OpenShift router is the Ingress point for all external traffic destined for OpenShift services.

When evaluating a single HAProxy router performance in terms of HTTP requests handled per second, the performance varies depending on many factors. In particular:

  • HTTP keep-alive/close mode
  • route type
  • TLS session resumption client support
  • number of concurrent connections per target route
  • number of target routes
  • back end server page size
  • underlying infrastructure (network/SDN solution, CPU, and so on)

While performance in your specific environment will vary, Red Hat lab tests on a public cloud instance of size 4 vCPU/16GB RAM, a single HAProxy router handling 100 routes terminated by backends serving 1kB static pages is able to handle the following number of transactions per second.

In HTTP keep-alive mode scenarios:

Encryption LoadBalancerService HostNetwork
none 21515 29622
edge 16743 22913
passthrough 36786 53295
re-encrypt 21583 25198

In HTTP close (no keep-alive) scenarios:

Encryption LoadBalancerService HostNetwork
none 5719 8273
edge 2729 4069
passthrough 4121 5344
re-encrypt 2320 2941

Default router configuration with ROUTER_THREADS=4 was used and two different endpoint publishing strategies (LoadBalancerService/HostNetwork) tested. TLS session resumption was used for encrypted routes. With HTTP keep-alive, a single HAProxy router is capable of saturating 1 Gbit NIC at page sizes as small as 8 kB.

When running on bare metal with modern processors, we can expect roughly twice the performance of the public cloud instance above. This overhead is introduced by the virtualization layer in place on public clouds and holds mostly true for private cloud-based virtualization as well. The following table is a guide on how many applications to use behind the router:

Number of applications Application type
5-10 static file/web server or caching proxy
100-1000 applications generating dynamic content

In general, HAProxy can support routes for 5 to 1000 applications, depending on the technology in use. Router performance might be limited by the capabilities and performance of the applications behind it, such as language or static versus dynamic content.

Router sharding should be used to serve more routes towards applications and help horizontally scale the routing tier.

Router performance optimizations

Set the maximum number of connections

One of the most important tunable parameters for HAProxy scalability is the maxconn parameter, which sets the maximum per-process number of concurrent connections to a given number. Adjust this parameter by editing the ROUTER_MAX_CONNECTIONS environment variable in the OpenShift HAProxy router's deployment configuration file.

A connection includes the front end and internal back end. This counts as two connections. Be sure to set ROUTER_MAX_CONNECTIONS to double than the number of connections you intend to create.

CPU and interrupt affinity

In OpenShift, the HAProxy router runs as a single process. The OpenShift HAProxy router typically performs better on a system with fewer but high frequency cores, rather than on an symmetric multiprocessing (SMP) system with a high number of lower frequency cores.

Pinning the HAProxy process to one CPU core and the network interrupts to another CPU core tends to increase network performance. Having processes and interrupts on the same non-uniform memory access (NUMA) node helps avoid memory accesses by ensuring a shared L3 cache. However, this level of control is generally not possible on a public cloud environment.

CPU pinning is performed either by taskset or by using HAProxy's cpu-map parameter. This directive takes two arguments: the process ID and the CPU core ID. For example, to pin HAProxy process 1 onto CPU core 0, add the following line to the global section of HAProxy’s configuration file:

      cpu-map 1 0

Increasing the number of threads

The HAProxy router comes with support for multithreading in OpenShift. On a multiple CPU core system, increasing the number of threads can help the performance, especially when terminating SSL on the router.

Impacts of buffer increases

The OpenShift HAProxy router request buffer configuration limits the size of headers in incoming requests and responses from applications. The HAProxy parameter tune.bufsize can be increased to allow processing of larger headers and to allow applications with very large cookies to work, such as those accepted by load balancers provided by many public cloud providers. However, this affects the total memory use, especially when large numbers of connections are open. With very large numbers of open connections, the memory usage will be nearly proportionate to the increase of this tunable parameter.

Optimizations for HAProxy reloads

Long-lasting connections, such as WebSocket connections, combined with long client/server HAProxy timeouts and short HAProxy reload intervals, can cause instantiation of many HAProxy processes. These processes must handle old connections, which were started before the HAProxy configuration reload. A large number of these processes is undesirable, as it will exert unnecessary load on the system and can lead to issues, such as out of memory conditions.


What huge pages do and how they are consumed by applications

What huge pages do

Memory is managed in blocks known as pages. On most systems, a page is 4Ki. 1Mi of memory is equal to 256 pages; 1Gi of memory is 256,000 pages, and so on. CPUs have a built-in memory management unit that manages a list of these pages in hardware. The Translation Lookaside Buffer (TLB) is a small hardware cache of virtual-to-physical page mappings. If the virtual address passed in a hardware instruction can be found in the TLB, the mapping can be determined quickly. If not, a TLB miss occurs, and the system falls back to slower, software-based address translation, resulting in performance issues. Since the size of the TLB is fixed, the only way to reduce the chance of a TLB miss is to increase the page size.

A huge page is a memory page that is larger than 4Ki. On x86_64 architectures, there are two common huge page sizes: 2Mi and 1Gi. Sizes vary on other architectures. To use huge pages, code must be written so that applications are aware of them. Transparent Huge Pages (THP) attempt to automate the management of huge pages without application knowledge, but they have limitations. In particular, they are limited to 2Mi page sizes. THP can lead to performance degradation on nodes with high memory utilization or fragmentation due to defragmenting efforts of THP, which can lock memory pages. For this reason, some applications may be designed to (or recommend) usage of pre-allocated huge pages instead of THP.

In OpenShift, applications in a pod can allocate and consume pre-allocated huge pages.

How huge pages are consumed by apps

Nodes must pre-allocate huge pages in order for the node to report its huge page capacity. A node can only pre-allocate huge pages for a single size.

Huge pages can be consumed through container-level resource requirements using the resource name hugepages-<size>, where size is the most compact binary notation using integer values supported on a particular node. For example, if a node supports 2048KiB page sizes, it exposes a schedulable resource hugepages-2Mi. Unlike CPU or memory, huge pages do not support over-commitment.

    apiVersion: v1
    kind: Pod
      generateName: hugepages-volume-
      - securityContext:
          privileged: true
        image: rhel7:latest
        - sleep
        - inf
        name: example
        - mountPath: /dev/hugepages
          name: hugepage
    hugepages-2Mi: 100Mi 1
    memory: "1Gi"
    cpu: "1"
      - name: hugepage
          medium: HugePages

1 Amount of memory for hugepages as the exact amount to be allocated. Do not specify this value as the amount of memory for hugepages multiplied by the size of the page. For example, given a huge page size of 2MB, to use 100MB of huge-page-backed RAM for our application, then you would allocate 50 huge pages. OpenShift handles the math for you. As in the above example, we can specify 100MB directly.

Allocating huge pages of a specific size

Some platforms support multiple huge page sizes. To allocate huge pages of a specific size, precede the huge pages boot command parameters with a huge page size selection parameter hugepagesz=<size>. The <size> value must be specified in bytes with an optional scale suffix [kKmMgG]. The default huge page size can be defined with the default_hugepagesz=<size> boot parameter.

Huge page requirements

  • Huge page requests must equal the limits. This is the default if limits are specified, but requests are not.
  • Huge pages are isolated at a pod scope. Container isolation is planned in a future iteration.
  • EmptyDir volumes backed by huge pages must not consume more huge page memory than the pod request.
  • Applications that consume huge pages via shmget() with SHM_HUGETLB must run with a supplemental group that matches proc/sys/vm/hugetlb_shm_group.

Additional resources

Configure huge pages

Nodes must pre-allocate huge pages used in an OpenShift cluster. Use the Node Tuning Operator to allocate huge pages on a specific node.


  1. Label the node so that the Node Tuning Operator knows on which node to apply the tuned profile, which describes how many huge pages should be allocated:

      $ oc label node <node_using_hugepages> hugepages=true

  2. Create a file with the following content and name it hugepages_tuning.yaml:

      apiVersion: tuned.openshift.io/v1
      kind: Tuned
        name: hugepages 1
        namespace: openshift-cluster-node-tuning-operator
        profile: 2
        - data: |
            summary=Configuration for hugepages
          name: node-hugepages
        - match: 3
          - label: hugepages
          priority: 30
          profile: node-hugepages

    1 Set the name parameter value to hugepages.

    2 Set the profile section to allocate huge pages.

    3 Set the match section to associate the profile to nodes with the hugepages label.

  3. Create the custom hugepages tuned profile using the hugepages_tuning.yaml file:

      $ oc create -f hugepages_tuning.yaml

  4. After creating the profile, the Operator applies the new profile to the correct node and allocates huge pages. Check the logs of a tuned pod on a node using huge pages to verify:

      $ oc logs <tuned_pod_on_node_using_hugepages> \
          -n openshift-cluster-node-tuning-operator | grep 'applied$' | tail -n1
      2019-08-08 07:20:41,286 INFO     tuned.daemon.daemon: static tuning from profile 'node-hugepages' applied

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