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Recommended host practices | Scalability and performance | OpenShift Container Platform 4.10
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Recommended host practices

The OpenShift Container Platform node configuration file contains important options. For example, two parameters control the maximum number of pods that can be scheduled to a node: podsPerCore and maxPods.

When both options are in use, the lower of the two values limits the number of pods on a node. Exceeding these values can result in:

  • Increased CPU utilization.

  • Slow pod scheduling.

  • Potential out-of-memory scenarios, depending on the amount of memory in the node.

  • Exhausting the pool of IP addresses.

  • Resource overcommitting, leading to poor user application performance.

In Kubernetes, a pod that is holding a single container actually uses two containers. The second container is used to set up networking prior to the actual container starting. Therefore, a system running 10 pods will actually have 20 containers running.

Disk IOPS throttling from the cloud provider might have an impact on CRI-O and kubelet. They might get overloaded when there are large number of I/O intensive pods running on the nodes. It is recommended that you monitor the disk I/O on the nodes and use volumes with sufficient throughput for the workload.

podsPerCore sets the number of pods the node can run based on the number of processor cores on the node. For example, if podsPerCore is set to 10 on a node with 4 processor cores, the maximum number of pods allowed on the node will be 40.

kubeletConfig:
  podsPerCore: 10

Setting podsPerCore to 0 disables this limit. The default is 0. podsPerCore cannot exceed maxPods.

maxPods sets the number of pods the node can run to a fixed value, regardless of the properties of the node.

 kubeletConfig:
    maxPods: 250

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 lets you use a KubeletConfig custom resource (CR) to edit the kubelet parameters.

As the fields in the kubeletConfig object are passed directly to the kubelet from upstream Kubernetes, the kubelet validates those values directly. Invalid values in the kubeletConfig object might cause cluster nodes to become unavailable. For valid values, see the Kubernetes documentation.

Consider the following guidance:

  • Create one KubeletConfig CR for each machine config pool with all the config changes you want for that pool. If you are applying the same content to all of the pools, you need only one KubeletConfig CR for all of the pools.

  • Edit an existing KubeletConfig CR to modify existing settings or add new settings, instead of creating a CR for each change. It is recommended that you create a CR only to modify a different machine config pool, or for changes that are intended to be temporary, so that you can revert the changes.

  • As needed, create multiple KubeletConfig CRs with a limit of 10 per cluster. For the first KubeletConfig CR, the Machine Config Operator (MCO) creates a machine config appended with kubelet. With each subsequent CR, the controller creates another kubelet machine config with a numeric suffix. For example, if you have a kubelet machine config with a -2 suffix, the next kubelet machine config is appended with -3.

If you want to delete the machine configs, delete them in reverse order to avoid exceeding the limit. For example, you delete the kubelet-3 machine config before deleting the kubelet-2 machine config.

If you have a machine config with a kubelet-9 suffix, and you create another KubeletConfig CR, a new machine config is not created, even if there are fewer than 10 kubelet machine configs.
Example KubeletConfig CR
$ oc get kubeletconfig
NAME                AGE
set-max-pods        15m
Example showing a KubeletConfig machine config
$ oc get mc | grep kubelet
...
99-worker-generated-kubelet-1                  b5c5119de007945b6fe6fb215db3b8e2ceb12511   3.2.0             26m
...

The following procedure is an example to show how to configure the maximum number of pods per node on the worker nodes.

Prerequisites

  1. Obtain the label associated with the static MachineConfigPool CR for the type of node you want to configure. Perform one of the following steps:

    1. View the machine config pool:

      $ oc describe machineconfigpool <name>

      For example:

      $ oc describe machineconfigpool worker
      Example output
      apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfigPool
metadata:
  creationTimestamp: 2019-02-08T14:52:39Z
  generation: 1
  labels:
    custom-kubelet: set-max-pods (1)
      1 If a label has been added it appears under labels.

    2. If the label is not present, add a key/value pair:

      $ oc label machineconfigpool worker custom-kubelet=set-max-pods
Procedure

  1. View the available machine configuration objects that you can select:

    $ oc get machineconfig

    By default, the two kubelet-related configs are 01-master-kubelet and 01-worker-kubelet.

  2. Check the current value for the maximum pods per node:

    $ oc describe node <node_name>

    For example:

    $ oc describe node ci-ln-5grqprb-f76d1-ncnqq-worker-a-mdv94

    Look for value: pods: <value> in the Allocatable stanza:

    Example output
    Allocatable:
 attachable-volumes-aws-ebs:  25
 cpu:                         3500m
 hugepages-1Gi:               0
 hugepages-2Mi:               0
 memory:                      15341844Ki
 pods:                        250

  3. Set the maximum pods per node on the worker nodes by creating a custom resource file that contains the kubelet configuration:

    apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
  name: set-max-pods
spec:
  machineConfigPoolSelector:
    matchLabels:
      custom-kubelet: set-max-pods (1)
  kubeletConfig:
    maxPods: 500 (2)
    1 Enter the label from the machine config pool.
    2 Add the kubelet configuration. In this example, use maxPods to set the maximum pods per node.

    The rate at which the kubelet talks to the API server depends on queries per second (QPS) and burst values. The default values, 50 for kubeAPIQPS and 100 for kubeAPIBurst, are sufficient if there are limited pods running on each node. It is recommended to update the kubelet QPS and burst rates if there are enough CPU and memory resources on the node.

    apiVersion: machineconfiguration.openshift.io/v1
kind: KubeletConfig
metadata:
  name: set-max-pods
spec:
  machineConfigPoolSelector:
    matchLabels:
      custom-kubelet: set-max-pods
  kubeletConfig:
    maxPods: <pod_count>
    kubeAPIBurst: <burst_rate>
    kubeAPIQPS: <QPS>

    1. Update the machine config pool for workers with the label:

      $ oc label machineconfigpool worker custom-kubelet=large-pods

    2. Create the KubeletConfig object:

      $ oc create -f change-maxPods-cr.yaml

    3. Verify that the KubeletConfig object is created:

      $ oc get kubeletconfig
      Example output
      NAME                AGE
set-max-pods        15m

      Depending on the number of worker nodes in the cluster, wait for the worker nodes to be rebooted one by one. For a cluster with 3 worker nodes, this could take about 10 to 15 minutes.

  4. Verify that the changes are applied to the node:

    1. Check on a worker node that the maxPods value changed:

      $ oc describe node <node_name>

    2. Locate the Allocatable stanza:

       ...
Allocatable:
  attachable-volumes-gce-pd:  127
  cpu:                        3500m
  ephemeral-storage:          123201474766
  hugepages-1Gi:              0
  hugepages-2Mi:              0
  memory:                     14225400Ki
  pods:                       500 (1)
 ...
      1 In this example, the pods parameter should report the value you set in the KubeletConfig object.

  5. Verify the change in the KubeletConfig object:

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

    This should show a status of True and type:Success, as shown in the following example:

    spec:
  kubeletConfig:
    maxPods: 500
  machineConfigPoolSelector:
    matchLabels:
      custom-kubelet: set-max-pods
status:
  conditions:
  - lastTransitionTime: "2021-06-30T17:04:07Z"
    message: Success
    status: "True"
    type: Success

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, you can adjust the number of machines that are updating to speed up the process.

Procedure

  1. Edit the worker machine config pool:

    $ oc edit machineconfigpool worker

  2. Set maxUnavailable to the value that you want:

    spec:
  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.

The control plane node resource requirements depend on the number and type of nodes and objects in the cluster. The following control plane node size recommendations are based on the results of a control plane density focused testing, or Cluster-density. This test creates the following objects across a given number of namespaces:

  • 1 image stream

  • 1 build

  • 5 deployments, with 2 pod replicas in a sleep state, mounting 4 secrets, 4 config maps, and 1 downward API volume each

  • 5 services, each one pointing to the TCP/8080 and TCP/8443 ports of one of the previous deployments

  • 1 route pointing to the first of the previous services

  • 10 secrets containing 2048 random string characters

  • 10 config maps containing 2048 random string characters

Number of worker nodes Cluster-density (namespaces) CPU cores Memory (GB)

24

500

4

16

120

1000

8

32

252

4000

16

64

501

4000

16

96

On a large and dense cluster with three masters or control plane nodes, the CPU and memory usage will spike up when one of the nodes is stopped, rebooted or fails. The failures can be due to unexpected issues with power, network or underlying infrastructure in addition to intentional cases where the cluster is restarted after shutting it down to save costs. The remaining two control plane nodes must handle the load in order to be highly available which leads to increase in the resource usage. This is also expected during upgrades because the masters are cordoned, drained, and rebooted serially to apply the operating system updates, as well as the control plane Operators update. To avoid cascading failures, keep the overall CPU and memory resource usage on the control plane nodes to at most 60% of all available capacity to handle the resource usage spikes. Increase the CPU and memory on the control plane nodes accordingly to avoid potential downtime due to lack of resources.

The node sizing varies depending on the number of nodes and object counts in the cluster. It also depends on whether the objects are actively being created on the cluster. During object creation, the control plane is more active in terms of resource usage compared to when the objects are in the running phase.

Operator Lifecycle Manager (OLM ) runs on the control plane nodes and it’s memory footprint depends on the number of namespaces and user installed operators that OLM needs to manage on the cluster. Control plane nodes need to be sized accordingly to avoid OOM kills. Following data points are based on the results from cluster maximums testing.

Number of namespaces OLM memory at idle state (GB) OLM memory with 5 user operators installed (GB)

500

0.823

1.7

1000

1.2

2.5

1500

1.7

3.2

2000

2

4.4

3000

2.7

5.6

4000

3.8

7.6

5000

4.2

9.02

6000

5.8

11.3

7000

6.6

12.9

8000

6.9

14.8

9000

8

17.7

10,000

9.9

21.6

You can modify the control plane node size in a running OpenShift Container Platform 4.10 cluster for the following configurations only:

  • Clusters installed with a user-provisioned installation method.

  • AWS clusters installed with an installer-provisioned infrastructure installation method.

For all other configurations, you must estimate your total node count and use the suggested control plane node size during installation.

The recommendations are based on the data points captured on OpenShift Container Platform clusters with OpenShift SDN as the network plugin.

In OpenShift Container Platform 4.10, half of a CPU core (500 millicore) is now reserved by the system by default compared to OpenShift Container Platform 3.11 and previous versions. The sizes are determined taking that into consideration.

If the control plane machines in an Amazon Web Services (AWS) cluster require more resources, you can select a larger AWS instance type for the control plane machines to use.

You can change the Amazon Web Services (AWS) instance type that your control plane machines use by updating the instance type in the AWS console.

Prerequisites

  • You have access to the AWS console with the permissions required to modify the EC2 Instance for your cluster.

  • You have access to the OpenShift Container Platform cluster as a user with the cluster-admin role.

Procedure

  1. Open the AWS console and fetch the instances for the control plane machines.

  2. Choose one control plane machine instance.

    1. For the selected control plane machine, back up the etcd data by creating an etcd snapshot. For more information, see "Backing up etcd".

    2. In the AWS console, stop the control plane machine instance.

    3. Select the stopped instance, and click Actions → Instance Settings → Change instance type.

    4. Change the instance to a larger type, ensuring that the type is the same base as the previous selection, and apply changes. For example, you can change m6i.xlarge to m6i.2xlarge or m6i.4xlarge.

    5. Start the instance.

    6. If your OpenShift Container Platform cluster has a corresponding Machine object for the instance, update the instance type of the object to match the instance type set in the AWS console.

  3. Repeat this process for each control plane machine.

Additional resources

Because etcd writes data to disk and persists proposals on disk, its performance depends on disk performance. Although etcd is not particularly I/O intensive, it requires a low latency block device for optimal performance and stability. Because etcd’s consensus protocol depends on persistently storing metadata to a log (WAL), etcd is sensitive to disk-write latency. Slow disks and disk activity from other processes can cause long fsync latencies.

Those latencies can cause etcd to miss heartbeats, not commit new proposals to the disk on time, and ultimately experience request timeouts and temporary leader loss. High write latencies also lead to an OpenShift API slowness, which affects cluster performance. Because of these reasons, avoid colocating other workloads on the control-plane nodes that are I/O sensitive or intensive and share the same underlying I/O infrastructure.

In terms of latency, run etcd on top of a block device that can write at least 50 IOPS of 8000 bytes long sequentially. That is, with a latency of 20ms, keep in mind that uses fdatasync to synchronize each write in the WAL. For heavy loaded clusters, sequential 500 IOPS of 8000 bytes (2 ms) are recommended. To measure those numbers, you can use a benchmarking tool, such as fio.

To achieve such performance, run etcd on machines that are backed by SSD or NVMe disks with low latency and high throughput. Consider single-level cell (SLC) solid-state drives (SSDs), which provide 1 bit per memory cell, are durable and reliable, and are ideal for write-intensive workloads.

The load on etcd arises from static factors, such as the number of nodes and pods, and dynamic factors, including changes in endpoints due to pod autoscaling, pod restarts, job executions, and other workload-related events. To accurately size your etcd setup, you must analyze the specific requirements of your workload. Consider the number of nodes, pods, and other relevant factors that impact the load on etcd.

The following hard disk features provide optimal etcd performance:

  • Low latency to support fast read operation.

  • High-bandwidth writes for faster compactions and defragmentation.

  • High-bandwidth reads for faster recovery from failures.

  • Solid state drives as a minimum selection, however NVMe drives are preferred.

  • Server-grade hardware from various manufacturers for increased reliability.

  • RAID 0 technology for increased performance.

  • Dedicated etcd drives. Do not place log files or other heavy workloads on etcd drives.

Avoid NAS or SAN setups and spinning drives. Ceph Rados Block Device (RBD) and other types of network-attached storage can result in unpredictable network latency. To provide fast storage to etcd nodes at scale, use PCI passthrough to pass NVM devices directly to the nodes.

Always benchmark by using utilities such as fio. You can use such utilities to continuously monitor the cluster performance as it increases.

Avoid using the Network File System (NFS) protocol or other network based file systems.

Some key metrics to monitor on a deployed OpenShift Container Platform cluster are p99 of etcd disk write ahead log duration and the number of etcd leader changes. Use Prometheus to track these metrics.

To validate the hardware for etcd before or after you create the OpenShift Container Platform cluster, you can use fio.

Prerequisites

  • Container runtimes such as Podman or Docker are installed on the machine that you’re testing.

  • Data is written to the /var/lib/etcd path.

Procedure

  • Run fio and analyze the results:

    • If you use Podman, run this command:

      $ sudo podman run --volume /var/lib/etcd:/var/lib/etcd:Z quay.io/openshift-scale/etcd-perf

    • If you use Docker, run this command:

      $ sudo docker run --volume /var/lib/etcd:/var/lib/etcd:Z quay.io/openshift-scale/etcd-perf

The output reports whether the disk is fast enough to host etcd by comparing the 99th percentile of the fsync metric captured from the run to see if it is less than 20 ms. A few of the most important etcd metrics that might affected by I/O performance are as follow:

  • etcd_disk_wal_fsync_duration_seconds_bucket metric reports the etcd’s WAL fsync duration

  • etcd_disk_backend_commit_duration_seconds_bucket metric reports the etcd backend commit latency duration

  • etcd_server_leader_changes_seen_total metric reports the leader changes

Because etcd replicates the requests among all the members, its performance strongly depends on network input/output (I/O) latency. High network latencies result in etcd heartbeats taking longer than the election timeout, which results in leader elections that are disruptive to the cluster. A key metric to monitor on a deployed OpenShift Container Platform cluster is the 99th percentile of etcd network peer latency on each etcd cluster member. Use Prometheus to track the metric.

The histogram_quantile(0.99, rate(etcd_network_peer_round_trip_time_seconds_bucket[2m])) metric reports the round trip time for etcd to finish replicating the client requests between the members. Ensure that it is less than 50 ms.

Additional resources

You can move etcd from a shared disk to a separate disk to prevent or resolve performance issues.

Prerequisites

  • The MachineConfigPool must match metadata.labels[machineconfiguration.openshift.io/role]. This applies to a controller, worker, or a custom pool.

  • The node’s auxiliary storage device, such as /dev/sdb, must match the sdb. Change this reference in all places in the file.

This procedure does not move parts of the root file system, such as /var/, to another disk or partition on an installed node.

The Machine Config Operator (MCO) is responsible for mounting a secondary disk for an OpenShift Container Platform 4.10 container storage.

Use the following steps to move etcd to a different device:

Procedure

  1. Create a machineconfig YAML file named etcd-mc.yml and add the following information:

    apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: master
  name: 98-var-lib-etcd
spec:
  config:
    ignition:
      version: 3.2.0
    systemd:
      units:
      - contents: |
          [Unit]
          Description=Make File System on /dev/sdb
          DefaultDependencies=no
          BindsTo=dev-sdb.device
          After=dev-sdb.device var.mount
          Before=systemd-fsck@dev-sdb.service

          [Service]
          Type=oneshot
          RemainAfterExit=yes
          ExecStart=/usr/lib/systemd/systemd-makefs xfs /dev/sdb
          TimeoutSec=0

          [Install]
          WantedBy=var-lib-containers.mount
        enabled: true
        name: systemd-mkfs@dev-sdb.service
      - contents: |
          [Unit]
          Description=Mount /dev/sdb to /var/lib/etcd
          Before=local-fs.target
          Requires=systemd-mkfs@dev-sdb.service
          After=systemd-mkfs@dev-sdb.service var.mount

          [Mount]
          What=/dev/sdb
          Where=/var/lib/etcd
          Type=xfs
          Options=defaults,prjquota

          [Install]
          WantedBy=local-fs.target
        enabled: true
        name: var-lib-etcd.mount
      - contents: |
          [Unit]
          Description=Sync etcd data if new mount is empty
          DefaultDependencies=no
          After=var-lib-etcd.mount var.mount
          Before=crio.service

          [Service]
          Type=oneshot
          RemainAfterExit=yes
          ExecCondition=/usr/bin/test ! -d /var/lib/etcd/member
          ExecStart=/usr/sbin/setenforce 0
          ExecStart=/bin/rsync -ar /sysroot/ostree/deploy/rhcos/var/lib/etcd/ /var/lib/etcd/
          ExecStart=/usr/sbin/setenforce 1
          TimeoutSec=0

          [Install]
          WantedBy=multi-user.target graphical.target
        enabled: true
        name: sync-var-lib-etcd-to-etcd.service
      - contents: |
          [Unit]
          Description=Restore recursive SELinux security contexts
          DefaultDependencies=no
          After=var-lib-etcd.mount
          Before=crio.service

          [Service]
          Type=oneshot
          RemainAfterExit=yes
          ExecStart=/sbin/restorecon -R /var/lib/etcd/
          TimeoutSec=0

          [Install]
          WantedBy=multi-user.target graphical.target
        enabled: true
        name: restorecon-var-lib-etcd.service

  2. Create the machine configuration by entering the following commands:

    $ oc login -u ${ADMIN} -p ${ADMINPASSWORD} ${API}
... output omitted ...
    $ oc create -f etcd-mc.yml
machineconfig.machineconfiguration.openshift.io/98-var-lib-etcd created
    $ oc login -u ${ADMIN} -p ${ADMINPASSWORD} ${API}
 [... output omitted ...]
    $ oc create -f etcd-mc.yml machineconfig.machineconfiguration.openshift.io/98-var-lib-etcd created

    The nodes are updated and rebooted. After the reboot completes, the following events occur:

    • An XFS file system is created on the specified disk.

    • The disk mounts to /var/lib/etc.

    • The content from /sysroot/ostree/deploy/rhcos/var/lib/etcd syncs to /var/lib/etcd.

    • A restore of SELinux labels is forced for /var/lib/etcd.

    • The old content is not removed.

  3. After the nodes are on a separate disk, update the machine configuration file, etcd-mc.yml with the following information:

    apiVersion: machineconfiguration.openshift.io/v1
kind: MachineConfig
metadata:
  labels:
    machineconfiguration.openshift.io/role: master
  name: 98-var-lib-etcd
spec:
  config:
    ignition:
      version: 3.2.0
    systemd:
      units:
      - contents: |
          [Unit]
          Description=Mount /dev/sdb to /var/lib/etcd
          Before=local-fs.target
          Requires=systemd-mkfs@dev-sdb.service
          After=systemd-mkfs@dev-sdb.service var.mount

          [Mount]
          What=/dev/sdb
          Where=/var/lib/etcd
          Type=xfs
          Options=defaults,prjquota

          [Install]
          WantedBy=local-fs.target
        enabled: true
        name: var-lib-etcd.mount

  4. Apply the modified version that removes the logic for creating and syncing the device by entering the following command:

    $ oc replace -f etcd-mc.yml

    The previous step prevents the nodes from rebooting.

Additional resources

For large and dense clusters, etcd can suffer from poor performance if the keyspace grows too large and exceeds the space quota. Periodically maintain and defragment etcd to free up space in the data store. Monitor Prometheus for etcd metrics and defragment it when required; otherwise, etcd can raise a cluster-wide alarm that puts the cluster into a maintenance mode that accepts only key reads and deletes.

Monitor these key metrics:

  • etcd_server_quota_backend_bytes, which is the current quota limit

  • etcd_mvcc_db_total_size_in_use_in_bytes, which indicates the actual database usage after a history compaction

  • etcd_mvcc_db_total_size_in_bytes, which shows the database size, including free space waiting for defragmentation

Defragment etcd data to reclaim disk space after events that cause disk fragmentation, such as etcd history compaction.

History compaction is performed automatically every five minutes and leaves gaps in the back-end database. This fragmented space is available for use by etcd, but is not available to the host file system. You must defragment etcd to make this space available to the host file system.

Defragmentation occurs automatically, but you can also trigger it manually.

Automatic defragmentation is good for most cases, because the etcd operator uses cluster information to determine the most efficient operation for the user.

The etcd Operator automatically defragments disks. No manual intervention is needed.

Verify that the defragmentation process is successful by viewing one of these logs:

  • etcd logs

  • cluster-etcd-operator pod

  • operator status error log

Automatic defragmentation can cause leader election failure in various OpenShift core components, such as the Kubernetes controller manager, which triggers a restart of the failing component. The restart is harmless and either triggers failover to the next running instance or the component resumes work again after the restart.
Example log output for successful defragmentation
etcd member has been defragmented: <member_name>, memberID: <member_id>
Example log output for unsuccessful defragmentation
failed defrag on member: <member_name>, memberID: <member_id>: <error_message>

A Prometheus alert indicates when you need to use manual defragmentation. The alert is displayed in two cases:

  • When etcd uses more than 50% of its available space for more than 10 minutes

  • When etcd is actively using less than 50% of its total database size for more than 10 minutes

You can also determine whether defragmentation is needed by checking the etcd database size in MB that will be freed by defragmentation with the PromQL expression: (etcd_mvcc_db_total_size_in_bytes - etcd_mvcc_db_total_size_in_use_in_bytes)/1024/1024

Defragmenting etcd is a blocking action. The etcd member will not respond until defragmentation is complete. For this reason, wait at least one minute between defragmentation actions on each of the pods to allow the cluster to recover.

Follow this procedure to defragment etcd data on each etcd member.

Prerequisites

  • You have access to the cluster as a user with the cluster-admin role.

Procedure

  1. Determine which etcd member is the leader, because the leader should be defragmented last.

    1. Get the list of etcd pods:

      $ oc -n openshift-etcd get pods -l k8s-app=etcd -o wide
      Example output
      etcd-ip-10-0-159-225.example.redhat.com                3/3     Running     0          175m   10.0.159.225   ip-10-0-159-225.example.redhat.com   <none>           <none>
etcd-ip-10-0-191-37.example.redhat.com                 3/3     Running     0          173m   10.0.191.37    ip-10-0-191-37.example.redhat.com    <none>           <none>
etcd-ip-10-0-199-170.example.redhat.com                3/3     Running     0          176m   10.0.199.170   ip-10-0-199-170.example.redhat.com   <none>           <none>

    2. Choose a pod and run the following command to determine which etcd member is the leader:

      $ oc rsh -n openshift-etcd etcd-ip-10-0-159-225.example.redhat.com etcdctl endpoint status --cluster -w table
      Example output
      Defaulting container name to etcdctl.
Use 'oc describe pod/etcd-ip-10-0-159-225.example.redhat.com -n openshift-etcd' to see all of the containers in this pod.
+---------------------------+------------------+---------+---------+-----------+------------+-----------+------------+--------------------+--------+
|         ENDPOINT          |        ID        | VERSION | DB SIZE | IS LEADER | IS LEARNER | RAFT TERM | RAFT INDEX | RAFT APPLIED INDEX | ERRORS |
+---------------------------+------------------+---------+---------+-----------+------------+-----------+------------+--------------------+--------+
|  https://10.0.191.37:2379 | 251cd44483d811c3 |   3.4.9 |  104 MB |     false |      false |         7 |      91624 |              91624 |        |
| https://10.0.159.225:2379 | 264c7c58ecbdabee |   3.4.9 |  104 MB |     false |      false |         7 |      91624 |              91624 |        |
| https://10.0.199.170:2379 | 9ac311f93915cc79 |   3.4.9 |  104 MB |      true |      false |         7 |      91624 |              91624 |        |
+---------------------------+------------------+---------+---------+-----------+------------+-----------+------------+--------------------+--------+

      Based on the IS LEADER column of this output, the https://10.0.199.170:2379 endpoint is the leader. Matching this endpoint with the output of the previous step, the pod name of the leader is etcd-ip-10-0-199-170.example.redhat.com.

  2. Defragment an etcd member.

    1. Connect to the running etcd container, passing in the name of a pod that is not the leader:

      $ oc rsh -n openshift-etcd etcd-ip-10-0-159-225.example.redhat.com

    2. Unset the ETCDCTL_ENDPOINTS environment variable:

      sh-4.4# unset ETCDCTL_ENDPOINTS

    3. Defragment the etcd member:

      sh-4.4# etcdctl --command-timeout=30s --endpoints=https://localhost:2379 defrag
      Example output
      Finished defragmenting etcd member[https://localhost:2379]

      If a timeout error occurs, increase the value for --command-timeout until the command succeeds.

    4. Verify that the database size was reduced:

      sh-4.4# etcdctl endpoint status -w table --cluster
      Example output
      +---------------------------+------------------+---------+---------+-----------+------------+-----------+------------+--------------------+--------+
|         ENDPOINT          |        ID        | VERSION | DB SIZE | IS LEADER | IS LEARNER | RAFT TERM | RAFT INDEX | RAFT APPLIED INDEX | ERRORS |
+---------------------------+------------------+---------+---------+-----------+------------+-----------+------------+--------------------+--------+
|  https://10.0.191.37:2379 | 251cd44483d811c3 |   3.4.9 |  104 MB |     false |      false |         7 |      91624 |              91624 |        |
| https://10.0.159.225:2379 | 264c7c58ecbdabee |   3.4.9 |   41 MB |     false |      false |         7 |      91624 |              91624 |        | (1)
| https://10.0.199.170:2379 | 9ac311f93915cc79 |   3.4.9 |  104 MB |      true |      false |         7 |      91624 |              91624 |        |
+---------------------------+------------------+---------+---------+-----------+------------+-----------+------------+--------------------+--------+

      This example shows that the database size for this etcd member is now 41 MB as opposed to the starting size of 104 MB.

    5. Repeat these steps to connect to each of the other etcd members and defragment them. Always defragment the leader last.

      Wait at least one minute between defragmentation actions to allow the etcd pod to recover. Until the etcd pod recovers, the etcd member will not respond.

  3. If any NOSPACE alarms were triggered due to the space quota being exceeded, clear them.

    1. Check if there are any NOSPACE alarms:

      sh-4.4# etcdctl alarm list
      Example output
      memberID:12345678912345678912 alarm:NOSPACE

    2. Clear the alarms:

      sh-4.4# etcdctl alarm disarm

The following infrastructure workloads do not incur OpenShift Container Platform worker subscriptions:

  • Kubernetes and OpenShift Container Platform control plane services that run on masters

  • The default router

  • The integrated container image registry

  • The HAProxy-based ingress Controller

  • The cluster metrics collection, or monitoring service, including components for monitoring user-defined projects

  • Cluster aggregated logging

  • Service brokers

  • Red Hat Quay

  • Red Hat OpenShift Data Foundation

  • Red Hat Advanced Cluster Manager

  • Red Hat Advanced Cluster Security for Kubernetes

  • Red Hat OpenShift GitOps

  • Red Hat OpenShift Pipelines

Any node that runs any other container, pod, or component is a worker node that your subscription must cover.

For information on infrastructure nodes and which components can run on infrastructure nodes, see the "Red Hat OpenShift control plane and infrastructure nodes" section in the OpenShift sizing and subscription guide for enterprise Kubernetes document.

The monitoring stack includes multiple components, including Prometheus, Grafana, and Alertmanager. The Cluster Monitoring Operator manages this stack. To redeploy the monitoring stack to infrastructure nodes, you can create and apply a custom config map.

Procedure

  1. Edit the cluster-monitoring-config config map and change the nodeSelector to use the infra label:

    $ oc edit configmap cluster-monitoring-config -n openshift-monitoring
    apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-monitoring-config
  namespace: openshift-monitoring
data:
  config.yaml: |+
    alertmanagerMain:
      nodeSelector: (1)
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    prometheusK8s:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    prometheusOperator:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    grafana:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    k8sPrometheusAdapter:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    kubeStateMetrics:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    telemeterClient:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    openshiftStateMetrics:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    thanosQuerier:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoSchedule
      - key: node-role.kubernetes.io/infra
        value: reserved
        effect: NoExecute
    1 Add a nodeSelector parameter with the appropriate value to the component you want to move. You can use a nodeSelector in the format shown or use <key>: <value> pairs, based on the value specified for the node. If you added a taint to the infrasructure node, also add a matching toleration.

  2. Watch the monitoring pods move to the new machines:

    $ watch 'oc get pod -n openshift-monitoring -o wide'

  3. If a component has not moved to the infra node, delete the pod with this component:

    $ oc delete pod -n openshift-monitoring <pod>

    The component from the deleted pod is re-created on the infra node.

You configure the registry Operator to deploy its pods to different nodes.

Prerequisites

  • Configure additional machine sets in your OpenShift Container Platform cluster.

Procedure

  1. View the config/instance object:

    $ oc get configs.imageregistry.operator.openshift.io/cluster -o yaml
    Example output
    apiVersion: imageregistry.operator.openshift.io/v1
kind: Config
metadata:
  creationTimestamp: 2019-02-05T13:52:05Z
  finalizers:
  - imageregistry.operator.openshift.io/finalizer
  generation: 1
  name: cluster
  resourceVersion: "56174"
  selfLink: /apis/imageregistry.operator.openshift.io/v1/configs/cluster
  uid: 36fd3724-294d-11e9-a524-12ffeee2931b
spec:
  httpSecret: d9a012ccd117b1e6616ceccb2c3bb66a5fed1b5e481623
  logging: 2
  managementState: Managed
  proxy: {}
  replicas: 1
  requests:
    read: {}
    write: {}
  storage:
    s3:
      bucket: image-registry-us-east-1-c92e88cad85b48ec8b312344dff03c82-392c
      region: us-east-1
status:
...

  2. Edit the config/instance object:

    $ oc edit configs.imageregistry.operator.openshift.io/cluster
    spec:
  affinity:
    podAntiAffinity:
      preferredDuringSchedulingIgnoredDuringExecution:
      - podAffinityTerm:
          namespaces:
          - openshift-image-registry
          topologyKey: kubernetes.io/hostname
        weight: 100
  logLevel: Normal
  managementState: Managed
  nodeSelector: (1)
    node-role.kubernetes.io/infra: ""
  tolerations:
  - effect: NoSchedule
    key: node-role.kubernetes.io/infra
    value: reserved
  - effect: NoExecute
    key: node-role.kubernetes.io/infra
    value: reserved
    1 Add a nodeSelector parameter with the appropriate value to the component you want to move. You can use a nodeSelector in the format shown or use <key>: <value> pairs, based on the value specified for the node. If you added a taint to the infrasructure node, also add a matching toleration.

  3. Verify the registry pod has been moved to the infrastructure node.

    1. Run the following command to identify the node where the registry pod is located:

      $ oc get pods -o wide -n openshift-image-registry

    2. Confirm the node has the label you specified:

      $ oc describe node <node_name>

      Review the command output and confirm that node-role.kubernetes.io/infra is in the LABELS list.

You can deploy the router pod to a different machine set. By default, the pod is deployed to a worker node.

Prerequisites

  • Configure additional machine sets in your OpenShift Container Platform cluster.

Procedure

  1. View the ingressController custom resource for the router Operator:

    $ oc get ingresscontroller default -n openshift-ingress-operator -o yaml

    The command output resembles the following text:

    apiVersion: operator.openshift.io/v1
kind: ingressController
metadata:
  creationTimestamp: 2019-04-18T12:35:39Z
  finalizers:
  - ingresscontroller.operator.openshift.io/finalizer-ingresscontroller
  generation: 1
  name: default
  namespace: openshift-ingress-operator
  resourceVersion: "11341"
  selfLink: /apis/operator.openshift.io/v1/namespaces/openshift-ingress-operator/ingresscontrollers/default
  uid: 79509e05-61d6-11e9-bc55-02ce4781844a
spec: {}
status:
  availableReplicas: 2
  conditions:
  - lastTransitionTime: 2019-04-18T12:36:15Z
    status: "True"
    type: Available
  domain: apps.<cluster>.example.com
  endpointPublishingStrategy:
    type: LoadBalancerService
  selector: ingresscontroller.operator.openshift.io/deployment-ingresscontroller=default

  2. Edit the ingresscontroller resource and change the nodeSelector to use the infra label:

    $ oc edit ingresscontroller default -n openshift-ingress-operator
      spec:
    nodePlacement:
      nodeSelector: (1)
        matchLabels:
          node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    1 Add a nodeSelector parameter with the appropriate value to the component you want to move. You can use a nodeSelector in the format shown or use <key>: <value> pairs, based on the value specified for the node. If you added a taint to the infrastructure node, also add a matching toleration.

  3. Confirm that the router pod is running on the infra node.

    1. View the list of router pods and note the node name of the running pod:

      $ oc get pod -n openshift-ingress -o wide
      Example output
      NAME                              READY     STATUS        RESTARTS   AGE       IP           NODE                           NOMINATED NODE   READINESS GATES
router-default-86798b4b5d-bdlvd   1/1      Running       0          28s       10.130.2.4   ip-10-0-217-226.ec2.internal   <none>           <none>
router-default-955d875f4-255g8    0/1      Terminating   0          19h       10.129.2.4   ip-10-0-148-172.ec2.internal   <none>           <none>

      In this example, the running pod is on the ip-10-0-217-226.ec2.internal node.

    2. View the node status of the running pod:

      $ oc get node <node_name> (1)
      1 Specify the <node_name> that you obtained from the pod list.
      Example output
      NAME                          STATUS  ROLES         AGE   VERSION
ip-10-0-217-226.ec2.internal  Ready   infra,worker  17h   v1.23.0

      Because the role list includes infra, the pod is running on the correct node.

Infrastructure nodes are nodes that are labeled to run pieces of the OpenShift Container Platform environment. The infrastructure node resource requirements depend on the cluster age, nodes, and objects in the cluster, as these factors can lead to an increase in the number of metrics or time series in Prometheus. The following infrastructure node size recommendations are based on the results of cluster maximums and control plane density focused testing.

Number of worker nodes Cluster density, or number of namespaces CPU cores Memory (GB)

27

500

4

24

120

1000

8

48

252

4000

16

128

501

4000

32

128

In general, three infrastructure nodes are recommended per cluster.

These sizing recommendations should be used as a guideline. Prometheus is a highly memory intensive application; the resource usage depends on various factors including the number of nodes, objects, the Prometheus metrics scraping interval, metrics or time series, and the age of the cluster. In addition, the router resource usage can also be affected by the number of routes and the amount/type of inbound requests.

These recommendations apply only to infrastructure nodes hosting Monitoring, ingress and Registry infrastructure components installed during cluster creation.

In OpenShift Container Platform 4.10, half of a CPU core (500 millicore) is now reserved by the system by default compared to OpenShift Container Platform 3.11 and previous versions. This influences the stated sizing recommendations.

Additional resources