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Chapter 40. ResourceTemplate schema reference	Chapter 40. ResourceTemplate schema reference Used in: CruiseControlTemplate , EntityOperatorTemplate , JmxTransTemplate , KafkaBridgeTemplate , KafkaClusterTemplate , KafkaConnectTemplate , KafkaExporterTemplate , KafkaMirrorMakerTemplate , KafkaNodePoolTemplate , KafkaUserTemplate , ZookeeperClusterTemplate Property Description metadata Metadata applied to the resource. MetadataTemplate	null	https://docs.redhat.com/en/documentation/red_hat_streams_for_apache_kafka/2.5/html/amq_streams_api_reference/type-ResourceTemplate-reference
Preface	Preface In this guide, the terms upgrade, update, and migrate have the following meanings: Upgrading The process of advancing your Satellite Server and Capsule Server installations from a y-stream release to the , for example Satellite 6.10 to Satellite 6.11. For more information, see Chapter 1, Upgrading Overview . Updating The process of advancing your Satellite Server and Capsule Server installations from a z-stream release to the , for example Satellite 6.11.0 to Satellite 6.11.1. Migrating The process of moving an existing Satellite installation to a new instance. For more information, see Chapter 5, Migrating Satellite to a New Red Hat Enterprise Linux System .	null	https://docs.redhat.com/en/documentation/red_hat_satellite/6.11/html/upgrading_and_updating_red_hat_satellite/pr01
25.4. Using Pre-Existing Keys and Certificates	25.4. Using Pre-Existing Keys and Certificates If you already have an existing key and certificate (for example, if you are installing the secure server to replace another company's secure server product), you can probably use your existing key and certificate with the secure server. The following two situations provide instances where you are not able to use your existing key and certificate: If you are changing your IP address or domain name - Certificates are issued for a particular IP address and domain name pair. You must get a new certificate if you are changing your IP address or domain name. If you have a certificate from VeriSign and you are changing your server software - VeriSign is a widely used CA. If you already have a VeriSign certificate for another purpose, you may have been considering using your existing VeriSign certificate with your new secure server. However, you are not be allowed to because VeriSign issues certificates for one specific server software and IP address/domain name combination. If you change either of those parameters (for example, if you previously used a different secure server product), the VeriSign certificate you obtained to use with the configuration will not work with the new configuration. You must obtain a new certificate. If you have an existing key and certificate that you can use, you do not have to generate a new key and obtain a new certificate. However, you may need to move and rename the files which contain your key and certificate. Move your existing key file to: Move your existing certificate file to: After you have moved your key and certificate, skip to Section 25.9, "Testing The Certificate" . If you are upgrading from the Red Hat Secure Web Server, your old key ( httpsd.key ) and certificate ( httpsd.crt ) are located in /etc/httpd/conf/ . Move and rename your key and certificate so that the secure server can use them. Use the following two commands to move and rename your key and certificate files: Then, start your secure server with the command: You are prompted to enter your passphrase. After you type it in and press Enter , the server starts.	[ "/etc/httpd/conf/ssl.key/server.key", "/etc/httpd/conf/ssl.crt/server.crt", "mv /etc/httpd/conf/httpsd.key /etc/httpd/conf/ssl.key/server.key mv /etc/httpd/conf/httpsd.crt /etc/httpd/conf/ssl.crt/server.crt", "/sbin/service httpd start" ]	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/4/html/system_administration_guide/apache_http_secure_server_configuration-using_pre_existing_keys_and_certificates
21.6. virt-rescue: The Rescue Shell	21.6. virt-rescue: The Rescue Shell This section provides information about the rescue shell. 21.6.1. Introduction This section describes virt-rescue , which can be considered analogous to a rescue CD for virtual machines. It boots a guest virtual machine into a rescue shell so that maintenance can be performed to correct errors and the guest virtual machine can be repaired. There is some overlap between virt-rescue and guestfish. It is important to distinguish their differing uses. virt-rescue is for making interactive, ad-hoc changes using ordinary Linux file system tools. It is particularly suited to rescuing a guest virtual machine that has failed . virt-rescue cannot be scripted. In contrast, guestfish is particularly useful for making scripted, structured changes through a formal set of commands (the libguestfs API), although it can also be used interactively. 21.6.2. Running virt-rescue Before you use virt-rescue on a guest virtual machine, make sure the guest virtual machine is not running, otherwise disk corruption will occur. When you are sure the guest virtual machine is not live, enter: (where GuestName is the guest name as known to libvirt), or: (where the path can be any file, any logical volume, LUN, or so on) containing a guest virtual machine disk. You will first see output scroll past, as virt-rescue boots the rescue VM. In the end you will see: The shell prompt here is an ordinary bash shell, and a reduced set of ordinary Red Hat Enterprise Linux commands is available. For example, you can enter: The command will list disk partitions. To mount a file system, it is suggested that you mount it under /sysroot , which is an empty directory in the rescue machine for the user to mount anything you like. Note that the files under / are files from the rescue VM itself: When you are finished rescuing the guest virtual machine, exit the shell by entering exit or Ctrl+d . virt-rescue has many command-line options. The options most often used are: --ro : Operate in read-only mode on the guest virtual machine. No changes will be saved. You can use this to experiment with the guest virtual machine. As soon as you exit from the shell, all of your changes are discarded. --network : Enable network access from the rescue shell. Use this for example if you need to download RPM or other files into the guest virtual machine.	[ "virt-rescue -d GuestName", "virt-rescue -a /path/to/disk/image", "Welcome to virt-rescue, the libguestfs rescue shell. Note: The contents of / are the rescue appliance. You have to mount the guest virtual machine's partitions under /sysroot before you can examine them. bash: cannot set terminal process group (-1): Inappropriate ioctl for device bash: no job control in this shell ><rescue>", "><rescue> fdisk -l /dev/vda", "><rescue> mount /dev/vda1 /sysroot/ EXT4-fs (vda1): mounted filesystem with ordered data mode. Opts: (null) ><rescue> ls -l /sysroot/grub/ total 324 -rw-r--r--. 1 root root 63 Sep 16 18:14 device.map -rw-r--r--. 1 root root 13200 Sep 16 18:14 e2fs_stage1_5 -rw-r--r--. 1 root root 12512 Sep 16 18:14 fat_stage1_5 -rw-r--r--. 1 root root 11744 Sep 16 18:14 ffs_stage1_5 -rw-------. 1 root root 1503 Oct 15 11:19 grub.conf [...]" ]	https://docs.redhat.com/en/documentation/Red_Hat_Enterprise_Linux/7/html/virtualization_deployment_and_administration_guide/sect-guest_virtual_machine_disk_access_with_offline_tools-virt_rescue_the_rescue_shell
6.14. Migrating Virtual Machines Between Hosts	6.14. Migrating Virtual Machines Between Hosts Live migration provides the ability to move a running virtual machine between physical hosts with no interruption to service. The virtual machine remains powered on and user applications continue to run while the virtual machine is relocated to a new physical host. In the background, the virtual machine's RAM is copied from the source host to the destination host. Storage and network connectivity are not altered. Note A virtual machine that is using a vGPU cannot be migrated to a different host. 6.14.1. Live Migration Prerequisites Note This is one in a series of topics that show how to set up and configure SR-IOV on Red Hat Virtualization. For more information, see Setting Up and Configuring SR-IOV You can use live migration to seamlessly move virtual machines to support a number of common maintenance tasks. Your Red Hat Virtualization environment must be correctly configured to support live migration well in advance of using it. At a minimum, the following prerequisites must be met to enable successful live migration of virtual machines: The source and destination hosts are members of the same cluster, ensuring CPU compatibility between them. Note Live migrating virtual machines between different clusters is generally not recommended. The source and destination hosts' status is Up . The source and destination hosts have access to the same virtual networks and VLANs. The source and destination hosts have access to the data storage domain on which the virtual machine resides. The destination host has sufficient CPU capacity to support the virtual machine's requirements. The destination host has sufficient unused RAM to support the virtual machine's requirements. The migrating virtual machine does not have the cache!=none custom property set. Live migration is performed using the management network and involves transferring large amounts of data between hosts. Concurrent migrations have the potential to saturate the management network. For best performance, Red Hat recommends creating separate logical networks for management, storage, display, and virtual machine data to minimize the risk of network saturation. Configuring Virtual Machines with SR-IOV-Enabled vNICs to Reduce Network Outage during Migration Virtual machines with vNICs that are directly connected to a virtual function (VF) of an SR-IOV-enabled host NIC can be further configured to reduce network outage during live migration: Ensure that the destination host has an available VF. Set the Passthrough and Migratable options in the passthrough vNIC's profile. See Enabling Passthrough on a vNIC Profile in the Administration Guide . Enable hotplugging for the virtual machine's network interface. Ensure that the virtual machine has a backup VirtIO vNIC, in addition to the passthrough vNIC, to maintain the virtual machine's network connection during migration. Set the VirtIO vNIC's No Network Filter option before configuring the bond. See Explanation of Settings in the VM Interface Profile Window in the Administration Guide . Add both vNICs as slaves under an active-backup bond on the virtual machine, with the passthrough vNIC as the primary interface. The bond and vNIC profiles can have one of the following configurations: Recommended : The bond is not configured with fail_over_mac=active and the VF vNIC is the primary slave. Disable the VirtIO vNIC profile's MAC-spoofing filter to ensure that traffic passing through the VirtIO vNIC is not dropped because it uses the VF vNIC MAC address. See Applying Network Filtering in the RHEL 7 Virtualization Deployment and Administration Guide . The bond is configured with fail_over_mac=active . This failover policy ensures that the MAC address of the bond is always the MAC address of the active slave. During failover, the virtual machine's MAC address changes, with a slight disruption in traffic. 6.14.2. Optimizing Live Migration Live virtual machine migration can be a resource-intensive operation. The following two options can be set globally for every virtual machine in the environment, at the cluster level, or at the individual virtual machine level to optimize live migration. The Auto Converge migrations option allows you to set whether auto-convergence is used during live migration of virtual machines. Large virtual machines with high workloads can dirty memory more quickly than the transfer rate achieved during live migration, and prevent the migration from converging. Auto-convergence capabilities in QEMU allow you to force convergence of virtual machine migrations. QEMU automatically detects a lack of convergence and triggers a throttle-down of the vCPUs on the virtual machine. The Enable migration compression option allows you to set whether migration compression is used during live migration of the virtual machine. This feature uses Xor Binary Zero Run-Length-Encoding to reduce virtual machine downtime and total live migration time for virtual machines running memory write-intensive workloads or for any application with a sparse memory update pattern. Both options are disabled globally by default. Configuring Auto-convergence and Migration Compression for Virtual Machine Migration Configure the optimization settings at the global level: Enable auto-convergence at the global level: Enable migration compression at the global level: Restart the ovirt-engine service to apply the changes: Configure the optimization settings at the cluster level: Click Compute Clusters and select a cluster. Click Edit . Click the Migration Policy tab. From the Auto Converge migrations list, select Inherit from global setting , Auto Converge , or Don't Auto Converge . From the Enable migration compression list, select Inherit from global setting , Compress , or Don't Compress . Click OK . Configure the optimization settings at the virtual machine level: Click Compute Virtual Machines and select a virtual machine. Click Edit . Click the Host tab. From the Auto Converge migrations list, select Inherit from cluster setting , Auto Converge , or Don't Auto Converge . From the Enable migration compression list, select Inherit from cluster setting , Compress , or Don't Compress . Click OK . 6.14.3. Guest Agent Hooks Hooks are scripts that trigger activity within a virtual machine when key events occur: Before migration After migration Before hibernation After hibernation The hooks configuration base directory is /etc/ovirt-guest-agent/hooks.d on Linux systems and C:\Program Files\Redhat\RHEV\Drivers\Agent on Windows systems. Each event has a corresponding subdirectory: before_migration and after_migration , before_hibernation and after_hibernation . All files or symbolic links in that directory will be executed. The executing user on Linux systems is ovirtagent . If the script needs root permissions, the elevation must be executed by the creator of the hook script. The executing user on Windows systems is the System Service user. 6.14.4. Automatic Virtual Machine Migration Red Hat Virtualization Manager automatically initiates live migration of all virtual machines running on a host when the host is moved into maintenance mode. The destination host for each virtual machine is assessed as the virtual machine is migrated, in order to spread the load across the cluster. From version 4.3, all virtual machines defined with manual or automatic migration modes are migrated when the host is moved into maintenance mode. However, for high performance and/or pinned virtual machines, a Maintenance Host window is displayed, asking you to confirm the action because the performance on the target host may be less than the performance on the current host. The Manager automatically initiates live migration of virtual machines in order to maintain load-balancing or power-saving levels in line with scheduling policy. Specify the scheduling policy that best suits the needs of your environment. You can also disable automatic, or even manual, live migration of specific virtual machines where required. If your virtual machines are configured for high performance, and/or if they have been pinned (by setting Passthrough Host CPU, CPU Pinning, or NUMA Pinning), the migration mode is set to Allow manual migration only . However, this can be changed to Allow Manual and Automatic mode if required. Special care should be taken when changing the default migration setting so that it does not result in a virtual machine migrating to a host that does not support high performance or pinning. 6.14.5. Preventing Automatic Migration of a Virtual Machine Red Hat Virtualization Manager allows you to disable automatic migration of virtual machines. You can also disable manual migration of virtual machines by setting the virtual machine to run only on a specific host. The ability to disable automatic migration and require a virtual machine to run on a particular host is useful when using application high availability products, such as Red Hat High Availability or Cluster Suite. Preventing Automatic Migration of Virtual Machines Click Compute Virtual Machines and select a virtual machine. Click Edit . Click the Host tab. In the Start Running On section, select Any Host in Cluster or Specific Host(s) , which enables you to select multiple hosts. Warning Explicitly assigning a virtual machine to a specific host and disabling migration are mutually exclusive with Red Hat Virtualization high availability. Important If the virtual machine has host devices directly attached to it, and a different host is specified, the host devices from the host will be automatically removed from the virtual machine. Select Allow manual migration only or Do not allow migration from the Migration Options drop-down list. Optionally, select the Use custom migration downtime check box and specify a value in milliseconds. Click OK . 6.14.6. Manually Migrating Virtual Machines A running virtual machine can be live migrated to any host within its designated host cluster. Live migration of virtual machines does not cause any service interruption. Migrating virtual machines to a different host is especially useful if the load on a particular host is too high. For live migration prerequisites, see Section 6.14.1, "Live Migration Prerequisites" . For high performance virtual machines and/or virtual machines defined with Pass-Through Host CPU , CPU Pinning , or NUMA Pinning , the default migration mode is Manual . Select Select Host Automatically so that the virtual machine migrates to the host that offers the best performance. Note When you place a host into maintenance mode, the virtual machines running on that host are automatically migrated to other hosts in the same cluster. You do not need to manually migrate these virtual machines. Note Live migrating virtual machines between different clusters is generally not recommended. The currently only supported use case is documented at https://access.redhat.com/articles/1390733 . Manually Migrating Virtual Machines Click Compute Virtual Machines and select a running virtual machine. Click Migrate . Use the radio buttons to select whether to Select Host Automatically or to Select Destination Host , specifying the host using the drop-down list. Note When the Select Host Automatically option is selected, the system determines the host to which the virtual machine is migrated according to the load balancing and power management rules set up in the scheduling policy. Click OK . During migration, progress is shown in the Migration progress bar. Once migration is complete the Host column will update to display the host the virtual machine has been migrated to. 6.14.7. Setting Migration Priority Red Hat Virtualization Manager queues concurrent requests for migration of virtual machines off of a given host. The load balancing process runs every minute. Hosts already involved in a migration event are not included in the migration cycle until their migration event has completed. When there is a migration request in the queue and available hosts in the cluster to action it, a migration event is triggered in line with the load balancing policy for the cluster. You can influence the ordering of the migration queue by setting the priority of each virtual machine; for example, setting mission critical virtual machines to migrate before others. Migrations will be ordered by priority; virtual machines with the highest priority will be migrated first. Setting Migration Priority Click Compute Virtual Machines and select a virtual machine. Click Edit . Select the High Availability tab. Select Low , Medium , or High from the Priority drop-down list. Click OK . 6.14.8. Canceling Ongoing Virtual Machine Migrations A virtual machine migration is taking longer than you expected. You'd like to be sure where all virtual machines are running before you make any changes to your environment. Canceling Ongoing Virtual Machine Migrations Select the migrating virtual machine. It is displayed in Compute Virtual Machines with a status of Migrating from . Click More Actions ( ), then click Cancel Migration . The virtual machine status returns from Migrating from to Up . 6.14.9. Event and Log Notification upon Automatic Migration of Highly Available Virtual Servers When a virtual server is automatically migrated because of the high availability function, the details of an automatic migration are documented in the Events tab and in the engine log to aid in troubleshooting, as illustrated in the following examples: Example 6.4. Notification in the Events Tab of the Administration Portal Highly Available Virtual_Machine_Name failed. It will be restarted automatically. Virtual_Machine_Name was restarted on Host Host_Name Example 6.5. Notification in the Manager engine.log This log can be found on the Red Hat Virtualization Manager at /var/log/ovirt-engine/engine.log : Failed to start Highly Available VM. Attempting to restart. VM Name: Virtual_Machine_Name , VM Id:_Virtual_Machine_ID_Number_	[ "engine-config -s DefaultAutoConvergence=True", "engine-config -s DefaultMigrationCompression=True", "systemctl restart ovirt-engine.service" ]	https://docs.redhat.com/en/documentation/red_hat_virtualization/4.3/html/virtual_machine_management_guide/sect-Migrating_Virtual_Machines_Between_Hosts
Chapter 16. Troubleshooting Logging	Chapter 16. Troubleshooting Logging 16.1. Viewing OpenShift Logging status You can view the status of the Red Hat OpenShift Logging Operator and for a number of logging subsystem components. 16.1.1. Viewing the status of the Red Hat OpenShift Logging Operator You can view the status of your Red Hat OpenShift Logging Operator. Prerequisites The Red Hat OpenShift Logging and Elasticsearch Operators must be installed. Procedure Change to the openshift-logging project. USD oc project openshift-logging To view the OpenShift Logging status: Get the OpenShift Logging status: USD oc get clusterlogging instance -o yaml Example output apiVersion: logging.openshift.io/v1 kind: ClusterLogging .... status: 1 collection: logs: fluentdStatus: daemonSet: fluentd 2 nodes: fluentd-2rhqp: ip-10-0-169-13.ec2.internal fluentd-6fgjh: ip-10-0-165-244.ec2.internal fluentd-6l2ff: ip-10-0-128-218.ec2.internal fluentd-54nx5: ip-10-0-139-30.ec2.internal fluentd-flpnn: ip-10-0-147-228.ec2.internal fluentd-n2frh: ip-10-0-157-45.ec2.internal pods: failed: [] notReady: [] ready: - fluentd-2rhqp - fluentd-54nx5 - fluentd-6fgjh - fluentd-6l2ff - fluentd-flpnn - fluentd-n2frh logstore: 3 elasticsearchStatus: - ShardAllocationEnabled: all cluster: activePrimaryShards: 5 activeShards: 5 initializingShards: 0 numDataNodes: 1 numNodes: 1 pendingTasks: 0 relocatingShards: 0 status: green unassignedShards: 0 clusterName: elasticsearch nodeConditions: elasticsearch-cdm-mkkdys93-1: nodeCount: 1 pods: client: failed: notReady: ready: - elasticsearch-cdm-mkkdys93-1-7f7c6-mjm7c data: failed: notReady: ready: - elasticsearch-cdm-mkkdys93-1-7f7c6-mjm7c master: failed: notReady: ready: - elasticsearch-cdm-mkkdys93-1-7f7c6-mjm7c visualization: 4 kibanaStatus: - deployment: kibana pods: failed: [] notReady: [] ready: - kibana-7fb4fd4cc9-f2nls replicaSets: - kibana-7fb4fd4cc9 replicas: 1 1 In the output, the cluster status fields appear in the status stanza. 2 Information on the Fluentd pods. 3 Information on the Elasticsearch pods, including Elasticsearch cluster health, green , yellow , or red . 4 Information on the Kibana pods. 16.1.1.1. Example condition messages The following are examples of some condition messages from the Status.Nodes section of the OpenShift Logging instance. A status message similar to the following indicates a node has exceeded the configured low watermark and no shard will be allocated to this node: Example output nodes: - conditions: - lastTransitionTime: 2019-03-15T15:57:22Z message: Disk storage usage for node is 27.5gb (36.74%). Shards will be not be allocated on this node. reason: Disk Watermark Low status: "True" type: NodeStorage deploymentName: example-elasticsearch-clientdatamaster-0-1 upgradeStatus: {} A status message similar to the following indicates a node has exceeded the configured high watermark and shards will be relocated to other nodes: Example output nodes: - conditions: - lastTransitionTime: 2019-03-15T16:04:45Z message: Disk storage usage for node is 27.5gb (36.74%). Shards will be relocated from this node. reason: Disk Watermark High status: "True" type: NodeStorage deploymentName: cluster-logging-operator upgradeStatus: {} A status message similar to the following indicates the Elasticsearch node selector in the CR does not match any nodes in the cluster: Example output Elasticsearch Status: Shard Allocation Enabled: shard allocation unknown Cluster: Active Primary Shards: 0 Active Shards: 0 Initializing Shards: 0 Num Data Nodes: 0 Num Nodes: 0 Pending Tasks: 0 Relocating Shards: 0 Status: cluster health unknown Unassigned Shards: 0 Cluster Name: elasticsearch Node Conditions: elasticsearch-cdm-mkkdys93-1: Last Transition Time: 2019-06-26T03:37:32Z Message: 0/5 nodes are available: 5 node(s) didn't match node selector. Reason: Unschedulable Status: True Type: Unschedulable elasticsearch-cdm-mkkdys93-2: Node Count: 2 Pods: Client: Failed: Not Ready: elasticsearch-cdm-mkkdys93-1-75dd69dccd-f7f49 elasticsearch-cdm-mkkdys93-2-67c64f5f4c-n58vl Ready: Data: Failed: Not Ready: elasticsearch-cdm-mkkdys93-1-75dd69dccd-f7f49 elasticsearch-cdm-mkkdys93-2-67c64f5f4c-n58vl Ready: Master: Failed: Not Ready: elasticsearch-cdm-mkkdys93-1-75dd69dccd-f7f49 elasticsearch-cdm-mkkdys93-2-67c64f5f4c-n58vl Ready: A status message similar to the following indicates that the requested PVC could not bind to PV: Example output Node Conditions: elasticsearch-cdm-mkkdys93-1: Last Transition Time: 2019-06-26T03:37:32Z Message: pod has unbound immediate PersistentVolumeClaims (repeated 5 times) Reason: Unschedulable Status: True Type: Unschedulable A status message similar to the following indicates that the Fluentd pods cannot be scheduled because the node selector did not match any nodes: Example output Status: Collection: Logs: Fluentd Status: Daemon Set: fluentd Nodes: Pods: Failed: Not Ready: Ready: 16.1.2. Viewing the status of logging subsystem components You can view the status for a number of logging subsystem components. Prerequisites The Red Hat OpenShift Logging and Elasticsearch Operators must be installed. Procedure Change to the openshift-logging project. USD oc project openshift-logging View the status of the logging subsystem for Red Hat OpenShift environment: USD oc describe deployment cluster-logging-operator Example output Name: cluster-logging-operator .... Conditions: Type Status Reason ---- ------ ------ Available True MinimumReplicasAvailable Progressing True NewReplicaSetAvailable .... Events: Type Reason Age From Message ---- ------ ---- ---- ------- Normal ScalingReplicaSet 62m deployment-controller Scaled up replica set cluster-logging-operator-574b8987df to 1---- View the status of the logging subsystem replica set: Get the name of a replica set: Example output USD oc get replicaset Example output NAME DESIRED CURRENT READY AGE cluster-logging-operator-574b8987df 1 1 1 159m elasticsearch-cdm-uhr537yu-1-6869694fb 1 1 1 157m elasticsearch-cdm-uhr537yu-2-857b6d676f 1 1 1 156m elasticsearch-cdm-uhr537yu-3-5b6fdd8cfd 1 1 1 155m kibana-5bd5544f87 1 1 1 157m Get the status of the replica set: USD oc describe replicaset cluster-logging-operator-574b8987df Example output Name: cluster-logging-operator-574b8987df .... Replicas: 1 current / 1 desired Pods Status: 1 Running / 0 Waiting / 0 Succeeded / 0 Failed .... Events: Type Reason Age From Message ---- ------ ---- ---- ------- Normal SuccessfulCreate 66m replicaset-controller Created pod: cluster-logging-operator-574b8987df-qjhqv---- 16.2. Viewing the status of the Elasticsearch log store You can view the status of the OpenShift Elasticsearch Operator and for a number of Elasticsearch components. 16.2.1. Viewing the status of the log store You can view the status of your log store. Prerequisites The Red Hat OpenShift Logging and Elasticsearch Operators must be installed. Procedure Change to the openshift-logging project. USD oc project openshift-logging To view the status: Get the name of the log store instance: USD oc get Elasticsearch Example output NAME AGE elasticsearch 5h9m Get the log store status: USD oc get Elasticsearch <Elasticsearch-instance> -o yaml For example: USD oc get Elasticsearch elasticsearch -n openshift-logging -o yaml The output includes information similar to the following: Example output status: 1 cluster: 2 activePrimaryShards: 30 activeShards: 60 initializingShards: 0 numDataNodes: 3 numNodes: 3 pendingTasks: 0 relocatingShards: 0 status: green unassignedShards: 0 clusterHealth: "" conditions: [] 3 nodes: 4 - deploymentName: elasticsearch-cdm-zjf34ved-1 upgradeStatus: {} - deploymentName: elasticsearch-cdm-zjf34ved-2 upgradeStatus: {} - deploymentName: elasticsearch-cdm-zjf34ved-3 upgradeStatus: {} pods: 5 client: failed: [] notReady: [] ready: - elasticsearch-cdm-zjf34ved-1-6d7fbf844f-sn422 - elasticsearch-cdm-zjf34ved-2-dfbd988bc-qkzjz - elasticsearch-cdm-zjf34ved-3-c8f566f7c-t7zkt data: failed: [] notReady: [] ready: - elasticsearch-cdm-zjf34ved-1-6d7fbf844f-sn422 - elasticsearch-cdm-zjf34ved-2-dfbd988bc-qkzjz - elasticsearch-cdm-zjf34ved-3-c8f566f7c-t7zkt master: failed: [] notReady: [] ready: - elasticsearch-cdm-zjf34ved-1-6d7fbf844f-sn422 - elasticsearch-cdm-zjf34ved-2-dfbd988bc-qkzjz - elasticsearch-cdm-zjf34ved-3-c8f566f7c-t7zkt shardAllocationEnabled: all 1 In the output, the cluster status fields appear in the status stanza. 2 The status of the log store: The number of active primary shards. The number of active shards. The number of shards that are initializing. The number of log store data nodes. The total number of log store nodes. The number of pending tasks. The log store status: green , red , yellow . The number of unassigned shards. 3 Any status conditions, if present. The log store status indicates the reasons from the scheduler if a pod could not be placed. Any events related to the following conditions are shown: Container Waiting for both the log store and proxy containers. Container Terminated for both the log store and proxy containers. Pod unschedulable. Also, a condition is shown for a number of issues; see Example condition messages . 4 The log store nodes in the cluster, with upgradeStatus . 5 The log store client, data, and master pods in the cluster, listed under 'failed`, notReady , or ready state. 16.2.1.1. Example condition messages The following are examples of some condition messages from the Status section of the Elasticsearch instance. The following status message indicates that a node has exceeded the configured low watermark, and no shard will be allocated to this node. status: nodes: - conditions: - lastTransitionTime: 2019-03-15T15:57:22Z message: Disk storage usage for node is 27.5gb (36.74%). Shards will be not be allocated on this node. reason: Disk Watermark Low status: "True" type: NodeStorage deploymentName: example-elasticsearch-cdm-0-1 upgradeStatus: {} The following status message indicates that a node has exceeded the configured high watermark, and shards will be relocated to other nodes. status: nodes: - conditions: - lastTransitionTime: 2019-03-15T16:04:45Z message: Disk storage usage for node is 27.5gb (36.74%). Shards will be relocated from this node. reason: Disk Watermark High status: "True" type: NodeStorage deploymentName: example-elasticsearch-cdm-0-1 upgradeStatus: {} The following status message indicates that the log store node selector in the CR does not match any nodes in the cluster: status: nodes: - conditions: - lastTransitionTime: 2019-04-10T02:26:24Z message: '0/8 nodes are available: 8 node(s) didn''t match node selector.' reason: Unschedulable status: "True" type: Unschedulable The following status message indicates that the log store CR uses a non-existent persistent volume claim (PVC). status: nodes: - conditions: - last Transition Time: 2019-04-10T05:55:51Z message: pod has unbound immediate PersistentVolumeClaims (repeated 5 times) reason: Unschedulable status: True type: Unschedulable The following status message indicates that your log store cluster does not have enough nodes to support the redundancy policy. status: clusterHealth: "" conditions: - lastTransitionTime: 2019-04-17T20:01:31Z message: Wrong RedundancyPolicy selected. Choose different RedundancyPolicy or add more nodes with data roles reason: Invalid Settings status: "True" type: InvalidRedundancy This status message indicates your cluster has too many control plane nodes: status: clusterHealth: green conditions: - lastTransitionTime: '2019-04-17T20:12:34Z' message: >- Invalid master nodes count. Please ensure there are no more than 3 total nodes with master roles reason: Invalid Settings status: 'True' type: InvalidMasters The following status message indicates that Elasticsearch storage does not support the change you tried to make. For example: status: clusterHealth: green conditions: - lastTransitionTime: "2021-05-07T01:05:13Z" message: Changing the storage structure for a custom resource is not supported reason: StorageStructureChangeIgnored status: 'True' type: StorageStructureChangeIgnored The reason and type fields specify the type of unsupported change: StorageClassNameChangeIgnored Unsupported change to the storage class name. StorageSizeChangeIgnored Unsupported change the storage size. StorageStructureChangeIgnored Unsupported change between ephemeral and persistent storage structures. Important If you try to configure the ClusterLogging custom resource (CR) to switch from ephemeral to persistent storage, the OpenShift Elasticsearch Operator creates a persistent volume claim (PVC) but does not create a persistent volume (PV). To clear the StorageStructureChangeIgnored status, you must revert the change to the ClusterLogging CR and delete the PVC. 16.2.2. Viewing the status of the log store components You can view the status for a number of the log store components. Elasticsearch indices You can view the status of the Elasticsearch indices. Get the name of an Elasticsearch pod: USD oc get pods --selector component=elasticsearch -o name Example output pod/elasticsearch-cdm-1godmszn-1-6f8495-vp4lw pod/elasticsearch-cdm-1godmszn-2-5769cf-9ms2n pod/elasticsearch-cdm-1godmszn-3-f66f7d-zqkz7 Get the status of the indices: USD oc exec elasticsearch-cdm-4vjor49p-2-6d4d7db474-q2w7z -- indices Example output Defaulting container name to elasticsearch. Use 'oc describe pod/elasticsearch-cdm-4vjor49p-2-6d4d7db474-q2w7z -n openshift-logging' to see all of the containers in this pod. green open infra-000002 S4QANnf1QP6NgCegfnrnbQ 3 1 119926 0 157 78 green open audit-000001 8_EQx77iQCSTzFOXtxRqFw 3 1 0 0 0 0 green open .security iDjscH7aSUGhIdq0LheLBQ 1 1 5 0 0 0 green open .kibana_-377444158_kubeadmin yBywZ9GfSrKebz5gWBZbjw 3 1 1 0 0 0 green open infra-000001 z6Dpe__ORgiopEpW6Yl44A 3 1 871000 0 874 436 green open app-000001 hIrazQCeSISewG3c2VIvsQ 3 1 2453 0 3 1 green open .kibana_1 JCitcBMSQxKOvIq6iQW6wg 1 1 0 0 0 0 green open .kibana_-1595131456_user1 gIYFIEGRRe-ka0W3okS-mQ 3 1 1 0 0 0 Log store pods You can view the status of the pods that host the log store. Get the name of a pod: USD oc get pods --selector component=elasticsearch -o name Example output pod/elasticsearch-cdm-1godmszn-1-6f8495-vp4lw pod/elasticsearch-cdm-1godmszn-2-5769cf-9ms2n pod/elasticsearch-cdm-1godmszn-3-f66f7d-zqkz7 Get the status of a pod: USD oc describe pod elasticsearch-cdm-1godmszn-1-6f8495-vp4lw The output includes the following status information: Example output .... Status: Running .... Containers: elasticsearch: Container ID: cri-o://b7d44e0a9ea486e27f47763f5bb4c39dfd2 State: Running Started: Mon, 08 Jun 2020 10:17:56 -0400 Ready: True Restart Count: 0 Readiness: exec [/usr/share/elasticsearch/probe/readiness.sh] delay=10s timeout=30s period=5s #success=1 #failure=3 .... proxy: Container ID: cri-o://3f77032abaddbb1652c116278652908dc01860320b8a4e741d06894b2f8f9aa1 State: Running Started: Mon, 08 Jun 2020 10:18:38 -0400 Ready: True Restart Count: 0 .... Conditions: Type Status Initialized True Ready True ContainersReady True PodScheduled True .... Events: <none> Log storage pod deployment configuration You can view the status of the log store deployment configuration. Get the name of a deployment configuration: USD oc get deployment --selector component=elasticsearch -o name Example output deployment.extensions/elasticsearch-cdm-1gon-1 deployment.extensions/elasticsearch-cdm-1gon-2 deployment.extensions/elasticsearch-cdm-1gon-3 Get the deployment configuration status: USD oc describe deployment elasticsearch-cdm-1gon-1 The output includes the following status information: Example output .... Containers: elasticsearch: Image: registry.redhat.io/openshift-logging/elasticsearch6-rhel8 Readiness: exec [/usr/share/elasticsearch/probe/readiness.sh] delay=10s timeout=30s period=5s #success=1 #failure=3 .... Conditions: Type Status Reason ---- ------ ------ Progressing Unknown DeploymentPaused Available True MinimumReplicasAvailable .... Events: <none> Log store replica set You can view the status of the log store replica set. Get the name of a replica set: USD oc get replicaSet --selector component=elasticsearch -o name replicaset.extensions/elasticsearch-cdm-1gon-1-6f8495 replicaset.extensions/elasticsearch-cdm-1gon-2-5769cf replicaset.extensions/elasticsearch-cdm-1gon-3-f66f7d Get the status of the replica set: USD oc describe replicaSet elasticsearch-cdm-1gon-1-6f8495 The output includes the following status information: Example output .... Containers: elasticsearch: Image: registry.redhat.io/openshift-logging/elasticsearch6-rhel8@sha256:4265742c7cdd85359140e2d7d703e4311b6497eec7676957f455d6908e7b1c25 Readiness: exec [/usr/share/elasticsearch/probe/readiness.sh] delay=10s timeout=30s period=5s #success=1 #failure=3 .... Events: <none> 16.2.3. Elasticsearch cluster status The Grafana dashboard in the Observe section of the OpenShift Container Platform web console displays the status of the Elasticsearch cluster. To get the status of the OpenShift Elasticsearch cluster, visit the Grafana dashboard in the Observe section of the OpenShift Container Platform web console at <cluster_url>/monitoring/dashboards/grafana-dashboard-cluster-logging . Elasticsearch status fields eo_elasticsearch_cr_cluster_management_state Shows whether the Elasticsearch cluster is in a managed or unmanaged state. For example: eo_elasticsearch_cr_cluster_management_state{state="managed"} 1 eo_elasticsearch_cr_cluster_management_state{state="unmanaged"} 0 eo_elasticsearch_cr_restart_total Shows the number of times the Elasticsearch nodes have restarted for certificate restarts, rolling restarts, or scheduled restarts. For example: eo_elasticsearch_cr_restart_total{reason="cert_restart"} 1 eo_elasticsearch_cr_restart_total{reason="rolling_restart"} 1 eo_elasticsearch_cr_restart_total{reason="scheduled_restart"} 3 es_index_namespaces_total Shows the total number of Elasticsearch index namespaces. For example: Total number of Namespaces. es_index_namespaces_total 5 es_index_document_count Shows the number of records for each namespace. For example: es_index_document_count{namespace="namespace_1"} 25 es_index_document_count{namespace="namespace_2"} 10 es_index_document_count{namespace="namespace_3"} 5 The "Secret Elasticsearch fields are either missing or empty" message If Elasticsearch is missing the admin-cert , admin-key , logging-es.crt , or logging-es.key files, the dashboard shows a status message similar to the following example: message": "Secret \"elasticsearch\" fields are either missing or empty: [admin-cert, admin-key, logging-es.crt, logging-es.key]", "reason": "Missing Required Secrets", 16.3. Understanding logging subsystem alerts All of the logging collector alerts are listed on the Alerting UI of the OpenShift Container Platform web console. 16.3.1. Viewing logging collector alerts Alerts are shown in the OpenShift Container Platform web console, on the Alerts tab of the Alerting UI. Alerts are in one of the following states: Firing . The alert condition is true for the duration of the timeout. Click the Options menu at the end of the firing alert to view more information or silence the alert. Pending The alert condition is currently true, but the timeout has not been reached. Not Firing . The alert is not currently triggered. Procedure To view the logging subsystem and other OpenShift Container Platform alerts: In the OpenShift Container Platform console, click Observe Alerting . Click the Alerts tab. The alerts are listed, based on the filters selected. Additional resources For more information on the Alerting UI, see Managing alerts . 16.3.2. About logging collector alerts The following alerts are generated by the logging collector. You can view these alerts in the OpenShift Container Platform web console, on the Alerts page of the Alerting UI. Table 16.1. Fluentd Prometheus alerts Alert Message Description Severity FluentDHighErrorRate <value> of records have resulted in an error by fluentd <instance>. The number of FluentD output errors is high, by default more than 10 in the 15 minutes. Warning FluentdNodeDown Prometheus could not scrape fluentd <instance> for more than 10m. Fluentd is reporting that Prometheus could not scrape a specific Fluentd instance. Critical FluentdQueueLengthIncreasing In the last 12h, fluentd <instance> buffer queue length constantly increased more than 1. Current value is <value>. Fluentd is reporting that the queue size is increasing. Critical FluentDVeryHighErrorRate <value> of records have resulted in an error by fluentd <instance>. The number of FluentD output errors is very high, by default more than 25 in the 15 minutes. Critical 16.3.3. About Elasticsearch alerting rules You can view these alerting rules in Prometheus. Table 16.2. Alerting rules Alert Description Severity ElasticsearchClusterNotHealthy The cluster health status has been RED for at least 2 minutes. The cluster does not accept writes, shards may be missing, or the master node hasn't been elected yet. Critical ElasticsearchClusterNotHealthy The cluster health status has been YELLOW for at least 20 minutes. Some shard replicas are not allocated. Warning ElasticsearchDiskSpaceRunningLow The cluster is expected to be out of disk space within the 6 hours. Critical ElasticsearchHighFileDescriptorUsage The cluster is predicted to be out of file descriptors within the hour. Warning ElasticsearchJVMHeapUseHigh The JVM Heap usage on the specified node is high. Alert ElasticsearchNodeDiskWatermarkReached The specified node has hit the low watermark due to low free disk space. Shards can not be allocated to this node anymore. You should consider adding more disk space to the node. Info ElasticsearchNodeDiskWatermarkReached The specified node has hit the high watermark due to low free disk space. Some shards will be re-allocated to different nodes if possible. Make sure more disk space is added to the node or drop old indices allocated to this node. Warning ElasticsearchNodeDiskWatermarkReached The specified node has hit the flood watermark due to low free disk space. Every index that has a shard allocated on this node is enforced a read-only block. The index block must be manually released when the disk use falls below the high watermark. Critical ElasticsearchJVMHeapUseHigh The JVM Heap usage on the specified node is too high. Alert ElasticsearchWriteRequestsRejectionJumps Elasticsearch is experiencing an increase in write rejections on the specified node. This node might not be keeping up with the indexing speed. Warning AggregatedLoggingSystemCPUHigh The CPU used by the system on the specified node is too high. Alert ElasticsearchProcessCPUHigh The CPU used by Elasticsearch on the specified node is too high. Alert 16.4. Collecting logging data for Red Hat Support When opening a support case, it is helpful to provide debugging information about your cluster to Red Hat Support. The must-gather tool enables you to collect diagnostic information for project-level resources, cluster-level resources, and each of the logging subsystem components. For prompt support, supply diagnostic information for both OpenShift Container Platform and OpenShift Logging. Note Do not use the hack/logging-dump.sh script. The script is no longer supported and does not collect data. 16.4.1. About the must-gather tool The oc adm must-gather CLI command collects the information from your cluster that is most likely needed for debugging issues. For your logging subsystem, must-gather collects the following information: Project-level resources, including pods, configuration maps, service accounts, roles, role bindings, and events at the project level Cluster-level resources, including nodes, roles, and role bindings at the cluster level OpenShift Logging resources in the openshift-logging and openshift-operators-redhat namespaces, including health status for the log collector, the log store, and the log visualizer When you run oc adm must-gather , a new pod is created on the cluster. The data is collected on that pod and saved in a new directory that starts with must-gather.local . This directory is created in the current working directory. 16.4.2. Prerequisites The logging subsystem and Elasticsearch must be installed. 16.4.3. Collecting OpenShift Logging data You can use the oc adm must-gather CLI command to collect information about your logging subsystem. Procedure To collect logging subsystem information with must-gather : Navigate to the directory where you want to store the must-gather information. Run the oc adm must-gather command against the OpenShift Logging image: USD oc adm must-gather --image=USD(oc -n openshift-logging get deployment.apps/cluster-logging-operator -o jsonpath='{.spec.template.spec.containers[?(@.name == "cluster-logging-operator")].image}') The must-gather tool creates a new directory that starts with must-gather.local within the current directory. For example: must-gather.local.4157245944708210408 . Create a compressed file from the must-gather directory that was just created. For example, on a computer that uses a Linux operating system, run the following command: USD tar -cvaf must-gather.tar.gz must-gather.local.4157245944708210408 Attach the compressed file to your support case on the Red Hat Customer Portal . 16.5. Troubleshooting for Critical Alerts 16.5.1. Elasticsearch Cluster Health is Red At least one primary shard and its replicas are not allocated to a node. Troubleshooting Check the Elasticsearch cluster health and verify that the cluster status is red. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- health List the nodes that have joined the cluster. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/nodes?v List the Elasticsearch pods and compare them with the nodes in the command output from the step. oc -n openshift-logging get pods -l component=elasticsearch If some of the Elasticsearch nodes have not joined the cluster, perform the following steps. Confirm that Elasticsearch has an elected control plane node. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/master?v Review the pod logs of the elected control plane node for issues. oc logs <elasticsearch_master_pod_name> -c elasticsearch -n openshift-logging Review the logs of nodes that have not joined the cluster for issues. oc logs <elasticsearch_node_name> -c elasticsearch -n openshift-logging If all the nodes have joined the cluster, perform the following steps, check if the cluster is in the process of recovering. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/recovery?active_only=true If there is no command output, the recovery process might be delayed or stalled by pending tasks. Check if there are pending tasks. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- health \|grep number_of_pending_tasks If there are pending tasks, monitor their status. If their status changes and indicates that the cluster is recovering, continue waiting. The recovery time varies according to the size of the cluster and other factors. Otherwise, if the status of the pending tasks does not change, this indicates that the recovery has stalled. If it seems like the recovery has stalled, check if cluster.routing.allocation.enable is set to none . oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cluster/settings?pretty If cluster.routing.allocation.enable is set to none , set it to all . oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cluster/settings?pretty -X PUT -d '{"persistent": {"cluster.routing.allocation.enable":"all"}}' Check which indices are still red. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/indices?v If any indices are still red, try to clear them by performing the following steps. Clear the cache. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name>/_cache/clear?pretty Increase the max allocation retries. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name>/_settings?pretty -X PUT -d '{"index.allocation.max_retries":10}' Delete all the scroll items. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_search/scroll/_all -X DELETE Increase the timeout. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name>/_settings?pretty -X PUT -d '{"index.unassigned.node_left.delayed_timeout":"10m"}' If the preceding steps do not clear the red indices, delete the indices individually. Identify the red index name. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/indices?v Delete the red index. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_red_index_name> -X DELETE If there are no red indices and the cluster status is red, check for a continuous heavy processing load on a data node. Check if the Elasticsearch JVM Heap usage is high. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_nodes/stats?pretty In the command output, review the node_name.jvm.mem.heap_used_percent field to determine the JVM Heap usage. Check for high CPU utilization. Additional resources Search for "Free up or increase disk space" in the Elasticsearch topic, Fix a red or yellow cluster status . 16.5.2. Elasticsearch Cluster Health is Yellow Replica shards for at least one primary shard are not allocated to nodes. Troubleshooting Increase the node count by adjusting nodeCount in the ClusterLogging CR. Additional resources About the Cluster Logging custom resource Configuring persistent storage for the log store Search for "Free up or increase disk space" in the Elasticsearch topic, Fix a red or yellow cluster status . 16.5.3. Elasticsearch Node Disk Low Watermark Reached Elasticsearch does not allocate shards to nodes that reach the low watermark . Troubleshooting Identify the node on which Elasticsearch is deployed. oc -n openshift-logging get po -o wide Check if there are unassigned shards . oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cluster/health?pretty \| grep unassigned_shards If there are unassigned shards, check the disk space on each node. for pod in `oc -n openshift-logging get po -l component=elasticsearch -o jsonpath='{.items[].metadata.name}'`; do echo USDpod; oc -n openshift-logging exec -c elasticsearch USDpod -- df -h /elasticsearch/persistent; done Check the nodes.node_name.fs field to determine the free disk space on that node. If the used disk percentage is above 85%, the node has exceeded the low watermark, and shards can no longer be allocated to this node. Try to increase the disk space on all nodes. If increasing the disk space is not possible, try adding a new data node to the cluster. If adding a new data node is problematic, decrease the total cluster redundancy policy. Check the current redundancyPolicy . oc -n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}' Note If you are using a ClusterLogging CR, enter: oc -n openshift-logging get cl -o jsonpath='{.items[].spec.logStore.elasticsearch.redundancyPolicy}' If the cluster redundancyPolicy is higher than SingleRedundancy , set it to SingleRedundancy and save this change. If the preceding steps do not fix the issue, delete the old indices. Check the status of all indices on Elasticsearch. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- indices Identify an old index that can be deleted. Delete the index. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name> -X DELETE Additional resources Search for "redundancyPolicy" in the "Sample ClusterLogging custom resource (CR)" in About the Cluster Logging custom resource 16.5.4. Elasticsearch Node Disk High Watermark Reached Elasticsearch attempts to relocate shards away from a node that has reached the high watermark . Troubleshooting Identify the node on which Elasticsearch is deployed. oc -n openshift-logging get po -o wide Check the disk space on each node. for pod in `oc -n openshift-logging get po -l component=elasticsearch -o jsonpath='{.items[].metadata.name}'`; do echo USDpod; oc -n openshift-logging exec -c elasticsearch USDpod -- df -h /elasticsearch/persistent; done Check if the cluster is rebalancing. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cluster/health?pretty \| grep relocating_shards If the command output shows relocating shards, the High Watermark has been exceeded. The default value of the High Watermark is 90%. The shards relocate to a node with low disk usage that has not crossed any watermark threshold limits. To allocate shards to a particular node, free up some space. Try to increase the disk space on all nodes. If increasing the disk space is not possible, try adding a new data node to the cluster. If adding a new data node is problematic, decrease the total cluster redundancy policy. Check the current redundancyPolicy . oc -n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}' Note If you are using a ClusterLogging CR, enter: oc -n openshift-logging get cl -o jsonpath='{.items[].spec.logStore.elasticsearch.redundancyPolicy}' If the cluster redundancyPolicy is higher than SingleRedundancy , set it to SingleRedundancy and save this change. If the preceding steps do not fix the issue, delete the old indices. Check the status of all indices on Elasticsearch. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- indices Identify an old index that can be deleted. Delete the index. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name> -X DELETE Additional resources Search for "redundancyPolicy" in the "Sample ClusterLogging custom resource (CR)" in About the Cluster Logging custom resource 16.5.5. Elasticsearch Node Disk Flood Watermark Reached Elasticsearch enforces a read-only index block on every index that has both of these conditions: One or more shards are allocated to the node. One or more disks exceed the flood stage . Troubleshooting Check the disk space of the Elasticsearch node. for pod in `oc -n openshift-logging get po -l component=elasticsearch -o jsonpath='{.items[].metadata.name}'`; do echo USDpod; oc -n openshift-logging exec -c elasticsearch USDpod -- df -h /elasticsearch/persistent; done Check the nodes.node_name.fs field to determine the free disk space on that node. If the used disk percentage is above 95%, it signifies that the node has crossed the flood watermark. Writing is blocked for shards allocated on this particular node. Try to increase the disk space on all nodes. If increasing the disk space is not possible, try adding a new data node to the cluster. If adding a new data node is problematic, decrease the total cluster redundancy policy. Check the current redundancyPolicy . oc -n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}' Note If you are using a ClusterLogging CR, enter: oc -n openshift-logging get cl -o jsonpath='{.items[].spec.logStore.elasticsearch.redundancyPolicy}' If the cluster redundancyPolicy is higher than SingleRedundancy , set it to SingleRedundancy and save this change. If the preceding steps do not fix the issue, delete the old indices. Check the status of all indices on Elasticsearch. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- indices Identify an old index that can be deleted. Delete the index. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name> -X DELETE Continue freeing up and monitoring the disk space until the used disk space drops below 90%. Then, unblock write to this particular node. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_all/_settings?pretty -X PUT -d '{"index.blocks.read_only_allow_delete": null}' Additional resources Search for "redundancyPolicy" in the "Sample ClusterLogging custom resource (CR)" in About the Cluster Logging custom resource 16.5.6. Elasticsearch JVM Heap Use is High The Elasticsearch node JVM Heap memory used is above 75%. Troubleshooting Consider increasing the heap size . 16.5.7. Aggregated Logging System CPU is High System CPU usage on the node is high. Troubleshooting Check the CPU of the cluster node. Consider allocating more CPU resources to the node. 16.5.8. Elasticsearch Process CPU is High Elasticsearch process CPU usage on the node is high. Troubleshooting Check the CPU of the cluster node. Consider allocating more CPU resources to the node. 16.5.9. Elasticsearch Disk Space is Running Low The Elasticsearch Cluster is predicted to be out of disk space within the 6 hours based on current disk usage. Troubleshooting Get the disk space of the Elasticsearch node. for pod in `oc -n openshift-logging get po -l component=elasticsearch -o jsonpath='{.items[].metadata.name}'`; do echo USDpod; oc -n openshift-logging exec -c elasticsearch USDpod -- df -h /elasticsearch/persistent; done In the command output, check the nodes.node_name.fs field to determine the free disk space on that node. Try to increase the disk space on all nodes. If increasing the disk space is not possible, try adding a new data node to the cluster. If adding a new data node is problematic, decrease the total cluster redundancy policy. Check the current redundancyPolicy . oc -n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}' Note If you are using a ClusterLogging CR, enter: oc -n openshift-logging get cl -o jsonpath='{.items[].spec.logStore.elasticsearch.redundancyPolicy}' If the cluster redundancyPolicy is higher than SingleRedundancy , set it to SingleRedundancy and save this change. If the preceding steps do not fix the issue, delete the old indices. Check the status of all indices on Elasticsearch. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- indices Identify an old index that can be deleted. Delete the index. oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name> -X DELETE Additional resources Search for "redundancyPolicy" in the "Sample ClusterLogging custom resource (CR)" in About the Cluster Logging custom resource Search for "ElasticsearchDiskSpaceRunningLow" in About Elasticsearch alerting rules . Search for "Free up or increase disk space" in the Elasticsearch topic, Fix a red or yellow cluster status . 16.5.10. Elasticsearch FileDescriptor Usage is high Based on current usage trends, the predicted number of file descriptors on the node is insufficient. Troubleshooting Check and, if needed, configure the value of max_file_descriptors for each node, as described in the Elasticsearch File descriptors topic. Additional resources Search for "ElasticsearchHighFileDescriptorUsage" in About Elasticsearch alerting rules . Search for "File Descriptors In Use" in OpenShift Logging dashboards .	[ "oc project openshift-logging", "oc get clusterlogging instance -o yaml", "apiVersion: logging.openshift.io/v1 kind: ClusterLogging . status: 1 collection: logs: fluentdStatus: daemonSet: fluentd 2 nodes: fluentd-2rhqp: ip-10-0-169-13.ec2.internal fluentd-6fgjh: ip-10-0-165-244.ec2.internal fluentd-6l2ff: ip-10-0-128-218.ec2.internal fluentd-54nx5: ip-10-0-139-30.ec2.internal fluentd-flpnn: ip-10-0-147-228.ec2.internal fluentd-n2frh: ip-10-0-157-45.ec2.internal pods: failed: [] notReady: [] ready: - fluentd-2rhqp - fluentd-54nx5 - fluentd-6fgjh - fluentd-6l2ff - fluentd-flpnn - fluentd-n2frh logstore: 3 elasticsearchStatus: - ShardAllocationEnabled: all cluster: activePrimaryShards: 5 activeShards: 5 initializingShards: 0 numDataNodes: 1 numNodes: 1 pendingTasks: 0 relocatingShards: 0 status: green unassignedShards: 0 clusterName: elasticsearch nodeConditions: elasticsearch-cdm-mkkdys93-1: nodeCount: 1 pods: client: failed: notReady: ready: - elasticsearch-cdm-mkkdys93-1-7f7c6-mjm7c data: failed: notReady: ready: - elasticsearch-cdm-mkkdys93-1-7f7c6-mjm7c master: failed: notReady: ready: - elasticsearch-cdm-mkkdys93-1-7f7c6-mjm7c visualization: 4 kibanaStatus: - deployment: kibana pods: failed: [] notReady: [] ready: - kibana-7fb4fd4cc9-f2nls replicaSets: - kibana-7fb4fd4cc9 replicas: 1", "nodes: - conditions: - lastTransitionTime: 2019-03-15T15:57:22Z message: Disk storage usage for node is 27.5gb (36.74%). Shards will be not be allocated on this node. reason: Disk Watermark Low status: \"True\" type: NodeStorage deploymentName: example-elasticsearch-clientdatamaster-0-1 upgradeStatus: {}", "nodes: - conditions: - lastTransitionTime: 2019-03-15T16:04:45Z message: Disk storage usage for node is 27.5gb (36.74%). Shards will be relocated from this node. reason: Disk Watermark High status: \"True\" type: NodeStorage deploymentName: cluster-logging-operator upgradeStatus: {}", "Elasticsearch Status: Shard Allocation Enabled: shard allocation unknown Cluster: Active Primary Shards: 0 Active Shards: 0 Initializing Shards: 0 Num Data Nodes: 0 Num Nodes: 0 Pending Tasks: 0 Relocating Shards: 0 Status: cluster health unknown Unassigned Shards: 0 Cluster Name: elasticsearch Node Conditions: elasticsearch-cdm-mkkdys93-1: Last Transition Time: 2019-06-26T03:37:32Z Message: 0/5 nodes are available: 5 node(s) didn't match node selector. Reason: Unschedulable Status: True Type: Unschedulable elasticsearch-cdm-mkkdys93-2: Node Count: 2 Pods: Client: Failed: Not Ready: elasticsearch-cdm-mkkdys93-1-75dd69dccd-f7f49 elasticsearch-cdm-mkkdys93-2-67c64f5f4c-n58vl Ready: Data: Failed: Not Ready: elasticsearch-cdm-mkkdys93-1-75dd69dccd-f7f49 elasticsearch-cdm-mkkdys93-2-67c64f5f4c-n58vl Ready: Master: Failed: Not Ready: elasticsearch-cdm-mkkdys93-1-75dd69dccd-f7f49 elasticsearch-cdm-mkkdys93-2-67c64f5f4c-n58vl Ready:", "Node Conditions: elasticsearch-cdm-mkkdys93-1: Last Transition Time: 2019-06-26T03:37:32Z Message: pod has unbound immediate PersistentVolumeClaims (repeated 5 times) Reason: Unschedulable Status: True Type: Unschedulable", "Status: Collection: Logs: Fluentd Status: Daemon Set: fluentd Nodes: Pods: Failed: Not Ready: Ready:", "oc project openshift-logging", "oc describe deployment cluster-logging-operator", "Name: cluster-logging-operator . Conditions: Type Status Reason ---- ------ ------ Available True MinimumReplicasAvailable Progressing True NewReplicaSetAvailable . Events: Type Reason Age From Message ---- ------ ---- ---- ------- Normal ScalingReplicaSet 62m deployment-controller Scaled up replica set cluster-logging-operator-574b8987df to 1----", "oc get replicaset", "NAME DESIRED CURRENT READY AGE cluster-logging-operator-574b8987df 1 1 1 159m elasticsearch-cdm-uhr537yu-1-6869694fb 1 1 1 157m elasticsearch-cdm-uhr537yu-2-857b6d676f 1 1 1 156m elasticsearch-cdm-uhr537yu-3-5b6fdd8cfd 1 1 1 155m kibana-5bd5544f87 1 1 1 157m", "oc describe replicaset cluster-logging-operator-574b8987df", "Name: cluster-logging-operator-574b8987df . Replicas: 1 current / 1 desired Pods Status: 1 Running / 0 Waiting / 0 Succeeded / 0 Failed . Events: Type Reason Age From Message ---- ------ ---- ---- ------- Normal SuccessfulCreate 66m replicaset-controller Created pod: cluster-logging-operator-574b8987df-qjhqv----", "oc project openshift-logging", "oc get Elasticsearch", "NAME AGE elasticsearch 5h9m", "oc get Elasticsearch <Elasticsearch-instance> -o yaml", "oc get Elasticsearch elasticsearch -n openshift-logging -o yaml", "status: 1 cluster: 2 activePrimaryShards: 30 activeShards: 60 initializingShards: 0 numDataNodes: 3 numNodes: 3 pendingTasks: 0 relocatingShards: 0 status: green unassignedShards: 0 clusterHealth: \"\" conditions: [] 3 nodes: 4 - deploymentName: elasticsearch-cdm-zjf34ved-1 upgradeStatus: {} - deploymentName: elasticsearch-cdm-zjf34ved-2 upgradeStatus: {} - deploymentName: elasticsearch-cdm-zjf34ved-3 upgradeStatus: {} pods: 5 client: failed: [] notReady: [] ready: - elasticsearch-cdm-zjf34ved-1-6d7fbf844f-sn422 - elasticsearch-cdm-zjf34ved-2-dfbd988bc-qkzjz - elasticsearch-cdm-zjf34ved-3-c8f566f7c-t7zkt data: failed: [] notReady: [] ready: - elasticsearch-cdm-zjf34ved-1-6d7fbf844f-sn422 - elasticsearch-cdm-zjf34ved-2-dfbd988bc-qkzjz - elasticsearch-cdm-zjf34ved-3-c8f566f7c-t7zkt master: failed: [] notReady: [] ready: - elasticsearch-cdm-zjf34ved-1-6d7fbf844f-sn422 - elasticsearch-cdm-zjf34ved-2-dfbd988bc-qkzjz - elasticsearch-cdm-zjf34ved-3-c8f566f7c-t7zkt shardAllocationEnabled: all", "status: nodes: - conditions: - lastTransitionTime: 2019-03-15T15:57:22Z message: Disk storage usage for node is 27.5gb (36.74%). Shards will be not be allocated on this node. reason: Disk Watermark Low status: \"True\" type: NodeStorage deploymentName: example-elasticsearch-cdm-0-1 upgradeStatus: {}", "status: nodes: - conditions: - lastTransitionTime: 2019-03-15T16:04:45Z message: Disk storage usage for node is 27.5gb (36.74%). Shards will be relocated from this node. reason: Disk Watermark High status: \"True\" type: NodeStorage deploymentName: example-elasticsearch-cdm-0-1 upgradeStatus: {}", "status: nodes: - conditions: - lastTransitionTime: 2019-04-10T02:26:24Z message: '0/8 nodes are available: 8 node(s) didn''t match node selector.' reason: Unschedulable status: \"True\" type: Unschedulable", "status: nodes: - conditions: - last Transition Time: 2019-04-10T05:55:51Z message: pod has unbound immediate PersistentVolumeClaims (repeated 5 times) reason: Unschedulable status: True type: Unschedulable", "status: clusterHealth: \"\" conditions: - lastTransitionTime: 2019-04-17T20:01:31Z message: Wrong RedundancyPolicy selected. Choose different RedundancyPolicy or add more nodes with data roles reason: Invalid Settings status: \"True\" type: InvalidRedundancy", "status: clusterHealth: green conditions: - lastTransitionTime: '2019-04-17T20:12:34Z' message: >- Invalid master nodes count. Please ensure there are no more than 3 total nodes with master roles reason: Invalid Settings status: 'True' type: InvalidMasters", "status: clusterHealth: green conditions: - lastTransitionTime: \"2021-05-07T01:05:13Z\" message: Changing the storage structure for a custom resource is not supported reason: StorageStructureChangeIgnored status: 'True' type: StorageStructureChangeIgnored", "oc get pods --selector component=elasticsearch -o name", "pod/elasticsearch-cdm-1godmszn-1-6f8495-vp4lw pod/elasticsearch-cdm-1godmszn-2-5769cf-9ms2n pod/elasticsearch-cdm-1godmszn-3-f66f7d-zqkz7", "oc exec elasticsearch-cdm-4vjor49p-2-6d4d7db474-q2w7z -- indices", "Defaulting container name to elasticsearch. Use 'oc describe pod/elasticsearch-cdm-4vjor49p-2-6d4d7db474-q2w7z -n openshift-logging' to see all of the containers in this pod. green open infra-000002 S4QANnf1QP6NgCegfnrnbQ 3 1 119926 0 157 78 green open audit-000001 8_EQx77iQCSTzFOXtxRqFw 3 1 0 0 0 0 green open .security iDjscH7aSUGhIdq0LheLBQ 1 1 5 0 0 0 green open .kibana_-377444158_kubeadmin yBywZ9GfSrKebz5gWBZbjw 3 1 1 0 0 0 green open infra-000001 z6Dpe__ORgiopEpW6Yl44A 3 1 871000 0 874 436 green open app-000001 hIrazQCeSISewG3c2VIvsQ 3 1 2453 0 3 1 green open .kibana_1 JCitcBMSQxKOvIq6iQW6wg 1 1 0 0 0 0 green open .kibana_-1595131456_user1 gIYFIEGRRe-ka0W3okS-mQ 3 1 1 0 0 0", "oc get pods --selector component=elasticsearch -o name", "pod/elasticsearch-cdm-1godmszn-1-6f8495-vp4lw pod/elasticsearch-cdm-1godmszn-2-5769cf-9ms2n pod/elasticsearch-cdm-1godmszn-3-f66f7d-zqkz7", "oc describe pod elasticsearch-cdm-1godmszn-1-6f8495-vp4lw", ". Status: Running . Containers: elasticsearch: Container ID: cri-o://b7d44e0a9ea486e27f47763f5bb4c39dfd2 State: Running Started: Mon, 08 Jun 2020 10:17:56 -0400 Ready: True Restart Count: 0 Readiness: exec [/usr/share/elasticsearch/probe/readiness.sh] delay=10s timeout=30s period=5s #success=1 #failure=3 . proxy: Container ID: cri-o://3f77032abaddbb1652c116278652908dc01860320b8a4e741d06894b2f8f9aa1 State: Running Started: Mon, 08 Jun 2020 10:18:38 -0400 Ready: True Restart Count: 0 . Conditions: Type Status Initialized True Ready True ContainersReady True PodScheduled True . Events: <none>", "oc get deployment --selector component=elasticsearch -o name", "deployment.extensions/elasticsearch-cdm-1gon-1 deployment.extensions/elasticsearch-cdm-1gon-2 deployment.extensions/elasticsearch-cdm-1gon-3", "oc describe deployment elasticsearch-cdm-1gon-1", ". Containers: elasticsearch: Image: registry.redhat.io/openshift-logging/elasticsearch6-rhel8 Readiness: exec [/usr/share/elasticsearch/probe/readiness.sh] delay=10s timeout=30s period=5s #success=1 #failure=3 . Conditions: Type Status Reason ---- ------ ------ Progressing Unknown DeploymentPaused Available True MinimumReplicasAvailable . Events: <none>", "oc get replicaSet --selector component=elasticsearch -o name replicaset.extensions/elasticsearch-cdm-1gon-1-6f8495 replicaset.extensions/elasticsearch-cdm-1gon-2-5769cf replicaset.extensions/elasticsearch-cdm-1gon-3-f66f7d", "oc describe replicaSet elasticsearch-cdm-1gon-1-6f8495", ". Containers: elasticsearch: Image: registry.redhat.io/openshift-logging/elasticsearch6-rhel8@sha256:4265742c7cdd85359140e2d7d703e4311b6497eec7676957f455d6908e7b1c25 Readiness: exec [/usr/share/elasticsearch/probe/readiness.sh] delay=10s timeout=30s period=5s #success=1 #failure=3 . Events: <none>", "eo_elasticsearch_cr_cluster_management_state{state=\"managed\"} 1 eo_elasticsearch_cr_cluster_management_state{state=\"unmanaged\"} 0", "eo_elasticsearch_cr_restart_total{reason=\"cert_restart\"} 1 eo_elasticsearch_cr_restart_total{reason=\"rolling_restart\"} 1 eo_elasticsearch_cr_restart_total{reason=\"scheduled_restart\"} 3", "Total number of Namespaces. es_index_namespaces_total 5", "es_index_document_count{namespace=\"namespace_1\"} 25 es_index_document_count{namespace=\"namespace_2\"} 10 es_index_document_count{namespace=\"namespace_3\"} 5", "message\": \"Secret \\\"elasticsearch\\\" fields are either missing or empty: [admin-cert, admin-key, logging-es.crt, logging-es.key]\", \"reason\": \"Missing Required Secrets\",", "oc adm must-gather --image=USD(oc -n openshift-logging get deployment.apps/cluster-logging-operator -o jsonpath='{.spec.template.spec.containers[?(@.name == \"cluster-logging-operator\")].image}')", "tar -cvaf must-gather.tar.gz must-gather.local.4157245944708210408", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- health", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/nodes?v", "-n openshift-logging get pods -l component=elasticsearch", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/master?v", "logs <elasticsearch_master_pod_name> -c elasticsearch -n openshift-logging", "logs <elasticsearch_node_name> -c elasticsearch -n openshift-logging", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/recovery?active_only=true", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- health \|grep number_of_pending_tasks", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cluster/settings?pretty", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cluster/settings?pretty -X PUT -d '{\"persistent\": {\"cluster.routing.allocation.enable\":\"all\"}}'", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/indices?v", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name>/_cache/clear?pretty", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name>/_settings?pretty -X PUT -d '{\"index.allocation.max_retries\":10}'", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_search/scroll/_all -X DELETE", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name>/_settings?pretty -X PUT -d '{\"index.unassigned.node_left.delayed_timeout\":\"10m\"}'", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/indices?v", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_red_index_name> -X DELETE", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_nodes/stats?pretty", "-n openshift-logging get po -o wide", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cluster/health?pretty \| grep unassigned_shards", "for pod in `oc -n openshift-logging get po -l component=elasticsearch -o jsonpath='{.items[].metadata.name}'`; do echo USDpod; oc -n openshift-logging exec -c elasticsearch USDpod -- df -h /elasticsearch/persistent; done", "-n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}'", "-n openshift-logging get cl -o jsonpath='{.items[].spec.logStore.elasticsearch.redundancyPolicy}'", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- indices", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name> -X DELETE", "-n openshift-logging get po -o wide", "for pod in `oc -n openshift-logging get po -l component=elasticsearch -o jsonpath='{.items[].metadata.name}'`; do echo USDpod; oc -n openshift-logging exec -c elasticsearch USDpod -- df -h /elasticsearch/persistent; done", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cluster/health?pretty \| grep relocating_shards", "-n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}'", "-n openshift-logging get cl -o jsonpath='{.items[].spec.logStore.elasticsearch.redundancyPolicy}'", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- indices", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name> -X DELETE", "for pod in `oc -n openshift-logging get po -l component=elasticsearch -o jsonpath='{.items[].metadata.name}'`; do echo USDpod; oc -n openshift-logging exec -c elasticsearch USDpod -- df -h /elasticsearch/persistent; done", "-n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}'", "-n openshift-logging get cl -o jsonpath='{.items[].spec.logStore.elasticsearch.redundancyPolicy}'", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- indices", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name> -X DELETE", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_all/_settings?pretty -X PUT -d '{\"index.blocks.read_only_allow_delete\": null}'", "for pod in `oc -n openshift-logging get po -l component=elasticsearch -o jsonpath='{.items[].metadata.name}'`; do echo USDpod; oc -n openshift-logging exec -c elasticsearch USDpod -- df -h /elasticsearch/persistent; done", "-n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}'", "-n openshift-logging get cl -o jsonpath='{.items[].spec.logStore.elasticsearch.redundancyPolicy}'", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- indices", "exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name> -X DELETE" ]	https://docs.redhat.com/en/documentation/openshift_container_platform/4.10/html/logging/troubleshooting-logging
Chapter 11. Migrating	Chapter 11. Migrating Warning The Red Hat OpenShift distributed tracing platform (Jaeger) 3.5 is the last release of the Red Hat OpenShift distributed tracing platform (Jaeger) that Red Hat plans to support. In the Red Hat OpenShift distributed tracing platform 3.5, Jaeger and support for Elasticsearch remain deprecated. Support for the Red Hat OpenShift distributed tracing platform (Jaeger) ends on November 3, 2025. The Red Hat OpenShift distributed tracing platform Operator (Jaeger) will be removed from the redhat-operators catalog on November 3, 2025. For more information, see the Red Hat Knowledgebase solution Jaeger Deprecation and Removal in OpenShift . You must migrate to the Red Hat build of OpenTelemetry Operator and the Tempo Operator for distributed tracing collection and storage. For more information, see "Migrating" in the Red Hat build of OpenTelemetry documentation, "Installing" in the Red Hat build of OpenTelemetry documentation, and "Installing" in the distributed tracing platform (Tempo) documentation. If you are already using the Red Hat OpenShift distributed tracing platform (Jaeger) for your applications, you can migrate to the Red Hat build of OpenTelemetry, which is based on the OpenTelemetry open-source project. The Red Hat build of OpenTelemetry provides a set of APIs, libraries, agents, and instrumentation to facilitate observability in distributed systems. The OpenTelemetry Collector in the Red Hat build of OpenTelemetry can ingest the Jaeger protocol, so you do not need to change the SDKs in your applications. Migration from the distributed tracing platform (Jaeger) to the Red Hat build of OpenTelemetry requires configuring the OpenTelemetry Collector and your applications to report traces seamlessly. You can migrate sidecar and sidecarless deployments. 11.1. Migrating with sidecars The Red Hat build of OpenTelemetry Operator supports sidecar injection into deployment workloads, so you can migrate from a distributed tracing platform (Jaeger) sidecar to a Red Hat build of OpenTelemetry sidecar. Prerequisites The Red Hat OpenShift distributed tracing platform (Jaeger) is used on the cluster. The Red Hat build of OpenTelemetry is installed. Procedure Configure the OpenTelemetry Collector as a sidecar. apiVersion: opentelemetry.io/v1beta1 kind: OpenTelemetryCollector metadata: name: otel namespace: <otel-collector-namespace> spec: mode: sidecar config: receivers: jaeger: protocols: grpc: {} thrift_binary: {} thrift_compact: {} thrift_http: {} processors: batch: {} memory_limiter: check_interval: 1s limit_percentage: 50 spike_limit_percentage: 30 resourcedetection: detectors: [openshift] timeout: 2s exporters: otlp: endpoint: "tempo-<example>-gateway:8090" 1 tls: insecure: true service: pipelines: traces: receivers: [jaeger] processors: [memory_limiter, resourcedetection, batch] exporters: [otlp] 1 This endpoint points to the Gateway of a TempoStack instance deployed by using the <example> Tempo Operator. Create a service account for running your application. apiVersion: v1 kind: ServiceAccount metadata: name: otel-collector-sidecar Create a cluster role for the permissions needed by some processors. apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: otel-collector-sidecar rules: 1 - apiGroups: ["config.openshift.io"] resources: ["infrastructures", "infrastructures/status"] verbs: ["get", "watch", "list"] 1 The resourcedetectionprocessor requires permissions for infrastructures and infrastructures/status. Create a ClusterRoleBinding to set the permissions for the service account. apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: otel-collector-sidecar subjects: - kind: ServiceAccount name: otel-collector-deployment namespace: otel-collector-example roleRef: kind: ClusterRole name: otel-collector apiGroup: rbac.authorization.k8s.io Deploy the OpenTelemetry Collector as a sidecar. Remove the injected Jaeger Agent from your application by removing the "sidecar.jaegertracing.io/inject": "true" annotation from your Deployment object. Enable automatic injection of the OpenTelemetry sidecar by adding the sidecar.opentelemetry.io/inject: "true" annotation to the .spec.template.metadata.annotations field of your Deployment object. Use the created service account for the deployment of your application to allow the processors to get the correct information and add it to your traces. 11.2. Migrating without sidecars You can migrate from the distributed tracing platform (Jaeger) to the Red Hat build of OpenTelemetry without sidecar deployment. Prerequisites The Red Hat OpenShift distributed tracing platform (Jaeger) is used on the cluster. The Red Hat build of OpenTelemetry is installed. Procedure Configure OpenTelemetry Collector deployment. Create the project where the OpenTelemetry Collector will be deployed. apiVersion: project.openshift.io/v1 kind: Project metadata: name: observability Create a service account for running the OpenTelemetry Collector instance. apiVersion: v1 kind: ServiceAccount metadata: name: otel-collector-deployment namespace: observability Create a cluster role for setting the required permissions for the processors. apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: otel-collector rules: 1 2 - apiGroups: ["", "config.openshift.io"] resources: ["pods", "namespaces", "infrastructures", "infrastructures/status"] verbs: ["get", "watch", "list"] 1 Permissions for the pods and namespaces resources are required for the k8sattributesprocessor . 2 Permissions for infrastructures and infrastructures/status are required for resourcedetectionprocessor . Create a ClusterRoleBinding to set the permissions for the service account. apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: otel-collector subjects: - kind: ServiceAccount name: otel-collector-deployment namespace: observability roleRef: kind: ClusterRole name: otel-collector apiGroup: rbac.authorization.k8s.io Create the OpenTelemetry Collector instance. Note This collector will export traces to a TempoStack instance. You must create your TempoStack instance by using the Red Hat Tempo Operator and place here the correct endpoint. apiVersion: opentelemetry.io/v1beta1 kind: OpenTelemetryCollector metadata: name: otel namespace: observability spec: mode: deployment serviceAccount: otel-collector-deployment config: receivers: jaeger: protocols: grpc: {} thrift_binary: {} thrift_compact: {} thrift_http: {} processors: batch: {} k8sattributes: {} memory_limiter: check_interval: 1s limit_percentage: 50 spike_limit_percentage: 30 resourcedetection: detectors: [openshift] exporters: otlp: endpoint: "tempo-example-gateway:8090" tls: insecure: true service: pipelines: traces: receivers: [jaeger] processors: [memory_limiter, k8sattributes, resourcedetection, batch] exporters: [otlp] Point your tracing endpoint to the OpenTelemetry Operator. If you are exporting your traces directly from your application to Jaeger, change the API endpoint from the Jaeger endpoint to the OpenTelemetry Collector endpoint. Example of exporting traces by using the jaegerexporter with Golang exp, err := jaeger.New(jaeger.WithCollectorEndpoint(jaeger.WithEndpoint(url))) 1 1 The URL points to the OpenTelemetry Collector API endpoint.	[ "apiVersion: opentelemetry.io/v1beta1 kind: OpenTelemetryCollector metadata: name: otel namespace: <otel-collector-namespace> spec: mode: sidecar config: receivers: jaeger: protocols: grpc: {} thrift_binary: {} thrift_compact: {} thrift_http: {} processors: batch: {} memory_limiter: check_interval: 1s limit_percentage: 50 spike_limit_percentage: 30 resourcedetection: detectors: [openshift] timeout: 2s exporters: otlp: endpoint: \"tempo-<example>-gateway:8090\" 1 tls: insecure: true service: pipelines: traces: receivers: [jaeger] processors: [memory_limiter, resourcedetection, batch] exporters: [otlp]", "apiVersion: v1 kind: ServiceAccount metadata: name: otel-collector-sidecar", "apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: otel-collector-sidecar rules: 1 - apiGroups: [\"config.openshift.io\"] resources: [\"infrastructures\", \"infrastructures/status\"] verbs: [\"get\", \"watch\", \"list\"]", "apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: otel-collector-sidecar subjects: - kind: ServiceAccount name: otel-collector-deployment namespace: otel-collector-example roleRef: kind: ClusterRole name: otel-collector apiGroup: rbac.authorization.k8s.io", "apiVersion: project.openshift.io/v1 kind: Project metadata: name: observability", "apiVersion: v1 kind: ServiceAccount metadata: name: otel-collector-deployment namespace: observability", "apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: otel-collector rules: 1 2 - apiGroups: [\"\", \"config.openshift.io\"] resources: [\"pods\", \"namespaces\", \"infrastructures\", \"infrastructures/status\"] verbs: [\"get\", \"watch\", \"list\"]", "apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: otel-collector subjects: - kind: ServiceAccount name: otel-collector-deployment namespace: observability roleRef: kind: ClusterRole name: otel-collector apiGroup: rbac.authorization.k8s.io", "apiVersion: opentelemetry.io/v1beta1 kind: OpenTelemetryCollector metadata: name: otel namespace: observability spec: mode: deployment serviceAccount: otel-collector-deployment config: receivers: jaeger: protocols: grpc: {} thrift_binary: {} thrift_compact: {} thrift_http: {} processors: batch: {} k8sattributes: {} memory_limiter: check_interval: 1s limit_percentage: 50 spike_limit_percentage: 30 resourcedetection: detectors: [openshift] exporters: otlp: endpoint: \"tempo-example-gateway:8090\" tls: insecure: true service: pipelines: traces: receivers: [jaeger] processors: [memory_limiter, k8sattributes, resourcedetection, batch] exporters: [otlp]", "exp, err := jaeger.New(jaeger.WithCollectorEndpoint(jaeger.WithEndpoint(url))) 1" ]	https://docs.redhat.com/en/documentation/openshift_container_platform/4.16/html/red_hat_build_of_opentelemetry/dist-tracing-otel-migrating
Chapter 1. Overview	Chapter 1. Overview From the perspective of a Ceph client, interacting with the Ceph storage cluster is remarkably simple: Connect to the Cluster Create a Pool I/O Context This remarkably simple interface is how a Ceph client selects one of the storage strategies you define. Storage strategies are invisible to the Ceph client in all but storage capacity and performance. The diagram below shows the logical data flow starting from the client into the Red Hat Ceph Storage cluster. 1.1. What are storage strategies? A storage strategy is a method of storing data that serves a particular use case. For example, if you need to store volumes and images for a cloud platform like OpenStack, you might choose to store data on reasonably performant SAS drives with SSD-based journals. By contrast, if you need to store object data for an S3- or Swift-compliant gateway, you might choose to use something more economical, like SATA drives. Ceph can accommodate both scenarios in the same Ceph cluster, but you need a means of providing the SAS/SSD storage strategy to the cloud platform (for example, Glance and Cinder in OpenStack), and a means of providing SATA storage for your object store. Storage strategies include the storage media (hard drives, SSDs, and the rest), the CRUSH maps that set up performance and failure domains for the storage media, the number of placement groups, and the pool interface. Ceph supports multiple storage strategies. Use cases, cost/benefit performance tradeoffs and data durability are the primary considerations that drive storage strategies. Use Cases: Ceph provides massive storage capacity, and it supports numerous use cases. For example, the Ceph Block Device client is a leading storage backend for cloud platforms like OpenStack- providing limitless storage for volumes and images with high performance features like copy-on-write cloning. Likewise, Ceph can provide container-based storage for OpenShift environments. By contrast, the Ceph Object Gateway client is a leading storage backend for cloud platforms that provides RESTful S3-compliant and Swift-compliant object storage for objects like audio, bitmap, video and other data. Cost/Benefit of Performance: Faster is better. Bigger is better. High durability is better. However, there is a price for each superlative quality, and a corresponding cost/benefit trade off. Consider the following use cases from a performance perspective: SSDs can provide very fast storage for relatively small amounts of data and journaling. Storing a database or object index might benefit from a pool of very fast SSDs, but prove too expensive for other data. SAS drives with SSD journaling provide fast performance at an economical price for volumes and images. SATA drives without SSD journaling provide cheap storage with lower overall performance. When you create a CRUSH hierarchy of OSDs, you need to consider the use case and an acceptable cost/performance trade off. Durability: In large scale clusters, hardware failure is an expectation, not an exception. However, data loss and service interruption remain unacceptable. For this reason, data durability is very important. Ceph addresses data durability with multiple deep copies of an object or with erasure coding and multiple coding chunks. Multiple copies or multiple coding chunks present an additional cost/benefit tradeoff: it's cheaper to store fewer copies or coding chunks, but it might lead to the inability to service write requests in a degraded state. Generally, one object with two additional copies (that is, size = 3 ) or two coding chunks might allow a cluster to service writes in a degraded state while the cluster recovers. The CRUSH algorithm aids this process by ensuring that Ceph stores additional copies or coding chunks in different locations within the cluster. This ensures that the failure of a single storage device or node doesn't lead to a loss of all of the copies or coding chunks necessary to preclude data loss. You can capture use cases, cost/benefit performance tradeoffs and data durability in a storage strategy and present it to a Ceph client as a storage pool. Important Ceph's object copies or coding chunks make RAID obsolete. Do not use RAID, because Ceph already handles data durability, a degraded RAID has a negative impact on performance, and recovering data using RAID is substantially slower than using deep copies or erasure coding chunks. 1.2. Configuring storage strategies Configuring storage strategies is about assigning Ceph OSDs to a CRUSH hierarchy, defining the number of placement groups for a pool, and creating a pool. The general steps are: Define a Storage Strategy: Storage strategies require you to analyze your use case, cost/benefit performance tradeoffs and data durability. Then, you create OSDs suitable for that use case. For example, you can create SSD-backed OSDs for a high performance pool; SAS drive/SSD journal-backed OSDs for high-performance block device volumes and images; or, SATA-backed OSDs for low cost storage. Ideally, each OSD for a use case should have the same hardware configuration so that you have a consistent performance profile. Define a CRUSH Hierarchy: Ceph rules select a node, usually the root , in a CRUSH hierarchy, and identify the appropriate OSDs for storing placement groups and the objects they contain. You must create a CRUSH hierarchy and a CRUSH rule for your storage strategy. CRUSH hierarchies get assigned directly to a pool by the CRUSH rule setting. Calculate Placement Groups: Ceph shards a pool into placement groups. You do not have to manually set the number of placement groups for your pool. PG autoscaler sets an appropriate number of placement groups for your pool that remains within a healthy maximum number of placement groups in the event that you assign multiple pools to the same CRUSH rule. Create a Pool: Finally, you must create a pool and determine whether it uses replicated or erasure-coded storage. You must set the number of placement groups for the pool, the rule for the pool and the durability, such as size or K+M coding chunks. Remember, the pool is the Ceph client's interface to the storage cluster, but the storage strategy is completely transparent to the Ceph client, except for capacity and performance.	null	https://docs.redhat.com/en/documentation/red_hat_ceph_storage/6/html/storage_strategies_guide/overview_strategy
Chapter 3. User tasks	Chapter 3. User tasks 3.1. Creating applications from installed Operators This guide walks developers through an example of creating applications from an installed Operator using the OpenShift Container Platform web console. 3.1.1. Creating an etcd cluster using an Operator This procedure walks through creating a new etcd cluster using the etcd Operator, managed by Operator Lifecycle Manager (OLM). Prerequisites Access to an OpenShift Container Platform 4.17 cluster. The etcd Operator already installed cluster-wide by an administrator. Procedure Create a new project in the OpenShift Container Platform web console for this procedure. This example uses a project called my-etcd . Navigate to the Operators Installed Operators page. The Operators that have been installed to the cluster by the cluster administrator and are available for use are shown here as a list of cluster service versions (CSVs). CSVs are used to launch and manage the software provided by the Operator. Tip You can get this list from the CLI using: USD oc get csv On the Installed Operators page, click the etcd Operator to view more details and available actions. As shown under Provided APIs , this Operator makes available three new resource types, including one for an etcd Cluster (the EtcdCluster resource). These objects work similar to the built-in native Kubernetes ones, such as Deployment or ReplicaSet , but contain logic specific to managing etcd. Create a new etcd cluster: In the etcd Cluster API box, click Create instance . The page allows you to make any modifications to the minimal starting template of an EtcdCluster object, such as the size of the cluster. For now, click Create to finalize. This triggers the Operator to start up the pods, services, and other components of the new etcd cluster. Click the example etcd cluster, then click the Resources tab to see that your project now contains a number of resources created and configured automatically by the Operator. Verify that a Kubernetes service has been created that allows you to access the database from other pods in your project. All users with the edit role in a given project can create, manage, and delete application instances (an etcd cluster, in this example) managed by Operators that have already been created in the project, in a self-service manner, just like a cloud service. If you want to enable additional users with this ability, project administrators can add the role using the following command: USD oc policy add-role-to-user edit <user> -n <target_project> You now have an etcd cluster that will react to failures and rebalance data as pods become unhealthy or are migrated between nodes in the cluster. Most importantly, cluster administrators or developers with proper access can now easily use the database with their applications. 3.2. Installing Operators in your namespace If a cluster administrator has delegated Operator installation permissions to your account, you can install and subscribe an Operator to your namespace in a self-service manner. 3.2.1. Prerequisites A cluster administrator must add certain permissions to your OpenShift Container Platform user account to allow self-service Operator installation to a namespace. See Allowing non-cluster administrators to install Operators for details. 3.2.2. About Operator installation with OperatorHub OperatorHub is a user interface for discovering Operators; it works in conjunction with Operator Lifecycle Manager (OLM), which installs and manages Operators on a cluster. As a user with the proper permissions, you can install an Operator from OperatorHub by using the OpenShift Container Platform web console or CLI. During installation, you must determine the following initial settings for the Operator: Installation Mode Choose a specific namespace in which to install the Operator. Update Channel If an Operator is available through multiple channels, you can choose which channel you want to subscribe to. For example, to deploy from the stable channel, if available, select it from the list. Approval Strategy You can choose automatic or manual updates. If you choose automatic updates for an installed Operator, when a new version of that Operator is available in the selected channel, Operator Lifecycle Manager (OLM) automatically upgrades the running instance of your Operator without human intervention. If you select manual updates, when a newer version of an Operator is available, OLM creates an update request. As a cluster administrator, you must then manually approve that update request to have the Operator updated to the new version. Understanding OperatorHub 3.2.3. Installing from OperatorHub by using the web console You can install and subscribe to an Operator from OperatorHub by using the OpenShift Container Platform web console. Prerequisites Access to an OpenShift Container Platform cluster using an account with Operator installation permissions. Procedure Navigate in the web console to the Operators OperatorHub page. Scroll or type a keyword into the Filter by keyword box to find the Operator you want. For example, type advanced to find the Advanced Cluster Management for Kubernetes Operator. You can also filter options by Infrastructure Features . For example, select Disconnected if you want to see Operators that work in disconnected environments, also known as restricted network environments. Select the Operator to display additional information. Note Choosing a Community Operator warns that Red Hat does not certify Community Operators; you must acknowledge the warning before continuing. Read the information about the Operator and click Install . On the Install Operator page, configure your Operator installation: If you want to install a specific version of an Operator, select an Update channel and Version from the lists. You can browse the various versions of an Operator across any channels it might have, view the metadata for that channel and version, and select the exact version you want to install. Note The version selection defaults to the latest version for the channel selected. If the latest version for the channel is selected, the Automatic approval strategy is enabled by default. Otherwise, Manual approval is required when not installing the latest version for the selected channel. Installing an Operator with Manual approval causes all Operators installed within the namespace to function with the Manual approval strategy and all Operators are updated together. If you want to update Operators independently, install Operators into separate namespaces. Choose a specific, single namespace in which to install the Operator. The Operator will only watch and be made available for use in this single namespace. For clusters on cloud providers with token authentication enabled: If the cluster uses AWS Security Token Service ( STS Mode in the web console), enter the Amazon Resource Name (ARN) of the AWS IAM role of your service account in the role ARN field. To create the role's ARN, follow the procedure described in Preparing AWS account . If the cluster uses Microsoft Entra Workload ID ( Workload Identity / Federated Identity Mode in the web console), add the client ID, tenant ID, and subscription ID in the appropriate fields. If the cluster uses Google Cloud Platform Workload Identity ( GCP Workload Identity / Federated Identity Mode in the web console), add the project number, pool ID, provider ID, and service account email in the appropriate fields. For Update approval , select either the Automatic or Manual approval strategy. Important If the web console shows that the cluster uses AWS STS, Microsoft Entra Workload ID, or GCP Workload Identity, you must set Update approval to Manual . Subscriptions with automatic approvals for updates are not recommended because there might be permission changes to make before updating. Subscriptions with manual approvals for updates ensure that administrators have the opportunity to verify the permissions of the later version, take any necessary steps, and then update. Click Install to make the Operator available to the selected namespaces on this OpenShift Container Platform cluster: If you selected a Manual approval strategy, the upgrade status of the subscription remains Upgrading until you review and approve the install plan. After approving on the Install Plan page, the subscription upgrade status moves to Up to date . If you selected an Automatic approval strategy, the upgrade status should resolve to Up to date without intervention. Verification After the upgrade status of the subscription is Up to date , select Operators Installed Operators to verify that the cluster service version (CSV) of the installed Operator eventually shows up. The Status should eventually resolve to Succeeded in the relevant namespace. Note For the All namespaces... installation mode, the status resolves to Succeeded in the openshift-operators namespace, but the status is Copied if you check in other namespaces. If it does not: Check the logs in any pods in the openshift-operators project (or other relevant namespace if A specific namespace... installation mode was selected) on the Workloads Pods page that are reporting issues to troubleshoot further. When the Operator is installed, the metadata indicates which channel and version are installed. Note The Channel and Version dropdown menus are still available for viewing other version metadata in this catalog context. 3.2.4. Installing from OperatorHub by using the CLI Instead of using the OpenShift Container Platform web console, you can install an Operator from OperatorHub by using the CLI. Use the oc command to create or update a Subscription object. For SingleNamespace install mode, you must also ensure an appropriate Operator group exists in the related namespace. An Operator group, defined by an OperatorGroup object, selects target namespaces in which to generate required RBAC access for all Operators in the same namespace as the Operator group. Tip In most cases, the web console method of this procedure is preferred because it automates tasks in the background, such as handling the creation of OperatorGroup and Subscription objects automatically when choosing SingleNamespace mode. Prerequisites Access to an OpenShift Container Platform cluster using an account with Operator installation permissions. You have installed the OpenShift CLI ( oc ). Procedure View the list of Operators available to the cluster from OperatorHub: USD oc get packagemanifests -n openshift-marketplace Example 3.1. Example output NAME CATALOG AGE 3scale-operator Red Hat Operators 91m advanced-cluster-management Red Hat Operators 91m amq7-cert-manager Red Hat Operators 91m # ... couchbase-enterprise-certified Certified Operators 91m crunchy-postgres-operator Certified Operators 91m mongodb-enterprise Certified Operators 91m # ... etcd Community Operators 91m jaeger Community Operators 91m kubefed Community Operators 91m # ... Note the catalog for your desired Operator. Inspect your desired Operator to verify its supported install modes and available channels: USD oc describe packagemanifests <operator_name> -n openshift-marketplace Example 3.2. Example output # ... Kind: PackageManifest # ... Install Modes: 1 Supported: true Type: OwnNamespace Supported: true Type: SingleNamespace Supported: false Type: MultiNamespace Supported: true Type: AllNamespaces # ... Entries: Name: example-operator.v3.7.11 Version: 3.7.11 Name: example-operator.v3.7.10 Version: 3.7.10 Name: stable-3.7 2 # ... Entries: Name: example-operator.v3.8.5 Version: 3.8.5 Name: example-operator.v3.8.4 Version: 3.8.4 Name: stable-3.8 3 Default Channel: stable-3.8 4 1 Indicates which install modes are supported. 2 3 Example channel names. 4 The channel selected by default if one is not specified. Tip You can print an Operator's version and channel information in YAML format by running the following command: USD oc get packagemanifests <operator_name> -n <catalog_namespace> -o yaml If more than one catalog is installed in a namespace, run the following command to look up the available versions and channels of an Operator from a specific catalog: USD oc get packagemanifest \ --selector=catalog=<catalogsource_name> \ --field-selector metadata.name=<operator_name> \ -n <catalog_namespace> -o yaml Important If you do not specify the Operator's catalog, running the oc get packagemanifest and oc describe packagemanifest commands might return a package from an unexpected catalog if the following conditions are met: Multiple catalogs are installed in the same namespace. The catalogs contain the same Operators or Operators with the same name. If the Operator you intend to install supports the AllNamespaces install mode, and you choose to use this mode, skip this step, because the openshift-operators namespace already has an appropriate Operator group in place by default, called global-operators . If the Operator you intend to install supports the SingleNamespace install mode, and you choose to use this mode, you must ensure an appropriate Operator group exists in the related namespace. If one does not exist, you can create create one by following these steps: Important You can only have one Operator group per namespace. For more information, see "Operator groups". Create an OperatorGroup object YAML file, for example operatorgroup.yaml , for SingleNamespace install mode: Example OperatorGroup object for SingleNamespace install mode apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: <operatorgroup_name> namespace: <namespace> 1 spec: targetNamespaces: - <namespace> 2 1 2 For SingleNamespace install mode, use the same <namespace> value for both the metadata.namespace and spec.targetNamespaces fields. Create the OperatorGroup object: USD oc apply -f operatorgroup.yaml Create a Subscription object to subscribe a namespace to an Operator: Create a YAML file for the Subscription object, for example subscription.yaml : Note If you want to subscribe to a specific version of an Operator, set the startingCSV field to the desired version and set the installPlanApproval field to Manual to prevent the Operator from automatically upgrading if a later version exists in the catalog. For details, see the following "Example Subscription object with a specific starting Operator version". Example 3.3. Example Subscription object apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: <subscription_name> namespace: <namespace_per_install_mode> 1 spec: channel: <channel_name> 2 name: <operator_name> 3 source: <catalog_name> 4 sourceNamespace: <catalog_source_namespace> 5 config: env: 6 - name: ARGS value: "-v=10" envFrom: 7 - secretRef: name: license-secret volumes: 8 - name: <volume_name> configMap: name: <configmap_name> volumeMounts: 9 - mountPath: <directory_name> name: <volume_name> tolerations: 10 - operator: "Exists" resources: 11 requests: memory: "64Mi" cpu: "250m" limits: memory: "128Mi" cpu: "500m" nodeSelector: 12 foo: bar 1 For default AllNamespaces install mode usage, specify the openshift-operators namespace. Alternatively, you can specify a custom global namespace, if you have created one. For SingleNamespace install mode usage, specify the relevant single namespace. 2 Name of the channel to subscribe to. 3 Name of the Operator to subscribe to. 4 Name of the catalog source that provides the Operator. 5 Namespace of the catalog source. Use openshift-marketplace for the default OperatorHub catalog sources. 6 The env parameter defines a list of environment variables that must exist in all containers in the pod created by OLM. 7 The envFrom parameter defines a list of sources to populate environment variables in the container. 8 The volumes parameter defines a list of volumes that must exist on the pod created by OLM. 9 The volumeMounts parameter defines a list of volume mounts that must exist in all containers in the pod created by OLM. If a volumeMount references a volume that does not exist, OLM fails to deploy the Operator. 10 The tolerations parameter defines a list of tolerations for the pod created by OLM. 11 The resources parameter defines resource constraints for all the containers in the pod created by OLM. 12 The nodeSelector parameter defines a NodeSelector for the pod created by OLM. Example 3.4. Example Subscription object with a specific starting Operator version apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: example-operator namespace: example-operator spec: channel: stable-3.7 installPlanApproval: Manual 1 name: example-operator source: custom-operators sourceNamespace: openshift-marketplace startingCSV: example-operator.v3.7.10 2 1 Set the approval strategy to Manual in case your specified version is superseded by a later version in the catalog. This plan prevents an automatic upgrade to a later version and requires manual approval before the starting CSV can complete the installation. 2 Set a specific version of an Operator CSV. For clusters on cloud providers with token authentication enabled, such as Amazon Web Services (AWS) Security Token Service (STS), Microsoft Entra Workload ID, or Google Cloud Platform Workload Identity, configure your Subscription object by following these steps: Ensure the Subscription object is set to manual update approvals: Example 3.5. Example Subscription object with manual update approvals kind: Subscription # ... spec: installPlanApproval: Manual 1 1 Subscriptions with automatic approvals for updates are not recommended because there might be permission changes to make before updating. Subscriptions with manual approvals for updates ensure that administrators have the opportunity to verify the permissions of the later version, take any necessary steps, and then update. Include the relevant cloud provider-specific fields in the Subscription object's config section: If the cluster is in AWS STS mode, include the following fields: Example 3.6. Example Subscription object with AWS STS variables kind: Subscription # ... spec: config: env: - name: ROLEARN value: "<role_arn>" 1 1 Include the role ARN details. If the cluster is in Workload ID mode, include the following fields: Example 3.7. Example Subscription object with Workload ID variables kind: Subscription # ... spec: config: env: - name: CLIENTID value: "<client_id>" 1 - name: TENANTID value: "<tenant_id>" 2 - name: SUBSCRIPTIONID value: "<subscription_id>" 3 1 Include the client ID. 2 Include the tenant ID. 3 Include the subscription ID. If the cluster is in GCP Workload Identity mode, include the following fields: Example 3.8. Example Subscription object with GCP Workload Identity variables kind: Subscription # ... spec: config: env: - name: AUDIENCE value: "<audience_url>" 1 - name: SERVICE_ACCOUNT_EMAIL value: "<service_account_email>" 2 where: <audience> Created in GCP by the administrator when they set up GCP Workload Identity, the AUDIENCE value must be a preformatted URL in the following format: //iam.googleapis.com/projects/<project_number>/locations/global/workloadIdentityPools/<pool_id>/providers/<provider_id> <service_account_email> The SERVICE_ACCOUNT_EMAIL value is a GCP service account email that is impersonated during Operator operation, for example: <service_account_name>@<project_id>.iam.gserviceaccount.com Create the Subscription object by running the following command: USD oc apply -f subscription.yaml If you set the installPlanApproval field to Manual , manually approve the pending install plan to complete the Operator installation. For more information, see "Manually approving a pending Operator update". At this point, OLM is now aware of the selected Operator. A cluster service version (CSV) for the Operator should appear in the target namespace, and APIs provided by the Operator should be available for creation. Verification Check the status of the Subscription object for your installed Operator by running the following command: USD oc describe subscription <subscription_name> -n <namespace> If you created an Operator group for SingleNamespace install mode, check the status of the OperatorGroup object by running the following command: USD oc describe operatorgroup <operatorgroup_name> -n <namespace> Additional resources Operator groups Channel names Additional resources Manually approving a pending Operator update	[ "oc get csv", "oc policy add-role-to-user edit <user> -n <target_project>", "oc get packagemanifests -n openshift-marketplace", "NAME CATALOG AGE 3scale-operator Red Hat Operators 91m advanced-cluster-management Red Hat Operators 91m amq7-cert-manager Red Hat Operators 91m couchbase-enterprise-certified Certified Operators 91m crunchy-postgres-operator Certified Operators 91m mongodb-enterprise Certified Operators 91m etcd Community Operators 91m jaeger Community Operators 91m kubefed Community Operators 91m", "oc describe packagemanifests <operator_name> -n openshift-marketplace", "Kind: PackageManifest Install Modes: 1 Supported: true Type: OwnNamespace Supported: true Type: SingleNamespace Supported: false Type: MultiNamespace Supported: true Type: AllNamespaces Entries: Name: example-operator.v3.7.11 Version: 3.7.11 Name: example-operator.v3.7.10 Version: 3.7.10 Name: stable-3.7 2 Entries: Name: example-operator.v3.8.5 Version: 3.8.5 Name: example-operator.v3.8.4 Version: 3.8.4 Name: stable-3.8 3 Default Channel: stable-3.8 4", "oc get packagemanifests <operator_name> -n <catalog_namespace> -o yaml", "oc get packagemanifest --selector=catalog=<catalogsource_name> --field-selector metadata.name=<operator_name> -n <catalog_namespace> -o yaml", "apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: <operatorgroup_name> namespace: <namespace> 1 spec: targetNamespaces: - <namespace> 2", "oc apply -f operatorgroup.yaml", "apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: <subscription_name> namespace: <namespace_per_install_mode> 1 spec: channel: <channel_name> 2 name: <operator_name> 3 source: <catalog_name> 4 sourceNamespace: <catalog_source_namespace> 5 config: env: 6 - name: ARGS value: \"-v=10\" envFrom: 7 - secretRef: name: license-secret volumes: 8 - name: <volume_name> configMap: name: <configmap_name> volumeMounts: 9 - mountPath: <directory_name> name: <volume_name> tolerations: 10 - operator: \"Exists\" resources: 11 requests: memory: \"64Mi\" cpu: \"250m\" limits: memory: \"128Mi\" cpu: \"500m\" nodeSelector: 12 foo: bar", "apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: example-operator namespace: example-operator spec: channel: stable-3.7 installPlanApproval: Manual 1 name: example-operator source: custom-operators sourceNamespace: openshift-marketplace startingCSV: example-operator.v3.7.10 2", "kind: Subscription spec: installPlanApproval: Manual 1", "kind: Subscription spec: config: env: - name: ROLEARN value: \"<role_arn>\" 1", "kind: Subscription spec: config: env: - name: CLIENTID value: \"<client_id>\" 1 - name: TENANTID value: \"<tenant_id>\" 2 - name: SUBSCRIPTIONID value: \"<subscription_id>\" 3", "kind: Subscription spec: config: env: - name: AUDIENCE value: \"<audience_url>\" 1 - name: SERVICE_ACCOUNT_EMAIL value: \"<service_account_email>\" 2", "//iam.googleapis.com/projects/<project_number>/locations/global/workloadIdentityPools/<pool_id>/providers/<provider_id>", "<service_account_name>@<project_id>.iam.gserviceaccount.com", "oc apply -f subscription.yaml", "oc describe subscription <subscription_name> -n <namespace>", "oc describe operatorgroup <operatorgroup_name> -n <namespace>" ]	https://docs.redhat.com/en/documentation/openshift_container_platform/4.17/html/operators/user-tasks
12.6.2. Deleting a Storage Pool Using virt-manager	12.6.2. Deleting a Storage Pool Using virt-manager This procedure demonstrates how to delete a storage pool. To avoid any issues with other guests using the same pool, it is best to stop the storage pool and release any resources in use by it. To do this, select the storage pool you want to stop and click the red X icon at the bottom of the Storage window. Figure 12.28. Stop Icon Delete the storage pool by clicking the Trash can icon. This icon is only enabled if you stop the storage pool first.	null	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/virtualization_administration_guide/del-stor-pool-nfs
Getting started	Getting started Red Hat OpenShift Service on AWS 4 Setting up clusters and accounts Red Hat OpenShift Documentation Team	null	https://docs.redhat.com/en/documentation/red_hat_openshift_service_on_aws/4/html/getting_started/index
Chapter 2. Understanding Operators	Chapter 2. Understanding Operators 2.1. What are Operators? Conceptually, Operators take human operational knowledge and encode it into software that is more easily shared with consumers. Operators are pieces of software that ease the operational complexity of running another piece of software. They act like an extension of the software vendor's engineering team, monitoring a Kubernetes environment (such as OpenShift Dedicated) and using its current state to make decisions in real time. Advanced Operators are designed to handle upgrades seamlessly, react to failures automatically, and not take shortcuts, like skipping a software backup process to save time. More technically, Operators are a method of packaging, deploying, and managing a Kubernetes application. A Kubernetes application is an app that is both deployed on Kubernetes and managed using the Kubernetes APIs and kubectl or oc tooling. To be able to make the most of Kubernetes, you require a set of cohesive APIs to extend in order to service and manage your apps that run on Kubernetes. Think of Operators as the runtime that manages this type of app on Kubernetes. 2.1.1. Why use Operators? Operators provide: Repeatability of installation and upgrade. Constant health checks of every system component. Over-the-air (OTA) updates for OpenShift components and ISV content. A place to encapsulate knowledge from field engineers and spread it to all users, not just one or two. Why deploy on Kubernetes? Kubernetes (and by extension, OpenShift Dedicated) contains all of the primitives needed to build complex distributed systems - secret handling, load balancing, service discovery, autoscaling - that work across on-premises and cloud providers. Why manage your app with Kubernetes APIs and kubectl tooling? These APIs are feature rich, have clients for all platforms and plug into the cluster's access control/auditing. An Operator uses the Kubernetes extension mechanism, custom resource definitions (CRDs), so your custom object, for example MongoDB , looks and acts just like the built-in, native Kubernetes objects. How do Operators compare with service brokers? A service broker is a step towards programmatic discovery and deployment of an app. However, because it is not a long running process, it cannot execute Day 2 operations like upgrade, failover, or scaling. Customizations and parameterization of tunables are provided at install time, versus an Operator that is constantly watching the current state of your cluster. Off-cluster services are a good match for a service broker, although Operators exist for these as well. 2.1.2. Operator Framework The Operator Framework is a family of tools and capabilities to deliver on the customer experience described above. It is not just about writing code; testing, delivering, and updating Operators is just as important. The Operator Framework components consist of open source tools to tackle these problems: Operator SDK The Operator SDK assists Operator authors in bootstrapping, building, testing, and packaging their own Operator based on their expertise without requiring knowledge of Kubernetes API complexities. Operator Lifecycle Manager Operator Lifecycle Manager (OLM) controls the installation, upgrade, and role-based access control (RBAC) of Operators in a cluster. It is deployed by default in OpenShift Dedicated 4. Operator Registry The Operator Registry stores cluster service versions (CSVs) and custom resource definitions (CRDs) for creation in a cluster and stores Operator metadata about packages and channels. It runs in a Kubernetes or OpenShift cluster to provide this Operator catalog data to OLM. OperatorHub OperatorHub is a web console for cluster administrators to discover and select Operators to install on their cluster. It is deployed by default in OpenShift Dedicated. These tools are designed to be composable, so you can use any that are useful to you. 2.1.3. Operator maturity model The level of sophistication of the management logic encapsulated within an Operator can vary. This logic is also in general highly dependent on the type of the service represented by the Operator. One can however generalize the scale of the maturity of the encapsulated operations of an Operator for certain set of capabilities that most Operators can include. To this end, the following Operator maturity model defines five phases of maturity for generic Day 2 operations of an Operator: Figure 2.1. Operator maturity model The above model also shows how these capabilities can best be developed through the Helm, Go, and Ansible capabilities of the Operator SDK. 2.2. Operator Framework packaging format This guide outlines the packaging format for Operators supported by Operator Lifecycle Manager (OLM) in OpenShift Dedicated. 2.2.1. Bundle format The bundle format for Operators is a packaging format introduced by the Operator Framework. To improve scalability and to better enable upstream users hosting their own catalogs, the bundle format specification simplifies the distribution of Operator metadata. An Operator bundle represents a single version of an Operator. On-disk bundle manifests are containerized and shipped as a bundle image , which is a non-runnable container image that stores the Kubernetes manifests and Operator metadata. Storage and distribution of the bundle image is then managed using existing container tools like podman and docker and container registries such as Quay. Operator metadata can include: Information that identifies the Operator, for example its name and version. Additional information that drives the UI, for example its icon and some example custom resources (CRs). Required and provided APIs. Related images. When loading manifests into the Operator Registry database, the following requirements are validated: The bundle must have at least one channel defined in the annotations. Every bundle has exactly one cluster service version (CSV). If a CSV owns a custom resource definition (CRD), that CRD must exist in the bundle. 2.2.1.1. Manifests Bundle manifests refer to a set of Kubernetes manifests that define the deployment and RBAC model of the Operator. A bundle includes one CSV per directory and typically the CRDs that define the owned APIs of the CSV in its /manifests directory. Example bundle format layout etcd ├── manifests │ ├── etcdcluster.crd.yaml │ └── etcdoperator.clusterserviceversion.yaml │ └── secret.yaml │ └── configmap.yaml └── metadata └── annotations.yaml └── dependencies.yaml Additionally supported objects The following object types can also be optionally included in the /manifests directory of a bundle: Supported optional object types ClusterRole ClusterRoleBinding ConfigMap ConsoleCLIDownload ConsoleLink ConsoleQuickStart ConsoleYamlSample PodDisruptionBudget PriorityClass PrometheusRule Role RoleBinding Secret Service ServiceAccount ServiceMonitor VerticalPodAutoscaler When these optional objects are included in a bundle, Operator Lifecycle Manager (OLM) can create them from the bundle and manage their lifecycle along with the CSV: Lifecycle for optional objects When the CSV is deleted, OLM deletes the optional object. When the CSV is upgraded: If the name of the optional object is the same, OLM updates it in place. If the name of the optional object has changed between versions, OLM deletes and recreates it. 2.2.1.2. Annotations A bundle also includes an annotations.yaml file in its /metadata directory. This file defines higher level aggregate data that helps describe the format and package information about how the bundle should be added into an index of bundles: Example annotations.yaml annotations: operators.operatorframework.io.bundle.mediatype.v1: "registry+v1" 1 operators.operatorframework.io.bundle.manifests.v1: "manifests/" 2 operators.operatorframework.io.bundle.metadata.v1: "metadata/" 3 operators.operatorframework.io.bundle.package.v1: "test-operator" 4 operators.operatorframework.io.bundle.channels.v1: "beta,stable" 5 operators.operatorframework.io.bundle.channel.default.v1: "stable" 6 1 The media type or format of the Operator bundle. The registry+v1 format means it contains a CSV and its associated Kubernetes objects. 2 The path in the image to the directory that contains the Operator manifests. This label is reserved for future use and currently defaults to manifests/ . The value manifests.v1 implies that the bundle contains Operator manifests. 3 The path in the image to the directory that contains metadata files about the bundle. This label is reserved for future use and currently defaults to metadata/ . The value metadata.v1 implies that this bundle has Operator metadata. 4 The package name of the bundle. 5 The list of channels the bundle is subscribing to when added into an Operator Registry. 6 The default channel an Operator should be subscribed to when installed from a registry. Note In case of a mismatch, the annotations.yaml file is authoritative because the on-cluster Operator Registry that relies on these annotations only has access to this file. 2.2.1.3. Dependencies The dependencies of an Operator are listed in a dependencies.yaml file in the metadata/ folder of a bundle. This file is optional and currently only used to specify explicit Operator-version dependencies. The dependency list contains a type field for each item to specify what kind of dependency this is. The following types of Operator dependencies are supported: olm.package This type indicates a dependency for a specific Operator version. The dependency information must include the package name and the version of the package in semver format. For example, you can specify an exact version such as 0.5.2 or a range of versions such as >0.5.1 . olm.gvk With this type, the author can specify a dependency with group/version/kind (GVK) information, similar to existing CRD and API-based usage in a CSV. This is a path to enable Operator authors to consolidate all dependencies, API or explicit versions, to be in the same place. olm.constraint This type declares generic constraints on arbitrary Operator properties. In the following example, dependencies are specified for a Prometheus Operator and etcd CRDs: Example dependencies.yaml file dependencies: - type: olm.package value: packageName: prometheus version: ">0.27.0" - type: olm.gvk value: group: etcd.database.coreos.com kind: EtcdCluster version: v1beta2 Additional resources Operator Lifecycle Manager dependency resolution 2.2.1.4. About the opm CLI The opm CLI tool is provided by the Operator Framework for use with the Operator bundle format. This tool allows you to create and maintain catalogs of Operators from a list of Operator bundles that are similar to software repositories. The result is a container image which can be stored in a container registry and then installed on a cluster. A catalog contains a database of pointers to Operator manifest content that can be queried through an included API that is served when the container image is run. On OpenShift Dedicated, Operator Lifecycle Manager (OLM) can reference the image in a catalog source, defined by a CatalogSource object, which polls the image at regular intervals to enable frequent updates to installed Operators on the cluster. See CLI tools for steps on installing the opm CLI. 2.2.2. Highlights File-based catalogs are the latest iteration of the catalog format in Operator Lifecycle Manager (OLM). It is a plain text-based (JSON or YAML) and declarative config evolution of the earlier SQLite database format, and it is fully backwards compatible. The goal of this format is to enable Operator catalog editing, composability, and extensibility. Editing With file-based catalogs, users interacting with the contents of a catalog are able to make direct changes to the format and verify that their changes are valid. Because this format is plain text JSON or YAML, catalog maintainers can easily manipulate catalog metadata by hand or with widely known and supported JSON or YAML tooling, such as the jq CLI. This editability enables the following features and user-defined extensions: Promoting an existing bundle to a new channel Changing the default channel of a package Custom algorithms for adding, updating, and removing upgrade paths Composability File-based catalogs are stored in an arbitrary directory hierarchy, which enables catalog composition. For example, consider two separate file-based catalog directories: catalogA and catalogB . A catalog maintainer can create a new combined catalog by making a new directory catalogC and copying catalogA and catalogB into it. This composability enables decentralized catalogs. The format permits Operator authors to maintain Operator-specific catalogs, and it permits maintainers to trivially build a catalog composed of individual Operator catalogs. File-based catalogs can be composed by combining multiple other catalogs, by extracting subsets of one catalog, or a combination of both of these. Note Duplicate packages and duplicate bundles within a package are not permitted. The opm validate command returns an error if any duplicates are found. Because Operator authors are most familiar with their Operator, its dependencies, and its upgrade compatibility, they are able to maintain their own Operator-specific catalog and have direct control over its contents. With file-based catalogs, Operator authors own the task of building and maintaining their packages in a catalog. Composite catalog maintainers, however, only own the task of curating the packages in their catalog and publishing the catalog to users. Extensibility The file-based catalog specification is a low-level representation of a catalog. While it can be maintained directly in its low-level form, catalog maintainers can build interesting extensions on top that can be used by their own custom tooling to make any number of mutations. For example, a tool could translate a high-level API, such as (mode=semver) , down to the low-level, file-based catalog format for upgrade paths. Or a catalog maintainer might need to customize all of the bundle metadata by adding a new property to bundles that meet a certain criteria. While this extensibility allows for additional official tooling to be developed on top of the low-level APIs for future OpenShift Dedicated releases, the major benefit is that catalog maintainers have this capability as well. Important As of OpenShift Dedicated 4.11, the default Red Hat-provided Operator catalog releases in the file-based catalog format. The default Red Hat-provided Operator catalogs for OpenShift Dedicated 4.6 through 4.10 released in the deprecated SQLite database format. The opm subcommands, flags, and functionality related to the SQLite database format are also deprecated and will be removed in a future release. The features are still supported and must be used for catalogs that use the deprecated SQLite database format. Many of the opm subcommands and flags for working with the SQLite database format, such as opm index prune , do not work with the file-based catalog format. For more information about working with file-based catalogs, see Managing custom catalogs . 2.2.2.1. Directory structure File-based catalogs can be stored and loaded from directory-based file systems. The opm CLI loads the catalog by walking the root directory and recursing into subdirectories. The CLI attempts to load every file it finds and fails if any errors occur. Non-catalog files can be ignored using .indexignore files, which have the same rules for patterns and precedence as .gitignore files. Example .indexignore file # Ignore everything except non-object .json and .yaml files */ !.json !.yaml */objects/.json */objects/.yaml Catalog maintainers have the flexibility to choose their desired layout, but it is recommended to store each package's file-based catalog blobs in separate subdirectories. Each individual file can be either JSON or YAML; it is not necessary for every file in a catalog to use the same format. Basic recommended structure catalog ├── packageA │ └── index.yaml ├── packageB │ ├── .indexignore │ ├── index.yaml │ └── objects │ └── packageB.v0.1.0.clusterserviceversion.yaml └── packageC └── index.json └── deprecations.yaml This recommended structure has the property that each subdirectory in the directory hierarchy is a self-contained catalog, which makes catalog composition, discovery, and navigation trivial file system operations. The catalog can also be included in a parent catalog by copying it into the parent catalog's root directory. 2.2.2.2. Schemas File-based catalogs use a format, based on the CUE language specification , that can be extended with arbitrary schemas. The following _Meta CUE schema defines the format that all file-based catalog blobs must adhere to: _Meta schema _Meta: { // schema is required and must be a non-empty string schema: string & !="" // package is optional, but if it's defined, it must be a non-empty string package?: string & !="" // properties is optional, but if it's defined, it must be a list of 0 or more properties properties?: [... #Property] } #Property: { // type is required type: string & !="" // value is required, and it must not be null value: !=null } Note No CUE schemas listed in this specification should be considered exhaustive. The opm validate command has additional validations that are difficult or impossible to express concisely in CUE. An Operator Lifecycle Manager (OLM) catalog currently uses three schemas ( olm.package , olm.channel , and olm.bundle ), which correspond to OLM's existing package and bundle concepts. Each Operator package in a catalog requires exactly one olm.package blob, at least one olm.channel blob, and one or more olm.bundle blobs. Note All olm.* schemas are reserved for OLM-defined schemas. Custom schemas must use a unique prefix, such as a domain that you own. 2.2.2.2.1. olm.package schema The olm.package schema defines package-level metadata for an Operator. This includes its name, description, default channel, and icon. Example 2.1. olm.package schema #Package: { schema: "olm.package" // Package name name: string & !="" // A description of the package description?: string // The package's default channel defaultChannel: string & !="" // An optional icon icon?: { base64data: string mediatype: string } } 2.2.2.2.2. olm.channel schema The olm.channel schema defines a channel within a package, the bundle entries that are members of the channel, and the upgrade paths for those bundles. If a bundle entry represents an edge in multiple olm.channel blobs, it can only appear once per channel. It is valid for an entry's replaces value to reference another bundle name that cannot be found in this catalog or another catalog. However, all other channel invariants must hold true, such as a channel not having multiple heads. Example 2.2. olm.channel schema #Channel: { schema: "olm.channel" package: string & !="" name: string & !="" entries: [...#ChannelEntry] } #ChannelEntry: { // name is required. It is the name of an `olm.bundle` that // is present in the channel. name: string & !="" // replaces is optional. It is the name of bundle that is replaced // by this entry. It does not have to be present in the entry list. replaces?: string & !="" // skips is optional. It is a list of bundle names that are skipped by // this entry. The skipped bundles do not have to be present in the // entry list. skips?: [...string & !=""] // skipRange is optional. It is the semver range of bundle versions // that are skipped by this entry. skipRange?: string & !="" } Warning When using the skipRange field, the skipped Operator versions are pruned from the update graph and are longer installable by users with the spec.startingCSV property of Subscription objects. You can update an Operator incrementally while keeping previously installed versions available to users for future installation by using both the skipRange and replaces field. Ensure that the replaces field points to the immediate version of the Operator version in question. 2.2.2.2.3. olm.bundle schema Example 2.3. olm.bundle schema #Bundle: { schema: "olm.bundle" package: string & !="" name: string & !="" image: string & !="" properties: [...#Property] relatedImages?: [...#RelatedImage] } #Property: { // type is required type: string & !="" // value is required, and it must not be null value: !=null } #RelatedImage: { // image is the image reference image: string & !="" // name is an optional descriptive name for an image that // helps identify its purpose in the context of the bundle name?: string & !="" } 2.2.2.2.4. olm.deprecations schema The optional olm.deprecations schema defines deprecation information for packages, bundles, and channels in a catalog. Operator authors can use this schema to provide relevant messages about their Operators, such as support status and recommended upgrade paths, to users running those Operators from a catalog. When this schema is defined, the OpenShift Dedicated web console displays warning badges for the affected elements of the Operator, including any custom deprecation messages, on both the pre- and post-installation pages of the OperatorHub. An olm.deprecations schema entry contains one or more of the following reference types, which indicates the deprecation scope. After the Operator is installed, any specified messages can be viewed as status conditions on the related Subscription object. Table 2.1. Deprecation reference types Type Scope Status condition olm.package Represents the entire package PackageDeprecated olm.channel Represents one channel ChannelDeprecated olm.bundle Represents one bundle version BundleDeprecated Each reference type has their own requirements, as detailed in the following example. Example 2.4. Example olm.deprecations schema with each reference type schema: olm.deprecations package: my-operator 1 entries: - reference: schema: olm.package 2 message: \| 3 The 'my-operator' package is end of life. Please use the 'my-operator-new' package for support. - reference: schema: olm.channel name: alpha 4 message: \| The 'alpha' channel is no longer supported. Please switch to the 'stable' channel. - reference: schema: olm.bundle name: my-operator.v1.68.0 5 message: \| my-operator.v1.68.0 is deprecated. Uninstall my-operator.v1.68.0 and install my-operator.v1.72.0 for support. 1 Each deprecation schema must have a package value, and that package reference must be unique across the catalog. There must not be an associated name field. 2 The olm.package schema must not include a name field, because it is determined by the package field defined earlier in the schema. 3 All message fields, for any reference type, must be a non-zero length and represented as an opaque text blob. 4 The name field for the olm.channel schema is required. 5 The name field for the olm.bundle schema is required. Note The deprecation feature does not consider overlapping deprecation, for example package versus channel versus bundle. Operator authors can save olm.deprecations schema entries as a deprecations.yaml file in the same directory as the package's index.yaml file: Example directory structure for a catalog with deprecations my-catalog └── my-operator ├── index.yaml └── deprecations.yaml Additional resources Updating or filtering a file-based catalog image 2.2.2.3. Properties Properties are arbitrary pieces of metadata that can be attached to file-based catalog schemas. The type field is a string that effectively specifies the semantic and syntactic meaning of the value field. The value can be any arbitrary JSON or YAML. OLM defines a handful of property types, again using the reserved olm.* prefix. 2.2.2.3.1. olm.package property The olm.package property defines the package name and version. This is a required property on bundles, and there must be exactly one of these properties. The packageName field must match the bundle's first-class package field, and the version field must be a valid semantic version. Example 2.5. olm.package property #PropertyPackage: { type: "olm.package" value: { packageName: string & !="" version: string & !="" } } 2.2.2.3.2. olm.gvk property The olm.gvk property defines the group/version/kind (GVK) of a Kubernetes API that is provided by this bundle. This property is used by OLM to resolve a bundle with this property as a dependency for other bundles that list the same GVK as a required API. The GVK must adhere to Kubernetes GVK validations. Example 2.6. olm.gvk property #PropertyGVK: { type: "olm.gvk" value: { group: string & !="" version: string & !="" kind: string & !="" } } 2.2.2.3.3. olm.package.required The olm.package.required property defines the package name and version range of another package that this bundle requires. For every required package property a bundle lists, OLM ensures there is an Operator installed on the cluster for the listed package and in the required version range. The versionRange field must be a valid semantic version (semver) range. Example 2.7. olm.package.required property #PropertyPackageRequired: { type: "olm.package.required" value: { packageName: string & !="" versionRange: string & !="" } } 2.2.2.3.4. olm.gvk.required The olm.gvk.required property defines the group/version/kind (GVK) of a Kubernetes API that this bundle requires. For every required GVK property a bundle lists, OLM ensures there is an Operator installed on the cluster that provides it. The GVK must adhere to Kubernetes GVK validations. Example 2.8. olm.gvk.required property #PropertyGVKRequired: { type: "olm.gvk.required" value: { group: string & !="" version: string & !="" kind: string & !="" } } 2.2.2.4. Example catalog With file-based catalogs, catalog maintainers can focus on Operator curation and compatibility. Because Operator authors have already produced Operator-specific catalogs for their Operators, catalog maintainers can build their catalog by rendering each Operator catalog into a subdirectory of the catalog's root directory. There are many possible ways to build a file-based catalog; the following steps outline a simple approach: Maintain a single configuration file for the catalog, containing image references for each Operator in the catalog: Example catalog configuration file name: community-operators repo: quay.io/community-operators/catalog tag: latest references: - name: etcd-operator image: quay.io/etcd-operator/index@sha256:5891b5b522d5df086d0ff0b110fbd9d21bb4fc7163af34d08286a2e846f6be03 - name: prometheus-operator image: quay.io/prometheus-operator/index@sha256:e258d248fda94c63753607f7c4494ee0fcbe92f1a76bfdac795c9d84101eb317 Run a script that parses the configuration file and creates a new catalog from its references: Example script name=USD(yq eval '.name' catalog.yaml) mkdir "USDname" yq eval '.name + "/" + .references[].name' catalog.yaml \| xargs mkdir for l in USD(yq e '.name as USDcatalog \| .references[] \| .image + "\|" + USDcatalog + "/" + .name + "/index.yaml"' catalog.yaml); do image=USD(echo USDl \| cut -d'\|' -f1) file=USD(echo USDl \| cut -d'\|' -f2) opm render "USDimage" > "USDfile" done opm generate dockerfile "USDname" indexImage=USD(yq eval '.repo + ":" + .tag' catalog.yaml) docker build -t "USDindexImage" -f "USDname.Dockerfile" . docker push "USDindexImage" 2.2.2.5. Guidelines Consider the following guidelines when maintaining file-based catalogs. 2.2.2.5.1. Immutable bundles The general advice with Operator Lifecycle Manager (OLM) is that bundle images and their metadata should be treated as immutable. If a broken bundle has been pushed to a catalog, you must assume that at least one of your users has upgraded to that bundle. Based on that assumption, you must release another bundle with an upgrade path from the broken bundle to ensure users with the broken bundle installed receive an upgrade. OLM will not reinstall an installed bundle if the contents of that bundle are updated in the catalog. However, there are some cases where a change in the catalog metadata is preferred: Channel promotion: If you already released a bundle and later decide that you would like to add it to another channel, you can add an entry for your bundle in another olm.channel blob. New upgrade paths: If you release a new 1.2.z bundle version, for example 1.2.4 , but 1.3.0 is already released, you can update the catalog metadata for 1.3.0 to skip 1.2.4 . 2.2.2.5.2. Source control Catalog metadata should be stored in source control and treated as the source of truth. Updates to catalog images should include the following steps: Update the source-controlled catalog directory with a new commit. Build and push the catalog image. Use a consistent tagging taxonomy, such as :latest or :<target_cluster_version> , so that users can receive updates to a catalog as they become available. 2.2.2.6. CLI usage For instructions about creating file-based catalogs by using the opm CLI, see Managing custom catalogs . For reference documentation about the opm CLI commands related to managing file-based catalogs, see CLI tools . 2.2.2.7. Automation Operator authors and catalog maintainers are encouraged to automate their catalog maintenance with CI/CD workflows. Catalog maintainers can further improve on this by building GitOps automation to accomplish the following tasks: Check that pull request (PR) authors are permitted to make the requested changes, for example by updating their package's image reference. Check that the catalog updates pass the opm validate command. Check that the updated bundle or catalog image references exist, the catalog images run successfully in a cluster, and Operators from that package can be successfully installed. Automatically merge PRs that pass the checks. Automatically rebuild and republish the catalog image. 2.3. Operator Framework glossary of common terms This topic provides a glossary of common terms related to the Operator Framework, including Operator Lifecycle Manager (OLM) and the Operator SDK. 2.3.1. Bundle In the bundle format, a bundle is a collection of an Operator CSV, manifests, and metadata. Together, they form a unique version of an Operator that can be installed onto the cluster. 2.3.2. Bundle image In the bundle format, a bundle image is a container image that is built from Operator manifests and that contains one bundle. Bundle images are stored and distributed by Open Container Initiative (OCI) spec container registries, such as Quay.io or DockerHub. 2.3.3. Catalog source A catalog source represents a store of metadata that OLM can query to discover and install Operators and their dependencies. 2.3.4. Channel A channel defines a stream of updates for an Operator and is used to roll out updates for subscribers. The head points to the latest version of that channel. For example, a stable channel would have all stable versions of an Operator arranged from the earliest to the latest. An Operator can have several channels, and a subscription binding to a certain channel would only look for updates in that channel. 2.3.5. Channel head A channel head refers to the latest known update in a particular channel. 2.3.6. Cluster service version A cluster service version (CSV) is a YAML manifest created from Operator metadata that assists OLM in running the Operator in a cluster. It is the metadata that accompanies an Operator container image, used to populate user interfaces with information such as its logo, description, and version. It is also a source of technical information that is required to run the Operator, like the RBAC rules it requires and which custom resources (CRs) it manages or depends on. 2.3.7. Dependency An Operator may have a dependency on another Operator being present in the cluster. For example, the Vault Operator has a dependency on the etcd Operator for its data persistence layer. OLM resolves dependencies by ensuring that all specified versions of Operators and CRDs are installed on the cluster during the installation phase. This dependency is resolved by finding and installing an Operator in a catalog that satisfies the required CRD API, and is not related to packages or bundles. 2.3.8. Extension Extensions enable cluster administrators to extend capabilities for users on their OpenShift Dedicated cluster. Extensions are managed by Operator Lifecycle Manager (OLM) v1. The ClusterExtension API streamlines management of installed extensions, which includes Operators via the registry+v1 bundle format, by consolidating user-facing APIs into a single object. Administrators and SREs can use the API to automate processes and define desired states by using GitOps principles. 2.3.9. Index image In the bundle format, an index image refers to an image of a database (a database snapshot) that contains information about Operator bundles including CSVs and CRDs of all versions. This index can host a history of Operators on a cluster and be maintained by adding or removing Operators using the opm CLI tool. 2.3.10. Install plan An install plan is a calculated list of resources to be created to automatically install or upgrade a CSV. 2.3.11. Multitenancy A tenant in OpenShift Dedicated is a user or group of users that share common access and privileges for a set of deployed workloads, typically represented by a namespace or project. You can use tenants to provide a level of isolation between different groups or teams. When a cluster is shared by multiple users or groups, it is considered a multitenant cluster. 2.3.12. Operator Operators are a method of packaging, deploying, and managing a Kubernetes application. A Kubernetes application is an app that is both deployed on Kubernetes and managed using the Kubernetes APIs and kubectl or oc tooling. In Operator Lifecycle Manager (OLM) v1, the ClusterExtension API streamlines management of installed extensions, which includes Operators via the registry+v1 bundle format. 2.3.13. Operator group An Operator group configures all Operators deployed in the same namespace as the OperatorGroup object to watch for their CR in a list of namespaces or cluster-wide. 2.3.14. Package In the bundle format, a package is a directory that encloses all released history of an Operator with each version. A released version of an Operator is described in a CSV manifest alongside the CRDs. 2.3.15. Registry A registry is a database that stores bundle images of Operators, each with all of its latest and historical versions in all channels. 2.3.16. Subscription A subscription keeps CSVs up to date by tracking a channel in a package. 2.3.17. Update graph An update graph links versions of CSVs together, similar to the update graph of any other packaged software. Operators can be installed sequentially, or certain versions can be skipped. The update graph is expected to grow only at the head with newer versions being added. Also known as update edges or update paths . 2.4. Operator Lifecycle Manager (OLM) 2.4.1. Operator Lifecycle Manager concepts and resources This guide provides an overview of the concepts that drive Operator Lifecycle Manager (OLM) in OpenShift Dedicated. 2.4.1.1. What is Operator Lifecycle Manager (OLM) Classic? Operator Lifecycle Manager (OLM) Classic helps users install, update, and manage the lifecycle of Kubernetes native applications (Operators) and their associated services running across their OpenShift Dedicated clusters. It is part of the Operator Framework , an open source toolkit designed to manage Operators in an effective, automated, and scalable way. Figure 2.2. OLM (Classic) workflow OLM runs by default in OpenShift Dedicated 4, which aids administrators with the dedicated-admin role in installing, upgrading, and granting access to Operators running on their cluster. The OpenShift Dedicated web console provides management screens for dedicated-admin administrators to install Operators, as well as grant specific projects access to use the catalog of Operators available on the cluster. For developers, a self-service experience allows provisioning and configuring instances of databases, monitoring, and big data services without having to be subject matter experts, because the Operator has that knowledge baked into it. 2.4.1.2. OLM resources The following custom resource definitions (CRDs) are defined and managed by Operator Lifecycle Manager (OLM): Table 2.2. CRDs managed by OLM and Catalog Operators Resource Short name Description ClusterServiceVersion (CSV) csv Application metadata. For example: name, version, icon, required resources. CatalogSource catsrc A repository of CSVs, CRDs, and packages that define an application. Subscription sub Keeps CSVs up to date by tracking a channel in a package. InstallPlan ip Calculated list of resources to be created to automatically install or upgrade a CSV. OperatorGroup og Configures all Operators deployed in the same namespace as the OperatorGroup object to watch for their custom resource (CR) in a list of namespaces or cluster-wide. OperatorConditions - Creates a communication channel between OLM and an Operator it manages. Operators can write to the Status.Conditions array to communicate complex states to OLM. 2.4.1.2.1. Cluster service version A cluster service version (CSV) represents a specific version of a running Operator on an OpenShift Dedicated cluster. It is a YAML manifest created from Operator metadata that assists Operator Lifecycle Manager (OLM) in running the Operator in the cluster. OLM requires this metadata about an Operator to ensure that it can be kept running safely on a cluster, and to provide information about how updates should be applied as new versions of the Operator are published. This is similar to packaging software for a traditional operating system; think of the packaging step for OLM as the stage at which you make your rpm , deb , or apk bundle. A CSV includes the metadata that accompanies an Operator container image, used to populate user interfaces with information such as its name, version, description, labels, repository link, and logo. A CSV is also a source of technical information required to run the Operator, such as which custom resources (CRs) it manages or depends on, RBAC rules, cluster requirements, and install strategies. This information tells OLM how to create required resources and set up the Operator as a deployment. 2.4.1.2.2. Catalog source A catalog source represents a store of metadata, typically by referencing an index image stored in a container registry. Operator Lifecycle Manager (OLM) queries catalog sources to discover and install Operators and their dependencies. OperatorHub in the OpenShift Dedicated web console also displays the Operators provided by catalog sources. Tip Cluster administrators can view the full list of Operators provided by an enabled catalog source on a cluster by using the Administration Cluster Settings Configuration OperatorHub page in the web console. The spec of a CatalogSource object indicates how to construct a pod or how to communicate with a service that serves the Operator Registry gRPC API. Example 2.9. Example CatalogSource object \ufeffapiVersion: operators.coreos.com/v1alpha1 kind: CatalogSource metadata: generation: 1 name: example-catalog 1 namespace: openshift-marketplace 2 annotations: olm.catalogImageTemplate: 3 "quay.io/example-org/example-catalog:v{kube_major_version}.{kube_minor_version}.{kube_patch_version}" spec: displayName: Example Catalog 4 image: quay.io/example-org/example-catalog:v1 5 priority: -400 6 publisher: Example Org sourceType: grpc 7 grpcPodConfig: securityContextConfig: <security_mode> 8 nodeSelector: 9 custom_label: <label> priorityClassName: system-cluster-critical 10 tolerations: 11 - key: "key1" operator: "Equal" value: "value1" effect: "NoSchedule" updateStrategy: registryPoll: 12 interval: 30m0s status: connectionState: address: example-catalog.openshift-marketplace.svc:50051 lastConnect: 2021-08-26T18:14:31Z lastObservedState: READY 13 latestImageRegistryPoll: 2021-08-26T18:46:25Z 14 registryService: 15 createdAt: 2021-08-26T16:16:37Z port: 50051 protocol: grpc serviceName: example-catalog serviceNamespace: openshift-marketplace 1 Name for the CatalogSource object. This value is also used as part of the name for the related pod that is created in the requested namespace. 2 Namespace to create the catalog in. To make the catalog available cluster-wide in all namespaces, set this value to openshift-marketplace . The default Red Hat-provided catalog sources also use the openshift-marketplace namespace. Otherwise, set the value to a specific namespace to make the Operator only available in that namespace. 3 Optional: To avoid cluster upgrades potentially leaving Operator installations in an unsupported state or without a continued update path, you can enable automatically changing your Operator catalog's index image version as part of cluster upgrades. Set the olm.catalogImageTemplate annotation to your index image name and use one or more of the Kubernetes cluster version variables as shown when constructing the template for the image tag. The annotation overwrites the spec.image field at run time. See the "Image template for custom catalog sources" section for more details. 4 Display name for the catalog in the web console and CLI. 5 Index image for the catalog. Optionally, can be omitted when using the olm.catalogImageTemplate annotation, which sets the pull spec at run time. 6 Weight for the catalog source. OLM uses the weight for prioritization during dependency resolution. A higher weight indicates the catalog is preferred over lower-weighted catalogs. 7 Source types include the following: grpc with an image reference: OLM pulls the image and runs the pod, which is expected to serve a compliant API. grpc with an address field: OLM attempts to contact the gRPC API at the given address. This should not be used in most cases. configmap : OLM parses config map data and runs a pod that can serve the gRPC API over it. 8 Specify the value of legacy or restricted . If the field is not set, the default value is legacy . In a future OpenShift Dedicated release, it is planned that the default value will be restricted . If your catalog cannot run with restricted permissions, it is recommended that you manually set this field to legacy . 9 Optional: For grpc type catalog sources, overrides the default node selector for the pod serving the content in spec.image , if defined. 10 Optional: For grpc type catalog sources, overrides the default priority class name for the pod serving the content in spec.image , if defined. Kubernetes provides system-cluster-critical and system-node-critical priority classes by default. Setting the field to empty ( "" ) assigns the pod the default priority. Other priority classes can be defined manually. 11 Optional: For grpc type catalog sources, overrides the default tolerations for the pod serving the content in spec.image , if defined. 12 Automatically check for new versions at a given interval to stay up-to-date. 13 Last observed state of the catalog connection. For example: READY : A connection is successfully established. CONNECTING : A connection is attempting to establish. TRANSIENT_FAILURE : A temporary problem has occurred while attempting to establish a connection, such as a timeout. The state will eventually switch back to CONNECTING and try again. See States of Connectivity in the gRPC documentation for more details. 14 Latest time the container registry storing the catalog image was polled to ensure the image is up-to-date. 15 Status information for the catalog's Operator Registry service. Referencing the name of a CatalogSource object in a subscription instructs OLM where to search to find a requested Operator: Example 2.10. Example Subscription object referencing a catalog source apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: example-operator namespace: example-namespace spec: channel: stable name: example-operator source: example-catalog sourceNamespace: openshift-marketplace Additional resources Understanding OperatorHub Red Hat-provided Operator catalogs Adding a catalog source to a cluster Catalog priority Viewing Operator catalog source status by using the CLI Catalog source pod scheduling 2.4.1.2.2.1. Image template for custom catalog sources Operator compatibility with the underlying cluster can be expressed by a catalog source in various ways. One way, which is used for the default Red Hat-provided catalog sources, is to identify image tags for index images that are specifically created for a particular platform release, for example OpenShift Dedicated 4. During a cluster upgrade, the index image tag for the default Red Hat-provided catalog sources are updated automatically by the Cluster Version Operator (CVO) so that Operator Lifecycle Manager (OLM) pulls the updated version of the catalog. For example during an upgrade from OpenShift Dedicated 4.17 to 4, the spec.image field in the CatalogSource object for the redhat-operators catalog is updated from: registry.redhat.io/redhat/redhat-operator-index:v4.18 to: registry.redhat.io/redhat/redhat-operator-index:v4.18 However, the CVO does not automatically update image tags for custom catalogs. To ensure users are left with a compatible and supported Operator installation after a cluster upgrade, custom catalogs should also be kept updated to reference an updated index image. Starting in OpenShift Dedicated 4.9, cluster administrators can add the olm.catalogImageTemplate annotation in the CatalogSource object for custom catalogs to an image reference that includes a template. The following Kubernetes version variables are supported for use in the template: kube_major_version kube_minor_version kube_patch_version Note You must specify the Kubernetes cluster version and not an OpenShift Dedicated cluster version, as the latter is not currently available for templating. Provided that you have created and pushed an index image with a tag specifying the updated Kubernetes version, setting this annotation enables the index image versions in custom catalogs to be automatically changed after a cluster upgrade. The annotation value is used to set or update the image reference in the spec.image field of the CatalogSource object. This helps avoid cluster upgrades leaving Operator installations in unsupported states or without a continued update path. Important You must ensure that the index image with the updated tag, in whichever registry it is stored in, is accessible by the cluster at the time of the cluster upgrade. Example 2.11. Example catalog source with an image template apiVersion: operators.coreos.com/v1alpha1 kind: CatalogSource metadata: generation: 1 name: example-catalog namespace: openshift-marketplace annotations: olm.catalogImageTemplate: "quay.io/example-org/example-catalog:v{kube_major_version}.{kube_minor_version}" spec: displayName: Example Catalog image: quay.io/example-org/example-catalog:v1.31 priority: -400 publisher: Example Org Note If the spec.image field and the olm.catalogImageTemplate annotation are both set, the spec.image field is overwritten by the resolved value from the annotation. If the annotation does not resolve to a usable pull spec, the catalog source falls back to the set spec.image value. If the spec.image field is not set and the annotation does not resolve to a usable pull spec, OLM stops reconciliation of the catalog source and sets it into a human-readable error condition. For an OpenShift Dedicated 4 cluster, which uses Kubernetes 1.31, the olm.catalogImageTemplate annotation in the preceding example resolves to the following image reference: quay.io/example-org/example-catalog:v1.31 For future releases of OpenShift Dedicated, you can create updated index images for your custom catalogs that target the later Kubernetes version that is used by the later OpenShift Dedicated version. With the olm.catalogImageTemplate annotation set before the upgrade, upgrading the cluster to the later OpenShift Dedicated version would then automatically update the catalog's index image as well. 2.4.1.2.2.2. Catalog health requirements Operator catalogs on a cluster are interchangeable from the perspective of installation resolution; a Subscription object might reference a specific catalog, but dependencies are resolved using all catalogs on the cluster. For example, if Catalog A is unhealthy, a subscription referencing Catalog A could resolve a dependency in Catalog B, which the cluster administrator might not have been expecting, because B normally had a lower catalog priority than A. As a result, OLM requires that all catalogs with a given global namespace (for example, the default openshift-marketplace namespace or a custom global namespace) are healthy. When a catalog is unhealthy, all Operator installation or update operations within its shared global namespace will fail with a CatalogSourcesUnhealthy condition. If these operations were permitted in an unhealthy state, OLM might make resolution and installation decisions that were unexpected to the cluster administrator. As a cluster administrator, if you observe an unhealthy catalog and want to consider the catalog as invalid and resume Operator installations, see the "Removing custom catalogs" or "Disabling the default OperatorHub catalog sources" sections for information about removing the unhealthy catalog. 2.4.1.2.3. Subscription A subscription , defined by a Subscription object, represents an intention to install an Operator. It is the custom resource that relates an Operator to a catalog source. Subscriptions describe which channel of an Operator package to subscribe to, and whether to perform updates automatically or manually. If set to automatic, the subscription ensures Operator Lifecycle Manager (OLM) manages and upgrades the Operator to ensure that the latest version is always running in the cluster. Example Subscription object apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: example-operator namespace: example-namespace spec: channel: stable name: example-operator source: example-catalog sourceNamespace: openshift-marketplace This Subscription object defines the name and namespace of the Operator, as well as the catalog from which the Operator data can be found. The channel, such as alpha , beta , or stable , helps determine which Operator stream should be installed from the catalog source. The names of channels in a subscription can differ between Operators, but the naming scheme should follow a common convention within a given Operator. For example, channel names might follow a minor release update stream for the application provided by the Operator ( 1.2 , 1.3 ) or a release frequency ( stable , fast ). In addition to being easily visible from the OpenShift Dedicated web console, it is possible to identify when there is a newer version of an Operator available by inspecting the status of the related subscription. The value associated with the currentCSV field is the newest version that is known to OLM, and installedCSV is the version that is installed on the cluster. Additional resources Viewing Operator subscription status by using the CLI 2.4.1.2.4. Install plan An install plan , defined by an InstallPlan object, describes a set of resources that Operator Lifecycle Manager (OLM) creates to install or upgrade to a specific version of an Operator. The version is defined by a cluster service version (CSV). To install an Operator, a cluster administrator, or a user who has been granted Operator installation permissions, must first create a Subscription object. A subscription represents the intent to subscribe to a stream of available versions of an Operator from a catalog source. The subscription then creates an InstallPlan object to facilitate the installation of the resources for the Operator. The install plan must then be approved according to one of the following approval strategies: If the subscription's spec.installPlanApproval field is set to Automatic , the install plan is approved automatically. If the subscription's spec.installPlanApproval field is set to Manual , the install plan must be manually approved by a cluster administrator or user with proper permissions. After the install plan is approved, OLM creates the specified resources and installs the Operator in the namespace that is specified by the subscription. Example 2.12. Example InstallPlan object apiVersion: operators.coreos.com/v1alpha1 kind: InstallPlan metadata: name: install-abcde namespace: operators spec: approval: Automatic approved: true clusterServiceVersionNames: - my-operator.v1.0.1 generation: 1 status: ... catalogSources: [] conditions: - lastTransitionTime: '2021-01-01T20:17:27Z' lastUpdateTime: '2021-01-01T20:17:27Z' status: 'True' type: Installed phase: Complete plan: - resolving: my-operator.v1.0.1 resource: group: operators.coreos.com kind: ClusterServiceVersion manifest: >- ... name: my-operator.v1.0.1 sourceName: redhat-operators sourceNamespace: openshift-marketplace version: v1alpha1 status: Created - resolving: my-operator.v1.0.1 resource: group: apiextensions.k8s.io kind: CustomResourceDefinition manifest: >- ... name: webservers.web.servers.org sourceName: redhat-operators sourceNamespace: openshift-marketplace version: v1beta1 status: Created - resolving: my-operator.v1.0.1 resource: group: '' kind: ServiceAccount manifest: >- ... name: my-operator sourceName: redhat-operators sourceNamespace: openshift-marketplace version: v1 status: Created - resolving: my-operator.v1.0.1 resource: group: rbac.authorization.k8s.io kind: Role manifest: >- ... name: my-operator.v1.0.1-my-operator-6d7cbc6f57 sourceName: redhat-operators sourceNamespace: openshift-marketplace version: v1 status: Created - resolving: my-operator.v1.0.1 resource: group: rbac.authorization.k8s.io kind: RoleBinding manifest: >- ... name: my-operator.v1.0.1-my-operator-6d7cbc6f57 sourceName: redhat-operators sourceNamespace: openshift-marketplace version: v1 status: Created ... 2.4.1.2.5. Operator groups An Operator group , defined by the OperatorGroup resource, provides multitenant configuration to OLM-installed Operators. An Operator group selects target namespaces in which to generate required RBAC access for its member Operators. The set of target namespaces is provided by a comma-delimited string stored in the olm.targetNamespaces annotation of a cluster service version (CSV). This annotation is applied to the CSV instances of member Operators and is projected into their deployments. Additional resources Operator groups 2.4.1.2.6. Operator conditions As part of its role in managing the lifecycle of an Operator, Operator Lifecycle Manager (OLM) infers the state of an Operator from the state of Kubernetes resources that define the Operator. While this approach provides some level of assurance that an Operator is in a given state, there are many instances where an Operator might need to communicate information to OLM that could not be inferred otherwise. This information can then be used by OLM to better manage the lifecycle of the Operator. OLM provides a custom resource definition (CRD) called OperatorCondition that allows Operators to communicate conditions to OLM. There are a set of supported conditions that influence management of the Operator by OLM when present in the Spec.Conditions array of an OperatorCondition resource. Note By default, the Spec.Conditions array is not present in an OperatorCondition object until it is either added by a user or as a result of custom Operator logic. Additional resources Operator conditions 2.4.2. Operator Lifecycle Manager architecture This guide outlines the component architecture of Operator Lifecycle Manager (OLM) in OpenShift Dedicated. 2.4.2.1. Component responsibilities Operator Lifecycle Manager (OLM) is composed of two Operators: the OLM Operator and the Catalog Operator. The OLM and Catalog Operators are responsible for managing the custom resource definitions (CRDs) that are the basis for the OLM framework: Table 2.3. CRDs managed by OLM and Catalog Operators Resource Short name Owner Description ClusterServiceVersion (CSV) csv OLM Application metadata: name, version, icon, required resources, installation, and so on. InstallPlan ip Catalog Calculated list of resources to be created to automatically install or upgrade a CSV. CatalogSource catsrc Catalog A repository of CSVs, CRDs, and packages that define an application. Subscription sub Catalog Used to keep CSVs up to date by tracking a channel in a package. OperatorGroup og OLM Configures all Operators deployed in the same namespace as the OperatorGroup object to watch for their custom resource (CR) in a list of namespaces or cluster-wide. Each of these Operators is also responsible for creating the following resources: Table 2.4. Resources created by OLM and Catalog Operators Resource Owner Deployments OLM ServiceAccounts (Cluster)Roles (Cluster)RoleBindings CustomResourceDefinitions (CRDs) Catalog ClusterServiceVersions 2.4.2.2. OLM Operator The OLM Operator is responsible for deploying applications defined by CSV resources after the required resources specified in the CSV are present in the cluster. The OLM Operator is not concerned with the creation of the required resources; you can choose to manually create these resources using the CLI or using the Catalog Operator. This separation of concern allows users incremental buy-in in terms of how much of the OLM framework they choose to leverage for their application. The OLM Operator uses the following workflow: Watch for cluster service versions (CSVs) in a namespace and check that requirements are met. If requirements are met, run the install strategy for the CSV. Note A CSV must be an active member of an Operator group for the install strategy to run. 2.4.2.3. Catalog Operator The Catalog Operator is responsible for resolving and installing cluster service versions (CSVs) and the required resources they specify. It is also responsible for watching catalog sources for updates to packages in channels and upgrading them, automatically if desired, to the latest available versions. To track a package in a channel, you can create a Subscription object configuring the desired package, channel, and the CatalogSource object you want to use for pulling updates. When updates are found, an appropriate InstallPlan object is written into the namespace on behalf of the user. The Catalog Operator uses the following workflow: Connect to each catalog source in the cluster. Watch for unresolved install plans created by a user, and if found: Find the CSV matching the name requested and add the CSV as a resolved resource. For each managed or required CRD, add the CRD as a resolved resource. For each required CRD, find the CSV that manages it. Watch for resolved install plans and create all of the discovered resources for it, if approved by a user or automatically. Watch for catalog sources and subscriptions and create install plans based on them. 2.4.2.4. Catalog Registry The Catalog Registry stores CSVs and CRDs for creation in a cluster and stores metadata about packages and channels. A package manifest is an entry in the Catalog Registry that associates a package identity with sets of CSVs. Within a package, channels point to a particular CSV. Because CSVs explicitly reference the CSV that they replace, a package manifest provides the Catalog Operator with all of the information that is required to update a CSV to the latest version in a channel, stepping through each intermediate version. 2.4.3. Operator Lifecycle Manager workflow This guide outlines the workflow of Operator Lifecycle Manager (OLM) in OpenShift Dedicated. 2.4.3.1. Operator installation and upgrade workflow in OLM In the Operator Lifecycle Manager (OLM) ecosystem, the following resources are used to resolve Operator installations and upgrades: ClusterServiceVersion (CSV) CatalogSource Subscription Operator metadata, defined in CSVs, can be stored in a collection called a catalog source. OLM uses catalog sources, which use the Operator Registry API , to query for available Operators as well as upgrades for installed Operators. Figure 2.3. Catalog source overview Within a catalog source, Operators are organized into packages and streams of updates called channels , which should be a familiar update pattern from OpenShift Dedicated or other software on a continuous release cycle like web browsers. Figure 2.4. Packages and channels in a Catalog source A user indicates a particular package and channel in a particular catalog source in a subscription , for example an etcd package and its alpha channel. If a subscription is made to a package that has not yet been installed in the namespace, the latest Operator for that package is installed. Note OLM deliberately avoids version comparisons, so the "latest" or "newest" Operator available from a given catalog channel package path does not necessarily need to be the highest version number. It should be thought of more as the head reference of a channel, similar to a Git repository. Each CSV has a replaces parameter that indicates which Operator it replaces. This builds a graph of CSVs that can be queried by OLM, and updates can be shared between channels. Channels can be thought of as entry points into the graph of updates: Figure 2.5. OLM graph of available channel updates Example channels in a package packageName: example channels: - name: alpha currentCSV: example.v0.1.2 - name: beta currentCSV: example.v0.1.3 defaultChannel: alpha For OLM to successfully query for updates, given a catalog source, package, channel, and CSV, a catalog must be able to return, unambiguously and deterministically, a single CSV that replaces the input CSV. 2.4.3.1.1. Example upgrade path For an example upgrade scenario, consider an installed Operator corresponding to CSV version 0.1.1 . OLM queries the catalog source and detects an upgrade in the subscribed channel with new CSV version 0.1.3 that replaces an older but not-installed CSV version 0.1.2 , which in turn replaces the older and installed CSV version 0.1.1 . OLM walks back from the channel head to versions via the replaces field specified in the CSVs to determine the upgrade path 0.1.3 0.1.2 0.1.1 ; the direction of the arrow indicates that the former replaces the latter. OLM upgrades the Operator one version at the time until it reaches the channel head. For this given scenario, OLM installs Operator version 0.1.2 to replace the existing Operator version 0.1.1 . Then, it installs Operator version 0.1.3 to replace the previously installed Operator version 0.1.2 . At this point, the installed operator version 0.1.3 matches the channel head and the upgrade is completed. 2.4.3.1.2. Skipping upgrades The basic path for upgrades in OLM is: A catalog source is updated with one or more updates to an Operator. OLM traverses every version of the Operator until reaching the latest version the catalog source contains. However, sometimes this is not a safe operation to perform. There will be cases where a published version of an Operator should never be installed on a cluster if it has not already, for example because a version introduces a serious vulnerability. In those cases, OLM must consider two cluster states and provide an update graph that supports both: The "bad" intermediate Operator has been seen by the cluster and installed. The "bad" intermediate Operator has not yet been installed onto the cluster. By shipping a new catalog and adding a skipped release, OLM is ensured that it can always get a single unique update regardless of the cluster state and whether it has seen the bad update yet. Example CSV with skipped release apiVersion: operators.coreos.com/v1alpha1 kind: ClusterServiceVersion metadata: name: etcdoperator.v0.9.2 namespace: placeholder annotations: spec: displayName: etcd description: Etcd Operator replaces: etcdoperator.v0.9.0 skips: - etcdoperator.v0.9.1 Consider the following example of Old CatalogSource and New CatalogSource . Figure 2.6. Skipping updates This graph maintains that: Any Operator found in Old CatalogSource has a single replacement in New CatalogSource . Any Operator found in New CatalogSource has a single replacement in New CatalogSource . If the bad update has not yet been installed, it will never be. 2.4.3.1.3. Replacing multiple Operators Creating New CatalogSource as described requires publishing CSVs that replace one Operator, but can skip several. This can be accomplished using the skipRange annotation: olm.skipRange: <semver_range> where <semver_range> has the version range format supported by the semver library . When searching catalogs for updates, if the head of a channel has a skipRange annotation and the currently installed Operator has a version field that falls in the range, OLM updates to the latest entry in the channel. The order of precedence is: Channel head in the source specified by sourceName on the subscription, if the other criteria for skipping are met. The Operator that replaces the current one, in the source specified by sourceName . Channel head in another source that is visible to the subscription, if the other criteria for skipping are met. The Operator that replaces the current one in any source visible to the subscription. Example CSV with skipRange apiVersion: operators.coreos.com/v1alpha1 kind: ClusterServiceVersion metadata: name: elasticsearch-operator.v4.1.2 namespace: <namespace> annotations: olm.skipRange: '>=4.1.0 <4.1.2' 2.4.3.1.4. Z-stream support A z-stream , or patch release, must replace all z-stream releases for the same minor version. OLM does not consider major, minor, or patch versions, it just needs to build the correct graph in a catalog. In other words, OLM must be able to take a graph as in Old CatalogSource and, similar to before, generate a graph as in New CatalogSource : Figure 2.7. Replacing several Operators This graph maintains that: Any Operator found in Old CatalogSource has a single replacement in New CatalogSource . Any Operator found in New CatalogSource has a single replacement in New CatalogSource . Any z-stream release in Old CatalogSource will update to the latest z-stream release in New CatalogSource . Unavailable releases can be considered "virtual" graph nodes; their content does not need to exist, the registry just needs to respond as if the graph looks like this. 2.4.4. Operator Lifecycle Manager dependency resolution This guide outlines dependency resolution and custom resource definition (CRD) upgrade lifecycles with Operator Lifecycle Manager (OLM) in OpenShift Dedicated. 2.4.4.1. About dependency resolution Operator Lifecycle Manager (OLM) manages the dependency resolution and upgrade lifecycle of running Operators. In many ways, the problems OLM faces are similar to other system or language package managers, such as yum and rpm . However, there is one constraint that similar systems do not generally have that OLM does: because Operators are always running, OLM attempts to ensure that you are never left with a set of Operators that do not work with each other. As a result, OLM must never create the following scenarios: Install a set of Operators that require APIs that cannot be provided Update an Operator in a way that breaks another that depends upon it This is made possible with two types of data: Properties Typed metadata about the Operator that constitutes the public interface for it in the dependency resolver. Examples include the group/version/kind (GVK) of the APIs provided by the Operator and the semantic version (semver) of the Operator. Constraints or dependencies An Operator's requirements that should be satisfied by other Operators that might or might not have already been installed on the target cluster. These act as queries or filters over all available Operators and constrain the selection during dependency resolution and installation. Examples include requiring a specific API to be available on the cluster or expecting a particular Operator with a particular version to be installed. OLM converts these properties and constraints into a system of Boolean formulas and passes them to a SAT solver, a program that establishes Boolean satisfiability, which does the work of determining what Operators should be installed. 2.4.4.2. Operator properties All Operators in a catalog have the following properties: olm.package Includes the name of the package and the version of the Operator olm.gvk A single property for each provided API from the cluster service version (CSV) Additional properties can also be directly declared by an Operator author by including a properties.yaml file in the metadata/ directory of the Operator bundle. Example arbitrary property properties: - type: olm.kubeversion value: version: "1.16.0" 2.4.4.2.1. Arbitrary properties Operator authors can declare arbitrary properties in a properties.yaml file in the metadata/ directory of the Operator bundle. These properties are translated into a map data structure that is used as an input to the Operator Lifecycle Manager (OLM) resolver at runtime. These properties are opaque to the resolver as it does not understand the properties, but it can evaluate the generic constraints against those properties to determine if the constraints can be satisfied given the properties list. Example arbitrary properties properties: - property: type: color value: red - property: type: shape value: square - property: type: olm.gvk value: group: olm.coreos.io version: v1alpha1 kind: myresource This structure can be used to construct a Common Expression Language (CEL) expression for generic constraints. Additional resources Common Expression Language (CEL) constraints 2.4.4.3. Operator dependencies The dependencies of an Operator are listed in a dependencies.yaml file in the metadata/ folder of a bundle. This file is optional and currently only used to specify explicit Operator-version dependencies. The dependency list contains a type field for each item to specify what kind of dependency this is. The following types of Operator dependencies are supported: olm.package This type indicates a dependency for a specific Operator version. The dependency information must include the package name and the version of the package in semver format. For example, you can specify an exact version such as 0.5.2 or a range of versions such as >0.5.1 . olm.gvk With this type, the author can specify a dependency with group/version/kind (GVK) information, similar to existing CRD and API-based usage in a CSV. This is a path to enable Operator authors to consolidate all dependencies, API or explicit versions, to be in the same place. olm.constraint This type declares generic constraints on arbitrary Operator properties. In the following example, dependencies are specified for a Prometheus Operator and etcd CRDs: Example dependencies.yaml file dependencies: - type: olm.package value: packageName: prometheus version: ">0.27.0" - type: olm.gvk value: group: etcd.database.coreos.com kind: EtcdCluster version: v1beta2 2.4.4.4. Generic constraints An olm.constraint property declares a dependency constraint of a particular type, differentiating non-constraint and constraint properties. Its value field is an object containing a failureMessage field holding a string-representation of the constraint message. This message is surfaced as an informative comment to users if the constraint is not satisfiable at runtime. The following keys denote the available constraint types: gvk Type whose value and interpretation is identical to the olm.gvk type package Type whose value and interpretation is identical to the olm.package type cel A Common Expression Language (CEL) expression evaluated at runtime by the Operator Lifecycle Manager (OLM) resolver over arbitrary bundle properties and cluster information all , any , not Conjunction, disjunction, and negation constraints, respectively, containing one or more concrete constraints, such as gvk or a nested compound constraint 2.4.4.4.1. Common Expression Language (CEL) constraints The cel constraint type supports Common Expression Language (CEL) as the expression language. The cel struct has a rule field which contains the CEL expression string that is evaluated against Operator properties at runtime to determine if the Operator satisfies the constraint. Example cel constraint type: olm.constraint value: failureMessage: 'require to have "certified"' cel: rule: 'properties.exists(p, p.type == "certified")' The CEL syntax supports a wide range of logical operators, such as AND and OR . As a result, a single CEL expression can have multiple rules for multiple conditions that are linked together by these logical operators. These rules are evaluated against a dataset of multiple different properties from a bundle or any given source, and the output is solved into a single bundle or Operator that satisfies all of those rules within a single constraint. Example cel constraint with multiple rules type: olm.constraint value: failureMessage: 'require to have "certified" and "stable" properties' cel: rule: 'properties.exists(p, p.type == "certified") && properties.exists(p, p.type == "stable")' 2.4.4.4.2. Compound constraints (all, any, not) Compound constraint types are evaluated following their logical definitions. The following is an example of a conjunctive constraint ( all ) of two packages and one GVK. That is, they must all be satisfied by installed bundles: Example all constraint schema: olm.bundle name: red.v1.0.0 properties: - type: olm.constraint value: failureMessage: All are required for Red because... all: constraints: - failureMessage: Package blue is needed for... package: name: blue versionRange: '>=1.0.0' - failureMessage: GVK Green/v1 is needed for... gvk: group: greens.example.com version: v1 kind: Green The following is an example of a disjunctive constraint ( any ) of three versions of the same GVK. That is, at least one must be satisfied by installed bundles: Example any constraint schema: olm.bundle name: red.v1.0.0 properties: - type: olm.constraint value: failureMessage: Any are required for Red because... any: constraints: - gvk: group: blues.example.com version: v1beta1 kind: Blue - gvk: group: blues.example.com version: v1beta2 kind: Blue - gvk: group: blues.example.com version: v1 kind: Blue The following is an example of a negation constraint ( not ) of one version of a GVK. That is, this GVK cannot be provided by any bundle in the result set: Example not constraint schema: olm.bundle name: red.v1.0.0 properties: - type: olm.constraint value: all: constraints: - failureMessage: Package blue is needed for... package: name: blue versionRange: '>=1.0.0' - failureMessage: Cannot be required for Red because... not: constraints: - gvk: group: greens.example.com version: v1alpha1 kind: greens The negation semantics might appear unclear in the not constraint context. To clarify, the negation is really instructing the resolver to remove any possible solution that includes a particular GVK, package at a version, or satisfies some child compound constraint from the result set. As a corollary, the not compound constraint should only be used within all or any constraints, because negating without first selecting a possible set of dependencies does not make sense. 2.4.4.4.3. Nested compound constraints A nested compound constraint, one that contains at least one child compound constraint along with zero or more simple constraints, is evaluated from the bottom up following the procedures for each previously described constraint type. The following is an example of a disjunction of conjunctions, where one, the other, or both can satisfy the constraint: Example nested compound constraint schema: olm.bundle name: red.v1.0.0 properties: - type: olm.constraint value: failureMessage: Required for Red because... any: constraints: - all: constraints: - package: name: blue versionRange: '>=1.0.0' - gvk: group: blues.example.com version: v1 kind: Blue - all: constraints: - package: name: blue versionRange: '<1.0.0' - gvk: group: blues.example.com version: v1beta1 kind: Blue Note The maximum raw size of an olm.constraint type is 64KB to limit resource exhaustion attacks. 2.4.4.5. Dependency preferences There can be many options that equally satisfy a dependency of an Operator. The dependency resolver in Operator Lifecycle Manager (OLM) determines which option best fits the requirements of the requested Operator. As an Operator author or user, it can be important to understand how these choices are made so that dependency resolution is clear. 2.4.4.5.1. Catalog priority On OpenShift Dedicated cluster, OLM reads catalog sources to know which Operators are available for installation. Example CatalogSource object apiVersion: "operators.coreos.com/v1alpha1" kind: "CatalogSource" metadata: name: "my-operators" namespace: "operators" spec: sourceType: grpc grpcPodConfig: securityContextConfig: <security_mode> 1 image: example.com/my/operator-index:v1 displayName: "My Operators" priority: 100 1 Specify the value of legacy or restricted . If the field is not set, the default value is legacy . In a future OpenShift Dedicated release, it is planned that the default value will be restricted . If your catalog cannot run with restricted permissions, it is recommended that you manually set this field to legacy . A CatalogSource object has a priority field, which is used by the resolver to know how to prefer options for a dependency. There are two rules that govern catalog preference: Options in higher-priority catalogs are preferred to options in lower-priority catalogs. Options in the same catalog as the dependent are preferred to any other catalogs. 2.4.4.5.2. Channel ordering An Operator package in a catalog is a collection of update channels that a user can subscribe to in an OpenShift Dedicated cluster. Channels can be used to provide a particular stream of updates for a minor release ( 1.2 , 1.3 ) or a release frequency ( stable , fast ). It is likely that a dependency might be satisfied by Operators in the same package, but different channels. For example, version 1.2 of an Operator might exist in both the stable and fast channels. Each package has a default channel, which is always preferred to non-default channels. If no option in the default channel can satisfy a dependency, options are considered from the remaining channels in lexicographic order of the channel name. 2.4.4.5.3. Order within a channel There are almost always multiple options to satisfy a dependency within a single channel. For example, Operators in one package and channel provide the same set of APIs. When a user creates a subscription, they indicate which channel to receive updates from. This immediately reduces the search to just that one channel. But within the channel, it is likely that many Operators satisfy a dependency. Within a channel, newer Operators that are higher up in the update graph are preferred. If the head of a channel satisfies a dependency, it will be tried first. 2.4.4.5.4. Other constraints In addition to the constraints supplied by package dependencies, OLM includes additional constraints to represent the desired user state and enforce resolution invariants. 2.4.4.5.4.1. Subscription constraint A subscription constraint filters the set of Operators that can satisfy a subscription. Subscriptions are user-supplied constraints for the dependency resolver. They declare the intent to either install a new Operator if it is not already on the cluster, or to keep an existing Operator updated. 2.4.4.5.4.2. Package constraint Within a namespace, no two Operators may come from the same package. 2.4.4.5.5. Additional resources Catalog health requirements 2.4.4.6. CRD upgrades OLM upgrades a custom resource definition (CRD) immediately if it is owned by a singular cluster service version (CSV). If a CRD is owned by multiple CSVs, then the CRD is upgraded when it has satisfied all of the following backward compatible conditions: All existing serving versions in the current CRD are present in the new CRD. All existing instances, or custom resources, that are associated with the serving versions of the CRD are valid when validated against the validation schema of the new CRD. Additional resources Adding a new CRD version Deprecating or removing a CRD version 2.4.4.7. Dependency best practices When specifying dependencies, there are best practices you should consider. Depend on APIs or a specific version range of Operators Operators can add or remove APIs at any time; always specify an olm.gvk dependency on any APIs your Operators requires. The exception to this is if you are specifying olm.package constraints instead. Set a minimum version The Kubernetes documentation on API changes describes what changes are allowed for Kubernetes-style Operators. These versioning conventions allow an Operator to update an API without bumping the API version, as long as the API is backwards-compatible. For Operator dependencies, this means that knowing the API version of a dependency might not be enough to ensure the dependent Operator works as intended. For example: TestOperator v1.0.0 provides v1alpha1 API version of the MyObject resource. TestOperator v1.0.1 adds a new field spec.newfield to MyObject , but still at v1alpha1. Your Operator might require the ability to write spec.newfield into the MyObject resource. An olm.gvk constraint alone is not enough for OLM to determine that you need TestOperator v1.0.1 and not TestOperator v1.0.0. Whenever possible, if a specific Operator that provides an API is known ahead of time, specify an additional olm.package constraint to set a minimum. Omit a maximum version or allow a very wide range Because Operators provide cluster-scoped resources such as API services and CRDs, an Operator that specifies a small window for a dependency might unnecessarily constrain updates for other consumers of that dependency. Whenever possible, do not set a maximum version. Alternatively, set a very wide semantic range to prevent conflicts with other Operators. For example, >1.0.0 <2.0.0 . Unlike with conventional package managers, Operator authors explicitly encode that updates are safe through channels in OLM. If an update is available for an existing subscription, it is assumed that the Operator author is indicating that it can update from the version. Setting a maximum version for a dependency overrides the update stream of the author by unnecessarily truncating it at a particular upper bound. Note Cluster administrators cannot override dependencies set by an Operator author. However, maximum versions can and should be set if there are known incompatibilities that must be avoided. Specific versions can be omitted with the version range syntax, for example > 1.0.0 !1.2.1 . Additional resources Kubernetes documentation: Changing the API 2.4.4.8. Dependency caveats When specifying dependencies, there are caveats you should consider. No compound constraints (AND) There is currently no method for specifying an AND relationship between constraints. In other words, there is no way to specify that one Operator depends on another Operator that both provides a given API and has version >1.1.0 . This means that when specifying a dependency such as: dependencies: - type: olm.package value: packageName: etcd version: ">3.1.0" - type: olm.gvk value: group: etcd.database.coreos.com kind: EtcdCluster version: v1beta2 It would be possible for OLM to satisfy this with two Operators: one that provides EtcdCluster and one that has version >3.1.0 . Whether that happens, or whether an Operator is selected that satisfies both constraints, depends on the ordering that potential options are visited. Dependency preferences and ordering options are well-defined and can be reasoned about, but to exercise caution, Operators should stick to one mechanism or the other. Cross-namespace compatibility OLM performs dependency resolution at the namespace scope. It is possible to get into an update deadlock if updating an Operator in one namespace would be an issue for an Operator in another namespace, and vice-versa. 2.4.4.9. Example dependency resolution scenarios In the following examples, a provider is an Operator which "owns" a CRD or API service. Example: Deprecating dependent APIs A and B are APIs (CRDs): The provider of A depends on B. The provider of B has a subscription. The provider of B updates to provide C but deprecates B. This results in: B no longer has a provider. A no longer works. This is a case OLM prevents with its upgrade strategy. Example: Version deadlock A and B are APIs: The provider of A requires B. The provider of B requires A. The provider of A updates to (provide A2, require B2) and deprecate A. The provider of B updates to (provide B2, require A2) and deprecate B. If OLM attempts to update A without simultaneously updating B, or vice-versa, it is unable to progress to new versions of the Operators, even though a new compatible set can be found. This is another case OLM prevents with its upgrade strategy. 2.4.5. Operator groups This guide outlines the use of Operator groups with Operator Lifecycle Manager (OLM) in OpenShift Dedicated. 2.4.5.1. About Operator groups An Operator group , defined by the OperatorGroup resource, provides multitenant configuration to OLM-installed Operators. An Operator group selects target namespaces in which to generate required RBAC access for its member Operators. The set of target namespaces is provided by a comma-delimited string stored in the olm.targetNamespaces annotation of a cluster service version (CSV). This annotation is applied to the CSV instances of member Operators and is projected into their deployments. 2.4.5.2. Operator group membership An Operator is considered a member of an Operator group if the following conditions are true: The CSV of the Operator exists in the same namespace as the Operator group. The install modes in the CSV of the Operator support the set of namespaces targeted by the Operator group. An install mode in a CSV consists of an InstallModeType field and a boolean Supported field. The spec of a CSV can contain a set of install modes of four distinct InstallModeTypes : Table 2.5. Install modes and supported Operator groups InstallModeType Description OwnNamespace The Operator can be a member of an Operator group that selects its own namespace. SingleNamespace The Operator can be a member of an Operator group that selects one namespace. MultiNamespace The Operator can be a member of an Operator group that selects more than one namespace. AllNamespaces The Operator can be a member of an Operator group that selects all namespaces (target namespace set is the empty string "" ). Note If the spec of a CSV omits an entry of InstallModeType , then that type is considered unsupported unless support can be inferred by an existing entry that implicitly supports it. 2.4.5.3. Target namespace selection You can explicitly name the target namespace for an Operator group using the spec.targetNamespaces parameter: apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: my-group namespace: my-namespace spec: targetNamespaces: - my-namespace You can alternatively specify a namespace using a label selector with the spec.selector parameter: apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: my-group namespace: my-namespace spec: selector: cool.io/prod: "true" Important Listing multiple namespaces via spec.targetNamespaces or use of a label selector via spec.selector is not recommended, as the support for more than one target namespace in an Operator group will likely be removed in a future release. If both spec.targetNamespaces and spec.selector are defined, spec.selector is ignored. Alternatively, you can omit both spec.selector and spec.targetNamespaces to specify a global Operator group, which selects all namespaces: apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: my-group namespace: my-namespace The resolved set of selected namespaces is shown in the status.namespaces parameter of an Opeator group. The status.namespace of a global Operator group contains the empty string ( "" ), which signals to a consuming Operator that it should watch all namespaces. 2.4.5.4. Operator group CSV annotations Member CSVs of an Operator group have the following annotations: Annotation Description olm.operatorGroup=<group_name> Contains the name of the Operator group. olm.operatorNamespace=<group_namespace> Contains the namespace of the Operator group. olm.targetNamespaces=<target_namespaces> Contains a comma-delimited string that lists the target namespace selection of the Operator group. Note All annotations except olm.targetNamespaces are included with copied CSVs. Omitting the olm.targetNamespaces annotation on copied CSVs prevents the duplication of target namespaces between tenants. 2.4.5.5. Provided APIs annotation A group/version/kind (GVK) is a unique identifier for a Kubernetes API. Information about what GVKs are provided by an Operator group are shown in an olm.providedAPIs annotation. The value of the annotation is a string consisting of <kind>.<version>.<group> delimited with commas. The GVKs of CRDs and API services provided by all active member CSVs of an Operator group are included. Review the following example of an OperatorGroup object with a single active member CSV that provides the PackageManifest resource: apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: annotations: olm.providedAPIs: PackageManifest.v1alpha1.packages.apps.redhat.com name: olm-operators namespace: local ... spec: selector: {} serviceAccountName: metadata: creationTimestamp: null targetNamespaces: - local status: lastUpdated: 2019-02-19T16:18:28Z namespaces: - local 2.4.5.6. Role-based access control When an Operator group is created, three cluster roles are generated. Each contains a single aggregation rule with a cluster role selector set to match a label, as shown below: Cluster role Label to match olm.og.<operatorgroup_name>-admin-<hash_value> olm.opgroup.permissions/aggregate-to-admin: <operatorgroup_name> olm.og.<operatorgroup_name>-edit-<hash_value> olm.opgroup.permissions/aggregate-to-edit: <operatorgroup_name> olm.og.<operatorgroup_name>-view-<hash_value> olm.opgroup.permissions/aggregate-to-view: <operatorgroup_name> The following RBAC resources are generated when a CSV becomes an active member of an Operator group, as long as the CSV is watching all namespaces with the AllNamespaces install mode and is not in a failed state with reason InterOperatorGroupOwnerConflict : Cluster roles for each API resource from a CRD Cluster roles for each API resource from an API service Additional roles and role bindings Table 2.6. Cluster roles generated for each API resource from a CRD Cluster role Settings <kind>.<group>-<version>-admin Verbs on <kind> : * Aggregation labels: rbac.authorization.k8s.io/aggregate-to-admin: true olm.opgroup.permissions/aggregate-to-admin: <operatorgroup_name> <kind>.<group>-<version>-edit Verbs on <kind> : create update patch delete Aggregation labels: rbac.authorization.k8s.io/aggregate-to-edit: true olm.opgroup.permissions/aggregate-to-edit: <operatorgroup_name> <kind>.<group>-<version>-view Verbs on <kind> : get list watch Aggregation labels: rbac.authorization.k8s.io/aggregate-to-view: true olm.opgroup.permissions/aggregate-to-view: <operatorgroup_name> <kind>.<group>-<version>-view-crdview Verbs on apiextensions.k8s.io customresourcedefinitions <crd-name> : get Aggregation labels: rbac.authorization.k8s.io/aggregate-to-view: true olm.opgroup.permissions/aggregate-to-view: <operatorgroup_name> Table 2.7. Cluster roles generated for each API resource from an API service Cluster role Settings <kind>.<group>-<version>-admin Verbs on <kind> : * Aggregation labels: rbac.authorization.k8s.io/aggregate-to-admin: true olm.opgroup.permissions/aggregate-to-admin: <operatorgroup_name> <kind>.<group>-<version>-edit Verbs on <kind> : create update patch delete Aggregation labels: rbac.authorization.k8s.io/aggregate-to-edit: true olm.opgroup.permissions/aggregate-to-edit: <operatorgroup_name> <kind>.<group>-<version>-view Verbs on <kind> : get list watch Aggregation labels: rbac.authorization.k8s.io/aggregate-to-view: true olm.opgroup.permissions/aggregate-to-view: <operatorgroup_name> Additional roles and role bindings If the CSV defines exactly one target namespace that contains * , then a cluster role and corresponding cluster role binding are generated for each permission defined in the permissions field of the CSV. All resources generated are given the olm.owner: <csv_name> and olm.owner.namespace: <csv_namespace> labels. If the CSV does not define exactly one target namespace that contains * , then all roles and role bindings in the Operator namespace with the olm.owner: <csv_name> and olm.owner.namespace: <csv_namespace> labels are copied into the target namespace. 2.4.5.7. Copied CSVs OLM creates copies of all active member CSVs of an Operator group in each of the target namespaces of that Operator group. The purpose of a copied CSV is to tell users of a target namespace that a specific Operator is configured to watch resources created there. Copied CSVs have a status reason Copied and are updated to match the status of their source CSV. The olm.targetNamespaces annotation is stripped from copied CSVs before they are created on the cluster. Omitting the target namespace selection avoids the duplication of target namespaces between tenants. Copied CSVs are deleted when their source CSV no longer exists or the Operator group that their source CSV belongs to no longer targets the namespace of the copied CSV. Note By default, the disableCopiedCSVs field is disabled. After enabling a disableCopiedCSVs field, the OLM deletes existing copied CSVs on a cluster. When a disableCopiedCSVs field is disabled, the OLM adds copied CSVs again. Disable the disableCopiedCSVs field: USD cat << EOF \| oc apply -f - apiVersion: operators.coreos.com/v1 kind: OLMConfig metadata: name: cluster spec: features: disableCopiedCSVs: false EOF Enable the disableCopiedCSVs field: USD cat << EOF \| oc apply -f - apiVersion: operators.coreos.com/v1 kind: OLMConfig metadata: name: cluster spec: features: disableCopiedCSVs: true EOF 2.4.5.8. Static Operator groups An Operator group is static if its spec.staticProvidedAPIs field is set to true . As a result, OLM does not modify the olm.providedAPIs annotation of an Operator group, which means that it can be set in advance. This is useful when a user wants to use an Operator group to prevent resource contention in a set of namespaces but does not have active member CSVs that provide the APIs for those resources. Below is an example of an Operator group that protects Prometheus resources in all namespaces with the something.cool.io/cluster-monitoring: "true" annotation: apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: cluster-monitoring namespace: cluster-monitoring annotations: olm.providedAPIs: Alertmanager.v1.monitoring.coreos.com,Prometheus.v1.monitoring.coreos.com,PrometheusRule.v1.monitoring.coreos.com,ServiceMonitor.v1.monitoring.coreos.com spec: staticProvidedAPIs: true selector: matchLabels: something.cool.io/cluster-monitoring: "true" 2.4.5.9. Operator group intersection Two Operator groups are said to have intersecting provided APIs if the intersection of their target namespace sets is not an empty set and the intersection of their provided API sets, defined by olm.providedAPIs annotations, is not an empty set. A potential issue is that Operator groups with intersecting provided APIs can compete for the same resources in the set of intersecting namespaces. Note When checking intersection rules, an Operator group namespace is always included as part of its selected target namespaces. Rules for intersection Each time an active member CSV synchronizes, OLM queries the cluster for the set of intersecting provided APIs between the Operator group of the CSV and all others. OLM then checks if that set is an empty set: If true and the CSV's provided APIs are a subset of the Operator group's: Continue transitioning. If true and the CSV's provided APIs are not a subset of the Operator group's: If the Operator group is static: Clean up any deployments that belong to the CSV. Transition the CSV to a failed state with status reason CannotModifyStaticOperatorGroupProvidedAPIs . If the Operator group is not static: Replace the Operator group's olm.providedAPIs annotation with the union of itself and the CSV's provided APIs. If false and the CSV's provided APIs are not a subset of the Operator group's: Clean up any deployments that belong to the CSV. Transition the CSV to a failed state with status reason InterOperatorGroupOwnerConflict . If false and the CSV's provided APIs are a subset of the Operator group's: If the Operator group is static: Clean up any deployments that belong to the CSV. Transition the CSV to a failed state with status reason CannotModifyStaticOperatorGroupProvidedAPIs . If the Operator group is not static: Replace the Operator group's olm.providedAPIs annotation with the difference between itself and the CSV's provided APIs. Note Failure states caused by Operator groups are non-terminal. The following actions are performed each time an Operator group synchronizes: The set of provided APIs from active member CSVs is calculated from the cluster. Note that copied CSVs are ignored. The cluster set is compared to olm.providedAPIs , and if olm.providedAPIs contains any extra APIs, then those APIs are pruned. All CSVs that provide the same APIs across all namespaces are requeued. This notifies conflicting CSVs in intersecting groups that their conflict has possibly been resolved, either through resizing or through deletion of the conflicting CSV. 2.4.5.10. Limitations for multitenant Operator management OpenShift Dedicated provides limited support for simultaneously installing different versions of an Operator on the same cluster. Operator Lifecycle Manager (OLM) installs Operators multiple times in different namespaces. One constraint of this is that the Operator's API versions must be the same. Operators are control plane extensions due to their usage of CustomResourceDefinition objects (CRDs), which are global resources in Kubernetes. Different major versions of an Operator often have incompatible CRDs. This makes them incompatible to install simultaneously in different namespaces on a cluster. All tenants, or namespaces, share the same control plane of a cluster. Therefore, tenants in a multitenant cluster also share global CRDs, which limits the scenarios in which different instances of the same Operator can be used in parallel on the same cluster. The supported scenarios include the following: Operators of different versions that ship the exact same CRD definition (in case of versioned CRDs, the exact same set of versions) Operators of different versions that do not ship a CRD, and instead have their CRD available in a separate bundle on the OperatorHub All other scenarios are not supported, because the integrity of the cluster data cannot be guaranteed if there are multiple competing or overlapping CRDs from different Operator versions to be reconciled on the same cluster. Additional resources Operators in multitenant clusters 2.4.5.11. Troubleshooting Operator groups Membership An install plan's namespace must contain only one Operator group. When attempting to generate a cluster service version (CSV) in a namespace, an install plan considers an Operator group invalid in the following scenarios: No Operator groups exist in the install plan's namespace. Multiple Operator groups exist in the install plan's namespace. An incorrect or non-existent service account name is specified in the Operator group. If an install plan encounters an invalid Operator group, the CSV is not generated and the InstallPlan resource continues to install with a relevant message. For example, the following message is provided if more than one Operator group exists in the same namespace: attenuated service account query failed - more than one operator group(s) are managing this namespace count=2 where count= specifies the number of Operator groups in the namespace. If the install modes of a CSV do not support the target namespace selection of the Operator group in its namespace, the CSV transitions to a failure state with the reason UnsupportedOperatorGroup . CSVs in a failed state for this reason transition to pending after either the target namespace selection of the Operator group changes to a supported configuration, or the install modes of the CSV are modified to support the target namespace selection. 2.4.6. Multitenancy and Operator colocation This guide outlines multitenancy and Operator colocation in Operator Lifecycle Manager (OLM). 2.4.6.1. Colocation of Operators in a namespace Operator Lifecycle Manager (OLM) handles OLM-managed Operators that are installed in the same namespace, meaning their Subscription resources are colocated in the same namespace, as related Operators. Even if they are not actually related, OLM considers their states, such as their version and update policy, when any one of them is updated. This default behavior manifests in two ways: InstallPlan resources of pending updates include ClusterServiceVersion (CSV) resources of all other Operators that are in the same namespace. All Operators in the same namespace share the same update policy. For example, if one Operator is set to manual updates, all other Operators' update policies are also set to manual. These scenarios can lead to the following issues: It becomes hard to reason about install plans for Operator updates, because there are many more resources defined in them than just the updated Operator. It becomes impossible to have some Operators in a namespace update automatically while other are updated manually, which is a common desire for cluster administrators. These issues usually surface because, when installing Operators with the OpenShift Dedicated web console, the default behavior installs Operators that support the All namespaces install mode into the default openshift-operators global namespace. As an administrator with the dedicated-admin role, you can bypass this default behavior manually by using the following workflow: Create a project for the installation of the Operator. Create a custom global Operator group , which is an Operator group that watches all namespaces. By associating this Operator group with the namespace you just created, it makes the installation namespace a global namespace, which makes Operators installed there available in all namespaces. Install the desired Operator in the installation namespace. If the Operator has dependencies, the dependencies are automatically installed in the pre-created namespace. As a result, it is then valid for the dependency Operators to have the same update policy and shared install plans. For a detailed procedure, see "Installing global Operators in custom namespaces". Additional resources Installing global Operators in custom namespaces Operators in multitenant clusters 2.4.7. Operator conditions This guide outlines how Operator Lifecycle Manager (OLM) uses Operator conditions. 2.4.7.1. About Operator conditions As part of its role in managing the lifecycle of an Operator, Operator Lifecycle Manager (OLM) infers the state of an Operator from the state of Kubernetes resources that define the Operator. While this approach provides some level of assurance that an Operator is in a given state, there are many instances where an Operator might need to communicate information to OLM that could not be inferred otherwise. This information can then be used by OLM to better manage the lifecycle of the Operator. OLM provides a custom resource definition (CRD) called OperatorCondition that allows Operators to communicate conditions to OLM. There are a set of supported conditions that influence management of the Operator by OLM when present in the Spec.Conditions array of an OperatorCondition resource. Note By default, the Spec.Conditions array is not present in an OperatorCondition object until it is either added by a user or as a result of custom Operator logic. 2.4.7.2. Supported conditions Operator Lifecycle Manager (OLM) supports the following Operator conditions. 2.4.7.2.1. Upgradeable condition The Upgradeable Operator condition prevents an existing cluster service version (CSV) from being replaced by a newer version of the CSV. This condition is useful when: An Operator is about to start a critical process and should not be upgraded until the process is completed. An Operator is performing a migration of custom resources (CRs) that must be completed before the Operator is ready to be upgraded. Important Setting the Upgradeable Operator condition to the False value does not avoid pod disruption. If you must ensure your pods are not disrupted, see "Using pod disruption budgets to specify the number of pods that must be up" and "Graceful termination" in the "Additional resources" section. Example Upgradeable Operator condition apiVersion: operators.coreos.com/v1 kind: OperatorCondition metadata: name: my-operator namespace: operators spec: conditions: - type: Upgradeable 1 status: "False" 2 reason: "migration" message: "The Operator is performing a migration." lastTransitionTime: "2020-08-24T23:15:55Z" 1 Name of the condition. 2 A False value indicates the Operator is not ready to be upgraded. OLM prevents a CSV that replaces the existing CSV of the Operator from leaving the Pending phase. A False value does not block cluster upgrades. 2.4.7.3. Additional resources Managing Operator conditions Enabling Operator conditions 2.4.8. Operator Lifecycle Manager metrics 2.4.8.1. Exposed metrics Operator Lifecycle Manager (OLM) exposes certain OLM-specific resources for use by the Prometheus-based OpenShift Dedicated cluster monitoring stack. Table 2.8. Metrics exposed by OLM Name Description catalog_source_count Number of catalog sources. catalogsource_ready State of a catalog source. The value 1 indicates that the catalog source is in a READY state. The value of 0 indicates that the catalog source is not in a READY state. csv_abnormal When reconciling a cluster service version (CSV), present whenever a CSV version is in any state other than Succeeded , for example when it is not installed. Includes the name , namespace , phase , reason , and version labels. A Prometheus alert is created when this metric is present. csv_count Number of CSVs successfully registered. csv_succeeded When reconciling a CSV, represents whether a CSV version is in a Succeeded state (value 1 ) or not (value 0 ). Includes the name , namespace , and version labels. csv_upgrade_count Monotonic count of CSV upgrades. install_plan_count Number of install plans. installplan_warnings_total Monotonic count of warnings generated by resources, such as deprecated resources, included in an install plan. olm_resolution_duration_seconds The duration of a dependency resolution attempt. subscription_count Number of subscriptions. subscription_sync_total Monotonic count of subscription syncs. Includes the channel , installed CSV, and subscription name labels. 2.4.9. Webhook management in Operator Lifecycle Manager Webhooks allow Operator authors to intercept, modify, and accept or reject resources before they are saved to the object store and handled by the Operator controller. Operator Lifecycle Manager (OLM) can manage the lifecycle of these webhooks when they are shipped alongside your Operator. See Defining cluster service versions (CSVs) for details on how an Operator developer can define webhooks for their Operator, as well as considerations when running on OLM. 2.4.9.1. Additional resources Kubernetes documentation: Validating admission webhooks Mutating admission webhooks Conversion webhooks 2.5. Understanding OperatorHub 2.5.1. About OperatorHub OperatorHub is the web console interface in OpenShift Dedicated that cluster administrators use to discover and install Operators. With one click, an Operator can be pulled from its off-cluster source, installed and subscribed on the cluster, and made ready for engineering teams to self-service manage the product across deployment environments using Operator Lifecycle Manager (OLM). Cluster administrators can choose from catalogs grouped into the following categories: Category Description Red Hat Operators Red Hat products packaged and shipped by Red Hat. Supported by Red Hat. Certified Operators Products from leading independent software vendors (ISVs). Red Hat partners with ISVs to package and ship. Supported by the ISV. Red Hat Marketplace Certified software that can be purchased from Red Hat Marketplace . Community Operators Optionally-visible software maintained by relevant representatives in the redhat-openshift-ecosystem/community-operators-prod/operators GitHub repository. No official support. Custom Operators Operators you add to the cluster yourself. If you have not added any custom Operators, the Custom category does not appear in the web console on your OperatorHub. Operators on OperatorHub are packaged to run on OLM. This includes a YAML file called a cluster service version (CSV) containing all of the CRDs, RBAC rules, deployments, and container images required to install and securely run the Operator. It also contains user-visible information like a description of its features and supported Kubernetes versions. The Operator SDK can be used to assist developers packaging their Operators for use on OLM and OperatorHub. If you have a commercial application that you want to make accessible to your customers, get it included using the certification workflow provided on the Red Hat Partner Connect portal at connect.redhat.com . 2.5.2. OperatorHub architecture The OperatorHub UI component is driven by the Marketplace Operator by default on OpenShift Dedicated in the openshift-marketplace namespace. 2.5.2.1. OperatorHub custom resource The Marketplace Operator manages an OperatorHub custom resource (CR) named cluster that manages the default CatalogSource objects provided with OperatorHub. 2.5.3. Additional resources Catalog source About the Operator SDK Defining cluster service versions (CSVs) Operator installation and upgrade workflow in OLM Red Hat Partner Connect Red Hat Marketplace 2.6. Red Hat-provided Operator catalogs Red Hat provides several Operator catalogs that are included with OpenShift Dedicated by default. Important As of OpenShift Dedicated 4.11, the default Red Hat-provided Operator catalog releases in the file-based catalog format. The default Red Hat-provided Operator catalogs for OpenShift Dedicated 4.6 through 4.10 released in the deprecated SQLite database format. The opm subcommands, flags, and functionality related to the SQLite database format are also deprecated and will be removed in a future release. The features are still supported and must be used for catalogs that use the deprecated SQLite database format. Many of the opm subcommands and flags for working with the SQLite database format, such as opm index prune , do not work with the file-based catalog format. For more information about working with file-based catalogs, see Managing custom catalogs , and Operator Framework packaging format . 2.6.1. About Operator catalogs An Operator catalog is a repository of metadata that Operator Lifecycle Manager (OLM) can query to discover and install Operators and their dependencies on a cluster. OLM always installs Operators from the latest version of a catalog. An index image, based on the Operator bundle format, is a containerized snapshot of a catalog. It is an immutable artifact that contains the database of pointers to a set of Operator manifest content. A catalog can reference an index image to source its content for OLM on the cluster. As catalogs are updated, the latest versions of Operators change, and older versions may be removed or altered. In addition, when OLM runs on an OpenShift Dedicated cluster in a restricted network environment, it is unable to access the catalogs directly from the internet to pull the latest content. As a cluster administrator, you can create your own custom index image, either based on a Red Hat-provided catalog or from scratch, which can be used to source the catalog content on the cluster. Creating and updating your own index image provides a method for customizing the set of Operators available on the cluster, while also avoiding the aforementioned restricted network environment issues. Important Kubernetes periodically deprecates certain APIs that are removed in subsequent releases. As a result, Operators are unable to use removed APIs starting with the version of OpenShift Dedicated that uses the Kubernetes version that removed the API. If your cluster is using custom catalogs, see Controlling Operator compatibility with OpenShift Dedicated versions for more details about how Operator authors can update their projects to help avoid workload issues and prevent incompatible upgrades. Note Support for the legacy package manifest format for Operators, including custom catalogs that were using the legacy format, is removed in OpenShift Dedicated 4.8 and later. When creating custom catalog images, versions of OpenShift Dedicated 4 required using the oc adm catalog build command, which was deprecated for several releases and is now removed. With the availability of Red Hat-provided index images starting in OpenShift Dedicated 4.6, catalog builders must use the opm index command to manage index images. Additional resources Managing custom catalogs Packaging format 2.6.2. About Red Hat-provided Operator catalogs The Red Hat-provided catalog sources are installed by default in the openshift-marketplace namespace, which makes the catalogs available cluster-wide in all namespaces. The following Operator catalogs are distributed by Red Hat: Catalog Index image Description redhat-operators registry.redhat.io/redhat/redhat-operator-index:v4 Red Hat products packaged and shipped by Red Hat. Supported by Red Hat. certified-operators registry.redhat.io/redhat/certified-operator-index:v4 Products from leading independent software vendors (ISVs). Red Hat partners with ISVs to package and ship. Supported by the ISV. redhat-marketplace registry.redhat.io/redhat/redhat-marketplace-index:v4 Certified software that can be purchased from Red Hat Marketplace . community-operators registry.redhat.io/redhat/community-operator-index:v4 Software maintained by relevant representatives in the redhat-openshift-ecosystem/community-operators-prod/operators GitHub repository. No official support. During a cluster upgrade, the index image tag for the default Red Hat-provided catalog sources are updated automatically by the Cluster Version Operator (CVO) so that Operator Lifecycle Manager (OLM) pulls the updated version of the catalog. For example during an upgrade from OpenShift Dedicated 4.8 to 4.9, the spec.image field in the CatalogSource object for the redhat-operators catalog is updated from: registry.redhat.io/redhat/redhat-operator-index:v4.8 to: registry.redhat.io/redhat/redhat-operator-index:v4.9 2.7. Operators in multitenant clusters The default behavior for Operator Lifecycle Manager (OLM) aims to provide simplicity during Operator installation. However, this behavior can lack flexibility, especially in multitenant clusters. In order for multiple tenants on a OpenShift Dedicated cluster to use an Operator, the default behavior of OLM requires that administrators install the Operator in All namespaces mode, which can be considered to violate the principle of least privilege. Consider the following scenarios to determine which Operator installation workflow works best for your environment and requirements. Additional resources Common terms: Multitenant Limitations for multitenant Operator management 2.7.1. Default Operator install modes and behavior When installing Operators with the web console as an administrator, you typically have two choices for the install mode, depending on the Operator's capabilities: Single namespace Installs the Operator in the chosen single namespace, and makes all permissions that the Operator requests available in that namespace. All namespaces Installs the Operator in the default openshift-operators namespace to watch and be made available to all namespaces in the cluster. Makes all permissions that the Operator requests available in all namespaces. In some cases, an Operator author can define metadata to give the user a second option for that Operator's suggested namespace. This choice also means that users in the affected namespaces get access to the Operators APIs, which can leverage the custom resources (CRs) they own, depending on their role in the namespace: The namespace-admin and namespace-edit roles can read/write to the Operator APIs, meaning they can use them. The namespace-view role can read CR objects of that Operator. For Single namespace mode, because the Operator itself installs in the chosen namespace, its pod and service account are also located there. For All namespaces mode, the Operator's privileges are all automatically elevated to cluster roles, meaning the Operator has those permissions in all namespaces. Additional resources Adding Operators to a cluster Install modes types Setting a suggested namespace 2.7.2. Recommended solution for multitenant clusters While a Multinamespace install mode does exist, it is supported by very few Operators. As a middle ground solution between the standard All namespaces and Single namespace install modes, you can install multiple instances of the same Operator, one for each tenant, by using the following workflow: Create a namespace for the tenant Operator that is separate from the tenant's namespace. You can do this by creating a project. Create an Operator group for the tenant Operator scoped only to the tenant's namespace. Install the Operator in the tenant Operator namespace. As a result, the Operator resides in the tenant Operator namespace and watches the tenant namespace, but neither the Operator's pod nor its service account are visible or usable by the tenant. This solution provides better tenant separation, least privilege principle at the cost of resource usage, and additional orchestration to ensure the constraints are met. For a detailed procedure, see "Preparing for multiple instances of an Operator for multitenant clusters". Limitations and considerations This solution only works when the following constraints are met: All instances of the same Operator must be the same version. The Operator cannot have dependencies on other Operators. The Operator cannot ship a CRD conversion webhook. Important You cannot use different versions of the same Operator on the same cluster. Eventually, the installation of another instance of the Operator would be blocked when it meets the following conditions: The instance is not the newest version of the Operator. The instance ships an older revision of the CRDs that lack information or versions that newer revisions have that are already in use on the cluster. Additional resources Preparing for multiple instances of an Operator for multitenant clusters 2.7.3. Operator colocation and Operator groups Operator Lifecycle Manager (OLM) handles OLM-managed Operators that are installed in the same namespace, meaning their Subscription resources are colocated in the same namespace, as related Operators. Even if they are not actually related, OLM considers their states, such as their version and update policy, when any one of them is updated. For more information on Operator colocation and using Operator groups effectively, see Operator Lifecycle Manager (OLM) Multitenancy and Operator colocation . 2.8. CRDs 2.8.1. Managing resources from custom resource definitions This guide describes how developers can manage custom resources (CRs) that come from custom resource definitions (CRDs). 2.8.1.1. Custom resource definitions In the Kubernetes API, a resource is an endpoint that stores a collection of API objects of a certain kind. For example, the built-in Pods resource contains a collection of Pod objects. A custom resource definition (CRD) object defines a new, unique object type, called a kind , in the cluster and lets the Kubernetes API server handle its entire lifecycle. Custom resource (CR) objects are created from CRDs that have been added to the cluster by a cluster administrator, allowing all cluster users to add the new resource type into projects. Operators in particular make use of CRDs by packaging them with any required RBAC policy and other software-specific logic. 2.8.1.2. Creating custom resources from a file After a custom resource definition (CRD) has been added to the cluster, custom resources (CRs) can be created with the CLI from a file using the CR specification. Procedure Create a YAML file for the CR. In the following example definition, the cronSpec and image custom fields are set in a CR of Kind: CronTab . The Kind comes from the spec.kind field of the CRD object: Example YAML file for a CR apiVersion: "stable.example.com/v1" 1 kind: CronTab 2 metadata: name: my-new-cron-object 3 finalizers: 4 - finalizer.stable.example.com spec: 5 cronSpec: "* * * * /5" image: my-awesome-cron-image 1 Specify the group name and API version (name/version) from the CRD. 2 Specify the type in the CRD. 3 Specify a name for the object. 4 Specify the finalizers for the object, if any. Finalizers allow controllers to implement conditions that must be completed before the object can be deleted. 5 Specify conditions specific to the type of object. After you create the file, create the object: USD oc create -f <file_name>.yaml 2.8.1.3. Inspecting custom resources You can inspect custom resource (CR) objects that exist in your cluster using the CLI. Prerequisites A CR object exists in a namespace to which you have access. Procedure To get information on a specific kind of a CR, run: USD oc get <kind> For example: USD oc get crontab Example output NAME KIND my-new-cron-object CronTab.v1.stable.example.com Resource names are not case-sensitive, and you can use either the singular or plural forms defined in the CRD, as well as any short name. For example: USD oc get crontabs USD oc get crontab USD oc get ct You can also view the raw YAML data for a CR: USD oc get <kind> -o yaml For example: USD oc get ct -o yaml Example output apiVersion: v1 items: - apiVersion: stable.example.com/v1 kind: CronTab metadata: clusterName: "" creationTimestamp: 2017-05-31T12:56:35Z deletionGracePeriodSeconds: null deletionTimestamp: null name: my-new-cron-object namespace: default resourceVersion: "285" selfLink: /apis/stable.example.com/v1/namespaces/default/crontabs/my-new-cron-object uid: 9423255b-4600-11e7-af6a-28d2447dc82b spec: cronSpec: '* * * * /5' 1 image: my-awesome-cron-image 2 1 2 Custom data from the YAML that you used to create the object displays.	[ "etcd ├── manifests │ ├── etcdcluster.crd.yaml │ └── etcdoperator.clusterserviceversion.yaml │ └── secret.yaml │ └── configmap.yaml └── metadata └── annotations.yaml └── dependencies.yaml", "annotations: operators.operatorframework.io.bundle.mediatype.v1: \"registry+v1\" 1 operators.operatorframework.io.bundle.manifests.v1: \"manifests/\" 2 operators.operatorframework.io.bundle.metadata.v1: \"metadata/\" 3 operators.operatorframework.io.bundle.package.v1: \"test-operator\" 4 operators.operatorframework.io.bundle.channels.v1: \"beta,stable\" 5 operators.operatorframework.io.bundle.channel.default.v1: \"stable\" 6", "dependencies: - type: olm.package value: packageName: prometheus version: \">0.27.0\" - type: olm.gvk value: group: etcd.database.coreos.com kind: EtcdCluster version: v1beta2", "Ignore everything except non-object .json and .yaml files */ !.json !.yaml */objects/.json */objects/.yaml", "catalog ├── packageA │ └── index.yaml ├── packageB │ ├── .indexignore │ ├── index.yaml │ └── objects │ └── packageB.v0.1.0.clusterserviceversion.yaml └── packageC └── index.json └── deprecations.yaml", "_Meta: { // schema is required and must be a non-empty string schema: string & !=\"\" // package is optional, but if it's defined, it must be a non-empty string package?: string & !=\"\" // properties is optional, but if it's defined, it must be a list of 0 or more properties properties?: [... #Property] } #Property: { // type is required type: string & !=\"\" // value is required, and it must not be null value: !=null }", "#Package: { schema: \"olm.package\" // Package name name: string & !=\"\" // A description of the package description?: string // The package's default channel defaultChannel: string & !=\"\" // An optional icon icon?: { base64data: string mediatype: string } }", "#Channel: { schema: \"olm.channel\" package: string & !=\"\" name: string & !=\"\" entries: [...#ChannelEntry] } #ChannelEntry: { // name is required. It is the name of an `olm.bundle` that // is present in the channel. name: string & !=\"\" // replaces is optional. It is the name of bundle that is replaced // by this entry. It does not have to be present in the entry list. replaces?: string & !=\"\" // skips is optional. It is a list of bundle names that are skipped by // this entry. The skipped bundles do not have to be present in the // entry list. skips?: [...string & !=\"\"] // skipRange is optional. It is the semver range of bundle versions // that are skipped by this entry. skipRange?: string & !=\"\" }", "#Bundle: { schema: \"olm.bundle\" package: string & !=\"\" name: string & !=\"\" image: string & !=\"\" properties: [...#Property] relatedImages?: [...#RelatedImage] } #Property: { // type is required type: string & !=\"\" // value is required, and it must not be null value: !=null } #RelatedImage: { // image is the image reference image: string & !=\"\" // name is an optional descriptive name for an image that // helps identify its purpose in the context of the bundle name?: string & !=\"\" }", "schema: olm.deprecations package: my-operator 1 entries: - reference: schema: olm.package 2 message: \| 3 The 'my-operator' package is end of life. Please use the 'my-operator-new' package for support. - reference: schema: olm.channel name: alpha 4 message: \| The 'alpha' channel is no longer supported. Please switch to the 'stable' channel. - reference: schema: olm.bundle name: my-operator.v1.68.0 5 message: \| my-operator.v1.68.0 is deprecated. Uninstall my-operator.v1.68.0 and install my-operator.v1.72.0 for support.", "my-catalog └── my-operator ├── index.yaml └── deprecations.yaml", "#PropertyPackage: { type: \"olm.package\" value: { packageName: string & !=\"\" version: string & !=\"\" } }", "#PropertyGVK: { type: \"olm.gvk\" value: { group: string & !=\"\" version: string & !=\"\" kind: string & !=\"\" } }", "#PropertyPackageRequired: { type: \"olm.package.required\" value: { packageName: string & !=\"\" versionRange: string & !=\"\" } }", "#PropertyGVKRequired: { type: \"olm.gvk.required\" value: { group: string & !=\"\" version: string & !=\"\" kind: string & !=\"\" } }", "name: community-operators repo: quay.io/community-operators/catalog tag: latest references: - name: etcd-operator image: quay.io/etcd-operator/index@sha256:5891b5b522d5df086d0ff0b110fbd9d21bb4fc7163af34d08286a2e846f6be03 - name: prometheus-operator image: quay.io/prometheus-operator/index@sha256:e258d248fda94c63753607f7c4494ee0fcbe92f1a76bfdac795c9d84101eb317", "name=USD(yq eval '.name' catalog.yaml) mkdir \"USDname\" yq eval '.name + \"/\" + .references[].name' catalog.yaml \| xargs mkdir for l in USD(yq e '.name as USDcatalog \| .references[] \| .image + \"\|\" + USDcatalog + \"/\" + .name + \"/index.yaml\"' catalog.yaml); do image=USD(echo USDl \| cut -d'\|' -f1) file=USD(echo USDl \| cut -d'\|' -f2) opm render \"USDimage\" > \"USDfile\" done opm generate dockerfile \"USDname\" indexImage=USD(yq eval '.repo + \":\" + .tag' catalog.yaml) docker build -t \"USDindexImage\" -f \"USDname.Dockerfile\" . docker push \"USDindexImage\"", "\\ufeffapiVersion: operators.coreos.com/v1alpha1 kind: CatalogSource metadata: generation: 1 name: example-catalog 1 namespace: openshift-marketplace 2 annotations: olm.catalogImageTemplate: 3 \"quay.io/example-org/example-catalog:v{kube_major_version}.{kube_minor_version}.{kube_patch_version}\" spec: displayName: Example Catalog 4 image: quay.io/example-org/example-catalog:v1 5 priority: -400 6 publisher: Example Org sourceType: grpc 7 grpcPodConfig: securityContextConfig: <security_mode> 8 nodeSelector: 9 custom_label: <label> priorityClassName: system-cluster-critical 10 tolerations: 11 - key: \"key1\" operator: \"Equal\" value: \"value1\" effect: \"NoSchedule\" updateStrategy: registryPoll: 12 interval: 30m0s status: connectionState: address: example-catalog.openshift-marketplace.svc:50051 lastConnect: 2021-08-26T18:14:31Z lastObservedState: READY 13 latestImageRegistryPoll: 2021-08-26T18:46:25Z 14 registryService: 15 createdAt: 2021-08-26T16:16:37Z port: 50051 protocol: grpc serviceName: example-catalog serviceNamespace: openshift-marketplace", "apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: example-operator namespace: example-namespace spec: channel: stable name: example-operator source: example-catalog sourceNamespace: openshift-marketplace", "registry.redhat.io/redhat/redhat-operator-index:v4.18", "registry.redhat.io/redhat/redhat-operator-index:v4.18", "apiVersion: operators.coreos.com/v1alpha1 kind: CatalogSource metadata: generation: 1 name: example-catalog namespace: openshift-marketplace annotations: olm.catalogImageTemplate: \"quay.io/example-org/example-catalog:v{kube_major_version}.{kube_minor_version}\" spec: displayName: Example Catalog image: quay.io/example-org/example-catalog:v1.31 priority: -400 publisher: Example Org", "quay.io/example-org/example-catalog:v1.31", "apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: example-operator namespace: example-namespace spec: channel: stable name: example-operator source: example-catalog sourceNamespace: openshift-marketplace", "apiVersion: operators.coreos.com/v1alpha1 kind: InstallPlan metadata: name: install-abcde namespace: operators spec: approval: Automatic approved: true clusterServiceVersionNames: - my-operator.v1.0.1 generation: 1 status: catalogSources: [] conditions: - lastTransitionTime: '2021-01-01T20:17:27Z' lastUpdateTime: '2021-01-01T20:17:27Z' status: 'True' type: Installed phase: Complete plan: - resolving: my-operator.v1.0.1 resource: group: operators.coreos.com kind: ClusterServiceVersion manifest: >- name: my-operator.v1.0.1 sourceName: redhat-operators sourceNamespace: openshift-marketplace version: v1alpha1 status: Created - resolving: my-operator.v1.0.1 resource: group: apiextensions.k8s.io kind: CustomResourceDefinition manifest: >- name: webservers.web.servers.org sourceName: redhat-operators sourceNamespace: openshift-marketplace version: v1beta1 status: Created - resolving: my-operator.v1.0.1 resource: group: '' kind: ServiceAccount manifest: >- name: my-operator sourceName: redhat-operators sourceNamespace: openshift-marketplace version: v1 status: Created - resolving: my-operator.v1.0.1 resource: group: rbac.authorization.k8s.io kind: Role manifest: >- name: my-operator.v1.0.1-my-operator-6d7cbc6f57 sourceName: redhat-operators sourceNamespace: openshift-marketplace version: v1 status: Created - resolving: my-operator.v1.0.1 resource: group: rbac.authorization.k8s.io kind: RoleBinding manifest: >- name: my-operator.v1.0.1-my-operator-6d7cbc6f57 sourceName: redhat-operators sourceNamespace: openshift-marketplace version: v1 status: Created", "packageName: example channels: - name: alpha currentCSV: example.v0.1.2 - name: beta currentCSV: example.v0.1.3 defaultChannel: alpha", "apiVersion: operators.coreos.com/v1alpha1 kind: ClusterServiceVersion metadata: name: etcdoperator.v0.9.2 namespace: placeholder annotations: spec: displayName: etcd description: Etcd Operator replaces: etcdoperator.v0.9.0 skips: - etcdoperator.v0.9.1", "olm.skipRange: <semver_range>", "apiVersion: operators.coreos.com/v1alpha1 kind: ClusterServiceVersion metadata: name: elasticsearch-operator.v4.1.2 namespace: <namespace> annotations: olm.skipRange: '>=4.1.0 <4.1.2'", "properties: - type: olm.kubeversion value: version: \"1.16.0\"", "properties: - property: type: color value: red - property: type: shape value: square - property: type: olm.gvk value: group: olm.coreos.io version: v1alpha1 kind: myresource", "dependencies: - type: olm.package value: packageName: prometheus version: \">0.27.0\" - type: olm.gvk value: group: etcd.database.coreos.com kind: EtcdCluster version: v1beta2", "type: olm.constraint value: failureMessage: 'require to have \"certified\"' cel: rule: 'properties.exists(p, p.type == \"certified\")'", "type: olm.constraint value: failureMessage: 'require to have \"certified\" and \"stable\" properties' cel: rule: 'properties.exists(p, p.type == \"certified\") && properties.exists(p, p.type == \"stable\")'", "schema: olm.bundle name: red.v1.0.0 properties: - type: olm.constraint value: failureMessage: All are required for Red because all: constraints: - failureMessage: Package blue is needed for package: name: blue versionRange: '>=1.0.0' - failureMessage: GVK Green/v1 is needed for gvk: group: greens.example.com version: v1 kind: Green", "schema: olm.bundle name: red.v1.0.0 properties: - type: olm.constraint value: failureMessage: Any are required for Red because any: constraints: - gvk: group: blues.example.com version: v1beta1 kind: Blue - gvk: group: blues.example.com version: v1beta2 kind: Blue - gvk: group: blues.example.com version: v1 kind: Blue", "schema: olm.bundle name: red.v1.0.0 properties: - type: olm.constraint value: all: constraints: - failureMessage: Package blue is needed for package: name: blue versionRange: '>=1.0.0' - failureMessage: Cannot be required for Red because not: constraints: - gvk: group: greens.example.com version: v1alpha1 kind: greens", "schema: olm.bundle name: red.v1.0.0 properties: - type: olm.constraint value: failureMessage: Required for Red because any: constraints: - all: constraints: - package: name: blue versionRange: '>=1.0.0' - gvk: group: blues.example.com version: v1 kind: Blue - all: constraints: - package: name: blue versionRange: '<1.0.0' - gvk: group: blues.example.com version: v1beta1 kind: Blue", "apiVersion: \"operators.coreos.com/v1alpha1\" kind: \"CatalogSource\" metadata: name: \"my-operators\" namespace: \"operators\" spec: sourceType: grpc grpcPodConfig: securityContextConfig: <security_mode> 1 image: example.com/my/operator-index:v1 displayName: \"My Operators\" priority: 100", "dependencies: - type: olm.package value: packageName: etcd version: \">3.1.0\" - type: olm.gvk value: group: etcd.database.coreos.com kind: EtcdCluster version: v1beta2", "apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: my-group namespace: my-namespace spec: targetNamespaces: - my-namespace", "apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: my-group namespace: my-namespace spec: selector: cool.io/prod: \"true\"", "apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: my-group namespace: my-namespace", "apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: annotations: olm.providedAPIs: PackageManifest.v1alpha1.packages.apps.redhat.com name: olm-operators namespace: local spec: selector: {} serviceAccountName: metadata: creationTimestamp: null targetNamespaces: - local status: lastUpdated: 2019-02-19T16:18:28Z namespaces: - local", "cat << EOF \| oc apply -f - apiVersion: operators.coreos.com/v1 kind: OLMConfig metadata: name: cluster spec: features: disableCopiedCSVs: false EOF", "cat << EOF \| oc apply -f - apiVersion: operators.coreos.com/v1 kind: OLMConfig metadata: name: cluster spec: features: disableCopiedCSVs: true EOF", "apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: cluster-monitoring namespace: cluster-monitoring annotations: olm.providedAPIs: Alertmanager.v1.monitoring.coreos.com,Prometheus.v1.monitoring.coreos.com,PrometheusRule.v1.monitoring.coreos.com,ServiceMonitor.v1.monitoring.coreos.com spec: staticProvidedAPIs: true selector: matchLabels: something.cool.io/cluster-monitoring: \"true\"", "attenuated service account query failed - more than one operator group(s) are managing this namespace count=2", "apiVersion: operators.coreos.com/v1 kind: OperatorCondition metadata: name: my-operator namespace: operators spec: conditions: - type: Upgradeable 1 status: \"False\" 2 reason: \"migration\" message: \"The Operator is performing a migration.\" lastTransitionTime: \"2020-08-24T23:15:55Z\"", "registry.redhat.io/redhat/redhat-operator-index:v4.8", "registry.redhat.io/redhat/redhat-operator-index:v4.9", "apiVersion: \"stable.example.com/v1\" 1 kind: CronTab 2 metadata: name: my-new-cron-object 3 finalizers: 4 - finalizer.stable.example.com spec: 5 cronSpec: \"* * * * /5\" image: my-awesome-cron-image", "oc create -f <file_name>.yaml", "oc get <kind>", "oc get crontab", "NAME KIND my-new-cron-object CronTab.v1.stable.example.com", "oc get crontabs", "oc get crontab", "oc get ct", "oc get <kind> -o yaml", "oc get ct -o yaml", "apiVersion: v1 items: - apiVersion: stable.example.com/v1 kind: CronTab metadata: clusterName: \"\" creationTimestamp: 2017-05-31T12:56:35Z deletionGracePeriodSeconds: null deletionTimestamp: null name: my-new-cron-object namespace: default resourceVersion: \"285\" selfLink: /apis/stable.example.com/v1/namespaces/default/crontabs/my-new-cron-object uid: 9423255b-4600-11e7-af6a-28d2447dc82b spec: cronSpec: '* * * * /5' 1 image: my-awesome-cron-image 2" ]	https://docs.redhat.com/en/documentation/openshift_dedicated/4/html/operators/understanding-operators
6.3. About Knowledge References	6.3. About Knowledge References After distributing the data over several databases, define the relationship between the distributed data using knowledge references , pointers to directory information held in different databases. The Directory Server provides the following types of knowledge references to help link the distributed data into a single directory tree: Referrals - The server returns a piece of information to the client application indicating that the client application needs to contact another server to fulfill the request. Chaining - The server contacts other servers on behalf of the client application and returns the combined results to the client application when the operation is finished. The following sections describe and compare these two types of knowledge references in more detail. 6.3.1. Using Referrals A referral is a piece of information returned by a server that informs a client application which server to contact to proceed with an operation request. This redirection mechanism occurs when a client application requests a directory entry that does not exist on the local server. Directory Server supports two types of referrals: Default referrals - The directory returns a default referral when a client application presents a DN for which the server does not have a matching suffix. Default referrals are stored in the configuration file of the server. One default referral can be set for the Directory Server and a separate default referral for each database. The default referral for each database is done through the suffix configuration information. When the suffix of the database is disabled, configure the directory service to return a default referral to client requests made to that suffix. For more information about suffixes, see Section 6.2.2, "About Suffixes" . For information on configuring suffixes, see the Red Hat Directory Server Administration Guide . Smart referrals - Smart referrals are stored on entries within the directory service itself. Smart referrals point to Directory Servers that have knowledge of the subtree whose DN matches the DN of the entry containing the smart referral. All referrals are returned in the format of an LDAP uniform resource locator, or LDAP URL. The following sections describe the structure of an LDAP referral, and then describe the two referral types supported by Directory Server. 6.3.1.1. The Structure of an LDAP Referral An LDAP referral contains information in the format of an LDAP URL. An LDAP URL contains the following information: The host name of the server to contact. The port number on the server that is configured to listen for LDAP requests. The base DN (for search operations) or target DN (for add, delete, and modify operations). For example, a client application searches dc=example,dc=com for entries with a surname value of Jensen . A referral returns the following LDAP URL to the client application: This referral instructs the client application to contact the host europe.example.com on port 389 and submit a search using the root suffix ou=people, l=europe,dc=example,dc=com . The LDAP client application determines how a referral is handled. Some client applications automatically retry the operation on the server to which they have been referred. Other client applications return the referral information to the user. Most LDAP client applications provided by Red Hat Directory Server (such as the command-line utilities) automatically follow the referral. The same bind credentials supplied on the initial directory request are used to access the server. Most client applications follow a limited number of referrals, or hops . The limit on the number of referrals that are followed reduces the time a client application spends trying to complete a directory lookup request and helps eliminate hung processes caused by circular referral patterns. 6.3.1.2. About Default Referrals Default referrals are returned to clients when the server or database that was contacted does not contain the requested data. Directory Server determines whether a default referral should be returned by comparing the DN of the requested directory object against the directory suffixes supported by the local server. If the DN does not match the supported suffixes, the Directory Server returns a default referral. For example, a directory client requests the following directory entry: uid=bjensen,ou=people,dc=example,dc=com However, the server only manages entries stored under the dc=europe,dc=example,dc=com suffix. The directory returns a referral to the client that indicates which server to contact for entries stored under the dc=example,dc=com suffix. The client then contacts the appropriate server and resubmits the original request. Configure the default referral to point to a Directory Server that has more information about the distribution of the directory service. Default referrals for the server are set by the nsslapd-referral attribute. Default referrals for each database in the directory installation are set by the nsslapd-referral attribute in the database entry in the configuration. These attribute values are stored in the dse.ldif file. For information on configuring default referrals, see the Red Hat Directory Server Administration Guide . 6.3.1.3. Smart Referrals The Directory Server can also use smart referrals . Smart referrals associate a directory entry or directory tree to a specific LDAP URL. This means that requests can be forwarded to any of the following: The same namespace contained on a different server. Different namespaces on a local server. Different namespaces on the same server. Unlike default referrals, smart referrals are stored within the directory service itself. For information on configuring and managing smart referrals, see the Red Hat Directory Server Administration Guide . For example, the directory service for the American office of the Example Corp. contains the ou=people,dc=example,dc=com directory branch point. Redirect all requests on this branch to the ou=people branch of the European office of Example Corp. by specifying a smart referral on the ou=people entry itself. The smart referral is ldap://europe.example.com:389/ou=people,dc=example,dc=com . Any requests made to the people branch of the American directory service are redirected to the European directory. This is illustrated below: Figure 6.7. Using Smart Referrals to Redirect Requests The same mechanism can be used to redirect queries to a different server that uses a different namespace. For example, an employee working in the Italian office of Example Corp. makes a request to the European directory service for the phone number of an Example Corp. employee in America. The directory service returns the referral ldap://europe.example.com:389/ou=US employees,dc=example,dc=com . Figure 6.8. Redirecting a Query to a Different Server and Namespace Finally, if multiple suffixes are served on the same server, queries can be redirected from one namespace to another namespace served on the same machine. For example, to redirect all queries on the local machine for o=example,c=us to dc=example,dc=com , then put the smart referral ldap:///dc=example,dc=com on the o=example,c=us entry. Figure 6.9. Redirecting a Query from One Namespace to Another Namespace on the Same Server Note The third slash in this LDAP URL indicates that the URL points to the same Directory Server. Creating a referral from one namespace to another works only for clients whose searches are based at that distinguished name. Other kinds of operations, such as searches below ou=people,o=example,c=US , are not performed correctly. For more information on LDAP URLS and on how to include smart URLs on Directory Server entries, see to the Red Hat Directory Server Administration Guide . 6.3.1.4. Tips for Designing Smart Referrals Even though smart referrals are easy to implement, consider the following points before using them: Keep the design simple. Deploying the directory service using a complex web of referrals makes administration difficult. Overusing smart referrals can also lead to circular referral patterns. For example, a referral points to an LDAP URL, which in turn points to another LDAP URL, and so on until a referral somewhere in the chain points back to the original server. This is illustrated below: Figure 6.10. A Circular Referral Pattern Redirect at major branchpoints. Limit referral usage to handle redirection at the suffix level of the directory tree. Smart referrals redirect lookup requests for leaf (non-branch) entries to different servers and DNs. As a result, it is tempting to use smart referrals as an aliasing mechanism, leading to a complex and difficult method to secure directory structure. Limiting referrals to the suffix or major branch points of the directory tree limits the number of referrals that have to be managed, subsequently reducing the directory's administrative overhead. Consider the security implications. Access control does not cross referral boundaries. Even if the server where the request originated allows access to an entry, when a smart referral sends a client request to another server, the client application may not be allowed access. In addition, the client's credentials need to be available on the server to which the client is referred for client authentication to occur. 6.3.2. Using Chaining Chaining is a method for relaying requests to another server. This method is implemented through database links. A database link, as described in Section 6.2, "Distributing the Directory Data" , contains no data. Instead, it redirects client application requests to remote servers that contain the data. During the chaining process, a server receives a request from a client application for data that the server does not contain. Using the database link, the server then contacts other servers on behalf of the client application and returns the results to the client application. Each database link is associated with a remote server holding data. Configure alternate remote servers containing replicas of the data for the database link to use in the event of a failure. For more information on configuring database links, see the Red Hat Directory Server Administration Guide . Database links provide the following features: Invisible access to remote data. Because the database link resolves client requests, data distribution is completely hidden from the client. Dynamic management. A part of the directory service can be added or removed from the system while the entire system remains available to client applications. The database link can temporarily return referrals to the application until entries have been redistributed across the directory service. This can also be implemented through the suffix itself, which can return a referral rather than forwarding a client application to the database. Access control. The database link impersonates the client application, providing the appropriate authorization identity to the remote server. User impersonation can be disabled on the remote servers when access control evaluation is not required. For more information on configuring database links, see the Red Hat Directory Server Administration Guide . 6.3.3. Deciding Between Referrals and Chaining Both methods of linking the directory partitions have advantages and disadvantages. The method, or combination of methods, to use depends upon the specific needs of the directory service. The major difference between the two knowledge references is the location of the intelligence that knows how to locate the distributed information. In a chained system, the intelligence is implemented in the servers. In a system that uses referrals, the intelligence is implemented in the client application. While chaining reduces client complexity, it does so at the cost of increased server complexity. Chained servers must work with remote servers and send the results to directory clients. With referrals, the client must handle locating the referral and collating search results. However, referrals offer more flexibility for the writers of client applications and allow developers to provide better feedback to users about the progress of a distributed directory operation. The following sections describe some of the more specific differences between referrals and chaining in greater detail. 6.3.3.1. Usage Differences Some client applications do not support referrals. Chaining allows client applications to communicate with a single server and still access the data stored on many servers. Sometimes referrals do not work when a company's network uses proxies. For example, a client application may have permissions to communicate with only one server inside a firewall. If that application is referred to a different server, it is not able to contact it successfully. A client must also be able to authenticate correctly when using referrals, which means that the servers to which clients are being referred need to contain the client's credentials. With chaining, client authentication takes place only once. Clients do not need to authenticate again on the servers to which their requests are chained. 6.3.3.2. Evaluating Access Controls Chaining evaluates access controls differently from referrals. With referrals, an entry for the client must exist on all of the target servers. With chaining, the client entry does not need to be on all of the target servers. Performing Search Requests Using Referrals The following diagram illustrates a client request to a server using referrals: Figure 6.11. Sending a Client Request to a Server Using Referrals In the illustration above, the client application performs the following steps: The client application first binds with Server A. Server A contains an entry for the client that provides a user name and password, so it returns a bind acceptance message. In order for the referral to work, the client entry must be present on server A. The client application sends the operation request to Server A. However, Server A does not contain the requested information. Instead, Server A returns a referral to the client application instructing it to contact Server B. The client application then sends a bind request to Server B. To bind successfully, Server B must also contain an entry for the client application. The bind is successful, and the client application can now resubmit its search operation to Server B. This approach requires Server B to have a replicated copy of the client's entry from Server A. Performing Search Requests Using Chaining The problem of replicating client entries across servers is resolved using chaining. On a chained system, the search request is forwarded multiple times until there is a response. Figure 6.12. Sending a Client Request to a Server Using Chaining In the illustration above, the following steps are performed: The client application binds with Server A, and Server A tries to confirm that the user name and password are correct. Server A does not contain an entry corresponding to the client application. Instead, it contains a database link to Server B, which contains the actual entry of the client. Server A sends a bind request to Server B. Server B sends an acceptance response to Server A. Server A then processes the client application's request using the database link. The database link contacts a remote data store located on Server B to process the search operation. In a chained system, the entry corresponding to the client application does not need to be located on the same server as the data the client requests. Figure 6.13. Authenticating a Client and Retrieving Data Using Different Servers In this illustration, the following steps are performed: The client application binds with Server A, and Server A tries to confirm that the user name and password are correct. Server A does not contain an entry corresponding to the client application. Instead, it contains a database link to Server B, which contains the actual entry of the client. Server A sends a bind request to Server B. Server B sends an acceptance response to Server A. Server A then processes the client application's request using another database link. The database link contacts a remote data store located on Server C to process the search operation. Unsupported Access Controls Database links do not support the following access controls: Controls that must access the content of the user entry are not supported when the user entry is located on a different server. This includes access controls based on groups, filters, and roles. Controls based on client IP addresses or DNS domains may be denied. This is because the database link impersonates the client when it contacts remote servers. If the remote database contains IP-based access controls, it evaluates them using the database link's domain rather than the original client domain.	[ "ldap://europe.example.com:389/ou=people, l=europe,dc=example,dc=com" ]	https://docs.redhat.com/en/documentation/red_hat_directory_server/11/html/deployment_guide/designing_the_directory_topology-about_knowledge_references
21.3. Installation	21.3. Installation To install libguestfs, guestfish, the libguestfs tools, and guestmount, enter the following command: To install every libguestfs-related package including the language bindings, enter the following command:	[ "yum install libguestfs libguestfs-tools", "yum install 'guestf'" ]	https://docs.redhat.com/en/documentation/Red_Hat_Enterprise_Linux/7/html/virtualization_deployment_and_administration_guide/sect-guest_virtual_machine_disk_access_with_offline_tools-installation
Chapter 14. Performing overcloud post-installation tasks	Chapter 14. Performing overcloud post-installation tasks This chapter contains information about tasks to perform immediately after you create your overcloud. These tasks ensure your overcloud is ready to use. 14.1. Checking overcloud deployment status To check the deployment status of the overcloud, use the openstack overcloud status command. This command returns the result of all deployment steps. Procedure Source the stackrc file: Run the deployment status command: The output of this command displays the status of the overcloud: If your overcloud uses a different name, use the --stack argument to select an overcloud with a different name: Replace <overcloud_name> with the name of your overcloud. 14.2. Creating basic overcloud flavors Validation steps in this guide assume that your installation contains flavors. If you have not already created at least one flavor, complete the following steps to create a basic set of default flavors that have a range of storage and processing capabilities: Procedure Source the overcloudrc file: Run the openstack flavor create command to create a flavor. Use the following options to specify the hardware requirements for each flavor: --disk Defines the hard disk space for a virtual machine volume. --ram Defines the RAM required for a virtual machine. --vcpus Defines the quantity of virtual CPUs for a virtual machine. The following example creates the default overcloud flavors: Note Use USD openstack flavor create --help to learn more about the openstack flavor create command. 14.3. Creating a default tenant network The overcloud requires a default Tenant network so that virtual machines can communicate internally. Procedure Source the overcloudrc file: Create the default Tenant network: Create a subnet on the network: Confirm the created network: These commands create a basic Networking service (neutron) network named default . The overcloud automatically assigns IP addresses from this network to virtual machines using an internal DHCP mechanism. 14.4. Creating a default floating IP network To access your virtual machines from outside of the overcloud, you must configure an external network that provides floating IP addresses to your virtual machines. This procedure contains two examples. Use the example that best suits your environment: Native VLAN (flat network) Non-Native VLAN (VLAN network) Both of these examples involve creating a network with the name public . The overcloud requires this specific name for the default floating IP pool. This name is also important for the validation tests in Section 14.7, "Validating the overcloud" . By default, Openstack Networking (neutron) maps a physical network name called datacentre to the br-ex bridge on your host nodes. You connect the public overcloud network to the physical datacentre and this provides a gateway through the br-ex bridge. Prerequisites A dedicated interface or native VLAN for the floating IP network. Procedure Source the overcloudrc file: Create the public network: Create a flat network for a native VLAN connection: Create a vlan network for non-native VLAN connections: Use the --provider-segment option to define the VLAN that you want to use. In this example, the VLAN is 201 . Create a subnet with an allocation pool for floating IP addresses. In this example, the IP range is 10.1.1.51 to 10.1.1.250 : Ensure that this range does not conflict with other IP addresses in your external network. 14.5. Creating a default provider network A provider network is another type of external network connection that routes traffic from private tenant networks to external infrastructure network. The provider network is similar to a floating IP network but the provider network uses a logical router to connect private networks to the provider network. This procedure contains two examples. Use the example that best suits your environment: Native VLAN (flat network) Non-Native VLAN (VLAN network) By default, Openstack Networking (neutron) maps a physical network name called datacentre to the br-ex bridge on your host nodes. You connect the public overcloud network to the physical datacentre and this provides a gateway through the br-ex bridge. Procedure Source the overcloudrc file: Create the provider network: Create a flat network for a native VLAN connection: Create a vlan network for non-native VLAN connections: Use the --provider-segment option to define the VLAN that you want to use. In this example, the VLAN is 201 . These example commands create a shared network. It is also possible to specify a tenant instead of specifying --share so that only the tenant has access to the new network. + If you mark a provider network as external, only the operator may create ports on that network. Add a subnet to the provider network to provide DHCP services: Create a router so that other networks can route traffic through the provider network: Set the external gateway for the router to the provider network: Attach other networks to this router. For example, run the following command to attach a subnet subnet1 to the router: This command adds subnet1 to the routing table and allows traffic from virtual machines using subnet1 to route to the provider network. 14.6. Creating additional bridge mappings Floating IP networks can use any bridge, not just br-ex , provided that you map the additional bridge during deployment. Procedure To map a new bridge called br-floating to the floating physical network, include the NeutronBridgeMappings parameter in an environment file: With this method, you can create separate external networks after creating the overcloud. For example, to create a floating IP network that maps to the floating physical network, run the following commands: 14.7. Validating the overcloud The overcloud uses the OpenStack Integration Test Suite (tempest) tool set to conduct a series of integration tests. This section contains information about preparations for running the integration tests. For full instructions about how to use the OpenStack Integration Test Suite, see the OpenStack Integration Test Suite Guide . The Integration Test Suite requires a few post-installation steps to ensure successful tests. Procedure If you run this test from the undercloud, ensure that the undercloud host has access to the Internal API network on the overcloud. For example, add a temporary VLAN on the undercloud host to access the Internal API network (ID: 201) using the 172.16.0.201/24 address: Run the integration tests as described in the OpenStack Integration Test Suite Guide . After completing the validation, remove any temporary connections to the overcloud Internal API. In this example, use the following commands to remove the previously created VLAN on the undercloud: 14.8. Protecting the overcloud from removal Set a custom policy for heat to protect your overcloud from being deleted. Procedure Create an environment file called prevent-stack-delete.yaml . Set the HeatApiPolicies parameter: Important The heat-deny-action is a default policy that you must include in your undercloud installation. Add the prevent-stack-delete.yaml environment file to the custom_env_files parameter in the undercloud.conf file: Run the undercloud installation command to refresh the configuration: This environment file prevents you from deleting any stacks in the overcloud, which means you cannot perform the following functions: Delete the overcloud Remove individual Compute nor Ceph Storage nodes Replace Controller nodes To enable stack deletion, remove the prevent-stack-delete.yaml file from the custom_env_files parameter and run the openstack undercloud install command.	[ "source ~/stackrc", "openstack overcloud status", "+-----------+---------------------+---------------------+-------------------+ \| Plan Name \| Created \| Updated \| Deployment Status \| +-----------+---------------------+---------------------+-------------------+ \| overcloud \| 2018-05-03 21:24:50 \| 2018-05-03 21:27:59 \| DEPLOY_SUCCESS \| +-----------+---------------------+---------------------+-------------------+", "openstack overcloud status --stack <overcloud_name>", "source ~/overcloudrc", "openstack flavor create m1.tiny --ram 512 --disk 0 --vcpus 1 openstack flavor create m1.smaller --ram 1024 --disk 0 --vcpus 1 openstack flavor create m1.small --ram 2048 --disk 10 --vcpus 1 openstack flavor create m1.medium --ram 3072 --disk 10 --vcpus 2 openstack flavor create m1.large --ram 8192 --disk 10 --vcpus 4 openstack flavor create m1.xlarge --ram 8192 --disk 10 --vcpus 8", "source ~/overcloudrc", "(overcloud) USD openstack network create default", "(overcloud) USD openstack subnet create default --network default --gateway 172.20.1.1 --subnet-range 172.20.0.0/16", "(overcloud) USD openstack network list +-----------------------+-------------+--------------------------------------+ \| id \| name \| subnets \| +-----------------------+-------------+--------------------------------------+ \| 95fadaa1-5dda-4777... \| default \| 7e060813-35c5-462c-a56a-1c6f8f4f332f \| +-----------------------+-------------+--------------------------------------+", "source ~/overcloudrc", "(overcloud) USD openstack network create public --external --provider-network-type flat --provider-physical-network datacentre", "(overcloud) USD openstack network create public --external --provider-network-type vlan --provider-physical-network datacentre --provider-segment 201", "(overcloud) USD openstack subnet create public --network public --dhcp --allocation-pool start=10.1.1.51,end=10.1.1.250 --gateway 10.1.1.1 --subnet-range 10.1.1.0/24", "source ~/overcloudrc", "(overcloud) USD openstack network create provider --external --provider-network-type flat --provider-physical-network datacentre --share", "(overcloud) USD openstack network create provider --external --provider-network-type vlan --provider-physical-network datacentre --provider-segment 201 --share", "(overcloud) USD openstack subnet create provider-subnet --network provider --dhcp --allocation-pool start=10.9.101.50,end=10.9.101.100 --gateway 10.9.101.254 --subnet-range 10.9.101.0/24", "(overcloud) USD openstack router create external", "(overcloud) USD openstack router set --external-gateway provider external", "(overcloud) USD openstack router add subnet external subnet1", "parameter_defaults: NeutronBridgeMappings: \"datacentre:br-ex,floating:br-floating\"", "source ~/overcloudrc (overcloud) USD openstack network create public --external --provider-physical-network floating --provider-network-type vlan --provider-segment 105 (overcloud) USD openstack subnet create public --network public --dhcp --allocation-pool start=10.1.2.51,end=10.1.2.250 --gateway 10.1.2.1 --subnet-range 10.1.2.0/24", "source ~/stackrc (undercloud) USD sudo ovs-vsctl add-port br-ctlplane vlan201 tag=201 -- set interface vlan201 type=internal (undercloud) USD sudo ip l set dev vlan201 up; sudo ip addr add 172.16.0.201/24 dev vlan201", "source ~/stackrc (undercloud) USD sudo ovs-vsctl del-port vlan201", "parameter_defaults: HeatApiPolicies: heat-deny-action: key: 'actions:action' value: 'rule:deny_everybody' heat-protect-overcloud: key: 'stacks:delete' value: 'rule:deny_everybody'", "custom_env_files = prevent-stack-delete.yaml", "openstack undercloud install" ]	https://docs.redhat.com/en/documentation/red_hat_openstack_platform/17.0/html/director_installation_and_usage/assembly_performing-overcloud-post-installation-tasks
Making open source more inclusive	Making open source more inclusive Red Hat is committed to replacing problematic language in our code, documentation, and web properties. We are beginning with these four terms: master, slave, blacklist, and whitelist. Because of the enormity of this endeavor, these changes will be implemented gradually over several upcoming releases. For more details, see our CTO Chris Wright's message .	null	https://docs.redhat.com/en/documentation/red_hat_build_of_openjdk/17/html/release_notes_for_eclipse_temurin_17.0.11/making-open-source-more-inclusive
7.91. java-1.8.0-openjdk	7.91. java-1.8.0-openjdk 7.91.1. RHBA-2015:1427 - java-1.8.0-openjdk bug fix and enhancement update Updated java-1.8.0-openjdk packages that fix several bugs and add one enhancement are now available for Red Hat Enterprise Linux 6. The java-1.8.0-openjdk packages contain the latest version of the Open Java Development Kit (OpenJDK), OpenJDK 8. These packages provide a fully compliant implementation of Java SE 8. Bug Fixes BZ# 1154143 In Red Hat Enterprise Linux 6, the java-1.8.0-openjdk packages mistakenly included the SunEC provider, which does not function properly on this system. With this update, SunEC has been removed from the Red Hat Enterprise Linux 6 version of java-1.8.0-openjdk. BZ# 1155783 Prior to this update, the java-1.8.0-openjdk packages incorrectly provided "java-devel", which could lead to their inclusion in inappropriate builds. As a consequence, the "yum install java-devel" command in some cases installed java-1.8.0-openjdk-devel instead of the intended Java package. This update removes the providing configuration, and java-1.8.0-openjdk-devel can now be installed only by using the "yum install java-1.8.0-openjdk-devel" command. BZ# 1182011 Previously, the OpenJDK utility displayed characters containing the umlaut diacritical mark (such as a, o, or u) and the eszett character (ss) in PostScript output incorrectly. A patch with support for umlaut and eszett characters has been applied, and OpenJDK now displays these characters correctly. BZ# 1189853 The java-1.8.0-openjdk package for Red Hat Enterprise Linux 6 did not provide the "java" virtual package. Consequently, when a package needed to use OpenJDK 8, it was necessary to require "java-1.8.0-openjdk" instead of commonly used "java". Now, it is sufficient to require "java" as expected. BZ# 1212592 OpenJDK used a copy of the system time zone data. This could cause a difference between OpenJDK time and the system time. Now, OpenJDK uses the system time zone data, and OpenJDK time and the system time are the same. Enhancement BZ# 1210007 Red Hat now provides debug builds of OpenJDK in optional channels. With installed debug builds and JVM or JDK switched to using them, it is possible to do detailed HotSpot debugging. The debug builds can be used via alternatives or direct execution, in the same way as regular Java builds. Note that debug builds are not suitable for use in production, as they operate at a slower rate. Users of java-1.8.0-openjdk are advised to upgrade to these updated packages, which fix these bugs and add this enhancement. All running instances of OpenJDK Java must be restarted for the update to take effect.	null	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/6.7_technical_notes/package-java-1.8.0-openjdk
Chapter 7. Viewing and exporting logs	Chapter 7. Viewing and exporting logs Activity logs are gathered for all repositories and namespace in Quay.io. Viewing usage logs of Quay.io. can provide valuable insights and benefits for both operational and security purposes. Usage logs might reveal the following information: Resource Planning : Usage logs can provide data on the number of image pulls, pushes, and overall traffic to your registry. User Activity : Logs can help you track user activity, showing which users are accessing and interacting with images in the registry. This can be useful for auditing, understanding user behavior, and managing access controls. Usage Patterns : By studying usage patterns, you can gain insights into which images are popular, which versions are frequently used, and which images are rarely accessed. This information can help prioritize image maintenance and cleanup efforts. Security Auditing : Usage logs enable you to track who is accessing images and when. This is crucial for security auditing, compliance, and investigating any unauthorized or suspicious activity. Image Lifecycle Management : Logs can reveal which images are being pulled, pushed, and deleted. This information is essential for managing image lifecycles, including deprecating old images and ensuring that only authorized images are used. Compliance and Regulatory Requirements : Many industries have compliance requirements that mandate tracking and auditing of access to sensitive resources. Usage logs can help you demonstrate compliance with such regulations. Identifying Abnormal Behavior : Unusual or abnormal patterns in usage logs can indicate potential security breaches or malicious activity. Monitoring these logs can help you detect and respond to security incidents more effectively. Trend Analysis : Over time, usage logs can provide trends and insights into how your registry is being used. This can help you make informed decisions about resource allocation, access controls, and image management strategies. There are multiple ways of accessing log files: Viewing logs through the web UI. Exporting logs so that they can be saved externally. Accessing log entries using the API. To access logs, you must have administrative privileges for the selected repository or namespace. Note A maximum of 100 log results are available at a time via the API. To gather more results that that, you must use the log exporter feature described in this chapter. 7.1. Viewing logs using the UI Use the following procedure to view log entries for a repository or namespace using the web UI. Procedure Navigate to a repository or namespace for which you are an administrator of. In the navigation pane, select Usage Logs . Optional. On the usage logs page: Set the date range for viewing log entries by adding dates to the From and to boxes. By default, the UI shows you the most recent week of log entries. Type a string into the Filter Logs box to display log entries that of the specified keyword. For example, you can type delete to filter the logs to show deleted tags. Under Description , toggle the arrow of a log entry to see more, or less, text associated with a specific log entry. 7.2. Exporting repository logs You can obtain a larger number of log files and save them outside of Quay.io by using the Export Logs feature. This feature has the following benefits and constraints: You can choose a range of dates for the logs you want to gather from a repository. You can request that the logs be sent to you by an email attachment or directed to a callback URL. To export logs, you must be an administrator of the repository or namespace. 30 days worth of logs are retained for all users. Export logs only gathers log data that was previously produced. It does not stream logging data. When logs are gathered and made available to you, you should immediately copy that data if you want to save it. By default, the data expires after one hour. Use the following procedure to export logs. Procedure Select a repository for which you have administrator privileges. In the navigation pane, select Usage Logs . Optional. If you want to specify specific dates, enter the range in the From and to boxes. Click the Export Logs button. An Export Usage Logs pop-up appears, as shown Enter an email address or callback URL to receive the exported log. For the callback URL, you can use a URL to a specified domain, for example, <webhook.site>. Select Start Logs Export to start the process for gather the selected log entries. Depending on the amount of logging data being gathered, this can take anywhere from a few minutes to several hours to complete. When the log export is completed, the one of following two events happens: An email is received, alerting you to the available of your requested exported log entries. A successful status of your log export request from the webhook URL is returned. Additionally, a link to the exported data is made available for you to delete to download the logs.	null	https://docs.redhat.com/en/documentation/red_hat_quay/3.10/html/about_quay_io/use-quay-view-export-logs
Chapter 144. KafkaNodePool schema reference	Chapter 144. KafkaNodePool schema reference Property Description spec The specification of the KafkaNodePool. KafkaNodePoolSpec status The status of the KafkaNodePool. KafkaNodePoolStatus	null	https://docs.redhat.com/en/documentation/red_hat_streams_for_apache_kafka/2.5/html/amq_streams_api_reference/type-KafkaNodePool-reference
Chapter 19. Preparing for Installation	Chapter 19. Preparing for Installation 19.1. Preparing for a Network Installation Note Make sure no installation DVD (or any other type of DVD or CD) is in your hosting partition's drive if you are performing a network-based installation. Having a DVD or CD in the drive might cause unexpected errors. Ensure that you have boot media available as described in Chapter 20, Booting (IPL) the Installer . The Red Hat Enterprise Linux installation medium must be available for either a network installation (via NFS, FTP, HTTP, or HTTPS) or installation via local storage. Use the following steps if you are performing an NFS, FTP, HTTP, or HTTPS installation. The NFS, FTP, HTTP, or HTTPS server to be used for installation over the network must be a separate, network-accessible server. The separate server can be a virtual machine, LPAR, or any other system (such as a Linux on Power Systems or x86 system). It must provide the complete contents of the installation DVD-ROM. Note The public directory used to access the installation files over FTP, NFS, HTTP, or HTTPS is mapped to local storage on the network server. For example, the local directory /var/www/inst/rhel6.9 on the network server can be accessed as http://network.server.com/inst/rhel6.9 . In the following examples, the directory on the installation staging server that will contain the installation files will be specified as /location/of/disk/space . The directory that will be made publicly available via FTP, NFS, HTTP, or HTTPS will be specified as /publicly_available_directory . For example, /location/of/disk/space may be a directory you create called /var/isos . /publicly_available_directory might be /var/www/html/rhel6.9 , for an HTTP install. In the following, you will require an ISO image . An ISO image is a file containing an exact copy of the content of a DVD. To create an ISO image from a DVD use the following command: where dvd is your DVD drive device, name_of_image is the name you give to the resulting ISO image file, and path_to_image is the path to the location on your system where the resulting ISO image will be stored. To copy the files from the installation DVD to a Linux instance, which acts as an installation staging server, continue with either Section 19.1.1, "Preparing for FTP, HTTP, and HTTPS Installation" or Section 19.1.2, "Preparing for an NFS Installation" . 19.1.1. Preparing for FTP, HTTP, and HTTPS Installation Warning If your Apache web server or tftp FTP server configuration enables SSL security, make sure to only enable the TLSv1 protocol, and disable SSLv2 and SSLv3 . This is due to the POODLE SSL vulnerability (CVE-2014-3566). See https://access.redhat.com/solutions/1232413 for details about securing Apache , and https://access.redhat.com/solutions/1234773 for information about securing tftp . Extract the files from the ISO image of the installation DVD and place them in a directory that is shared over FTP, HTTP, or HTTPS. , make sure that the directory is shared via FTP, HTTP, or HTTPS, and verify client access. Test to see whether the directory is accessible from the server itself, and then from another machine on the same subnet to which you will be installing. 19.1.2. Preparing for an NFS Installation For NFS installation it is not necessary to extract all the files from the ISO image. It is sufficient to make the ISO image itself, the install.img file, and optionally the product.img file available on the network server via NFS. Transfer the ISO image to the NFS exported directory. On a Linux system, run: where path_to_image is the path to the ISO image file, name_of_image is the name of the ISO image file, and publicly_available_directory is a directory that is available over NFS or that you intend to make available over NFS. Use a SHA256 checksum program to verify that the ISO image that you copied is intact. Many SHA256 checksum programs are available for various operating systems. On a Linux system, run: where name_of_image is the name of the ISO image file. The SHA256 checksum program displays a string of 64 characters called a hash . Compare this hash to the hash displayed for this particular image on the Downloads page in the Red Hat Customer Portal (refer to Chapter 1, Obtaining Red Hat Enterprise Linux ). The two hashes should be identical. Copy the images/ directory from inside the ISO image to the same directory in which you stored the ISO image file itself. Enter the following commands: where path_to_image is the path to the ISO image file, name_of_image is the name of the ISO image file, and mount_point is a mount point on which to mount the image while you copy files from the image. For example: The ISO image file and an images/ directory are now present, side-by-side, in the same directory. Verify that the images/ directory contains at least the install.img file, without which installation cannot proceed. Optionally, the images/ directory should contain the product.img file, without which only the packages for a Minimal installation will be available during the package group selection stage (refer to Section 23.17, "Package Group Selection" ). Ensure that an entry for the publicly available directory exists in the /etc/exports file on the network server so that the directory is available via NFS. To export a directory read-only to a specific system, use: To export a directory read-only to all systems, use: On the network server, start the NFS daemon (on a Red Hat Enterprise Linux system, use /sbin/service nfs start ). If NFS is already running, reload the configuration file (on a Red Hat Enterprise Linux system use /sbin/service nfs reload ). Be sure to test the NFS share following the directions in the Red Hat Enterprise Linux Deployment Guide . Refer to your NFS documentation for details on starting and stopping the NFS server. Note anaconda has the ability to test the integrity of the installation media. It works with the DVD, hard drive ISO, and NFS ISO installation methods. We recommend that you test all installation media before starting the installation process, and before reporting any installation-related bugs (many of the bugs reported are actually due to improperly-burned DVDs). To use this test, type the following command at the boot: prompt:	[ "dd if=/dev/ dvd of=/ path_to_image / name_of_image .iso", "mv / path_to_image / name_of_image .iso / publicly_available_directory /", "sha256sum name_of_image .iso", "mount -t iso9660 / path_to_image / name_of_image .iso / mount_point -o loop,ro cp -pr / mount_point /images / publicly_available_directory / umount / mount_point", "mount -t iso9660 /var/isos/RHEL6.iso /mnt/tmp -o loop,ro cp -pr /mnt/tmp/images /var/isos/ umount /mnt/tmp", "/publicly_available_directory client.ip.address (ro)", "/publicly_available_directory * (ro)", "linux mediacheck" ]	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/installation_guide/ch-preparing-s390
Kafka configuration properties	Kafka configuration properties Red Hat Streams for Apache Kafka 2.9 Use configuration properties to configure Kafka components	[ "Further, when in `read_committed` the seekToEnd method will return the LSO .", "dnf install <package_name>", "dnf install <path_to_download_package>" ]	https://docs.redhat.com/en/documentation/red_hat_streams_for_apache_kafka/2.9/html-single/kafka_configuration_properties/index
Chapter 3. Red Hat Advanced Cluster Security Cloud Service architecture	Chapter 3. Red Hat Advanced Cluster Security Cloud Service architecture Discover Red Hat Advanced Cluster Security Cloud Service (RHACS Cloud Service) architecture and concepts. 3.1. Red Hat Advanced Cluster Security Cloud Service architecture overview Red Hat Advanced Cluster Security Cloud Service (RHACS Cloud Service) is a Red Hat managed Software-as-a-Service (SaaS) platform that lets you protect your Kubernetes and OpenShift Container Platform clusters and applications throughout the build, deploy, and runtime lifecycles. RHACS Cloud Service includes many built-in DevOps enforcement controls and security-focused best practices based on industry standards such as the Center for Internet Security (CIS) benchmarks and the National Institute of Standards Technology (NIST) guidelines. You can also integrate it with your existing DevOps tools and workflows to improve security and compliance. RHACS Cloud Service architecture The following graphic shows the architecture with the StackRox Scanner and Scanner V4. Installation of Scanner V4 is optional, but provides additional benefits. Central services include the user interface (UI), data storage, RHACS application programming interface (API), and image scanning capabilities. You deploy your Central service through the Red Hat Hybrid Cloud Console. When you create a new ACS instance, Red Hat creates your individual control plane for RHACS. RHACS Cloud Service allows you to secure self-managed clusters that communicate with a Central instance. The clusters you secure, called Secured Clusters, are managed by you, and not by Red Hat. Secured Cluster services include optional vulnerability scanning services, admission control services, and data collection services used for runtime monitoring and compliance. You install Secured Cluster services on any OpenShift or Kubernetes cluster you want to secure. 3.2. Central Red Hat manages Central, the control plane for RHACS Cloud Service. These services include the following components: Central : Central is the RHACS application management interface and services. It handles API interactions and user interface (RHACS Portal) access. Central DB : Central DB is the database for RHACS and handles all data persistence. It is currently based on PostgreSQL 13. Scanner V4 : Beginning with version 4.4, RHACS contains the Scanner V4 vulnerability scanner for scanning container images. Scanner V4 is built on ClairCore , which also powers the Clair scanner. Scanner V4 includes the Indexer, Matcher, and Scanner V4 DB components, which are used in scanning. StackRox Scanner : The StackRox Scanner is the default scanner in RHACS. The StackRox Scanner originates from a fork of the Clair v2 open source scanner. Scanner-DB : This database contains data for the StackRox Scanner. RHACS scanners analyze each image layer to determine the base operating system and identify programming language packages and packages that were installed by the operating system package manager. They match the findings against known vulnerabilities from various vulnerability sources. In addition, the StackRox Scanner identifies vulnerabilities in the node's operating system and platform. These capabilities are planned for Scanner V4 in a future release. 3.2.1. Vulnerability data sources Sources for vulnerabilities depend on the scanner that is used in your system. RHACS contains two scanners: StackRox Scanner and Scanner V4. StackRox Scanner is the default scanner and is deprecated beginning with release 4.6. Scanner V4 was introduced in release 4.4 and is the recommended image scanner. 3.2.1.1. StackRox Scanner sources StackRox Scanner uses the following vulnerability sources: Red Hat OVAL v2 Alpine Security Database Data tracked in Amazon Linux Security Center Debian Security Tracker Ubuntu CVE Tracker NVD : This is used for various purposes such as filling in information gaps when vendors do not provide information. For example, Alpine does not provide a description, CVSS score, severity, or published date. Note This product uses the NVD API but is not endorsed or certified by the NVD. Linux manual entries and NVD manual entries : The upstream StackRox project maintains a set of vulnerabilities that might not be discovered due to data formatting from other sources or absence of data. repository-to-cpe.json : Maps RPM repositories to their related CPEs, which is required for matching vulnerabilities for RHEL-based images. 3.2.1.2. Scanner V4 sources Scanner V4 uses the following vulnerability sources: Red Hat VEX Used with release 4.6 and later. This source provides vulnerability data in Vulnerability Exploitability eXchange(VEX) format. RHACS takes advantage of VEX benefits to significantly decrease the time needed for the initial loading of vulnerability data, and the space needed to store vulnerability data. RHACS might list a different number of vulnerabilities when you are scanning with a RHACS version that uses OVAL, such as RHACS version 4.5, and a version that uses VEX, such as version 4.6. For example, RHACS no longer displays vulnerabilities with a status of "under investigation," while these vulnerabilities were included with versions that used OVAL data. For more information about Red Hat security data, including information about the use of OVAL, Common Security Advisory Framework Version 2.0 (CSAF), and VEX, see The future of Red Hat security data . Red Hat CVE Map This is used in addition with VEX data for images which appear in the Red Hat Container Catalog . OSV This is used for language-related vulnerabilities, such as Go, Java, JavaScript, Python, and Ruby. This source might provide vulnerability IDs other than CVE IDs for vulnerabilities, such as a GitHub Security Advisory (GHSA) ID. Note RHACS uses the OSV database available at OSV.dev under Apache License 2.0 . NVD This is used for various purposes such as filling in information gaps when vendors do not provide information. For example, Alpine does not provide a description, CVSS score, severity, or published date. Note This product uses the NVD API but is not endorsed or certified by the NVD. Additional vulnerability sources Alpine Security Database Data tracked in Amazon Linux Security Center Debian Security Tracker Oracle OVAL Photon OVAL SUSE OVAL Ubuntu OVAL StackRox : The upstream StackRox project maintains a set of vulnerabilities that might not be discovered due to data formatting from other sources or absence of data. Scanner V4 Indexer sources Scanner V4 indexer uses the following files to index Red Hat containers: repository-to-cpe.json : Maps RPM repositories to their related CPEs, which is required for matching vulnerabilities for RHEL-based images. container-name-repos-map.json : This matches container names to their respective repositories. 3.3. Secured cluster services You install the secured cluster services on each cluster that you want to secure by using the Red Hat Advanced Cluster Security Cloud Service. Secured cluster services include the following components: Sensor : Sensor is the service responsible for analyzing and monitoring the cluster. Sensor listens to the OpenShift Container Platform or Kubernetes API and Collector events to report the current state of the cluster. Sensor also triggers deploy-time and runtime violations based on RHACS Cloud Service policies. In addition, Sensor is responsible for all cluster interactions, such as applying network policies, initiating reprocessing of RHACS Cloud Service policies, and interacting with the Admission controller. Admission controller : The Admission controller prevents users from creating workloads that violate security policies in RHACS Cloud Service. Collector : Collector analyzes and monitors container activity on cluster nodes. It collects container runtime and network activity information and sends the collected data to Sensor. StackRox Scanner : In Kubernetes, the secured cluster services include Scanner-slim as an optional component. However, on OpenShift Container Platform, RHACS Cloud Service installs a Scanner-slim version on each secured cluster to scan images in the OpenShift Container Platform integrated registry and optionally other registries. Scanner-DB : This database contains data for the StackRox Scanner. Scanner V4 : Scanner V4 components are installed on the secured cluster if enabled. Scanner V4 Indexer : The Scanner V4 Indexer performs image indexing, previously known as image analysis. Given an image and registry credentials, the Indexer pulls the image from the registry. It finds the base operating system, if it exists, and looks for packages. It stores and outputs an index report, which contains the findings for the given image. Scanner V4 DB : This component is installed if Scanner V4 is enabled. This database stores information for Scanner V4, including index reports. For best performance, configure a persistent volume claim (PVC) for Scanner V4 DB. Note When secured cluster services are installed on the same cluster as Central services and installed in the same namespace, secured cluster services do not deploy Scanner V4 components. Instead, it is assumed that Central services already include a deployment of Scanner V4. Additional resources External components 3.4. Data access and permissions Red Hat does not have access to the clusters on which you install the secured cluster services. Also, RHACS Cloud Service does not need permission to access the secured clusters. For example, you do not need to create new IAM policies, access roles, or API tokens. However, RHACS Cloud Service stores the data that secured cluster services send. All data is encrypted within RHACS Cloud Service. Encrypting the data within the RHACS Cloud Service platform helps to ensure the confidentiality and integrity of the data. When you install secured cluster services on a cluster, it generates data and transmits it to the RHACS Cloud Service. This data is kept secure within the RHACS Cloud Service platform, and only authorized SRE team members and systems can access this data. RHACS Cloud Service uses this data to monitor the security and compliance of your cluster and applications, and to provide valuable insights and analytics that can help you optimize your deployments.	null	https://docs.redhat.com/en/documentation/red_hat_advanced_cluster_security_for_kubernetes/4.7/html/rhacs_cloud_service/rhacs-cloud-service-architecture
20.4. Known Issues	20.4. Known Issues SELinux MLS policy is not supported with kernel version 4.14 SELinux Multi-Level Security (MLS) Policy denies unknown classes and permissions, and kernel version 4.14 in the kernel-alt packages recognizes the map permission, which is not defined in any policy. Consequently, every command on a system with active MLS policy and SELinux in enforcing mode terminates with the Segmentation fault error. A lot of SELinux denial warnings occurs on systems with active MLS policy and SELinux in permissive mode. The combination of SELinux MLS policy with kernel version 4.14 is not supported. kdump saves the vmcore only if mpt3sas is blacklisted When kdump kernel loads the mpt3sas driver, the kdump kernel crashes and fails to save the vmcore on certain POWER9 systems. To work around this problem, blacklist mpt3sas from the kdump kernel environment by appending the module_blacklist=mpt3sas string to the KDUMP_COMMANDLINE_APPEND variable in the /etc/sysconfig/kdump file: Then restart the kdump service to pick up the changes to the configuration file by running the systemctl restart command as the root user: As a result, kdump is now able to save the vmcore on the POWER9 systems. (BZ#1496273) Recovering from OOM situation fails due to incorrect function of OOM-killer Recovering from an out-of-memory (OOM) situation does not work correctly on systems with large amounts of memory. Kernel's OOM-killer kills the process using the most memory and frees the memory to be used again. However, sometimes the OOM-killer does not wait long enough before killing a second process. Eventually, the OOM-killer kills all the processes on the system and logs this error: If this happens, the operating system must be rebooted. There is no available workaround. (BZ#1405748) HTM is disabled for guests running on IBM POWER systems The Hardware Transactional Memory (HTM) feature currently prevents migrating guest virtual machines from IBM POWER8 to IBM POWER9 hosts, and has therefore been disabled by default. As a consequence, guest virtual machines running on IBM POWER8 and IBM POWER9 hosts cannot use HTM, unless the feature is manually enabled. To do so, change the default pseries-rhel7.5 machine type of these guests to pseries-rhel7.4 . Note that guests configured this way cannot be migrated from an IBM POWER8 host to an IBM POWER9 host. (BZ#1525599) Migrating guests with huge pages from IBM POWER8 to IBM POWER9 fails IBM POWER8 hosts can only use 16MB and 16GB huge pages, but these huge-page sizes are not supported on IBM POWER9. As a consequence, migrating a guest from an IBM POWER8 host to an IBM POWER9 host fails if the guest is configured with static huge pages. To work around this problem, disable huge pages on the guest and reboot it prior to migration. (BZ#1538959) modprobe succeeds to load kernel modules with incorrect parameters When attempting to load a kernel module with an incorrect parameter using the modprobe command, the incorrect parameter is ignored, and the module loads as expected on Red Hat Enterprise Linux 7 for ARM and for IBM Power LE (POWER9). Note that this is a different behavior compared to Red Hat Enterprise Linux for traditional architectures, such as AMD64 and Intel 64, IBM Z and IBM Power Systems. On these systems, modprobe exits with an error, and the module with an incorrect parameter does not load in the described situation. On all architectures, an error message is recorded in the dmesg output. (BZ#1449439)	[ "KDUMP_COMMANDLINE_APPEND=\"irqpoll maxcpus=1 ... module_blacklist=mpt3sas\"", "~]# systemctl restart kdump.service", "Kernel panic - not syncing: Out of memory and no killable processes" ]	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/7/html/7.5_release_notes/power9-known-issues
Chapter 28. Milestones	Chapter 28. Milestones Milestones are a special service task that can be configured in the case definition designer by adding the milestone node to the process designer palette. When creating a new case definition, a milestone configured as AdHoc Autostart is included on the design palette by default. Newly created milestones are not set to AdHoc Autostart by default. Case management milestones generally occur at the end of a stage, but they can also be the result of achieving other milestones. A milestone always requires a condition to be defined in order to track progress. Milestones react to case file data when data is added to a case. A milestone represents a single point of achievement within the case instance. It can be used to flag certain events, which can be useful for Key Performance Indicator (KPI) tracking or identifying the tasks that are still to be completed. Milestones can be in any of the following states during case execution: Active : The condition has been defined on the milestone but it has not been met. Completed : The milestone condition has been met, the milestone has been achieved, and the case can proceed to the task. Terminated : The milestone is no longer a part of the case process and is no longer required. While a milestone is available or completed it can be triggered manually by a signal or automatically if AdHoc Autostart is configured when a case instance starts. Milestones can be triggered as many times as required, however, it is directly achieved when the condition is met. 28.1. Creating the Hardware spec ready milestone Create a HardwareSpecReady milestone that is reached when the required hardware specification document is completed. Procedure In the process designer, expand Milestone in the Object Library and drag a new milestone on the canvas and place it on the right side of the Place order end event. Click the new milestone and click the Properties icon in the upper-right corner. Input Hardware spec ready in the Name field. Expand Implementation/Execution and select AdHoc Autostart . Expand Data Assignments , click in the Assignments field, and add the following: Click the Source column drop-down, select Constant , and input org.kie.api.runtime.process.CaseData(data.get("hwSpec") != null) . Click OK . 28.2. Creating the Manager decision milestone This milestone is reached when the managerDecision variable has been given a response. Procedure In the process designer, expand Milestone in the Object Library and drag a new milestone onto the canvas below the HardwareSpecReady milestone. Click the new milestone and click the Properties icon in the upper-right corner. Input Manager decision in the Name field. Expand Implementation/Execution and select AdHoc Autostart . Expand Data Assignments and click in the Assignments field and add the following: Click the Source column drop-down, select Constant , and input org.kie.api.runtime.process.CaseData(data.get("managerDecision") != null) . Click OK . 28.3. Creating the Order placed milestone This milestone is reached when the ordered variable, which is part of the Place order sub-process, has been given a response. Procedure In the process designer, expand Milestone in the Object Library and drag a new milestone on the canvas below the Prepare hardware spec user task. Click the new milestone and click the Properties icon in the upper-right corner. Input Milestone 1: Order placed in the Name field. Expand Implementation/Execution and select AdHoc Autostart . Expand Data Assignments , click in the Assignments field, and add the following: Click the Source column drop-down, select Constant , and input org.kie.api.runtime.process.CaseData(data.get("ordered") == true) . This means that a case variable named ordered exists with the value true . Click OK . Click Milestone 1: Order placed and create a new script task. Click the new script task and click the Properties icon in the upper-right corner. Input Notify requestor in the Name field. Expand Implementation/Execution and input System.out.println("Notification::Order placed"); . Click the Notify requestor script task and create a signal end event. Click the signal event and in the upper-right corner click the Properties . icon. Expand Implementation/Execution , click the down arrow in the Signal field, and select New . Input Milestone 2: Order shipped . Click the down arrow in the Signal Scope field, select Process Instance . Click Save . Figure 28.1. Order placed milestone 28.4. Creating the Order shipped milestone The condition for this milestone is that a case file variable named shipped is true . AdHoc Autostart is not enabled for this milestone. Instead, it is triggered by a signal event when the order is ready to be sent. Procedure In the process designer, expand Milestone in the Object Library and drag a new milestone on the canvas below the Notify requestor script task. Click the new milestone and click the Properties icon in the upper-right corner. Input Milestone 2: Order shipped in the Name field. Expand Implementation/Execution and ensure that AdHoc Autostart is not selected. Expand Data Assignments , click in the Assignments field, and add the following: Click the Source column drop-down, select Constant , and input org.kie.api.runtime.process.CaseData(data.get("shipped") == true) . This means that a case variable named shipped exists with the value true . Click OK . Click Milestone 2: Order shipped and create a new script task. Click the new script task and click the Properties icon in the upper-right corner. Input Send to tracking system in the Name field. Expand Implementation/Execution and input System.out.println("Order added to tracking system"); . Click the Send to tracking system script task and create a signal end event. Click the signal event and in the upper-right corner click the Properties . icon. Expand Implementation/Execution , click the down arrow in the Signal field, and select New . Input Milestone 3: Delivered to customer . Click the down arrow in the Signal Scope field, select Process Instance . Click Save . Figure 28.2. Order shipped milestone 28.5. Creating the Delivered to customer milestone The condition for this milestone is that a case file variable named delivered is true . AdHoc Autostart is not enabled for this milestone. Instead, it is triggered by a signal event after the order has successfully shipped to the customer. Procedure In the process designer, expand Milestone in the Object Library and drag a new milestone on the canvas below the Send to tracking system script task. Click the new milestone and click the Properties icon in the upper-right corner. Input Milestone 3: Delivered to customer in the Name field. Expand Implementation/Execution and ensure that AdHoc Autostart is not selected. Expand Data Assignments , click in the Assignments field, and add the following: Click the Source column drop-down, select Constant , and input org.kie.api.runtime.process.CaseData(data.get("delivered") == true) . This means that a case variable named delivered exists with the value true . Click OK . Click Milestone 3: Delivered to customer and create a new user task. Click the new user task and click the Properties icon in the upper-right corner. Input Customer satisfaction survey in the Name field. Expand Implementation/Execution , click Add below the Actors menu, click Select New , and input owner . Input CustomerSurvey in the Task Name field. Select the Skippable check box and enter the following text in the in the Description field: Satisfaction survey for order #{CaseId} click in the Assignments field and add the following: Click OK . Click the Customer satisfaction survey user task and create an end event. Click Save to confirm your changes. Figure 28.3. Delivered to customer milestone The IT Orders case can be closed after all milestone sequences are completed. However, due to the ad hoc nature of cases, the case could be reopened if, for example, the order was never received by the customer or the item is faulty. Tasks can be re-triggered or added to the case definition as required, even during run time.	null	https://docs.redhat.com/en/documentation/red_hat_process_automation_manager/7.13/html/getting_started_with_red_hat_process_automation_manager/case-management-milestones-con
Appendix E. Permissions required to provision hosts	Appendix E. Permissions required to provision hosts The following list provides an overview of the permissions a non-admin user requires to provision hosts. Resource name Permissions Additional details Activation Keys view_activation_keys Ansible role view_ansible_roles Required if Ansible is used. Architecture view_architectures Compute profile view_compute_profiles Compute resource view_compute_resources, create_compute_resources, destroy_compute_resources, power_compute_resources Required to provision bare-metal hosts. view_compute_resources_vms, create_compute_resources_vms, destroy_compute_resources_vms, power_compute_resources_vms Required to provision virtual machines. Content Views view_content_views Domain view_domains Environment view_environments Host view_hosts, create_hosts, edit_hosts, destroy_hosts, build_hosts, power_hosts, play_roles_on_host view_discovered_hosts, submit_discovered_hosts, auto_provision_discovered_hosts, provision_discovered_hosts, edit_discovered_hosts, destroy_discovered_hosts Required if the Discovery service is enabled. Hostgroup view_hostgroups, create_hostgroups, edit_hostgroups, play_roles_on_hostgroup Image view_images Lifecycle environment view_lifecycle_environments Location view_locations Medium view_media Operatingsystem view_operatingsystems Organization view_organizations Parameter view_params, create_params, edit_params, destroy_params Product and Repositories view_products Provisioning template view_provisioning_templates Ptable view_ptables Capsule view_smart_proxies, view_smart_proxies_puppetca view_openscap_proxies Required if the OpenSCAP plugin is enabled. Subnet view_subnets Additional resources Creating a Role in Administering Red Hat Satellite Adding Permissions to a Role in Administering Red Hat Satellite Assigning Roles to a User in Administering Red Hat Satellite	null	https://docs.redhat.com/en/documentation/red_hat_satellite/6.16/html/provisioning_hosts/permissions-required-to-provision-hosts_provisioning
9.8. Time Zone Configuration	9.8. Time Zone Configuration Set your time zone by selecting the city closest to your computer's physical location. Click on the map to zoom in to a particular geographical region of the world. Specify a time zone even if you plan to use NTP (Network Time Protocol) to maintain the accuracy of the system clock. From here there are two ways for you to select your time zone: Using your mouse, click on the interactive map to select a specific city (represented by a yellow dot). A red X appears indicating your selection. You can also scroll through the list at the bottom of the screen to select your time zone. Using your mouse, click on a location to highlight your selection. If Red Hat Enterprise Linux is the only operating system on your computer, select System clock uses UTC . The system clock is a piece of hardware on your computer system. Red Hat Enterprise Linux uses the timezone setting to determine the offset between the local time and UTC on the system clock. This behavior is standard for systems that use UNIX, Linux, and similar operating systems. Click to proceed. Warning Do not enable the System clock uses UTC option if your machine also runs Microsoft Windows. Microsoft operating systems change the BIOS clock to match local time rather than UTC. This may cause unexpected behavior under Red Hat Enterprise Linux. Note To change your time zone configuration after you have completed the installation, use the Time and Date Properties Tool . Type the system-config-date command in a shell prompt to launch the Time and Date Properties Tool . If you are not root, it prompts you for the root password to continue.	null	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/installation_guide/s1-timezone-x86
Chapter 9. Managing users and roles	Chapter 9. Managing users and roles A User defines a set of details for individuals using the system. Users can be associated with organizations and environments, so that when they create new entities, the default settings are automatically used. Users can also have one or more roles attached, which grants them rights to view and manage organizations and environments. See Section 9.1, "Managing users" for more information on working with users. You can manage permissions of several users at once by organizing them into user groups. User groups themselves can be further grouped to create a hierarchy of permissions. For more information on creating user groups, see Section 9.4, "Creating and managing user groups" . Roles define a set of permissions and access levels. Each role contains one on more permission filters that specify the actions allowed for the role. Actions are grouped according to the Resource type . Once a role has been created, users and user groups can be associated with that role. This way, you can assign the same set of permissions to large groups of users. Satellite provides a set of predefined roles and also enables creating custom roles and permission filters as described in Section 9.5, "Creating and managing roles" . 9.1. Managing users As an administrator, you can create, modify and remove Satellite users. You can also configure access permissions for a user or a group of users by assigning them different roles . 9.1.1. Creating a user Use this procedure to create a user. To use the CLI instead of the Satellite web UI, see the CLI procedure . Procedure In the Satellite web UI, navigate to Administer > Users . Click Create User . Enter the account details for the new user. Click Submit to create the user. The user account details that you can specify include the following: On the User tab, select an authentication source from the Authorized by list: INTERNAL : to manage the user inside Satellite Server. EXTERNAL : to manage the user with external authentication. For more information, see Configuring External Authentication in Installing Satellite Server in a connected network environment . On the Organizations tab, select an organization for the user. Specify the default organization Satellite selects for the user after login from the Default on login list. Important If a user is not assigned to an organization, their access is limited. CLI procedure Create a user: The --auth-source-id 1 setting means that the user is authenticated internally, you can specify an external authentication source as an alternative. Add the --admin option to grant administrator privileges to the user. Specifying organization IDs is not required. You can modify the user details later by using the hammer user update command. Additional resources For more information about creating user accounts by using Hammer, enter hammer user create --help . 9.1.2. Assigning roles to a user Use this procedure to assign roles to a user. To use the CLI instead of the Satellite web UI, see the CLI procedure . Procedure In the Satellite web UI, navigate to Administer > Users . Click the username of the user to be assigned one or more roles. Note If a user account is not listed, check that you are currently viewing the correct organization. To list all the users in Satellite, click Default Organization and then Any Organization . Click the Locations tab, and select a location if none is assigned. Click the Organizations tab, and check that an organization is assigned. Click the Roles tab to display the list of available roles. Select the roles to assign from the Roles list. To grant all the available permissions, select the Administrator checkbox. Click Submit . To view the roles assigned to a user, click the Roles tab; the assigned roles are listed under Selected items . To remove an assigned role, click the role name in Selected items . CLI procedure To assign roles to a user, enter the following command: 9.1.3. Impersonating a different user account Administrators can impersonate other authenticated users for testing and troubleshooting purposes by temporarily logging on to the Satellite web UI as a different user. When impersonating another user, the administrator has permissions to access exactly what the impersonated user can access in the system, including the same menus. Audits are created to record the actions that the administrator performs while impersonating another user. However, all actions that an administrator performs while impersonating another user are recorded as having been performed by the impersonated user. Prerequisites Ensure that you are logged on to the Satellite web UI as a user with administrator privileges for Satellite. Procedure In the Satellite web UI, navigate to Administer > Users . To the right of the user that you want to impersonate, from the list in the Actions column, select Impersonate . When you want to stop the impersonation session, in the upper right of the main menu, click the impersonation icon. 9.1.4. Creating an API-only user You can create users that can interact only with the Satellite API. Prerequisites You have created a user and assigned roles to them. Note that this user must be authorized internally. For more information, see the following sections: Section 9.1.1, "Creating a user" Section 9.1.2, "Assigning roles to a user" Procedure Log in to your Satellite as admin. Navigate to Administer > Users and select a user. On the User tab, set a password. Do not save or communicate this password with others. You can create pseudo-random strings on your console: Create a Personal Access Token for the user. For more information, see Section 9.3.1, "Creating a Personal Access Token" . 9.2. Managing SSH keys Adding SSH keys to a user allows deployment of SSH keys during provisioning. For information on deploying SSH keys during provisioning, see Deploying SSH Keys during Provisioning in Provisioning hosts . For information on SSH keys and SSH key creation, see Using SSH-based Authentication in Red Hat Enterprise Linux 8 Configuring basic system settings . 9.2.1. Managing SSH keys for a user Use this procedure to add or remove SSH keys for a user. To use the CLI instead of the Satellite web UI, see the CLI procedure . Prerequisites Ensure that you are logged in to the Satellite web UI as an Admin user of Red Hat Satellite or a user with the create_ssh_key permission enabled for adding SSH key and destroy_ssh_key permission for removing a key. Procedure In the Satellite web UI, navigate to Administer > Users . From the Username column, click on the username of the required user. Click on the SSH Keys tab. To Add SSH key Prepare the content of the public SSH key in a clipboard. Click Add SSH Key . In the Key field, paste the public SSH key content from the clipboard. In the Name field, enter a name for the SSH key. Click Submit . To Remove SSH key Click Delete on the row of the SSH key to be deleted. Click OK in the confirmation prompt. CLI procedure To add an SSH key to a user, you must specify either the path to the public SSH key file, or the content of the public SSH key copied to the clipboard. If you have the public SSH key file, enter the following command: If you have the content of the public SSH key, enter the following command: To delete an SSH key from a user, enter the following command: To view an SSH key attached to a user, enter the following command: To list SSH keys attached to a user, enter the following command: 9.3. Managing Personal Access Tokens Personal Access Tokens allow you to authenticate API requests without using your password. You can set an expiration date for your Personal Access Token and you can revoke it if you decide it should expire before the expiration date. 9.3.1. Creating a Personal Access Token Use this procedure to create a Personal Access Token. Procedure In the Satellite web UI, navigate to Administer > Users . Select a user for which you want to create a Personal Access Token. On the Personal Access Tokens tab, click Add Personal Access Token . Enter a Name for you Personal Access Token. Optional: Select the Expires date to set an expiration date. If you do not set an expiration date, your Personal Access Token will never expire unless revoked. Click Submit. You now have the Personal Access Token available to you on the Personal Access Tokens tab. Important Ensure to store your Personal Access Token as you will not be able to access it again after you leave the page or create a new Personal Access Token. You can click Copy to clipboard to copy your Personal Access Token. Verification Make an API request to your Satellite Server and authenticate with your Personal Access Token: You should receive a response with status 200 , for example: If you go back to Personal Access Tokens tab, you can see the updated Last Used time to your Personal Access Token. 9.3.2. Revoking a Personal Access Token Use this procedure to revoke a Personal Access Token before its expiration date. Procedure In the Satellite web UI, navigate to Administer > Users . Select a user for which you want to revoke the Personal Access Token. On the Personal Access Tokens tab, locate the Personal Access Token you want to revoke. Click Revoke in the Actions column to the Personal Access Token you want to revoke. Verification Make an API request to your Satellite Server and try to authenticate with the revoked Personal Access Token: You receive the following error message: 9.4. Creating and managing user groups 9.4.1. User groups With Satellite, you can assign permissions to groups of users. You can also create user groups as collections of other user groups. If using an external authentication source, you can map Satellite user groups to external user groups as described in Configuring External User Groups in Installing Satellite Server in a connected network environment . User groups are defined in an organizational context, meaning that you must select an organization before you can access user groups. 9.4.2. Creating a user group Use this procedure to create a user group. Procedure In the Satellite web UI, navigate to Administer > User Groups . Click Create User group . On the User Group tab, specify the name of the new user group and select group members: Select the previously created user groups from the User Groups list. Select users from the Users list. On the Roles tab, select the roles you want to assign to the user group. Alternatively, select the Admin checkbox to assign all available permissions. Click Submit . CLI procedure To create a user group, enter the following command: 9.4.3. Removing a user group Use the following procedure to remove a user group from Satellite. Procedure In the Satellite web UI, navigate to Administer > User Groups . Click Delete to the right of the user group you want to delete. Click Confirm to delete the user group. 9.5. Creating and managing roles Satellite provides a set of predefined roles with permissions sufficient for standard tasks, as listed in Section 9.6, "Predefined roles available in Satellite" . It is also possible to configure custom roles, and assign one or more permission filters to them. Permission filters define the actions allowed for a certain resource type. Certain Satellite plugins create roles automatically. 9.5.1. Creating a role Use this procedure to create a role. Procedure In the Satellite web UI, navigate to Administer > Roles . Click Create Role . Provide a Name for the role. Click Submit to save your new role. CLI procedure To create a role, enter the following command: To serve its purpose, a role must contain permissions. After creating a role, proceed to Section 9.5.3, "Adding permissions to a role" . 9.5.2. Cloning a role Use the Satellite web UI to clone a role. Procedure In the Satellite web UI, navigate to Administer > Roles and select Clone from the drop-down menu to the right of the required role. Provide a Name for the role. Click Submit to clone the role. Click the name of the cloned role and navigate to Filters . Edit the permissions as required. Click Submit to save your new role. 9.5.3. Adding permissions to a role Use this procedure to add permissions to a role. To use the CLI instead of the Satellite web UI, see the CLI procedure . Procedure In the Satellite web UI, navigate to Administer > Roles . Select Add Filter from the drop-down list to the right of the required role. Select the Resource type from the drop-down list. The (Miscellaneous) group gathers permissions that are not associated with any resource group. Click the permissions you want to select from the Permission list. Depending on the Resource type selected, you can select or deselect the Unlimited and Override checkbox. The Unlimited checkbox is selected by default, which means that the permission is applied on all resources of the selected type. When you disable the Unlimited checkbox, the Search field activates. In this field you can specify further filtering with use of the Satellite search syntax. For more information, see Section 9.7, "Granular permission filtering" . When you enable the Override checkbox, you can add additional locations and organizations to allow the role to access the resource type in the additional locations and organizations; you can also remove an already associated location and organization from the resource type to restrict access. Click . Click Submit to save changes. CLI procedure List all available permissions: Add permissions to a role: For more information about roles and permissions parameters, enter the hammer role --help and hammer filter --help commands. 9.5.4. Viewing permissions of a role Use the Satellite web UI to view the permissions of a role. Procedure In the Satellite web UI, navigate to Administer > Roles . Click Filters to the right of the required role to get to the Filters page. The Filters page contains a table of permissions assigned to a role grouped by the resource type. It is also possible to generate a complete table of permissions and actions that you can use on your Satellite system. For more information, see Section 9.5.5, "Creating a complete permission table" . 9.5.5. Creating a complete permission table Use the Satellite CLI to create a permission table. Procedure Start the Satellite console with the following command: Insert the following code into the console: The above syntax creates a table of permissions and saves it to the /tmp/table.html file. Press Ctrl + D to exit the Satellite console. Insert the following text at the first line of /tmp/table.html : Append the following text at the end of /tmp/table.html : Open /tmp/table.html in a web browser to view the table. 9.5.6. Removing a role Use the following procedure to remove a role from Satellite. Procedure In the Satellite web UI, navigate to Administer > Roles . Select Delete from the drop-down list to the right of the role to be deleted. Click Confirm to delete the role. 9.6. Predefined roles available in Satellite The following table provides an overview of permissions that predefined roles in Satellite grant to a user. For a complete set of predefined roles and the permissions they grant, log in to Satellite web UI as the privileged user and navigate to Administer > Roles . For more information, see Section 9.5.4, "Viewing permissions of a role" . Predefined role Permissions the role provides Additional information Auditor View the Audit log. Default role View tasks and jobs invocations. Satellite automatically assigns this role to every user in the system. Manager View and edit global settings. Organization admin All permissions except permissions for managing organizations. An administrator role defined per organization. The role has no visibility into resources in other organizations. By cloning this role and assigning an organization, you can delegate administration of that organization to a user. Site manager View permissions for various items. Permissions to manage hosts in the infrastructure. A restrained version of the Manager role. System admin Edit global settings in Administer > Settings . View, create, edit, and destroy users, user groups, and roles. View, create, edit, destroy, and assign organizations and locations but not view resources within them. Users with this role can create users and assign all roles to them. Give this role only to trusted users. Viewer View the configuration of every element of the Satellite structure, logs, reports, and statistics. 9.7. Granular permission filtering As mentioned in Section 9.5.3, "Adding permissions to a role" , Red Hat Satellite provides the ability to limit the configured user permissions to selected instances of a resource type. These granular filters are queries to the Satellite database and are supported by the majority of resource types. 9.7.1. Creating a granular permission filter Use this procedure to create a granular filter. To use the CLI instead of the Satellite web UI, see the CLI procedure . Satellite does not apply search conditions to create actions. For example, limiting the create_locations action with name = "Default Location" expression in the search field does not prevent the user from assigning a custom name to the newly created location. Procedure Specify a query in the Search field on the Edit Filter page. Deselect the Unlimited checkbox for the field to be active. Queries have the following form: field_name marks the field to be queried. The range of available field names depends on the resource type. For example, the Partition Table resource type offers family , layout , and name as query parameters. operator specifies the type of comparison between field_name and value . See Section 9.7.3, "Supported operators for granular search" for an overview of applicable operators. value is the value used for filtering. This can be for example a name of an organization. Two types of wildcard characters are supported: underscore (_) provides single character replacement, while percent sign (%) replaces zero or more characters. For most resource types, the Search field provides a drop-down list suggesting the available parameters. This list appears after placing the cursor in the search field. For many resource types, you can combine queries using logical operators such as and , not and has operators. CLI procedure To create a granular filter, enter the hammer filter create command with the --search option to limit permission filters, for example: This command adds to the qa-user role a permission to view, create, edit, and destroy content views that only applies to content views with name starting with ccv . 9.7.2. Examples of using granular permission filters As an administrator, you can allow selected users to make changes in a certain part of the environment path. The following filter allows you to work with content while it is in the development stage of the application lifecycle, but the content becomes inaccessible once is pushed to production. 9.7.2.1. Applying permissions for the host resource type The following query applies any permissions specified for the Host resource type only to hosts in the group named host-editors. The following query returns records where the name matches XXXX , Yyyy , or zzzz example strings: You can also limit permissions to a selected environment. To do so, specify the environment name in the Search field, for example: You can limit user permissions to a certain organization or location with the use of the granular permission filter in the Search field. However, some resource types provide a GUI alternative, an Override checkbox that provides the Locations and Organizations tabs. On these tabs, you can select from the list of available organizations and locations. For more information, see Section 9.7.2.2, "Creating an organization-specific manager role" . 9.7.2.2. Creating an organization-specific manager role Use the Satellite web UI to create an administrative role restricted to a single organization named org-1 . Procedure In the Satellite web UI, navigate to Administer > Roles . Clone the existing Organization admin role. Select Clone from the drop-down list to the Filters button. You are then prompted to insert a name for the cloned role, for example org-1 admin . Click the desired locations and organizations to associate them with the role. Click Submit to create the role. Click org-1 admin , and click Filters to view all associated filters. The default filters work for most use cases. However, you can optionally click Edit to change the properties for each filter. For some filters, you can enable the Override option if you want the role to be able to access resources in additional locations and organizations. For example, by selecting the Domain resource type, the Override option, and then additional locations and organizations using the Locations and Organizations tabs, you allow this role to access domains in the additional locations and organizations that is not associated with this role. You can also click New filter to associate new filters with this role. 9.7.3. Supported operators for granular search Table 9.1. Logical operators Operator Description and Combines search criteria. not Negates an expression. has Object must have a specified property. Table 9.2. Symbolic operators Operator Description = Is equal to . An equality comparison that is case-sensitive for text fields. != Is not equal to . An inversion of the = operator. ~ Like . A case-insensitive occurrence search for text fields. !~ Not like . An inversion of the ~ operator. ^ In . An equality comparison that is case-sensitive search for text fields. This generates a different SQL query to the Is equal to comparison, and is more efficient for multiple value comparison. !^ Not in . An inversion of the ^ operator. >, >= Greater than , greater than or equal to . Supported for numerical fields only. <, ⇐ Less than , less than or equal to . Supported for numerical fields only.	[ "hammer user create --auth-source-id My_Authentication_Source --login My_User_Name --mail My_User_Mail --organization-ids My_Organization_ID_1 , My_Organization_ID_2 --password My_User_Password", "hammer user add-role --id user_id --role role_name", "openssl rand -hex 32", "hammer user ssh-keys add --user-id user_id --name key_name --key-file ~/.ssh/id_rsa.pub", "hammer user ssh-keys add --user-id user_id --name key_name --key ecdsa-sha2-nistp256 AAAAE2VjZHNhLXNoYTItbmlzdHAyNTYAAAAIbmlzdHAyNtYAAABBBHHS2KmNyIYa27Qaa7EHp+2l99ucGStx4P77e03ZvE3yVRJEFikpoP3MJtYYfIe8k 1/46MTIZo9CPTX4CYUHeN8= host@user", "hammer user ssh-keys delete --id key_id --user-id user_id", "hammer user ssh-keys info --id key_id --user-id user_id", "hammer user ssh-keys list --user-id user_id", "curl https:// satellite.example.com /api/status --user My_Username : My_Personal_Access_Token", "{\"satellite_version\":\"6.15.0\",\"result\":\"ok\",\"status\":200,\"version\":\"3.5.1.10\",\"api_version\":2}", "curl https:// satellite.example.com /api/status --user My_Username : My_Personal_Access_Token", "{ \"error\": {\"message\":\"Unable to authenticate user My_Username \"} }", "hammer user-group create --name My_User_Group_Name --role-ids My_Role_ID_1 , My_Role_ID_2 --user-ids My_User_ID_1 , My_User_ID_2", "hammer role create --name My_Role_Name", "hammer filter available-permissions", "hammer filter create --permission-ids My_Permission_ID_1 , My_Permission_ID_2 --role My_Role_Name", "foreman-rake console", "f = File.open('/tmp/table.html', 'w') result = Foreman::AccessControl.permissions {\|a,b\| a.security_block <=> b.security_block}.collect do \|p\| actions = p.actions.collect { \|a\| \"<li>#{a}</li>\" } \"<tr><td>#{p.name}</td><td><ul>#{actions.join('')}</ul></td><td>#{p.resource_type}</td></tr>\" end.join(\"\\n\") f.write(result)", "<table border=\"1\"><tr><td>Permission name</td><td>Actions</td><td>Resource type</td></tr>", "</table>", "field_name operator value", "hammer filter create --permission-ids 91 --search \"name ~ ccv*\" --role qa-user", "hostgroup = host-editors", "name ^ (XXXX, Yyyy, zzzz)", "Dev" ]	https://docs.redhat.com/en/documentation/red_hat_satellite/6.15/html/administering_red_hat_satellite/managing_users_and_roles_admin
Chapter 6. Managing alerts	Chapter 6. Managing alerts In OpenShift Container Platform 4.9, the Alerting UI enables you to manage alerts, silences, and alerting rules. Alerting rules . Alerting rules contain a set of conditions that outline a particular state within a cluster. Alerts are triggered when those conditions are true. An alerting rule can be assigned a severity that defines how the alerts are routed. Alerts . An alert is fired when the conditions defined in an alerting rule are true. Alerts provide a notification that a set of circumstances are apparent within an OpenShift Container Platform cluster. Silences . A silence can be applied to an alert to prevent notifications from being sent when the conditions for an alert are true. You can mute an alert after the initial notification, while you work on resolving the underlying issue. Note The alerts, silences, and alerting rules that are available in the Alerting UI relate to the projects that you have access to. For example, if you are logged in with cluster-admin privileges, you can access all alerts, silences, and alerting rules. If you are a non-administator user, you can create and silence alerts if you are assigned the following user roles: The cluster-monitoring-view role, which allows you to access Alertmanager The monitoring-alertmanager-edit role, which permits you to create and silence alerts in the Administrator perspective in the web console The monitoring-rules-edit role, which permits you to create and silence alerts in the Developer perspective in the web console 6.1. Accessing the Alerting UI in the Administrator and Developer perspectives The Alerting UI is accessible through the Administrator perspective and the Developer perspective in the OpenShift Container Platform web console. In the Administrator perspective, select Observe Alerting . The three main pages in the Alerting UI in this perspective are the Alerts , Silences , and Alerting Rules pages. In the Developer perspective, select Observe <project_name> Alerts . In this perspective, alerts, silences, and alerting rules are all managed from the Alerts page. The results shown in the Alerts page are specific to the selected project. Note In the Developer perspective, you can select from core OpenShift Container Platform and user-defined projects that you have access to in the Project: list. However, alerts, silences, and alerting rules relating to core OpenShift Container Platform projects are not displayed if you do not have cluster-admin privileges. 6.2. Searching and filtering alerts, silences, and alerting rules You can filter the alerts, silences, and alerting rules that are displayed in the Alerting UI. This section provides a description of each of the available filtering options. Understanding alert filters In the Administrator perspective, the Alerts page in the Alerting UI provides details about alerts relating to default OpenShift Container Platform and user-defined projects. The page includes a summary of severity, state, and source for each alert. The time at which an alert went into its current state is also shown. You can filter by alert state, severity, and source. By default, only Platform alerts that are Firing are displayed. The following describes each alert filtering option: Alert State filters: Firing . The alert is firing because the alert condition is true and the optional for duration has passed. The alert will continue to fire as long as the condition remains true. Pending . The alert is active but is waiting for the duration that is specified in the alerting rule before it fires. Silenced . The alert is now silenced for a defined time period. Silences temporarily mute alerts based on a set of label selectors that you define. Notifications will not be sent for alerts that match all the listed values or regular expressions. Severity filters: Critical . The condition that triggered the alert could have a critical impact. The alert requires immediate attention when fired and is typically paged to an individual or to a critical response team. Warning . The alert provides a warning notification about something that might require attention to prevent a problem from occurring. Warnings are typically routed to a ticketing system for non-immediate review. Info . The alert is provided for informational purposes only. None . The alert has no defined severity. You can also create custom severity definitions for alerts relating to user-defined projects. Source filters: Platform . Platform-level alerts relate only to default OpenShift Container Platform projects. These projects provide core OpenShift Container Platform functionality. User . User alerts relate to user-defined projects. These alerts are user-created and are customizable. User-defined workload monitoring can be enabled post-installation to provide observability into your own workloads. Understanding silence filters In the Administrator perspective, the Silences page in the Alerting UI provides details about silences applied to alerts in default OpenShift Container Platform and user-defined projects. The page includes a summary of the state of each silence and the time at which a silence ends. You can filter by silence state. By default, only Active and Pending silences are displayed. The following describes each silence state filter option: Silence State filters: Active . The silence is active and the alert will be muted until the silence is expired. Pending . The silence has been scheduled and it is not yet active. Expired . The silence has expired and notifications will be sent if the conditions for an alert are true. Understanding alerting rule filters In the Administrator perspective, the Alerting Rules page in the Alerting UI provides details about alerting rules relating to default OpenShift Container Platform and user-defined projects. The page includes a summary of the state, severity, and source for each alerting rule. You can filter alerting rules by alert state, severity, and source. By default, only Platform alerting rules are displayed. The following describes each alerting rule filtering option: Alert State filters: Firing . The alert is firing because the alert condition is true and the optional for duration has passed. The alert will continue to fire as long as the condition remains true. Pending . The alert is active but is waiting for the duration that is specified in the alerting rule before it fires. Silenced . The alert is now silenced for a defined time period. Silences temporarily mute alerts based on a set of label selectors that you define. Notifications will not be sent for alerts that match all the listed values or regular expressions. Not Firing . The alert is not firing. Severity filters: Critical . The conditions defined in the alerting rule could have a critical impact. When true, these conditions require immediate attention. Alerts relating to the rule are typically paged to an individual or to a critical response team. Warning . The conditions defined in the alerting rule might require attention to prevent a problem from occurring. Alerts relating to the rule are typically routed to a ticketing system for non-immediate review. Info . The alerting rule provides informational alerts only. None . The alerting rule has no defined severity. You can also create custom severity definitions for alerting rules relating to user-defined projects. Source filters: Platform . Platform-level alerting rules relate only to default OpenShift Container Platform projects. These projects provide core OpenShift Container Platform functionality. User . User-defined workload alerting rules relate to user-defined projects. These alerting rules are user-created and are customizable. User-defined workload monitoring can be enabled post-installation to provide observability into your own workloads. Searching and filtering alerts, silences, and alerting rules in the Developer perspective In the Developer perspective, the Alerts page in the Alerting UI provides a combined view of alerts and silences relating to the selected project. A link to the governing alerting rule is provided for each displayed alert. In this view, you can filter by alert state and severity. By default, all alerts in the selected project are displayed if you have permission to access the project. These filters are the same as those described for the Administrator perspective. 6.3. Getting information about alerts, silences, and alerting rules The Alerting UI provides detailed information about alerts and their governing alerting rules and silences. Prerequisites You have access to the cluster as a developer or as a user with view permissions for the project that you are viewing metrics for. Procedure To obtain information about alerts in the Administrator perspective : Open the OpenShift Container Platform web console and navigate to the Observe Alerting Alerts page. Optional: Search for alerts by name using the Name field in the search list. Optional: Filter alerts by state, severity, and source by selecting filters in the Filter list. Optional: Sort the alerts by clicking one or more of the Name , Severity , State , and Source column headers. Select the name of an alert to navigate to its Alert Details page. The page includes a graph that illustrates alert time series data. It also provides information about the alert, including: A description of the alert Messages associated with the alerts Labels attached to the alert A link to its governing alerting rule Silences for the alert, if any exist To obtain information about silences in the Administrator perspective : Navigate to the Observe Alerting Silences page. Optional: Filter the silences by name using the Search by name field. Optional: Filter silences by state by selecting filters in the Filter list. By default, Active and Pending filters are applied. Optional: Sort the silences by clicking one or more of the Name , Firing Alerts , and State column headers. Select the name of a silence to navigate to its Silence Details page. The page includes the following details: Alert specification Start time End time Silence state Number and list of firing alerts To obtain information about alerting rules in the Administrator perspective : Navigate to the Observe Alerting Alerting Rules page. Optional: Filter alerting rules by state, severity, and source by selecting filters in the Filter list. Optional: Sort the alerting rules by clicking one or more of the Name , Severity , Alert State , and Source column headers. Select the name of an alerting rule to navigate to its Alerting Rule Details page. The page provides the following details about the alerting rule: Alerting rule name, severity, and description The expression that defines the condition for firing the alert The time for which the condition should be true for an alert to fire A graph for each alert governed by the alerting rule, showing the value with which the alert is firing A table of all alerts governed by the alerting rule To obtain information about alerts, silences, and alerting rules in the Developer perspective : Navigate to the Observe <project_name> Alerts page. View details for an alert, silence, or an alerting rule: Alert Details can be viewed by selecting > to the left of an alert name and then selecting the alert in the list. Silence Details can be viewed by selecting a silence in the Silenced By section of the Alert Details page. The Silence Details page includes the following information: Alert specification Start time End time Silence state Number and list of firing alerts Alerting Rule Details can be viewed by selecting View Alerting Rule in the menu on the right of an alert in the Alerts page. Note Only alerts, silences, and alerting rules relating to the selected project are displayed in the Developer perspective. 6.4. Managing alerting rules OpenShift Container Platform monitoring ships with a set of default alerting rules. As a cluster administrator, you can view the default alerting rules. In OpenShift Container Platform 4.9, you can create, view, edit, and remove alerting rules in user-defined projects. Alerting rule considerations The default alerting rules are used specifically for the OpenShift Container Platform cluster. Some alerting rules intentionally have identical names. They send alerts about the same event with different thresholds, different severity, or both. Inhibition rules prevent notifications for lower severity alerts that are firing when a higher severity alert is also firing. 6.4.1. Optimizing alerting for user-defined projects You can optimize alerting for your own projects by considering the following recommendations when creating alerting rules: Minimize the number of alerting rules that you create for your project . Create alerting rules that notify you of conditions that impact you. It is more difficult to notice relevant alerts if you generate many alerts for conditions that do not impact you. Create alerting rules for symptoms instead of causes . Create alerting rules that notify you of conditions regardless of the underlying cause. The cause can then be investigated. You will need many more alerting rules if each relates only to a specific cause. Some causes are then likely to be missed. Plan before you write your alerting rules . Determine what symptoms are important to you and what actions you want to take if they occur. Then build an alerting rule for each symptom. Provide clear alert messaging . State the symptom and recommended actions in the alert message. Include severity levels in your alerting rules . The severity of an alert depends on how you need to react if the reported symptom occurs. For example, a critical alert should be triggered if a symptom requires immediate attention by an individual or a critical response team. Optimize alert routing . Deploy an alerting rule directly on the Prometheus instance in the openshift-user-workload-monitoring project if the rule does not query default OpenShift Container Platform metrics. This reduces latency for alerting rules and minimizes the load on monitoring components. Warning Default OpenShift Container Platform metrics for user-defined projects provide information about CPU and memory usage, bandwidth statistics, and packet rate information. Those metrics cannot be included in an alerting rule if you route the rule directly to the Prometheus instance in the openshift-user-workload-monitoring project. Alerting rule optimization should be used only if you have read the documentation and have a comprehensive understanding of the monitoring architecture. Additional resources See the Prometheus alerting documentation for further guidelines on optimizing alerts See Monitoring overview for details about OpenShift Container Platform 4.9 monitoring architecture 6.4.2. Creating alerting rules for user-defined projects You can create alerting rules for user-defined projects. Those alerting rules will fire alerts based on the values of chosen metrics. Prerequisites You have enabled monitoring for user-defined projects. You are logged in as a user that has the monitoring-rules-edit role for the project where you want to create an alerting rule. You have installed the OpenShift CLI ( oc ). Procedure Create a YAML file for alerting rules. In this example, it is called example-app-alerting-rule.yaml . Add an alerting rule configuration to the YAML file. For example: Note When you create an alerting rule, a project label is enforced on it if a rule with the same name exists in another project. apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule metadata: name: example-alert namespace: ns1 spec: groups: - name: example rules: - alert: VersionAlert expr: version{job="prometheus-example-app"} == 0 This configuration creates an alerting rule named example-alert . The alerting rule fires an alert when the version metric exposed by the sample service becomes 0 . Important A user-defined alerting rule can include metrics for its own project and cluster metrics. You cannot include metrics for another user-defined project. For example, an alerting rule for the user-defined project ns1 can have metrics from ns1 and cluster metrics, such as the CPU and memory metrics. However, the rule cannot include metrics from ns2 . Additionally, you cannot create alerting rules for the openshift-* core OpenShift Container Platform projects. OpenShift Container Platform monitoring by default provides a set of alerting rules for these projects. Apply the configuration file to the cluster: USD oc apply -f example-app-alerting-rule.yaml It takes some time to create the alerting rule. 6.4.3. Reducing latency for alerting rules that do not query platform metrics If an alerting rule for a user-defined project does not query default cluster metrics, you can deploy the rule directly on the Prometheus instance in the openshift-user-workload-monitoring project. This reduces latency for alerting rules by bypassing Thanos Ruler when it is not required. This also helps to minimize the overall load on monitoring components. Warning Default OpenShift Container Platform metrics for user-defined projects provide information about CPU and memory usage, bandwidth statistics, and packet rate information. Those metrics cannot be included in an alerting rule if you deploy the rule directly to the Prometheus instance in the openshift-user-workload-monitoring project. The procedure outlined in this section should only be used if you have read the documentation and have a comprehensive understanding of the monitoring architecture. Prerequisites You have enabled monitoring for user-defined projects. You are logged in as a user that has the monitoring-rules-edit role for the project where you want to create an alerting rule. You have installed the OpenShift CLI ( oc ). Procedure Create a YAML file for alerting rules. In this example, it is called example-app-alerting-rule.yaml . Add an alerting rule configuration to the YAML file that includes a label with the key openshift.io/prometheus-rule-evaluation-scope and value leaf-prometheus . For example: apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule metadata: name: example-alert namespace: ns1 labels: openshift.io/prometheus-rule-evaluation-scope: leaf-prometheus spec: groups: - name: example rules: - alert: VersionAlert expr: version{job="prometheus-example-app"} == 0 If that label is present, the alerting rule is deployed on the Prometheus instance in the openshift-user-workload-monitoring project. If the label is not present, the alerting rule is deployed to Thanos Ruler. Apply the configuration file to the cluster: USD oc apply -f example-app-alerting-rule.yaml It takes some time to create the alerting rule. See Monitoring overview for details about OpenShift Container Platform 4.9 monitoring architecture. 6.4.4. Accessing alerting rules for user-defined projects To list alerting rules for a user-defined project, you must have been assigned the monitoring-rules-view role for the project. Prerequisites You have enabled monitoring for user-defined projects. You are logged in as a user that has the monitoring-rules-view role for your project. You have installed the OpenShift CLI ( oc ). Procedure You can list alerting rules in <project> : USD oc -n <project> get prometheusrule To list the configuration of an alerting rule, run the following: USD oc -n <project> get prometheusrule <rule> -o yaml 6.4.5. Listing alerting rules for all projects in a single view As a cluster administrator, you can list alerting rules for core OpenShift Container Platform and user-defined projects together in a single view. Prerequisites You have access to the cluster as a user with the cluster-admin role. You have installed the OpenShift CLI ( oc ). Procedure In the Administrator perspective, navigate to Observe Alerting Alerting Rules . Select the Platform and User sources in the Filter drop-down menu. Note The Platform source is selected by default. 6.4.6. Removing alerting rules for user-defined projects You can remove alerting rules for user-defined projects. Prerequisites You have enabled monitoring for user-defined projects. You are logged in as a user that has the monitoring-rules-edit role for the project where you want to create an alerting rule. You have installed the OpenShift CLI ( oc ). Procedure To remove rule <foo> in <namespace> , run the following: USD oc -n <namespace> delete prometheusrule <foo> Additional resources See the Alertmanager documentation 6.5. Managing silences You can create a silence to stop receiving notifications about an alert when it is firing. It might be useful to silence an alert after being first notified, while you resolve the underlying issue. When creating a silence, you must specify whether it becomes active immediately or at a later time. You must also set a duration period after which the silence expires. You can view, edit, and expire existing silences. 6.5.1. Silencing alerts You can either silence a specific alert or silence alerts that match a specification that you define. Prerequisites You are a cluster administrator and have access to the cluster as a user with the cluster-admin cluster role. You are a non-administator user and have access to the cluster as a user with the following user roles: The cluster-monitoring-view cluster role, which allows you to access Alertmanager. The monitoring-alertmanager-edit role, which permits you to create and silence alerts in the Administrator perspective in the web console. The monitoring-rules-edit role, which permits you to create and silence alerts in the Developer perspective in the web console. Procedure To silence a specific alert: In the Administrator perspective: Navigate to the Observe Alerting Alerts page of the OpenShift Container Platform web console. For the alert that you want to silence, select the in the right-hand column and select Silence Alert . The Silence Alert form will appear with a pre-populated specification for the chosen alert. Optional: Modify the silence. You must add a comment before creating the silence. To create the silence, select Silence . In the Developer perspective: Navigate to the Observe <project_name> Alerts page in the OpenShift Container Platform web console. Expand the details for an alert by selecting > to the left of the alert name. Select the name of the alert in the expanded view to open the Alert Details page for the alert. Select Silence Alert . The Silence Alert form will appear with a prepopulated specification for the chosen alert. Optional: Modify the silence. You must add a comment before creating the silence. To create the silence, select Silence . To silence a set of alerts by creating an alert specification in the Administrator perspective: Navigate to the Observe Alerting Silences page in the OpenShift Container Platform web console. Select Create Silence . Set the schedule, duration, and label details for an alert in the Create Silence form. You must also add a comment for the silence. To create silences for alerts that match the label sectors that you entered in the step, select Silence . 6.5.2. Editing silences You can edit a silence, which will expire the existing silence and create a new one with the changed configuration. Procedure To edit a silence in the Administrator perspective: Navigate to the Observe Alerting Silences page. For the silence you want to modify, select the in the last column and choose Edit silence . Alternatively, you can select Actions Edit Silence in the Silence Details page for a silence. In the Edit Silence page, enter your changes and select Silence . This will expire the existing silence and create one with the chosen configuration. To edit a silence in the Developer perspective: Navigate to the Observe <project_name> Alerts page. Expand the details for an alert by selecting > to the left of the alert name. Select the name of the alert in the expanded view to open the Alert Details page for the alert. Select the name of a silence in the Silenced By section in that page to navigate to the Silence Details page for the silence. Select the name of a silence to navigate to its Silence Details page. Select Actions Edit Silence in the Silence Details page for a silence. In the Edit Silence page, enter your changes and select Silence . This will expire the existing silence and create one with the chosen configuration. 6.5.3. Expiring silences You can expire a silence. Expiring a silence deactivates it forever. Procedure To expire a silence in the Administrator perspective: Navigate to the Observe Alerting Silences page. For the silence you want to modify, select the in the last column and choose Expire silence . Alternatively, you can select Actions Expire Silence in the Silence Details page for a silence. To expire a silence in the Developer perspective: Navigate to the Observe <project_name> Alerts page. Expand the details for an alert by selecting > to the left of the alert name. Select the name of the alert in the expanded view to open the Alert Details page for the alert. Select the name of a silence in the Silenced By section in that page to navigate to the Silence Details page for the silence. Select the name of a silence to navigate to its Silence Details page. Select Actions Expire Silence in the Silence Details page for a silence. 6.6. Sending notifications to external systems In OpenShift Container Platform 4.9, firing alerts can be viewed in the Alerting UI. Alerts are not configured by default to be sent to any notification systems. You can configure OpenShift Container Platform to send alerts to the following receiver types: PagerDuty Webhook Email Slack Routing alerts to receivers enables you to send timely notifications to the appropriate teams when failures occur. For example, critical alerts require immediate attention and are typically paged to an individual or a critical response team. Alerts that provide non-critical warning notifications might instead be routed to a ticketing system for non-immediate review. Checking that alerting is operational by using the watchdog alert OpenShift Container Platform monitoring includes a watchdog alert that fires continuously. Alertmanager repeatedly sends watchdog alert notifications to configured notification providers. The provider is usually configured to notify an administrator when it stops receiving the watchdog alert. This mechanism helps you quickly identify any communication issues between Alertmanager and the notification provider. 6.6.1. Configuring alert receivers You can configure alert receivers to ensure that you learn about important issues with your cluster. Prerequisites You have access to the cluster as a user with the cluster-admin role. Procedure In the Administrator perspective, navigate to Administration Cluster Settings Configuration Alertmanager . Note Alternatively, you can navigate to the same page through the notification drawer. Select the bell icon at the top right of the OpenShift Container Platform web console and choose Configure in the AlertmanagerReceiverNotConfigured alert. Select Create Receiver in the Receivers section of the page. In the Create Receiver form, add a Receiver Name and choose a Receiver Type from the list. Edit the receiver configuration: For PagerDuty receivers: Choose an integration type and add a PagerDuty integration key. Add the URL of your PagerDuty installation. Select Show advanced configuration if you want to edit the client and incident details or the severity specification. For webhook receivers: Add the endpoint to send HTTP POST requests to. Select Show advanced configuration if you want to edit the default option to send resolved alerts to the receiver. For email receivers: Add the email address to send notifications to. Add SMTP configuration details, including the address to send notifications from, the smarthost and port number used for sending emails, the hostname of the SMTP server, and authentication details. Choose whether TLS is required. Select Show advanced configuration if you want to edit the default option not to send resolved alerts to the receiver or edit the body of email notifications configuration. For Slack receivers: Add the URL of the Slack webhook. Add the Slack channel or user name to send notifications to. Select Show advanced configuration if you want to edit the default option not to send resolved alerts to the receiver or edit the icon and username configuration. You can also choose whether to find and link channel names and usernames. By default, firing alerts with labels that match all of the selectors will be sent to the receiver. If you want label values for firing alerts to be matched exactly before they are sent to the receiver: Add routing label names and values in the Routing Labels section of the form. Select Regular Expression if want to use a regular expression. Select Add Label to add further routing labels. Select Create to create the receiver. 6.7. Applying a custom Alertmanager configuration You can overwrite the default Alertmanager configuration by editing the alertmanager-main secret inside the openshift-monitoring project. Prerequisites You have access to the cluster as a user with the cluster-admin role. Procedure To change the Alertmanager configuration from the CLI: Print the currently active Alertmanager configuration into file alertmanager.yaml : USD oc -n openshift-monitoring get secret alertmanager-main --template='{{ index .data "alertmanager.yaml" }}' \| base64 --decode > alertmanager.yaml Edit the configuration in alertmanager.yaml : global: resolve_timeout: 5m route: group_wait: 30s group_interval: 5m repeat_interval: 12h receiver: default routes: - matchers: - "alertname=Watchdog" repeat_interval: 5m receiver: watchdog - matchers: - "service=<your_service>" 1 routes: - matchers: - <your_matching_rules> 2 receiver: <receiver> 3 receivers: - name: default - name: watchdog - name: <receiver> # <receiver_configuration> 1 service specifies the service that fires the alerts. 2 <your_matching_rules> specifies the target alerts. 3 receiver specifies the receiver to use for the alert. Note Use the matchers key name to indicate the matchers that an alert has to fulfill to match the node. Do not use the match or match_re key names, which are both deprecated and planned for removal in a future release. In addition, if you define inhibition rules, use the target_matchers key name to indicate the target matchers and the source_matchers key name to indicate the source matchers. Do not use the target_match , target_match_re , source_match , or source_match_re key names, which are deprecated and planned for removal in a future release. The following Alertmanager configuration example configures PagerDuty as an alert receiver: global: resolve_timeout: 5m route: group_wait: 30s group_interval: 5m repeat_interval: 12h receiver: default routes: - matchers: - "alertname=Watchdog" repeat_interval: 5m receiver: watchdog - matchers: - "service=example-app" routes: - matchers: - "severity=critical" receiver: team-frontend-page receivers: - name: default - name: watchdog - name: team-frontend-page pagerduty_configs: - service_key: " your-key " With this configuration, alerts of critical severity that are fired by the example-app service are sent using the team-frontend-page receiver. Typically these types of alerts would be paged to an individual or a critical response team. Apply the new configuration in the file: USD oc -n openshift-monitoring create secret generic alertmanager-main --from-file=alertmanager.yaml --dry-run=client -o=yaml \| oc -n openshift-monitoring replace secret --filename=- To change the Alertmanager configuration from the OpenShift Container Platform web console: Navigate to the Administration Cluster Settings Configuration Alertmanager YAML page of the web console. Modify the YAML configuration file. Select Save . Additional resources See the PagerDuty official site for more information on PagerDuty See the PagerDuty Prometheus Integration Guide to learn how to retrieve the service_key See Alertmanager configuration for configuring alerting through different alert receivers 6.8. steps Reviewing monitoring dashboards	[ "apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule metadata: name: example-alert namespace: ns1 spec: groups: - name: example rules: - alert: VersionAlert expr: version{job=\"prometheus-example-app\"} == 0", "oc apply -f example-app-alerting-rule.yaml", "apiVersion: monitoring.coreos.com/v1 kind: PrometheusRule metadata: name: example-alert namespace: ns1 labels: openshift.io/prometheus-rule-evaluation-scope: leaf-prometheus spec: groups: - name: example rules: - alert: VersionAlert expr: version{job=\"prometheus-example-app\"} == 0", "oc apply -f example-app-alerting-rule.yaml", "oc -n <project> get prometheusrule", "oc -n <project> get prometheusrule <rule> -o yaml", "oc -n <namespace> delete prometheusrule <foo>", "oc -n openshift-monitoring get secret alertmanager-main --template='{{ index .data \"alertmanager.yaml\" }}' \| base64 --decode > alertmanager.yaml", "global: resolve_timeout: 5m route: group_wait: 30s group_interval: 5m repeat_interval: 12h receiver: default routes: - matchers: - \"alertname=Watchdog\" repeat_interval: 5m receiver: watchdog - matchers: - \"service=<your_service>\" 1 routes: - matchers: - <your_matching_rules> 2 receiver: <receiver> 3 receivers: - name: default - name: watchdog - name: <receiver> <receiver_configuration>", "global: resolve_timeout: 5m route: group_wait: 30s group_interval: 5m repeat_interval: 12h receiver: default routes: - matchers: - \"alertname=Watchdog\" repeat_interval: 5m receiver: watchdog - matchers: - \"service=example-app\" routes: - matchers: - \"severity=critical\" receiver: team-frontend-page receivers: - name: default - name: watchdog - name: team-frontend-page pagerduty_configs: - service_key: \" your-key \"", "oc -n openshift-monitoring create secret generic alertmanager-main --from-file=alertmanager.yaml --dry-run=client -o=yaml \| oc -n openshift-monitoring replace secret --filename=-" ]	https://docs.redhat.com/en/documentation/openshift_container_platform/4.9/html/monitoring/managing-alerts
1.5. MAC Address Pools	1.5. MAC Address Pools MAC address pools define the range(s) of MAC addresses allocated for each cluster. A MAC address pool is specified for each cluster. By using MAC address pools, Red Hat Virtualization can automatically generate and assign MAC addresses to new virtual network devices, which helps to prevent MAC address duplication. MAC address pools are more memory efficient when all MAC addresses related to a cluster are within the range for the assigned MAC address pool. The same MAC address pool can be shared by multiple clusters, but each cluster has a single MAC address pool assigned. A default MAC address pool is created by Red Hat Virtualization and is used if another MAC address pool is not assigned. For more information about assigning MAC address pools to clusters see Section 8.2.1, "Creating a New Cluster" . Note If more than one Red Hat Virtualization cluster shares a network, do not rely solely on the default MAC address pool because the virtual machines of each cluster will try to use the same range of MAC addresses, leading to conflicts. To avoid MAC address conflicts, check the MAC address pool ranges to ensure that each cluster is assigned a unique MAC address range. The MAC address pool assigns the available MAC address following the last address that was returned to the pool. If there are no further addresses left in the range, the search starts again from the beginning of the range. If there are multiple MAC address ranges with available MAC addresses defined in a single MAC address pool, the ranges take turns in serving incoming requests in the same way available MAC addresses are selected. 1.5.1. Creating MAC Address Pools You can create new MAC address pools. Creating a MAC Address Pool Click Administration Configure . Click the MAC Address Pools tab. Click Add . Enter the Name and Description of the new MAC address pool. Select the Allow Duplicates check box to allow a MAC address to be used multiple times in a pool. The MAC address pool will not automatically use a duplicate MAC address, but enabling the duplicates option means a user can manually use a duplicate MAC address. Note If one MAC address pool has duplicates disabled, and another has duplicates enabled, each MAC address can be used once in the pool with duplicates disabled but can be used multiple times in the pool with duplicates enabled. Enter the required MAC Address Ranges . To enter multiple ranges click the plus button to the From and To fields. Click OK . 1.5.2. Editing MAC Address Pools You can edit MAC address pools to change the details, including the range of MAC addresses available in the pool and whether duplicates are allowed. Editing MAC Address Pool Properties Click Administration Configure . Click the MAC Address Pools tab. Select the MAC address pool to be edited. Click Edit . Change the Name , Description , Allow Duplicates , and MAC Address Ranges fields as required. Note When a MAC address range is updated, the MAC addresses of existing NICs are not reassigned. MAC addresses that were already assigned, but are outside of the new MAC address range, are added as user-specified MAC addresses and are still tracked by that MAC address pool. Click OK . 1.5.3. Editing MAC Address Pool Permissions After a MAC address pool has been created, you can edit its user permissions. The user permissions control which data centers can use the MAC address pool. See Section 1.1, "Roles" for more information on adding new user permissions. Editing MAC Address Pool Permissions Click Administration Configure . Click the MAC Address Pools tab. Select the required MAC address pool. Edit the user permissions for the MAC address pool: To add user permissions to a MAC address pool: Click Add in the user permissions pane at the bottom of the Configure window. Search for and select the required users. Select the required role from the Role to Assign drop-down list. Click OK to add the user permissions. To remove user permissions from a MAC address pool: Select the user permission to be removed in the user permissions pane at the bottom of the Configure window. Click Remove to remove the user permissions. 1.5.4. Removing MAC Address Pools You can remove a created MAC address pool if the pool is not associated with a cluster, but the default MAC address pool cannot be removed. Removing a MAC Address Pool Click Administration Configure . Click the MAC Address Pools tab. Select the MAC address pool to be removed. Click the Remove . Click OK .	null	https://docs.redhat.com/en/documentation/red_hat_virtualization/4.3/html/administration_guide/sect-mac_address_pools
Chapter 4. Avro Jackson	Chapter 4. Avro Jackson Jackson Avro is a Data Format which uses the Jackson library with the Avro extension to unmarshal an Avro payload into Java objects or to marshal Java objects into an Avro payload. Note If you are familiar with Jackson, this Avro data format behaves in the same way as its JSON counterpart, and thus can be used with classes annotated for JSON serialization/deserialization. from("kafka:topic"). unmarshal().avro(AvroLibrary.Jackson, JsonNode.class). to("log:info"); 4.1. Dependencies When using avro-jackson with Red Hat build of Camel Spring Boot make sure to add the Maven dependency to have support for auto configuration. <dependency> <groupId>org.apache.camel.springboot</groupId> <artifactId>camel-jackson-avro-starter</artifactId> </dependency> 4.2. Configuring the SchemaResolver Since Avro serialization is schema-based, this data format requires that you provide a SchemaResolver object that is able to lookup the schema for each exchange that is going to be marshalled/unmarshalled. You can add a single SchemaResolver to the registry and it will be looked up automatically. Or you can explicitly specify the reference to a custom SchemaResolver. 4.3. Avro Jackson Options The Avro Jackson dataformat supports 18 options, which are listed below. Name Default Java Type Description objectMapper String Lookup and use the existing ObjectMapper with the given id when using Jackson. useDefaultObjectMapper Boolean Whether to lookup and use default Jackson ObjectMapper from the registry. unmarshalType String Class name of the java type to use when unmarshalling. jsonView String When marshalling a POJO to JSON you might want to exclude certain fields from the JSON output. With Jackson you can use JSON views to accomplish this. This option is to refer to the class which has JsonView annotations. include String If you want to marshal a pojo to JSON, and the pojo has some fields with null values. And you want to skip these null values, you can set this option to NON_NULL. allowJmsType Boolean Used for JMS users to allow the JMSType header from the JMS spec to specify a FQN classname to use to unmarshal to. collectionType String Refers to a custom collection type to lookup in the registry to use. This option should rarely be used, but allows to use different collection types than java.util.Collection based as default. useList Boolean To unmarshal to a List of Map or a List of Pojo. moduleClassNames String To use custom Jackson modules com.fasterxml.jackson.databind.Module specified as a String with FQN class names. Multiple classes can be separated by comma. moduleRefs String To use custom Jackson modules referred from the Camel registry. Multiple modules can be separated by comma. enableFeatures String Set of features to enable on the Jackson com.fasterxml.jackson.databind.ObjectMapper. The features should be a name that matches a enum from com.fasterxml.jackson.databind.SerializationFeature, com.fasterxml.jackson.databind.DeserializationFeature, or com.fasterxml.jackson.databind.MapperFeature Multiple features can be separated by comma. disableFeatures String Set of features to disable on the Jackson com.fasterxml.jackson.databind.ObjectMapper. The features should be a name that matches a enum from com.fasterxml.jackson.databind.SerializationFeature, com.fasterxml.jackson.databind.DeserializationFeature, or com.fasterxml.jackson.databind.MapperFeature Multiple features can be separated by comma. allowUnmarshallType Boolean If enabled then Jackson is allowed to attempt to use the CamelJacksonUnmarshalType header during the unmarshalling. This should only be enabled when desired to be used. timezone String If set then Jackson will use the Timezone when marshalling/unmarshalling. autoDiscoverObjectMapper Boolean If set to true then Jackson will lookup for an objectMapper into the registry. contentTypeHeader Boolean Whether the data format should set the Content-Type header with the type from the data format. For example application/xml for data formats marshalling to XML, or application/json for data formats marshalling to JSON. schemaResolver String Optional schema resolver used to lookup schemas for the data in transit. autoDiscoverSchemaResolver Boolean When not disabled, the SchemaResolver will be looked up into the registry. 4.4. Using custom AvroMapper You can configure JacksonAvroDataFormat to use a custom AvroMapper in case you need more control of the mapping configuration. If you setup a single AvroMapper in the registry, then Camel will automatic lookup and use this AvroMapper . 4.5. Spring Boot Auto-Configuration The component supports 19 options, which are listed below. Name Description Default Type camel.dataformat.avro-jackson.allow-jms-type Used for JMS users to allow the JMSType header from the JMS spec to specify a FQN classname to use to unmarshal to. false Boolean camel.dataformat.avro-jackson.allow-unmarshall-type If enabled then Jackson is allowed to attempt to use the CamelJacksonUnmarshalType header during the unmarshalling. This should only be enabled when desired to be used. false Boolean camel.dataformat.avro-jackson.auto-discover-object-mapper If set to true then Jackson will lookup for an objectMapper into the registry. false Boolean camel.dataformat.avro-jackson.auto-discover-schema-resolver When not disabled, the SchemaResolver will be looked up into the registry. true Boolean camel.dataformat.avro-jackson.collection-type Refers to a custom collection type to lookup in the registry to use. This option should rarely be used, but allows to use different collection types than java.util.Collection based as default. String camel.dataformat.avro-jackson.content-type-header Whether the data format should set the Content-Type header with the type from the data format. For example application/xml for data formats marshalling to XML, or application/json for data formats marshalling to JSON. true Boolean camel.dataformat.avro-jackson.disable-features Set of features to disable on the Jackson com.fasterxml.jackson.databind.ObjectMapper. The features should be a name that matches a enum from com.fasterxml.jackson.databind.SerializationFeature, com.fasterxml.jackson.databind.DeserializationFeature, or com.fasterxml.jackson.databind.MapperFeature Multiple features can be separated by comma. String camel.dataformat.avro-jackson.enable-features Set of features to enable on the Jackson com.fasterxml.jackson.databind.ObjectMapper. The features should be a name that matches a enum from com.fasterxml.jackson.databind.SerializationFeature, com.fasterxml.jackson.databind.DeserializationFeature, or com.fasterxml.jackson.databind.MapperFeature Multiple features can be separated by comma. String camel.dataformat.avro-jackson.enabled Whether to enable auto configuration of the avro-jackson data format. This is enabled by default. Boolean camel.dataformat.avro-jackson.include If you want to marshal a pojo to JSON, and the pojo has some fields with null values. And you want to skip these null values, you can set this option to NON_NULL. String camel.dataformat.avro-jackson.json-view When marshalling a POJO to JSON you might want to exclude certain fields from the JSON output. With Jackson you can use JSON views to accomplish this. This option is to refer to the class which has JsonView annotations. String camel.dataformat.avro-jackson.module-class-names To use custom Jackson modules com.fasterxml.jackson.databind.Module specified as a String with FQN class names. Multiple classes can be separated by comma. String camel.dataformat.avro-jackson.module-refs To use custom Jackson modules referred from the Camel registry. Multiple modules can be separated by comma. String camel.dataformat.avro-jackson.object-mapper Lookup and use the existing ObjectMapper with the given id when using Jackson. String camel.dataformat.avro-jackson.schema-resolver Optional schema resolver used to lookup schemas for the data in transit. String camel.dataformat.avro-jackson.timezone If set then Jackson will use the Timezone when marshalling/unmarshalling. String camel.dataformat.avro-jackson.unmarshal-type Class name of the java type to use when unmarshalling. String camel.dataformat.avro-jackson.use-default-object-mapper Whether to lookup and use default Jackson ObjectMapper from the registry. true Boolean camel.dataformat.avro-jackson.use-list To unmarshal to a List of Map or a List of Pojo. false Boolean	[ "from(\"kafka:topic\"). unmarshal().avro(AvroLibrary.Jackson, JsonNode.class). to(\"log:info\");", "<dependency> <groupId>org.apache.camel.springboot</groupId> <artifactId>camel-jackson-avro-starter</artifactId> </dependency>" ]	https://docs.redhat.com/en/documentation/red_hat_build_of_apache_camel/4.4/html/red_hat_build_of_apache_camel_for_spring_boot_reference/csb-camel-avro-jackson-dataformat-starter
Chapter 19. Troubleshooting issues in provider mode	Chapter 19. Troubleshooting issues in provider mode 19.1. Force deletion of storage in provider clusters When a client cluster is deleted without performing the offboarding process to remove all the resources from the corresponding provider cluster, you need to perform force deletion of the corresponding storage consumer from the provider cluster. This helps to release the storage space that was claimed by the client. Caution It is recommended to use this method only in unavoidable situations such as accidental deletion of storage client clusters. Prerequisites Access to the OpenShift Data Foundation storage cluster in provider mode. Procedure Click Storage Storage Clients from the OpenShift console. Click the delete icon at the far right of the listed storage client cluster. The delete icon is enabled only after 5 minutes of the last heartbeat of the cluster. Click Confirm .	null	https://docs.redhat.com/en/documentation/red_hat_openshift_data_foundation/4.18/html/troubleshooting_openshift_data_foundation/troubleshooting_issues_in_provider_mode
Chapter 6. File Integrity Operator	Chapter 6. File Integrity Operator 6.1. File Integrity Operator release notes The File Integrity Operator for OpenShift Container Platform continually runs file integrity checks on RHCOS nodes. These release notes track the development of the File Integrity Operator in the OpenShift Container Platform. For an overview of the File Integrity Operator, see Understanding the File Integrity Operator . To access the latest release, see Updating the File Integrity Operator . 6.1.1. OpenShift File Integrity Operator 1.3.1 The following advisory is available for the OpenShift File Integrity Operator 1.3.1: RHBA-2023:3600 OpenShift File Integrity Operator Bug Fix Update 6.1.1.1. New features and enhancements FIO now includes kubelet certificates as default files, excluding them from issuing warnings when they're managed by OpenShift Container Platform. ( OCPBUGS-14348 ) FIO now correctly directs email to the address for Red Hat Technical Support. ( OCPBUGS-5023 ) 6.1.1.2. Bug fixes Previously, FIO would not clean up FileIntegrityNodeStatus CRDs when nodes are removed from the cluster. FIO has been updated to correctly clean up node status CRDs on node removal. ( OCPBUGS-4321 ) Previously, FIO would also erroneously indicate that new nodes failed integrity checks. FIO has been updated to correctly show node status CRDs when adding new nodes to the cluster. This provides correct node status notifications. ( OCPBUGS-8502 ) Previously, when FIO was reconciling FileIntegrity CRDs, it would pause scanning until the reconciliation was done. This caused an overly aggressive re-initiatization process on nodes not impacted by the reconciliation. This problem also resulted in unnecessary daemonsets for machine config pools which are unrelated to the FileIntegrity being changed. FIO correctly handles these cases and only pauses AIDE scanning for nodes that are affected by file integrity changes. ( CMP-1097 ) 6.1.1.3. Known Issues In FIO 1.3.1, increasing nodes in IBM Z clusters might result in Failed File Integrity node status. For more information, see Adding nodes in IBM Power clusters can result in failed File Integrity node status . 6.1.2. OpenShift File Integrity Operator 1.2.1 The following advisory is available for the OpenShift File Integrity Operator 1.2.1: RHBA-2023:1684 OpenShift File Integrity Operator Bug Fix Update This release includes updated container dependencies. 6.1.3. OpenShift File Integrity Operator 1.2.0 The following advisory is available for the OpenShift File Integrity Operator 1.2.0: RHBA-2023:1273 OpenShift File Integrity Operator Enhancement Update 6.1.3.1. New features and enhancements The File Integrity Operator Custom Resource (CR) now contains an initialDelay feature that specifies the number of seconds to wait before starting the first AIDE integrity check. For more information, see Creating the FileIntegrity custom resource . The File Integrity Operator is now stable and the release channel is upgraded to stable . Future releases will follow Semantic Versioning . To access the latest release, see Updating the File Integrity Operator . 6.1.4. OpenShift File Integrity Operator 1.0.0 The following advisory is available for the OpenShift File Integrity Operator 1.0.0: RHBA-2023:0037 OpenShift File Integrity Operator Bug Fix Update 6.1.5. OpenShift File Integrity Operator 0.1.32 The following advisory is available for the OpenShift File Integrity Operator 0.1.32: RHBA-2022:7095 OpenShift File Integrity Operator Bug Fix Update 6.1.5.1. Bug fixes Previously, alerts issued by the File Integrity Operator did not set a namespace, making it difficult to understand from which namespace the alert originated. Now, the Operator sets the appropriate namespace, providing more information about the alert. ( BZ#2112394 ) Previously, The File Integrity Operator did not update the metrics service on Operator startup, causing the metrics targets to be unreachable. With this release, the File Integrity Operator now ensures the metrics service is updated on Operator startup. ( BZ#2115821 ) 6.1.6. OpenShift File Integrity Operator 0.1.30 The following advisory is available for the OpenShift File Integrity Operator 0.1.30: RHBA-2022:5538 OpenShift File Integrity Operator Bug Fix and Enhancement Update 6.1.6.1. New features and enhancements The File Integrity Operator is now supported on the following architectures: IBM Power IBM Z and LinuxONE 6.1.6.2. Bug fixes Previously, alerts issued by the File Integrity Operator did not set a namespace, making it difficult to understand where the alert originated. Now, the Operator sets the appropriate namespace, increasing understanding of the alert. ( BZ#2101393 ) 6.1.7. OpenShift File Integrity Operator 0.1.24 The following advisory is available for the OpenShift File Integrity Operator 0.1.24: RHBA-2022:1331 OpenShift File Integrity Operator Bug Fix 6.1.7.1. New features and enhancements You can now configure the maximum number of backups stored in the FileIntegrity Custom Resource (CR) with the config.maxBackups attribute. This attribute specifies the number of AIDE database and log backups left over from the re-init process to keep on the node. Older backups beyond the configured number are automatically pruned. The default is set to five backups. 6.1.7.2. Bug fixes Previously, upgrading the Operator from versions older than 0.1.21 to 0.1.22 could cause the re-init feature to fail. This was a result of the Operator failing to update configMap resource labels. Now, upgrading to the latest version fixes the resource labels. ( BZ#2049206 ) Previously, when enforcing the default configMap script contents, the wrong data keys were compared. This resulted in the aide-reinit script not being updated properly after an Operator upgrade, and caused the re-init process to fail. Now, daemonSets run to completion and the AIDE database re-init process executes successfully. ( BZ#2072058 ) 6.1.8. OpenShift File Integrity Operator 0.1.22 The following advisory is available for the OpenShift File Integrity Operator 0.1.22: RHBA-2022:0142 OpenShift File Integrity Operator Bug Fix 6.1.8.1. Bug fixes Previously, a system with a File Integrity Operator installed might interrupt the OpenShift Container Platform update, due to the /etc/kubernetes/aide.reinit file. This occurred if the /etc/kubernetes/aide.reinit file was present, but later removed prior to the ostree validation. With this update, /etc/kubernetes/aide.reinit is moved to the /run directory so that it does not conflict with the OpenShift Container Platform update. ( BZ#2033311 ) 6.1.9. OpenShift File Integrity Operator 0.1.21 The following advisory is available for the OpenShift File Integrity Operator 0.1.21: RHBA-2021:4631 OpenShift File Integrity Operator Bug Fix and Enhancement Update 6.1.9.1. New features and enhancements The metrics related to FileIntegrity scan results and processing metrics are displayed on the monitoring dashboard on the web console. The results are labeled with the prefix of file_integrity_operator_ . If a node has an integrity failure for more than 1 second, the default PrometheusRule provided in the operator namespace alerts with a warning. The following dynamic Machine Config Operator and Cluster Version Operator related filepaths are excluded from the default AIDE policy to help prevent false positives during node updates: /etc/machine-config-daemon/currentconfig /etc/pki/ca-trust/extracted/java/cacerts /etc/cvo/updatepayloads /root/.kube The AIDE daemon process has stability improvements over v0.1.16, and is more resilient to errors that might occur when the AIDE database is initialized. 6.1.9.2. Bug fixes Previously, when the Operator automatically upgraded, outdated daemon sets were not removed. With this release, outdated daemon sets are removed during the automatic upgrade. 6.1.10. Additional resources Understanding the File Integrity Operator 6.2. Installing the File Integrity Operator 6.2.1. Installing the File Integrity Operator using the web console Prerequisites You must have admin privileges. Procedure In the OpenShift Container Platform web console, navigate to Operators OperatorHub . Search for the File Integrity Operator, then click Install . Keep the default selection of Installation mode and namespace to ensure that the Operator will be installed to the openshift-file-integrity namespace. Click Install . Verification To confirm that the installation is successful: Navigate to the Operators Installed Operators page. Check that the Operator is installed in the openshift-file-integrity namespace and its status is Succeeded . If the Operator is not installed successfully: Navigate to the Operators Installed Operators page and inspect the Status column for any errors or failures. Navigate to the Workloads Pods page and check the logs in any pods in the openshift-file-integrity project that are reporting issues. 6.2.2. Installing the File Integrity Operator using the CLI Prerequisites You must have admin privileges. Procedure Create a Namespace object YAML file by running: USD oc create -f <file-name>.yaml Example output apiVersion: v1 kind: Namespace metadata: labels: openshift.io/cluster-monitoring: "true" name: openshift-file-integrity Create the OperatorGroup object YAML file: USD oc create -f <file-name>.yaml Example output apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: file-integrity-operator namespace: openshift-file-integrity spec: targetNamespaces: - openshift-file-integrity Create the Subscription object YAML file: USD oc create -f <file-name>.yaml Example output apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: file-integrity-operator namespace: openshift-file-integrity spec: channel: "stable" installPlanApproval: Automatic name: file-integrity-operator source: redhat-operators sourceNamespace: openshift-marketplace Verification Verify the installation succeeded by inspecting the CSV file: USD oc get csv -n openshift-file-integrity Verify that the File Integrity Operator is up and running: USD oc get deploy -n openshift-file-integrity 6.2.3. Additional resources The File Integrity Operator is supported in a restricted network environment. For more information, see Using Operator Lifecycle Manager on restricted networks . 6.3. Updating the File Integrity Operator As a cluster administrator, you can update the File Integrity Operator on your OpenShift Container Platform cluster. 6.3.1. Preparing for an Operator update The subscription of an installed Operator specifies an update channel that tracks and receives updates for the Operator. You can change the update channel to start tracking and receiving updates from a newer channel. The names of update channels in a subscription can differ between Operators, but the naming scheme typically follows a common convention within a given Operator. For example, channel names might follow a minor release update stream for the application provided by the Operator ( 1.2 , 1.3 ) or a release frequency ( stable , fast ). Note You cannot change installed Operators to a channel that is older than the current channel. Red Hat Customer Portal Labs include the following application that helps administrators prepare to update their Operators: Red Hat OpenShift Container Platform Operator Update Information Checker You can use the application to search for Operator Lifecycle Manager-based Operators and verify the available Operator version per update channel across different versions of OpenShift Container Platform. Cluster Version Operator-based Operators are not included. 6.3.2. Changing the update channel for an Operator You can change the update channel for an Operator by using the OpenShift Container Platform web console. Tip If the approval strategy in the subscription is set to Automatic , the update process initiates as soon as a new Operator version is available in the selected channel. If the approval strategy is set to Manual , you must manually approve pending updates. Prerequisites An Operator previously installed using Operator Lifecycle Manager (OLM). Procedure In the Administrator perspective of the web console, navigate to Operators Installed Operators . Click the name of the Operator you want to change the update channel for. Click the Subscription tab. Click the name of the update channel under Channel . Click the newer update channel that you want to change to, then click Save . For subscriptions with an Automatic approval strategy, the update begins automatically. Navigate back to the Operators Installed Operators page to monitor the progress of the update. When complete, the status changes to Succeeded and Up to date . For subscriptions with a Manual approval strategy, you can manually approve the update from the Subscription tab. 6.3.3. Manually approving a pending Operator update If an installed Operator has the approval strategy in its subscription set to Manual , when new updates are released in its current update channel, the update must be manually approved before installation can begin. Prerequisites An Operator previously installed using Operator Lifecycle Manager (OLM). Procedure In the Administrator perspective of the OpenShift Container Platform web console, navigate to Operators Installed Operators . Operators that have a pending update display a status with Upgrade available . Click the name of the Operator you want to update. Click the Subscription tab. Any update requiring approval are displayed to Upgrade Status . For example, it might display 1 requires approval . Click 1 requires approval , then click Preview Install Plan . Review the resources that are listed as available for update. When satisfied, click Approve . Navigate back to the Operators Installed Operators page to monitor the progress of the update. When complete, the status changes to Succeeded and Up to date . 6.4. Understanding the File Integrity Operator The File Integrity Operator is an OpenShift Container Platform Operator that continually runs file integrity checks on the cluster nodes. It deploys a daemon set that initializes and runs privileged advanced intrusion detection environment (AIDE) containers on each node, providing a status object with a log of files that are modified during the initial run of the daemon set pods. Important Currently, only Red Hat Enterprise Linux CoreOS (RHCOS) nodes are supported. 6.4.1. Creating the FileIntegrity custom resource An instance of a FileIntegrity custom resource (CR) represents a set of continuous file integrity scans for one or more nodes. Each FileIntegrity CR is backed by a daemon set running AIDE on the nodes matching the FileIntegrity CR specification. Procedure Create the following example FileIntegrity CR named worker-fileintegrity.yaml to enable scans on worker nodes: Example FileIntegrity CR apiVersion: fileintegrity.openshift.io/v1alpha1 kind: FileIntegrity metadata: name: worker-fileintegrity namespace: openshift-file-integrity spec: nodeSelector: 1 node-role.kubernetes.io/worker: "" tolerations: 2 - key: "myNode" operator: "Exists" effect: "NoSchedule" config: 3 name: "myconfig" namespace: "openshift-file-integrity" key: "config" gracePeriod: 20 4 maxBackups: 5 5 initialDelay: 60 6 debug: false status: phase: Active 7 1 Defines the selector for scheduling node scans. 2 Specify tolerations to schedule on nodes with custom taints. When not specified, a default toleration allowing running on main and infra nodes is applied. 3 Define a ConfigMap containing an AIDE configuration to use. 4 The number of seconds to pause in between AIDE integrity checks. Frequent AIDE checks on a node might be resource intensive, so it can be useful to specify a longer interval. Default is 900 seconds (15 minutes). 5 The maximum number of AIDE database and log backups (leftover from the re-init process) to keep on a node. Older backups beyond this number are automatically pruned by the daemon. Default is set to 5. 6 The number of seconds to wait before starting the first AIDE integrity check. Default is set to 0. 7 The running status of the FileIntegrity instance. Statuses are Initializing , Pending , or Active . Initializing The FileIntegrity object is currently initializing or re-initializing the AIDE database. Pending The FileIntegrity deployment is still being created. Active The scans are active and ongoing. Apply the YAML file to the openshift-file-integrity namespace: USD oc apply -f worker-fileintegrity.yaml -n openshift-file-integrity Verification Confirm the FileIntegrity object was created successfully by running the following command: USD oc get fileintegrities -n openshift-file-integrity Example output NAME AGE worker-fileintegrity 14s 6.4.2. Checking the FileIntegrity custom resource status The FileIntegrity custom resource (CR) reports its status through the . status.phase subresource. Procedure To query the FileIntegrity CR status, run: USD oc get fileintegrities/worker-fileintegrity -o jsonpath="{ .status.phase }" Example output Active 6.4.3. FileIntegrity custom resource phases Pending - The phase after the custom resource (CR) is created. Active - The phase when the backing daemon set is up and running. Initializing - The phase when the AIDE database is being reinitialized. 6.4.4. Understanding the FileIntegrityNodeStatuses object The scan results of the FileIntegrity CR are reported in another object called FileIntegrityNodeStatuses . USD oc get fileintegritynodestatuses Example output NAME AGE worker-fileintegrity-ip-10-0-130-192.ec2.internal 101s worker-fileintegrity-ip-10-0-147-133.ec2.internal 109s worker-fileintegrity-ip-10-0-165-160.ec2.internal 102s Note It might take some time for the FileIntegrityNodeStatus object results to be available. There is one result object per node. The nodeName attribute of each FileIntegrityNodeStatus object corresponds to the node being scanned. The status of the file integrity scan is represented in the results array, which holds scan conditions. USD oc get fileintegritynodestatuses.fileintegrity.openshift.io -ojsonpath='{.items[].results}' \| jq The fileintegritynodestatus object reports the latest status of an AIDE run and exposes the status as Failed , Succeeded , or Errored in a status field. USD oc get fileintegritynodestatuses -w Example output NAME NODE STATUS example-fileintegrity-ip-10-0-134-186.us-east-2.compute.internal ip-10-0-134-186.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-150-230.us-east-2.compute.internal ip-10-0-150-230.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-169-137.us-east-2.compute.internal ip-10-0-169-137.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-180-200.us-east-2.compute.internal ip-10-0-180-200.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-194-66.us-east-2.compute.internal ip-10-0-194-66.us-east-2.compute.internal Failed example-fileintegrity-ip-10-0-222-188.us-east-2.compute.internal ip-10-0-222-188.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-134-186.us-east-2.compute.internal ip-10-0-134-186.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-222-188.us-east-2.compute.internal ip-10-0-222-188.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-194-66.us-east-2.compute.internal ip-10-0-194-66.us-east-2.compute.internal Failed example-fileintegrity-ip-10-0-150-230.us-east-2.compute.internal ip-10-0-150-230.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-180-200.us-east-2.compute.internal ip-10-0-180-200.us-east-2.compute.internal Succeeded 6.4.5. FileIntegrityNodeStatus CR status types These conditions are reported in the results array of the corresponding FileIntegrityNodeStatus CR status: Succeeded - The integrity check passed; the files and directories covered by the AIDE check have not been modified since the database was last initialized. Failed - The integrity check failed; some files or directories covered by the AIDE check have been modified since the database was last initialized. Errored - The AIDE scanner encountered an internal error. 6.4.5.1. FileIntegrityNodeStatus CR success example Example output of a condition with a success status [ { "condition": "Succeeded", "lastProbeTime": "2020-09-15T12:45:57Z" } ] [ { "condition": "Succeeded", "lastProbeTime": "2020-09-15T12:46:03Z" } ] [ { "condition": "Succeeded", "lastProbeTime": "2020-09-15T12:45:48Z" } ] In this case, all three scans succeeded and so far there are no other conditions. 6.4.5.2. FileIntegrityNodeStatus CR failure status example To simulate a failure condition, modify one of the files AIDE tracks. For example, modify /etc/resolv.conf on one of the worker nodes: USD oc debug node/ip-10-0-130-192.ec2.internal Example output Creating debug namespace/openshift-debug-node-ldfbj ... Starting pod/ip-10-0-130-192ec2internal-debug ... To use host binaries, run `chroot /host` Pod IP: 10.0.130.192 If you don't see a command prompt, try pressing enter. sh-4.2# echo "# integrity test" >> /host/etc/resolv.conf sh-4.2# exit Removing debug pod ... Removing debug namespace/openshift-debug-node-ldfbj ... After some time, the Failed condition is reported in the results array of the corresponding FileIntegrityNodeStatus object. The Succeeded condition is retained, which allows you to pinpoint the time the check failed. USD oc get fileintegritynodestatuses.fileintegrity.openshift.io/worker-fileintegrity-ip-10-0-130-192.ec2.internal -ojsonpath='{.results}' \| jq -r Alternatively, if you are not mentioning the object name, run: USD oc get fileintegritynodestatuses.fileintegrity.openshift.io -ojsonpath='{.items[].results}' \| jq Example output [ { "condition": "Succeeded", "lastProbeTime": "2020-09-15T12:54:14Z" }, { "condition": "Failed", "filesChanged": 1, "lastProbeTime": "2020-09-15T12:57:20Z", "resultConfigMapName": "aide-ds-worker-fileintegrity-ip-10-0-130-192.ec2.internal-failed", "resultConfigMapNamespace": "openshift-file-integrity" } ] The Failed condition points to a config map that gives more details about what exactly failed and why: USD oc describe cm aide-ds-worker-fileintegrity-ip-10-0-130-192.ec2.internal-failed Example output Name: aide-ds-worker-fileintegrity-ip-10-0-130-192.ec2.internal-failed Namespace: openshift-file-integrity Labels: file-integrity.openshift.io/node=ip-10-0-130-192.ec2.internal file-integrity.openshift.io/owner=worker-fileintegrity file-integrity.openshift.io/result-log= Annotations: file-integrity.openshift.io/files-added: 0 file-integrity.openshift.io/files-changed: 1 file-integrity.openshift.io/files-removed: 0 Data integritylog: ------ AIDE 0.15.1 found differences between database and filesystem!! Start timestamp: 2020-09-15 12:58:15 Summary: Total number of files: 31553 Added files: 0 Removed files: 0 Changed files: 1 --------------------------------------------------- Changed files: --------------------------------------------------- changed: /hostroot/etc/resolv.conf --------------------------------------------------- Detailed information about changes: --------------------------------------------------- File: /hostroot/etc/resolv.conf SHA512 : sTQYpB/AL7FeoGtu/1g7opv6C+KT1CBJ , qAeM+a8yTgHPnIHMaRlS+so61EN8VOpg Events: <none> Due to the config map data size limit, AIDE logs over 1 MB are added to the failure config map as a base64-encoded gzip archive. In this case, you want to pipe the output of the above command to base64 --decode \| gunzip . Compressed logs are indicated by the presence of a file-integrity.openshift.io/compressed annotation key in the config map. 6.4.6. Understanding events Transitions in the status of the FileIntegrity and FileIntegrityNodeStatus objects are logged by events . The creation time of the event reflects the latest transition, such as Initializing to Active , and not necessarily the latest scan result. However, the newest event always reflects the most recent status. USD oc get events --field-selector reason=FileIntegrityStatus Example output LAST SEEN TYPE REASON OBJECT MESSAGE 97s Normal FileIntegrityStatus fileintegrity/example-fileintegrity Pending 67s Normal FileIntegrityStatus fileintegrity/example-fileintegrity Initializing 37s Normal FileIntegrityStatus fileintegrity/example-fileintegrity Active When a node scan fails, an event is created with the add/changed/removed and config map information. USD oc get events --field-selector reason=NodeIntegrityStatus Example output LAST SEEN TYPE REASON OBJECT MESSAGE 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-134-173.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-168-238.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-169-175.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-152-92.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-158-144.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-131-30.ec2.internal 87m Warning NodeIntegrityStatus fileintegrity/example-fileintegrity node ip-10-0-152-92.ec2.internal has changed! a:1,c:1,r:0 \ log:openshift-file-integrity/aide-ds-example-fileintegrity-ip-10-0-152-92.ec2.internal-failed Changes to the number of added, changed, or removed files results in a new event, even if the status of the node has not transitioned. USD oc get events --field-selector reason=NodeIntegrityStatus Example output LAST SEEN TYPE REASON OBJECT MESSAGE 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-134-173.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-168-238.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-169-175.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-152-92.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-158-144.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-131-30.ec2.internal 87m Warning NodeIntegrityStatus fileintegrity/example-fileintegrity node ip-10-0-152-92.ec2.internal has changed! a:1,c:1,r:0 \ log:openshift-file-integrity/aide-ds-example-fileintegrity-ip-10-0-152-92.ec2.internal-failed 40m Warning NodeIntegrityStatus fileintegrity/example-fileintegrity node ip-10-0-152-92.ec2.internal has changed! a:3,c:1,r:0 \ log:openshift-file-integrity/aide-ds-example-fileintegrity-ip-10-0-152-92.ec2.internal-failed 6.5. Configuring the Custom File Integrity Operator 6.5.1. Viewing FileIntegrity object attributes As with any Kubernetes custom resources (CRs), you can run oc explain fileintegrity , and then look at the individual attributes using: USD oc explain fileintegrity.spec USD oc explain fileintegrity.spec.config 6.5.2. Important attributes Table 6.1. Important spec and spec.config attributes Attribute Description spec.nodeSelector A map of key-values pairs that must match with node's labels in order for the AIDE pods to be schedulable on that node. The typical use is to set only a single key-value pair where node-role.kubernetes.io/worker: "" schedules AIDE on all worker nodes, node.openshift.io/os_id: "rhcos" schedules on all Red Hat Enterprise Linux CoreOS (RHCOS) nodes. spec.debug A boolean attribute. If set to true , the daemon running in the AIDE deamon set's pods would output extra information. spec.tolerations Specify tolerations to schedule on nodes with custom taints. When not specified, a default toleration is applied, which allows tolerations to run on control plane nodes. spec.config.gracePeriod The number of seconds to pause in between AIDE integrity checks. Frequent AIDE checks on a node can be resource intensive, so it can be useful to specify a longer interval. Defaults to 900 , or 15 minutes. maxBackups The maximum number of AIDE database and log backups leftover from the re-init process to keep on a node. Older backups beyond this number are automatically pruned by the daemon. spec.config.name Name of a configMap that contains custom AIDE configuration. If omitted, a default configuration is created. spec.config.namespace Namespace of a configMap that contains custom AIDE configuration. If unset, the FIO generates a default configuration suitable for RHCOS systems. spec.config.key Key that contains actual AIDE configuration in a config map specified by name and namespace . The default value is aide.conf . spec.config.initialDelay The number of seconds to wait before starting the first AIDE integrity check. Default is set to 0. This attribute is optional. 6.5.3. Examine the default configuration The default File Integrity Operator configuration is stored in a config map with the same name as the FileIntegrity CR. Procedure To examine the default config, run: USD oc describe cm/worker-fileintegrity 6.5.4. Understanding the default File Integrity Operator configuration Below is an excerpt from the aide.conf key of the config map: @@define DBDIR /hostroot/etc/kubernetes @@define LOGDIR /hostroot/etc/kubernetes database=file:@@{DBDIR}/aide.db.gz database_out=file:@@{DBDIR}/aide.db.gz gzip_dbout=yes verbose=5 report_url=file:@@{LOGDIR}/aide.log report_url=stdout PERMS = p+u+g+acl+selinux+xattrs CONTENT_EX = sha512+ftype+p+u+g+n+acl+selinux+xattrs /hostroot/boot/ CONTENT_EX /hostroot/root/\..* PERMS /hostroot/root/ CONTENT_EX The default configuration for a FileIntegrity instance provides coverage for files under the following directories: /root /boot /usr /etc The following directories are not covered: /var /opt Some OpenShift Container Platform-specific excludes under /etc/ 6.5.5. Supplying a custom AIDE configuration Any entries that configure AIDE internal behavior such as DBDIR , LOGDIR , database , and database_out are overwritten by the Operator. The Operator would add a prefix to /hostroot/ before all paths to be watched for integrity changes. This makes reusing existing AIDE configs that might often not be tailored for a containerized environment and start from the root directory easier. Note /hostroot is the directory where the pods running AIDE mount the host's file system. Changing the configuration triggers a reinitializing of the database. 6.5.6. Defining a custom File Integrity Operator configuration This example focuses on defining a custom configuration for a scanner that runs on the control plane nodes based on the default configuration provided for the worker-fileintegrity CR. This workflow might be useful if you are planning to deploy a custom software running as a daemon set and storing its data under /opt/mydaemon on the control plane nodes. Procedure Make a copy of the default configuration. Edit the default configuration with the files that must be watched or excluded. Store the edited contents in a new config map. Point the FileIntegrity object to the new config map through the attributes in spec.config . Extract the default configuration: USD oc extract cm/worker-fileintegrity --keys=aide.conf This creates a file named aide.conf that you can edit. To illustrate how the Operator post-processes the paths, this example adds an exclude directory without the prefix: USD vim aide.conf Example output /hostroot/etc/kubernetes/static-pod-resources !/hostroot/etc/kubernetes/aide.* !/hostroot/etc/kubernetes/manifests !/hostroot/etc/docker/certs.d !/hostroot/etc/selinux/targeted !/hostroot/etc/openvswitch/conf.db Exclude a path specific to control plane nodes: !/opt/mydaemon/ Store the other content in /etc : /hostroot/etc/ CONTENT_EX Create a config map based on this file: USD oc create cm master-aide-conf --from-file=aide.conf Define a FileIntegrity CR manifest that references the config map: apiVersion: fileintegrity.openshift.io/v1alpha1 kind: FileIntegrity metadata: name: master-fileintegrity namespace: openshift-file-integrity spec: nodeSelector: node-role.kubernetes.io/master: "" config: name: master-aide-conf namespace: openshift-file-integrity The Operator processes the provided config map file and stores the result in a config map with the same name as the FileIntegrity object: USD oc describe cm/master-fileintegrity \| grep /opt/mydaemon Example output !/hostroot/opt/mydaemon 6.5.7. Changing the custom File Integrity configuration To change the File Integrity configuration, never change the generated config map. Instead, change the config map that is linked to the FileIntegrity object through the spec.name , namespace , and key attributes. 6.6. Performing advanced Custom File Integrity Operator tasks 6.6.1. Reinitializing the database If the File Integrity Operator detects a change that was planned, it might be required to reinitialize the database. Procedure Annotate the FileIntegrity custom resource (CR) with file-integrity.openshift.io/re-init : USD oc annotate fileintegrities/worker-fileintegrity file-integrity.openshift.io/re-init= The old database and log files are backed up and a new database is initialized. The old database and logs are retained on the nodes under /etc/kubernetes , as seen in the following output from a pod spawned using oc debug : Example output ls -lR /host/etc/kubernetes/aide.* -rw-------. 1 root root 1839782 Sep 17 15:08 /host/etc/kubernetes/aide.db.gz -rw-------. 1 root root 1839783 Sep 17 14:30 /host/etc/kubernetes/aide.db.gz.backup-20200917T15_07_38 -rw-------. 1 root root 73728 Sep 17 15:07 /host/etc/kubernetes/aide.db.gz.backup-20200917T15_07_55 -rw-r--r--. 1 root root 0 Sep 17 15:08 /host/etc/kubernetes/aide.log -rw-------. 1 root root 613 Sep 17 15:07 /host/etc/kubernetes/aide.log.backup-20200917T15_07_38 -rw-r--r--. 1 root root 0 Sep 17 15:07 /host/etc/kubernetes/aide.log.backup-20200917T15_07_55 To provide some permanence of record, the resulting config maps are not owned by the FileIntegrity object, so manual cleanup is necessary. As a result, any integrity failures would still be visible in the FileIntegrityNodeStatus object. 6.6.2. Machine config integration In OpenShift Container Platform 4, the cluster node configuration is delivered through MachineConfig objects. You can assume that the changes to files that are caused by a MachineConfig object are expected and should not cause the file integrity scan to fail. To suppress changes to files caused by MachineConfig object updates, the File Integrity Operator watches the node objects; when a node is being updated, the AIDE scans are suspended for the duration of the update. When the update finishes, the database is reinitialized and the scans resume. This pause and resume logic only applies to updates through the MachineConfig API, as they are reflected in the node object annotations. 6.6.3. Exploring the daemon sets Each FileIntegrity object represents a scan on a number of nodes. The scan itself is performed by pods managed by a daemon set. To find the daemon set that represents a FileIntegrity object, run: USD oc -n openshift-file-integrity get ds/aide-worker-fileintegrity To list the pods in that daemon set, run: USD oc -n openshift-file-integrity get pods -lapp=aide-worker-fileintegrity To view logs of a single AIDE pod, call oc logs on one of the pods. USD oc -n openshift-file-integrity logs pod/aide-worker-fileintegrity-mr8x6 Example output Starting the AIDE runner daemon initializing AIDE db initialization finished running aide check ... The config maps created by the AIDE daemon are not retained and are deleted after the File Integrity Operator processes them. However, on failure and error, the contents of these config maps are copied to the config map that the FileIntegrityNodeStatus object points to. 6.7. Troubleshooting the File Integrity Operator 6.7.1. General troubleshooting Issue You want to generally troubleshoot issues with the File Integrity Operator. Resolution Enable the debug flag in the FileIntegrity object. The debug flag increases the verbosity of the daemons that run in the DaemonSet pods and run the AIDE checks. 6.7.2. Checking the AIDE configuration Issue You want to check the AIDE configuration. Resolution The AIDE configuration is stored in a config map with the same name as the FileIntegrity object. All AIDE configuration config maps are labeled with file-integrity.openshift.io/aide-conf . 6.7.3. Determining the FileIntegrity object's phase Issue You want to determine if the FileIntegrity object exists and see its current status. Resolution To see the FileIntegrity object's current status, run: USD oc get fileintegrities/worker-fileintegrity -o jsonpath="{ .status }" Once the FileIntegrity object and the backing daemon set are created, the status should switch to Active . If it does not, check the Operator pod logs. 6.7.4. Determining that the daemon set's pods are running on the expected nodes Issue You want to confirm that the daemon set exists and that its pods are running on the nodes you expect them to run on. Resolution Run: USD oc -n openshift-file-integrity get pods -lapp=aide-worker-fileintegrity Note Adding -owide includes the IP address of the node that the pod is running on. To check the logs of the daemon pods, run oc logs . Check the return value of the AIDE command to see if the check passed or failed.	[ "oc create -f <file-name>.yaml", "apiVersion: v1 kind: Namespace metadata: labels: openshift.io/cluster-monitoring: \"true\" name: openshift-file-integrity", "oc create -f <file-name>.yaml", "apiVersion: operators.coreos.com/v1 kind: OperatorGroup metadata: name: file-integrity-operator namespace: openshift-file-integrity spec: targetNamespaces: - openshift-file-integrity", "oc create -f <file-name>.yaml", "apiVersion: operators.coreos.com/v1alpha1 kind: Subscription metadata: name: file-integrity-operator namespace: openshift-file-integrity spec: channel: \"stable\" installPlanApproval: Automatic name: file-integrity-operator source: redhat-operators sourceNamespace: openshift-marketplace", "oc get csv -n openshift-file-integrity", "oc get deploy -n openshift-file-integrity", "apiVersion: fileintegrity.openshift.io/v1alpha1 kind: FileIntegrity metadata: name: worker-fileintegrity namespace: openshift-file-integrity spec: nodeSelector: 1 node-role.kubernetes.io/worker: \"\" tolerations: 2 - key: \"myNode\" operator: \"Exists\" effect: \"NoSchedule\" config: 3 name: \"myconfig\" namespace: \"openshift-file-integrity\" key: \"config\" gracePeriod: 20 4 maxBackups: 5 5 initialDelay: 60 6 debug: false status: phase: Active 7", "oc apply -f worker-fileintegrity.yaml -n openshift-file-integrity", "oc get fileintegrities -n openshift-file-integrity", "NAME AGE worker-fileintegrity 14s", "oc get fileintegrities/worker-fileintegrity -o jsonpath=\"{ .status.phase }\"", "Active", "oc get fileintegritynodestatuses", "NAME AGE worker-fileintegrity-ip-10-0-130-192.ec2.internal 101s worker-fileintegrity-ip-10-0-147-133.ec2.internal 109s worker-fileintegrity-ip-10-0-165-160.ec2.internal 102s", "oc get fileintegritynodestatuses.fileintegrity.openshift.io -ojsonpath='{.items[].results}' \| jq", "oc get fileintegritynodestatuses -w", "NAME NODE STATUS example-fileintegrity-ip-10-0-134-186.us-east-2.compute.internal ip-10-0-134-186.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-150-230.us-east-2.compute.internal ip-10-0-150-230.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-169-137.us-east-2.compute.internal ip-10-0-169-137.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-180-200.us-east-2.compute.internal ip-10-0-180-200.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-194-66.us-east-2.compute.internal ip-10-0-194-66.us-east-2.compute.internal Failed example-fileintegrity-ip-10-0-222-188.us-east-2.compute.internal ip-10-0-222-188.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-134-186.us-east-2.compute.internal ip-10-0-134-186.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-222-188.us-east-2.compute.internal ip-10-0-222-188.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-194-66.us-east-2.compute.internal ip-10-0-194-66.us-east-2.compute.internal Failed example-fileintegrity-ip-10-0-150-230.us-east-2.compute.internal ip-10-0-150-230.us-east-2.compute.internal Succeeded example-fileintegrity-ip-10-0-180-200.us-east-2.compute.internal ip-10-0-180-200.us-east-2.compute.internal Succeeded", "[ { \"condition\": \"Succeeded\", \"lastProbeTime\": \"2020-09-15T12:45:57Z\" } ] [ { \"condition\": \"Succeeded\", \"lastProbeTime\": \"2020-09-15T12:46:03Z\" } ] [ { \"condition\": \"Succeeded\", \"lastProbeTime\": \"2020-09-15T12:45:48Z\" } ]", "oc debug node/ip-10-0-130-192.ec2.internal", "Creating debug namespace/openshift-debug-node-ldfbj Starting pod/ip-10-0-130-192ec2internal-debug To use host binaries, run `chroot /host` Pod IP: 10.0.130.192 If you don't see a command prompt, try pressing enter. sh-4.2# echo \"# integrity test\" >> /host/etc/resolv.conf sh-4.2# exit Removing debug pod Removing debug namespace/openshift-debug-node-ldfbj", "oc get fileintegritynodestatuses.fileintegrity.openshift.io/worker-fileintegrity-ip-10-0-130-192.ec2.internal -ojsonpath='{.results}' \| jq -r", "oc get fileintegritynodestatuses.fileintegrity.openshift.io -ojsonpath='{.items[].results}' \| jq", "[ { \"condition\": \"Succeeded\", \"lastProbeTime\": \"2020-09-15T12:54:14Z\" }, { \"condition\": \"Failed\", \"filesChanged\": 1, \"lastProbeTime\": \"2020-09-15T12:57:20Z\", \"resultConfigMapName\": \"aide-ds-worker-fileintegrity-ip-10-0-130-192.ec2.internal-failed\", \"resultConfigMapNamespace\": \"openshift-file-integrity\" } ]", "oc describe cm aide-ds-worker-fileintegrity-ip-10-0-130-192.ec2.internal-failed", "Name: aide-ds-worker-fileintegrity-ip-10-0-130-192.ec2.internal-failed Namespace: openshift-file-integrity Labels: file-integrity.openshift.io/node=ip-10-0-130-192.ec2.internal file-integrity.openshift.io/owner=worker-fileintegrity file-integrity.openshift.io/result-log= Annotations: file-integrity.openshift.io/files-added: 0 file-integrity.openshift.io/files-changed: 1 file-integrity.openshift.io/files-removed: 0 Data integritylog: ------ AIDE 0.15.1 found differences between database and filesystem!! Start timestamp: 2020-09-15 12:58:15 Summary: Total number of files: 31553 Added files: 0 Removed files: 0 Changed files: 1 --------------------------------------------------- Changed files: --------------------------------------------------- changed: /hostroot/etc/resolv.conf --------------------------------------------------- Detailed information about changes: --------------------------------------------------- File: /hostroot/etc/resolv.conf SHA512 : sTQYpB/AL7FeoGtu/1g7opv6C+KT1CBJ , qAeM+a8yTgHPnIHMaRlS+so61EN8VOpg Events: <none>", "oc get events --field-selector reason=FileIntegrityStatus", "LAST SEEN TYPE REASON OBJECT MESSAGE 97s Normal FileIntegrityStatus fileintegrity/example-fileintegrity Pending 67s Normal FileIntegrityStatus fileintegrity/example-fileintegrity Initializing 37s Normal FileIntegrityStatus fileintegrity/example-fileintegrity Active", "oc get events --field-selector reason=NodeIntegrityStatus", "LAST SEEN TYPE REASON OBJECT MESSAGE 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-134-173.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-168-238.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-169-175.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-152-92.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-158-144.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-131-30.ec2.internal 87m Warning NodeIntegrityStatus fileintegrity/example-fileintegrity node ip-10-0-152-92.ec2.internal has changed! a:1,c:1,r:0 \\ log:openshift-file-integrity/aide-ds-example-fileintegrity-ip-10-0-152-92.ec2.internal-failed", "oc get events --field-selector reason=NodeIntegrityStatus", "LAST SEEN TYPE REASON OBJECT MESSAGE 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-134-173.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-168-238.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-169-175.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-152-92.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-158-144.ec2.internal 114m Normal NodeIntegrityStatus fileintegrity/example-fileintegrity no changes to node ip-10-0-131-30.ec2.internal 87m Warning NodeIntegrityStatus fileintegrity/example-fileintegrity node ip-10-0-152-92.ec2.internal has changed! a:1,c:1,r:0 \\ log:openshift-file-integrity/aide-ds-example-fileintegrity-ip-10-0-152-92.ec2.internal-failed 40m Warning NodeIntegrityStatus fileintegrity/example-fileintegrity node ip-10-0-152-92.ec2.internal has changed! a:3,c:1,r:0 \\ log:openshift-file-integrity/aide-ds-example-fileintegrity-ip-10-0-152-92.ec2.internal-failed", "oc explain fileintegrity.spec", "oc explain fileintegrity.spec.config", "oc describe cm/worker-fileintegrity", "@@define DBDIR /hostroot/etc/kubernetes @@define LOGDIR /hostroot/etc/kubernetes database=file:@@{DBDIR}/aide.db.gz database_out=file:@@{DBDIR}/aide.db.gz gzip_dbout=yes verbose=5 report_url=file:@@{LOGDIR}/aide.log report_url=stdout PERMS = p+u+g+acl+selinux+xattrs CONTENT_EX = sha512+ftype+p+u+g+n+acl+selinux+xattrs /hostroot/boot/ CONTENT_EX /hostroot/root/\\..* PERMS /hostroot/root/ CONTENT_EX", "oc extract cm/worker-fileintegrity --keys=aide.conf", "vim aide.conf", "/hostroot/etc/kubernetes/static-pod-resources !/hostroot/etc/kubernetes/aide.* !/hostroot/etc/kubernetes/manifests !/hostroot/etc/docker/certs.d !/hostroot/etc/selinux/targeted !/hostroot/etc/openvswitch/conf.db", "!/opt/mydaemon/", "/hostroot/etc/ CONTENT_EX", "oc create cm master-aide-conf --from-file=aide.conf", "apiVersion: fileintegrity.openshift.io/v1alpha1 kind: FileIntegrity metadata: name: master-fileintegrity namespace: openshift-file-integrity spec: nodeSelector: node-role.kubernetes.io/master: \"\" config: name: master-aide-conf namespace: openshift-file-integrity", "oc describe cm/master-fileintegrity \| grep /opt/mydaemon", "!/hostroot/opt/mydaemon", "oc annotate fileintegrities/worker-fileintegrity file-integrity.openshift.io/re-init=", "ls -lR /host/etc/kubernetes/aide.* -rw-------. 1 root root 1839782 Sep 17 15:08 /host/etc/kubernetes/aide.db.gz -rw-------. 1 root root 1839783 Sep 17 14:30 /host/etc/kubernetes/aide.db.gz.backup-20200917T15_07_38 -rw-------. 1 root root 73728 Sep 17 15:07 /host/etc/kubernetes/aide.db.gz.backup-20200917T15_07_55 -rw-r--r--. 1 root root 0 Sep 17 15:08 /host/etc/kubernetes/aide.log -rw-------. 1 root root 613 Sep 17 15:07 /host/etc/kubernetes/aide.log.backup-20200917T15_07_38 -rw-r--r--. 1 root root 0 Sep 17 15:07 /host/etc/kubernetes/aide.log.backup-20200917T15_07_55", "oc -n openshift-file-integrity get ds/aide-worker-fileintegrity", "oc -n openshift-file-integrity get pods -lapp=aide-worker-fileintegrity", "oc -n openshift-file-integrity logs pod/aide-worker-fileintegrity-mr8x6", "Starting the AIDE runner daemon initializing AIDE db initialization finished running aide check", "oc get fileintegrities/worker-fileintegrity -o jsonpath=\"{ .status }\"", "oc -n openshift-file-integrity get pods -lapp=aide-worker-fileintegrity" ]	https://docs.redhat.com/en/documentation/openshift_container_platform/4.10/html/security_and_compliance/file-integrity-operator
Chapter 8. File and directory layouts	Chapter 8. File and directory layouts As a storage administrator, you can control how file or directory data is mapped to objects. This section describes how to: Understand file and directory layouts Set file and directory layouts View file and directory layout fields View individual layout fields Remove the directory layouts 8.1. Prerequisites A running, and healthy Red Hat Ceph Storage cluster. Deployment of a Ceph File System. The installation of the attr package. 8.2. Overview of file and directory layouts This section explains what file and directory layouts are in the context for the Ceph File System. A layout of a file or directory controls how its content is mapped to Ceph RADOS objects. The directory layouts serve primarily for setting an inherited layout for new files in that directory. To view and set a file or directory layout, use virtual extended attributes or extended file attributes ( xattrs ). The name of the layout attributes depends on whether a file is a regular file or a directory: Regular files layout attributes are called ceph.file.layout . Directories layout attributes are called ceph.dir.layout . Layouts Inheritance Files inherit the layout of their parent directory when you create them. However, subsequent changes to the parent directory layout do not affect children. If a directory does not have any layouts set, files inherit the layout from the closest directory to the layout in the directory structure. 8.3. Setting file and directory layout fields Use the setfattr command to set layout fields on a file or directory. Important When you modify the layout fields of a file, the file must be empty, otherwise an error occurs. Prerequisites Root-level access to the node. Procedure To modify layout fields on a file or directory: Syntax Replace: TYPE with file or dir . FIELD with the name of the field. VALUE with the new value of the field. PATH with the path to the file or directory. Example Additional Resources See the table in the Overview of file and directory layouts section of the Red Hat Ceph Storage File System Guide for more details. See the setfattr(1) manual page. 8.4. Viewing file and directory layout fields To use the getfattr command to view layout fields on a file or directory. Prerequisites A running Red Hat Ceph Storage cluster. Root-level access to all nodes in the storage cluster. Procedure To view layout fields on a file or directory as a single string: Syntax Replace PATH with the path to the file or directory. TYPE with file or dir . Example Note A directory does not have an explicit layout until you set it. Consequently, attempting to view the layout without first setting it fails because there are no changes to display. Additional Resources The getfattr(1) manual page. For more information, see Setting file and directory layout fields section in the Red Hat Ceph Storage File System Guide . 8.5. Viewing individual layout fields Use the getfattr command to view individual layout fields for a file or directory. Prerequisites A running Red Hat Ceph Storage cluster. Root-level access to all nodes in the storage cluster. Procedure To view individual layout fields on a file or directory: Syntax Replace TYPE with file or dir . FIELD with the name of the field. PATH with the path to the file or directory. Example Note Pools in the pool field are indicated by name. However, newly created pools can be indicated by ID. Additional Resources The getfattr(1) manual page. For more information, see File and directory layouts section in the Red Hat Ceph Storage File System Guide . 8.6. Removing directory layouts Use the setfattr command to remove layouts from a directory. Note When you set a file layout, you cannot change or remove it. Prerequisites A directory with a layout. Procedure To remove a layout from a directory: Syntax Example To remove the pool_namespace field: Syntax Example Note The pool_namespace field is the only field you can remove separately. Additional Resources The setfattr(1) manual page 8.7. Additional Resources See the Deployment of the Ceph File System section in the Red Hat Ceph Storage File System Guide . See the getfattr(1) manual page for more information. See the setfattr(1) manual page for more information.	[ "setfattr -n ceph. TYPE .layout. FIELD -v VALUE PATH", "setfattr -n ceph.file.layout.stripe_unit -v 1048576 test", "getfattr -n ceph. TYPE .layout PATH", "getfattr -n ceph.dir.layout /home/test ceph.dir.layout=\"stripe_unit=4194304 stripe_count=2 object_size=4194304 pool=cephfs_data\"", "getfattr -n ceph. TYPE .layout. FIELD _PATH", "getfattr -n ceph.file.layout.pool test ceph.file.layout.pool=\"cephfs_data\"", "setfattr -x ceph.dir.layout DIRECTORY_PATH", "[user@client ~]USD setfattr -x ceph.dir.layout /home/cephfs", "setfattr -x ceph.dir.layout.pool_namespace DIRECTORY_PATH", "[user@client ~]USD setfattr -x ceph.dir.layout.pool_namespace /home/cephfs" ]	https://docs.redhat.com/en/documentation/red_hat_ceph_storage/5/html/file_system_guide/file-and-directory-layouts
Chapter 2. Architecture	Chapter 2. Architecture The director advocates the use of native OpenStack APIs to configure, deploy, and manage OpenStack environments itself. This means integration with director requires integrating with these native OpenStack APIs and supporting components. The major benefit of utilizing such APIs is that they are well documented, undergo extensive integration testing upstream, are mature, and makes understanding how the director works easier for those that have a foundational knowledge of OpenStack. This also means the director automatically inherits core OpenStack feature enhancements, security patches, and bug fixes. The Red Hat OpenStack Platform director is a toolset for installing and managing a complete OpenStack environment. It is based primarily on the OpenStack project TripleO, which is an abbreviation for "OpenStack-On-OpenStack". This project takes advantage of OpenStack components to install a fully operational OpenStack environment. This includes new OpenStack components that provision and control bare metal systems to use as OpenStack nodes. This provides a simple method for installing a complete Red Hat OpenStack Platform environment that is both lean and robust. The Red Hat OpenStack Platform director uses two main concepts: an Undercloud and an Overcloud. This director itself is comprised of a subset of OpenStack components that form a single-system OpenStack environment, otherwise known as the Undercloud. The Undercloud acts as a management system that can create a production-level cloud for workloads to run. This production-level cloud is the Overcloud. For more information on the Overcloud and the Undercloud, see the Director Installation and Usage guide. Director ships with tools, utilities, and example templates for creating an Overcloud configuration. The director captures configuration data, parameters, and network topology information then uses this information in conjunction with components such as Ironic, Heat, and Puppet to orchestrate an Overcloud installation. Partners have varied requirements. Understanding the director's architecture aids in understand which components matter for a given integration effort. 2.1. Core Components This section examines some of the core components of the Red Hat OpenStack Platform director and describes how they contribute to Overcloud creation. 2.1.1. Ironic Ironic provides dedicated bare metal hosts to end users through self-service provisioning. The director uses Ironic to manage the lifecycle of the bare metal hardware in our Overcloud. Ironic has its own native API for defining bare metal nodes. Administrators aiming to provision OpenStack environments with the director must register their nodes with Ironic using a specific driver. The main supported driver is The Intelligent Platform Management Interface (IPMI) as most hardware contains some support for IPMI power management functions. However, ironic also contains vendor specific equivalents such as HP iLO or Dell DRAC. Ironic controls the power management of the nodes and gathers hardware information or facts using a introspection mechanism. The director uses the information obtained from the introspection process to match node to various OpenStack environment roles, such as Controller nodes, Compute nodes, and storage nodes. For example, a discovered node with 10 disks will more than likely be provisioned as a storage node. Partners wishing to have director support for their hardware will need to have driver coverage in Ironic. 2.1.2. Heat Heat acts as an application stack orchestration engine. This allows organizations to define elements for a given application before deploying it to a cloud. This involves creating a stack template that includes a number of infrastructure resources (e.g. instances, networks, storage volumes, elastic IPs, etc) along with a set of parameters for configuration. Heat creates these resources based on a given dependency chain, monitors them for availability, and scales them where necessary. These templates enable application stacks to become portable and achieve repeatable results. The director uses the native OpenStack Heat APIs to provision and manage the resources associated with deploying an Overcloud. This includes precise details such as defining the number of nodes to provision per node role, the software components to configure for each node, and the order in which the director configures these components and node types. The director also uses Heat for troubleshooting a deployment and making changes post-deployment with ease. The following example is a snippet from a Heat template that defines parameters of a Controller node: Heat consumes templates included with the director to facilitate the creation of an Overcloud, which includes calling Ironic to power the nodes. We can view the resources (and their status) of an in-progress Overcloud using the standard Heat tools. For example, you can use the Heat tools to display the Overcloud as a nested application stack. Heat provides a comprehensive and powerful syntax for declaring and creating production OpenStack clouds. However, it requires some prior understanding and proficiency for partner integration. Every partner integration use case requires Heat templates. 2.1.3. Puppet Puppet is a configuration management and enforcement tool. It is used as a mechanism to describe the end state of a machine and keep it that way. You define this end state in a Puppet manifest. Puppet supports two models: A standalone mode in which instructions in the form of manifests are ran locally A server mode where it retrieves its manifests from a central server, called a Puppet Master. Administrators make changes in two ways: either uploading new manifests to a node and executing them locally, or in the client/server model by making modifications on the Puppet Master. We use Puppet in many areas of director: We use Puppet on the Undercloud host locally to install and configure packages as per the configuration laid out in undercloud.conf . We inject the openstack-puppet-modules package into the base Overcloud image. These Puppet modules are ready for post-deployment configuration. By default, we create an image that contains all OpenStack services and use it for each node. We provide additional Puppet manifests and parameters to the nodes via Heat, and apply the configuration after the Overcloud's deployment. This includes the services to enable and start and the OpenStack configuration to apply, which are dependent on the node type. We provide Puppet hieradata to the nodes. The Puppet modules and manifests are free from site or node-specific parameters to keep the manifests consistent. The hieradata acts as a form of parameterized values that you can push to a Puppet module and reference in other areas. For example, to reference the MySQL password inside of a manifest, save this information as hieradata and reference it within the manifest. Viewing the hieradata: Referencing it in the Puppet manifest: Partner integrated services that need package installation and service enablement should consider creating Puppet modules to meet their requirement. For examples, see Section 4.2, "Obtaining OpenStack Puppet Modules" for information on how to obtain current OpenStack Puppet modules. 2.1.4. TripleO and TripleO Heat Templates As mentioned previously, the director is based on the upstream TripleO project. This project combines a set of OpenStack services that: Store Overcloud images (Glance) Orchestrate the Overcloud (Heat) Provision bare metal machines (Ironic and Nova) TripleO also includes a Heat template collection that defines a Red Hat-supported Overcloud environment. The director, using Heat, reads this template collection and orchestrates the Overcloud stack. 2.1.5. Composable Services Each aspect of Red Hat OpenStack Platform is broken into a composable service. This means you can define different roles using different combinations of services. For example, an administrator might aim to move the networking agents from the default Controller node to a standalone Networker node. For more information about the composable service architecture, see Chapter 6, Composable Services . 2.1.6. Containerized Services and Kolla Each of the main Red Hat OpenStack Platform services run in containers. This provides a method of keep each service within its own isolated namespace separated from the host. This means: The deployment of services is performed by pulling container images from the Red Hat Custom Portal and running them. The management functions, like starting and stopping services, operate through the podman command. Upgrading containers require pulling new container images and replacing the existing containers with newer versions. Red Hat OpenStack Platform uses a set of containers built and managed with the kolla toolset. 2.1.7. Ansible OpenStack Platform uses Ansible is used to drive certain functions in relation to composable service upgrades. This includes functions such as starting and stopping certain services and perfoming database upgrades. These upgrade tasks are defined within composable service templates.	[ "NeutronExternalNetworkBridge: description: Name of bridge used for external network traffic. type: string default: 'br-ex' NeutronBridgeMappings: description: > The OVS logical->physical bridge mappings to use. See the Neutron documentation for details. Defaults to mapping br-ex - the external bridge on hosts - to a physical name 'datacentre' which can be used to create provider networks (and we use this for the default floating network) - if changing this either use different post-install network scripts or be sure to keep 'datacentre' as a mapping network name. type: string default: \"datacentre:br-ex\"", "grep mysql_root_password hieradata.yaml # View the data in the hieradata file openstack::controller::mysql_root_password: 'redhat123'", "grep mysql_root_password example.pp # Now referenced in the Puppet manifest mysql_root_password => hiera('openstack::controller::mysql_root_password')" ]	https://docs.redhat.com/en/documentation/red_hat_openstack_platform/16.0/html/partner_integration/architecture
9.5. Pacemaker Support for Docker Containers (Technology Preview)	9.5. Pacemaker Support for Docker Containers (Technology Preview) Important Pacemaker support for Docker containers is provided for technology preview only. For details on what "technology preview" means, see Technology Preview Features Support Scope . There is one exception to this feature being Technology Preview: As of Red Hat Enterprise Linux 7.4, Red Hat fully supports the usage of Pacemaker bundles for Red Hat Openstack Platform (RHOSP) deployments. Pacemaker supports a special syntax for launching a Docker container with any infrastructure it requires: the bundle . After you have created a Pacemaker bundle, you can create a Pacemaker resource that the bundle encapsulates. Section 9.5.1, "Configuring a Pacemaker Bundle Resource" describes the syntax for the command to create a Pacemaker bundle and provides tables summarizing the parameters you can define for each bundle parameter. Section 9.5.2, "Configuring a Pacemaker Resource in a Bundle" provides information on configuring a resource contained in a Pacemaker bundle. Section 9.5.3, "Limitations of Pacemaker Bundles" notes the limitations of Pacemaker bundles. Section 9.5.4, "Pacemaker Bundle Configuration Example" provides a Pacemaker bundle configuration example. 9.5.1. Configuring a Pacemaker Bundle Resource The syntax for the command to create a Pacemaker bundle for a Docker container is as follows. This command creates a bundle that encapsulates no other resources. For information on creating a cluster resource in a bundle see Section 9.5.2, "Configuring a Pacemaker Resource in a Bundle" . The required bundle_id parameter must be a unique name for the bundle. If the --disabled option is specified, the bundle is not started automatically. If the --wait option is specified, Pacemaker will wait up to n seconds for the bundle to start and then return 0 on success or 1 on error. If n is not specified it defaults to 60 minutes. The following sections describe the parameters you can configure for each element of a Pacemaker bundle. 9.5.1.1. Docker Parameters Table 9.6, "Docker Container Parameters" describes the docker container options you can set for a bundle. Note Before configuring a docker bundle in Pacemaker, you must install Docker and supply a fully configured Docker image on every node allowed to run the bundle. Table 9.6. Docker Container Parameters Field Default Description image Docker image tag (required) replicas Value of promoted-max if that is positive, otherwise 1. A positive integer specifying the number of container instances to launch replicas-per-host 1 A positive integer specifying the number of container instances allowed to run on a single node promoted-max 0 A non-negative integer that, if positive, indicates that the containerized service should be treated as a multistate service, with this many replicas allowed to run the service in the master role network If specified, this will be passed to the docker run command as the network setting for the Docker container. run-command /usr/sbin/pacemaker_remoted if the bundle contains a resource, otherwise none This command will be run inside the container when launching it ("PID 1"). If the bundle contains a resource, this command must start the pacemaker_remoted daemon (but it could, for example, be a script that performs others tasks as well). options Extra command-line options to pass to the docker run command 9.5.1.2. Bundle Network Parameters Table 9.7, "Bundle Resource Network Parameters" describes the network options you can set for a bundle. Table 9.7. Bundle Resource Network Parameters Field Default Description add-host TRUE If TRUE, and ip-range-start is used, Pacemaker will automatically ensure that the /etc/hosts file inside the containers has entries for each replica name and its assigned IP. ip-range-start If specified, Pacemaker will create an implicit ocf:heartbeat:IPaddr2 resource for each container instance, starting with this IP address, using as many sequential addresses as were specified as the replicas parameter for the Docker element. These addresses can be used from the host's network to reach the service inside the container, although it is not visible within the container itself. Only IPv4 addresses are currently supported. host-netmask 32 If ip-range-start is specified, the IP addresses are created with this CIDR netmask (as a number of bits). host-interface If ip-range-start is specified, the IP addresses are created on this host interface (by default, it will be determined from the IP address). control-port 3121 If the bundle contains a Pacemaker resource, the cluster will use this integer TCP port for communication with Pacemaker Remote inside the container. Changing this is useful when the container is unable to listen on the default port, which could happen when the container uses the host's network rather than ip-range-start (in which case replicas-per-host must be 1), or when the bundle may run on a Pacemaker Remote node that is already listening on the default port. Any PCMK_remote_port environment variable set on the host or in the container is ignored for bundle connections. When a Pacemaker bundle configuration uses the control-port parameter, then if the bundle has its own IP address the port needs to be open on that IP address on and from all full cluster nodes running corosync. If, instead, the bundle has set the network="host" container parameter, the port needs to be open on each cluster node's IP address from all cluster nodes. Note Replicas are named by the bundle ID plus a dash and an integer counter starting with zero. For example, if a bundle named httpd-bundle has configured replicas=2 , its containers will be named httpd-bundle-0 and httpd-bundle-1 . In addition to the network parameters, you can optionally specify port-map parameters for a bundle. Table 9.8, "Bundle Resource port-map Parameters" describes these port-map parameters. Table 9.8. Bundle Resource port-map Parameters Field Default Description id A unique name for the port mapping (required) port If this is specified, connections to this TCP port number on the host network (on the container's assigned IP address, if ip-range-start is specified) will be forwarded to the container network. Exactly one of port or range must be specified in a port-mapping. internal-port Value of port If port and internal-port are specified, connections to port on the host's network will be forwarded to this port on the container network. range If range is specified, connections to these TCP port numbers (expressed as first_port-last_port ) on the host network (on the container's assigned IP address, if ip-range-start is specified) will be forwarded to the same ports in the container network. Exactly one of port or range must be specified in a port mapping. Note If the bundle contains a resource, Pacemaker will automatically map the control-port , so it is not necessary to specify that port in a port mapping. 9.5.1.3. Bundle Storage Parameters You can optionally configure storage-map parameters for a bundle. Table 9.9, "Bundle Resource Storage Mapping Parameters" describes these parameters. Table 9.9. Bundle Resource Storage Mapping Parameters Field Default Description id A unique name for the storage mapping (required) source-dir The absolute path on the host's filesystem that will be mapped into the container. Exactly one of source-dir and source-dir-root parameter must be specified when configuring a storage-map parameter. source-dir-root The start of a path on the host's filesystem that will be mapped into the container, using a different subdirectory on the host for each container instance. The subdirectory will be named with the same name as the bundle name, plus a dash and an integer counter starting with 0. Exactly one source-dir and source-dir-root parameter must be specified when configuring a storage-map parameter. target-dir The path name within the container where the host storage will be mapped (required) options File system mount options to use when mapping the storage As an example of how subdirectories on a host are named using the source-dir-root parameter, if source-dir-root=/path/to/my/directory , target-dir=/srv/appdata , and the bundle is named mybundle with replicas=2 , then the cluster will create two container instances with host names mybundle-0 and mybundle-1 and create two directories on the host running the containers: /path/to/my/directory/mybundle-0 and /path/to/my/directory/mybundle-1 . Each container will be given one of those directories, and any application running inside the container will see the directory as /srv/appdata . Note Pacemaker does not define the behavior if the source directory does not already exist on the host. However, it is expected that the container technology or its resource agent will create the source directory in that case. Note If the bundle contains a Pacemaker resource, Pacemaker will automatically map the equivalent of source-dir=/etc/pacemaker/authkey target-dir=/etc/pacemaker/authkey and source-dir-root=/var/log/pacemaker/bundles target-dir=/var/log into the container, so it is not necessary to specify those paths in when configuring storage-map parameters. Important The PCMK_authkey_location environment variable must not be set to anything other than the default of /etc/pacemaker/authkey on any node in the cluster. 9.5.2. Configuring a Pacemaker Resource in a Bundle A bundle may optionally contain one Pacemaker cluster resource. As with a resource that is not contained in a bundle, the cluster resource may have operations, instance attributes, and metadata attributes defined. If a bundle contains a resource, the container image must include the Pacemaker Remote daemon, and ip-range-start or control-port must be configured in the bundle. Pacemaker will create an implicit ocf:pacemaker:remote resource for the connection, launch Pacemaker Remote within the container, and monitor and manage the resource by means of Pacemaker Remote. If the bundle has more than one container instance (replica), the Pacemaker resource will function as an implicit clone, which will be a multistate clone if the bundle has configured the promoted-max option as greater than zero. You create a resource in a Pacemaker bundle with the pcs resource create command by specifying the bundle parameter for the command and the bundle ID in which to include the resource. For an example of creating a Pacemaker bundle that contains a resource, see Section 9.5.4, "Pacemaker Bundle Configuration Example" . Important Containers in bundles that contain a resource must have an accessible networking environment, so that Pacemaker on the cluster nodes can contact Pacemaker Remote inside the container. For example, the docker option --net=none should not be used with a resource. The default (using a distinct network space inside the container) works in combination with the ip-range-start parameter. If the docker option --net=host is used (making the container share the host's network space), a unique control-port parameter should be specified for each bundle. Any firewall must allow access to the control-port . 9.5.2.1. Node Attributes and Bundle Resources If the bundle contains a cluster resource, the resource agent may want to set node attributes such as master scores. However, with containers, it is not apparent which node should get the attribute. If the container uses shared storage that is the same no matter which node the container is hosted on, then it is appropriate to use the master score on the bundle node itself. On the other hand, if the container uses storage exported from the underlying host, then it may be more appropriate to use the master score on the underlying host. Since this depends on the particular situation, the container-attribute-target resource metadata attribute allows the user to specify which approach to use. If it is set to host , then user-defined node attributes will be checked on the underlying host. If it is anything else, the local node (in this case the bundle node) is used. This behavior applies only to user-defined attributes; the cluster will always check the local node for cluster-defined attributes such as #uname . If container-attribute-target is set to host , the cluster will pass additional environment variables to the resource agent that allow it to set node attributes appropriately. 9.5.2.2. Metadata Attributes and Bundle Resources Any metadata attribute set on a bundle will be inherited by the resource contained in a bundle and any resources implicitly created by Pacemaker for the bundle. This includes options such as priority , target-role , and is-managed . 9.5.3. Limitations of Pacemaker Bundles Pacemaker bundles operate with the following limitations: Bundles may not be included in groups or explicitly cloned with a pcs command. This includes a resource that the bundle contains, and any resources implicitly created by Pacemaker for the bundle. Note, however, that if a bundle is configured with a value of replicas greater than one, the bundle behaves as if it were a clone. Restarting Pacemaker while a bundle is unmanaged or the cluster is in maintenance mode may cause the bundle to fail. Bundles do not have instance attributes, utilization attributes, or operations, although a resource contained in a bundle may have them. A bundle that contains a resource can run on a Pacemaker Remote node only if the bundle uses a distinct control-port . 9.5.4. Pacemaker Bundle Configuration Example The following example creates a Pacemaker bundle resource with a bundle ID of httpd-bundle that contains an ocf:heartbeat:apache resource with a resource ID of httpd . This procedure requires the following prerequisite configuration: Docker has been installed and enabled on every node in the cluster. There is an existing Docker image, named pcmktest:http The container image includes the Pacemaker Remote daemon. The container image includes a configured Apache web server. Every node in the cluster has directories /var/local/containers/httpd-bundle-0 , /var/local/containers/httpd-bundle-1 , and /var/local/containers/httpd-bundle-2 , containing an index.html file for the web server root. In production, a single, shared document root would be more likely, but for the example this configuration allows you to make the index.html file on each host different so that you can connect to the web server and verify which index.html file is being served. This procedure configures the following parameters for the Pacemaker bundle: The bundle ID is httpd-bundle . The previously-configured Docker container image is pcmktest:http . This example will launch three container instances. This example will pass the command-line option --log-driver=journald to the docker run command. This parameter is not required, but is included to show how to pass an extra option to the docker command. A value of --log-driver=journald means that the system logs inside the container will be logged in the underlying hosts's systemd journal. Pacemaker will create three sequential implicit ocf:heartbeat:IPaddr2 resources, one for each container image, starting with the IP address 192.168.122.131. The IP addresses are created on the host interface eth0. The IP addresses are created with a CIDR netmask of 24. This example creates a port map ID of http-port ; connections to port 80 on the container's assigned IP address will be forwarded to the container network. This example creates a storage map ID of httpd-root . For this storage mapping: The value of source-dir-root is /var/local/containers , which specifies the start of the path on the host's file system that will be mapped into the container, using a different subdirectory on the host for each container instance. The value of target-dir is /var/www/html , which specifies the path name within the container where the host storage will be mapped. The file system rw mount option will be used when mapping the storage. Since this example container includes a resource, Pacemaker will automatically map the equivalent of source-dir=/etc/pacemaker/authkey in the container, so you do not need to specify that path in the storage mapping. In this example, the existing cluster configuration is put into a temporary file named temp-cib.xml , which is then copied to a file named temp-cib.xml.deltasrc . All modifications to the cluster configuration are made to the tmp-cib.xml file. When the udpates are complete, this procedure uses the diff-against option of the pcs cluster cib-push command so that only the updates to the configuration file are pushed to the active configuration file.	[ "pcs resource bundle create bundle_id container docker [ container_options ] [network network_options ] [port-map port_options ]... [storage-map storage_options ]... [meta meta_options ] [--disabled] [--wait[=n]]", "pcs cluster cib tmp-cib.xml cp tmp-cib.xml tmp-cib.xml.deltasrc pcs -f tmp.cib.xml resource bundle create httpd-bundle container docker image=pcmktest:http replicas=3 options=--log-driver=journald network ip-range-start=192.168.122.131 host-interface=eth0 host-netmask=24 port-map id=httpd-port port=80 storage-map id=httpd-root source-dir-root=/var/local/containers target-dir=/var/www/html options=rw pcs -f tmp-cib.xml resource create httpd ocf:heartbeat:apache statusurl=http://localhost/server-status bundle httpd-bundle pcs cluster cib-push tmp-cib.xml diff-against=tmp-cib.xml.deltasrc" ]	https://docs.redhat.com/en/documentation/Red_Hat_Enterprise_Linux/7/html/high_availability_add-on_reference/s1-containers-HAAR
Chapter 1. Preparing to deploy OpenShift Data Foundation	Chapter 1. Preparing to deploy OpenShift Data Foundation Deploying OpenShift Data Foundation on OpenShift Container Platform using dynamic or local storage devices provides you with the option to create internal cluster resources. This will result in the internal provisioning of the base services, which helps to make additional storage classes available to applications. Before you begin the deployment of Red Hat OpenShift Data Foundation using dynamic or local storage, ensure that your resource requirements are met. See the Resource requirements section in the Planning guide. Verify the rotational flag on your VMDKs before deploying object storage devices (OSDs) on them. For more information, see the knowledgebase article Override device rotational flag in ODF environment . Optional: If you want to enable cluster-wide encryption using the external Key Management System (KMS) HashiCorp Vault, follow these steps: Ensure that you have a valid Red Hat OpenShift Data Foundation Advanced subscription. To know how subscriptions for OpenShift Data Foundation work, see knowledgebase article on OpenShift Data Foundation subscriptions . When the Token authentication method is selected for encryption then refer to Enabling cluster-wide encryption with KMS using the Token authentication method . When the Kubernetes authentication method is selected for encryption then refer to Enabling cluster-wide encryption with KMS using the Kubernetes authentication method . Ensure that you are using signed certificates on your Vault servers. Optional: If you want to enable cluster-wide encryption using the external Key Management System (KMS) Thales CipherTrust Manager, you must first enable the Key Management Interoperability Protocol (KMIP) and use signed certificates on your server. Follow these steps: Create a KMIP client if one does not exist. From the user interface, select KMIP Client Profile Add Profile . Add the CipherTrust username to the Common Name field during profile creation. Create a token by navigating to KMIP Registration Token New Registration Token . Copy the token for the step. To register the client, navigate to KMIP Registered Clients Add Client . Specify the Name . Paste the Registration Token from the step, then click Save . Download the Private Key and Client Certificate by clicking Save Private Key and Save Certificate respectively. To create a new KMIP interface, navigate to Admin Settings Interfaces Add Interface . Select KMIP Key Management Interoperability Protocol and click . Select a free Port . Select Network Interface as all . Select Interface Mode as TLS, verify client cert, user name taken from client cert, auth request is optional . (Optional) You can enable hard delete to delete both metadata and material when the key is deleted. It is disabled by default. Select the CA to be used, and click Save . To get the server CA certificate, click on the Action menu (...) on the right of the newly created interface, and click Download Certificate . Optional: If StorageClass encryption is to be enabled during deployment, create a key to act as the Key Encryption Key (KEK): Navigate to Keys Add Key . Enter Key Name . Set the Algorithm and Size to AES and 256 respectively. Enable Create a key in Pre-Active state and set the date and time for activation. Ensure that Encrypt and Decrypt are enabled under Key Usage . Copy the ID of the newly created Key to be used as the Unique Identifier during deployment. Minimum starting node requirements An OpenShift Data Foundation cluster will be deployed with minimum configuration when the standard deployment resource requirement is not met. See Resource requirements section in Planning guide. Disaster recovery requirements Disaster Recovery features supported by Red Hat OpenShift Data Foundation require all of the following prerequisites to successfully implement a disaster recovery solution: A valid Red Hat OpenShift Data Foundation Advanced subscription A valid Red Hat Advanced Cluster Management for Kubernetes subscription To know how subscriptions for OpenShift Data Foundation work, see knowledgebase article on OpenShift Data Foundation subscriptions . For detailed requirements, see Configuring OpenShift Data Foundation Disaster Recovery for OpenShift Workloads guide, and Requirements and recommendations section of the Install guide in Red Hat Advanced Cluster Management for Kubernetes documentation. For deploying using local storage devices, see requirements for installing OpenShift Data Foundation using local storage devices . These are not applicable for deployment using dynamic storage devices. 1.1. Requirements for installing OpenShift Data Foundation using local storage devices Node requirements The cluster must consist of at least three OpenShift Container Platform worker or infrastructure nodes with locally attached-storage devices on each of them. Each of the three selected nodes must have at least one raw block device available. OpenShift Data Foundation uses the one or more available raw block devices. Note Make sure that the devices have a unique by-id device name for each available raw block device. The devices you use must be empty, the disks must not include Physical Volumes (PVs), Volume Groups (VGs), or Logical Volumes (LVs) remaining on the disk. For more information, see the Resource requirements section in the Planning guide . Disaster recovery requirements Disaster Recovery features supported by Red Hat OpenShift Data Foundation require all of the following prerequisites to successfully implement a disaster recovery solution: A valid Red Hat OpenShift Data Foundation Advanced subscription. A valid Red Hat Advanced Cluster Management (RHACM) for Kubernetes subscription. To know in detail how subscriptions for OpenShift Data Foundation work, see knowledgebase article on OpenShift Data Foundation subscriptions . For detailed disaster recovery solution requirements, see Configuring OpenShift Data Foundation Disaster Recovery for OpenShift Workloads guide, and Requirements and recommendations section of the Install guide in Red Hat Advanced Cluster Management for Kubernetes documentation. Arbiter stretch cluster requirements In this case, a single cluster is stretched across two zones with a third zone as the location for the arbiter. This solution is currently intended for deployment in the OpenShift Container Platform on-premises and in the same data center. This solution is not recommended for deployments stretching over multiple data centers. Instead, consider Metro-DR as a first option for no data loss DR solution deployed over multiple data centers with low latency networks. To know in detail how subscriptions for OpenShift Data Foundation work, see knowledgebase article on OpenShift Data Foundation subscriptions . Note You cannot enable Flexible scaling and Arbiter both at the same time as they have conflicting scaling logic. With Flexible scaling, you can add one node at a time to your OpenShift Data Foundation cluster. Whereas, in an Arbiter cluster, you need to add at least one node in each of the two data zones. Minimum starting node requirements An OpenShift Data Foundation cluster is deployed with a minimum configuration when the resource requirement for a standard deployment is not met. For more information, see the Resource requirements section in the Planning guide .	null	https://docs.redhat.com/en/documentation/red_hat_openshift_data_foundation/4.18/html/deploying_openshift_data_foundation_on_vmware_vsphere/preparing_to_deploy_openshift_data_foundation
15.3. Live Migration and Red Hat Enterprise Linux Version Compatibility	15.3. Live Migration and Red Hat Enterprise Linux Version Compatibility Live Migration is supported as shown in Table 15.1, "Live Migration Compatibility" : Table 15.1. Live Migration Compatibility Migration Method Release Type Example Live Migration Support Notes Forward Major release 6.5+ 7.x Fully supported Any issues should be reported Backward Major release 7.x 6.y Not supported Forward Minor release 7.x 7.y (7.0 7.1) Fully supported Any issues should be reported Backward Minor release 7.y 7.x (7.1 7.0) Fully supported Any issues should be reported Troubleshooting problems with migration Issues with the migration protocol - If backward migration ends with "unknown section error", repeating the migration process can repair the issue as it may be a transient error. If not, report the problem. Issues with audio devices - When migrating from Red Hat Enterprise Linux 6.x to Red Hat Enterprise Linux 7.y, note that the es1370 audio card is no longer supported. Use the ac97 audio card instead. Issues with network cards - When migrating from Red Hat Enterprise Linux 6.x to Red Hat Enterprise Linux 7.y, note that the pcnet and ne2k_pci network cards are no longer supported. Use the virtio-net network device instead. Configuring Network Storage Configure shared storage and install a guest virtual machine on the shared storage. Alternatively, use the NFS example in Section 15.4, "Shared Storage Example: NFS for a Simple Migration"	null	https://docs.redhat.com/en/documentation/Red_Hat_Enterprise_Linux/7/html/virtualization_deployment_and_administration_guide/sect-kvm_live_migration-live_migration_and_red_hat_enterprise_linux_version_compatibility_
Chapter 4. Helm CLI	Chapter 4. Helm CLI 4.1. Getting started with Helm 3 4.1.1. Understanding Helm Helm is a software package manager that simplifies deployment of applications and services to OpenShift Container Platform clusters. Helm uses a packaging format called charts . A Helm chart is a collection of files that describes the OpenShift Container Platform resources. A running instance of the chart in a cluster is called a release . A new release is created every time a chart is installed on the cluster. Each time a chart is installed, or a release is upgraded or rolled back, an incremental revision is created. 4.1.1.1. Key features Helm provides the ability to: Search through a large collection of charts stored in the chart repository. Modify existing charts. Create your own charts with OpenShift Container Platform or Kubernetes resources. Package and share your applications as charts. 4.1.2. Installing Helm The following section describes how to install Helm on different platforms using the CLI. You can also find the URL to the latest binaries from the OpenShift Container Platform web console by clicking the ? icon in the upper-right corner and selecting Command Line Tools . Prerequisites You have installed Go, version 1.13 or higher. 4.1.2.1. On Linux Download the Helm binary and add it to your path: # curl -L https://mirror.openshift.com/pub/openshift-v4/clients/helm/latest/helm-linux-amd64 -o /usr/local/bin/helm Make the binary file executable: # chmod +x /usr/local/bin/helm Check the installed version: USD helm version Example output version.BuildInfo{Version:"v3.0", GitCommit:"b31719aab7963acf4887a1c1e6d5e53378e34d93", GitTreeState:"clean", GoVersion:"go1.13.4"} 4.1.2.2. On Windows 7/8 Download the latest .exe file and put in a directory of your preference. Right click Start and click Control Panel . Select System and Security and then click System . From the menu on the left, select Advanced systems settings and click Environment Variables at the bottom. Select Path from the Variable section and click Edit . Click New and type the path to the folder with the .exe file into the field or click Browse and select the directory, and click OK . 4.1.2.3. On Windows 10 Download the latest .exe file and put in a directory of your preference. Click Search and type env or environment . Select Edit environment variables for your account . Select Path from the Variable section and click Edit . Click New and type the path to the directory with the exe file into the field or click Browse and select the directory, and click OK . 4.1.2.4. On MacOS Download the Helm binary and add it to your path: # curl -L https://mirror.openshift.com/pub/openshift-v4/clients/helm/latest/helm-darwin-amd64 -o /usr/local/bin/helm Make the binary file executable: # chmod +x /usr/local/bin/helm Check the installed version: USD helm version Example output version.BuildInfo{Version:"v3.0", GitCommit:"b31719aab7963acf4887a1c1e6d5e53378e34d93", GitTreeState:"clean", GoVersion:"go1.13.4"} 4.1.3. Installing a Helm chart on an OpenShift Container Platform cluster Prerequisites You have a running OpenShift Container Platform cluster and you have logged into it. You have installed Helm. Procedure Create a new project: USD oc new-project mysql Add a repository of Helm charts to your local Helm client: USD helm repo add stable https://kubernetes-charts.storage.googleapis.com/ Example output "stable" has been added to your repositories Update the repository: USD helm repo update Install an example MySQL chart: USD helm install example-mysql stable/mysql Verify that the chart has installed successfully: USD helm list Example output NAME NAMESPACE REVISION UPDATED STATUS CHART APP VERSION example-mysql mysql 1 2019-12-05 15:06:51.379134163 -0500 EST deployed mysql-1.5.0 5.7.27 4.1.4. Creating a custom Helm chart on OpenShift Container Platform Procedure Create a new project: USD oc new-project nodejs-ex-k Download an example Node.js chart that contains OpenShift Container Platform objects: USD git clone https://github.com/redhat-developer/redhat-helm-charts Go to the directory with the sample chart: USD cd redhat-helm-charts/alpha/nodejs-ex-k/ Edit the Chart.yaml file and add a description of your chart: apiVersion: v2 1 name: nodejs-ex-k 2 description: A Helm chart for OpenShift 3 icon: https://static.redhat.com/libs/redhat/brand-assets/latest/corp/logo.svg 4 1 The chart API version. It should be v2 for Helm charts that require at least Helm 3. 2 The name of your chart. 3 The description of your chart. 4 The URL to an image to be used as an icon. Verify that the chart is formatted properly: USD helm lint Example output [INFO] Chart.yaml: icon is recommended 1 chart(s) linted, 0 chart(s) failed Navigate to the directory level: USD cd .. Install the chart: USD helm install nodejs-chart nodejs-ex-k Verify that the chart has installed successfully: USD helm list Example output NAME NAMESPACE REVISION UPDATED STATUS CHART APP VERSION nodejs-chart nodejs-ex-k 1 2019-12-05 15:06:51.379134163 -0500 EST deployed nodejs-0.1.0 1.16.0 4.2. Configuring custom Helm chart repositories The Developer Catalog , in the Developer perspective of the web console, displays the Helm charts available in the cluster. By default, it lists the Helm charts from the Red Hat Helm chart repository. For a list of the charts see the Red Hat Helm index file . As a cluster administrator, you can add multiple Helm chart repositories, apart from the default one, and display the Helm charts from these repositories in the Developer Catalog . 4.2.1. Adding custom Helm chart repositories As a cluster administrator, you can add custom Helm chart repositories to your cluster and enable access to the Helm charts from these repositories in the Developer Catalog . Procedure To add a new Helm Chart Repository, you must add the Helm Chart Repository custom resource (CR) to your cluster. Sample Helm Chart Repository CR apiVersion: helm.openshift.io/v1beta1 kind: HelmChartRepository metadata: name: <name> spec: # optional name that might be used by console # name: <chart-display-name> connectionConfig: url: <helm-chart-repository-url> For example, to add an Azure sample chart repository, run: USD cat <<EOF \| oc apply -f - apiVersion: helm.openshift.io/v1beta1 kind: HelmChartRepository metadata: name: azure-sample-repo spec: name: azure-sample-repo connectionConfig: url: https://raw.githubusercontent.com/Azure-Samples/helm-charts/master/docs EOF Navigate to the Developer Catalog in the web console to verify that the Helm charts from the chart repository are displayed. For example, use the Chart repositories filter to search for a Helm chart from the repository. Figure 4.1. Chart repositories filter Note If a cluster administrator removes all of the chart repositories, then you cannot view the Helm option in the +Add view, Developer Catalog , and left navigation panel. 4.2.2. Creating credentials and CA certificates to add Helm chart repositories Some Helm chart repositories need credentials and custom certificate authority (CA) certificates to connect to it. You can use the web console as well as the CLI to add credentials and certificates. Procedure To configure the credentials and certificates, and then add a Helm chart repository using the CLI: In the openshift-config namespace, create a ConfigMap object with a custom CA certificate in PEM encoded format, and store it under the ca-bundle.crt key within the config map: USD oc create configmap helm-ca-cert \ --from-file=ca-bundle.crt=/path/to/certs/ca.crt \ -n openshift-config In the openshift-config namespace, create a Secret object to add the client TLS configurations: USD oc create secret generic helm-tls-configs \ --from-file=tls.crt=/path/to/certs/client.crt \ --from-file=tls.key=/path/to/certs//client.key \ -n openshift-config Note that the client certificate and key must be in PEM encoded format and stored under the keys tls.crt and tls.key , respectively. Add the Helm repository as follows: USD cat <<EOF \| oc apply -f - apiVersion: helm.openshift.io/v1beta1 kind: HelmChartRepository metadata: name: <helm-repository> spec: name: <helm-repository> connectionConfig: url: <URL for the Helm repository> tlsConfig: name: helm-tls-configs ca: name: helm-ca-cert EOF The ConfigMap and Secret are consumed in the HelmChartRepository CR using the tlsConfig and ca fields. These certificates are used to connect to the Helm repository URL. By default, all authenticated users have access to all configured charts. However, for chart repositories where certificates are needed, you must provide users with read access to the helm-ca-cert config map and helm-tls-configs secret in the openshift-config namespace, as follows: USD cat <<EOF \| kubectl apply -f - apiVersion: rbac.authorization.k8s.io/v1 kind: Role metadata: namespace: openshift-config name: helm-chartrepos-tls-conf-viewer rules: - apiGroups: [""] resources: ["configmaps"] resourceNames: ["helm-ca-cert"] verbs: ["get"] - apiGroups: [""] resources: ["secrets"] resourceNames: ["helm-tls-configs"] verbs: ["get"] --- kind: RoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: namespace: openshift-config name: helm-chartrepos-tls-conf-viewer subjects: - kind: Group apiGroup: rbac.authorization.k8s.io name: 'system:authenticated' roleRef: apiGroup: rbac.authorization.k8s.io kind: Role name: helm-chartrepos-tls-conf-viewer EOF 4.3. Disabling Helm hart repositories As a cluster administrator, you can remove Helm chart repositories in your cluster so they are no longer visible in the Developer Catalog . 4.3.1. Disabling Helm Chart repository in the cluster You can disable Helm Charts in the catalog by adding the disabled property in the HelmChartRepository custom resource. Procedure To disable a Helm Chart repository by using CLI, add the disabled: true flag to the custom resource. For example, to remove an Azure sample chart repository, run: To disable a recently added Helm Chart repository by using Web Console: Go to Custom Resource Definitions and search for the HelmChartRepository custom resource. Go to Instances , find the repository you want to disable, and click its name. Go to the YAML tab, add the disabled: true flag in the spec section, and click Save . Example The repository is now disabled and will not appear in the catalog.	[ "curl -L https://mirror.openshift.com/pub/openshift-v4/clients/helm/latest/helm-linux-amd64 -o /usr/local/bin/helm", "chmod +x /usr/local/bin/helm", "helm version", "version.BuildInfo{Version:\"v3.0\", GitCommit:\"b31719aab7963acf4887a1c1e6d5e53378e34d93\", GitTreeState:\"clean\", GoVersion:\"go1.13.4\"}", "curl -L https://mirror.openshift.com/pub/openshift-v4/clients/helm/latest/helm-darwin-amd64 -o /usr/local/bin/helm", "chmod +x /usr/local/bin/helm", "helm version", "version.BuildInfo{Version:\"v3.0\", GitCommit:\"b31719aab7963acf4887a1c1e6d5e53378e34d93\", GitTreeState:\"clean\", GoVersion:\"go1.13.4\"}", "oc new-project mysql", "helm repo add stable https://kubernetes-charts.storage.googleapis.com/", "\"stable\" has been added to your repositories", "helm repo update", "helm install example-mysql stable/mysql", "helm list", "NAME NAMESPACE REVISION UPDATED STATUS CHART APP VERSION example-mysql mysql 1 2019-12-05 15:06:51.379134163 -0500 EST deployed mysql-1.5.0 5.7.27", "oc new-project nodejs-ex-k", "git clone https://github.com/redhat-developer/redhat-helm-charts", "cd redhat-helm-charts/alpha/nodejs-ex-k/", "apiVersion: v2 1 name: nodejs-ex-k 2 description: A Helm chart for OpenShift 3 icon: https://static.redhat.com/libs/redhat/brand-assets/latest/corp/logo.svg 4", "helm lint", "[INFO] Chart.yaml: icon is recommended 1 chart(s) linted, 0 chart(s) failed", "cd ..", "helm install nodejs-chart nodejs-ex-k", "helm list", "NAME NAMESPACE REVISION UPDATED STATUS CHART APP VERSION nodejs-chart nodejs-ex-k 1 2019-12-05 15:06:51.379134163 -0500 EST deployed nodejs-0.1.0 1.16.0", "apiVersion: helm.openshift.io/v1beta1 kind: HelmChartRepository metadata: name: <name> spec: # optional name that might be used by console # name: <chart-display-name> connectionConfig: url: <helm-chart-repository-url>", "cat <<EOF \| oc apply -f - apiVersion: helm.openshift.io/v1beta1 kind: HelmChartRepository metadata: name: azure-sample-repo spec: name: azure-sample-repo connectionConfig: url: https://raw.githubusercontent.com/Azure-Samples/helm-charts/master/docs EOF", "oc create configmap helm-ca-cert --from-file=ca-bundle.crt=/path/to/certs/ca.crt -n openshift-config", "oc create secret generic helm-tls-configs --from-file=tls.crt=/path/to/certs/client.crt --from-file=tls.key=/path/to/certs//client.key -n openshift-config", "cat <<EOF \| oc apply -f - apiVersion: helm.openshift.io/v1beta1 kind: HelmChartRepository metadata: name: <helm-repository> spec: name: <helm-repository> connectionConfig: url: <URL for the Helm repository> tlsConfig: name: helm-tls-configs ca: name: helm-ca-cert EOF", "cat <<EOF \| kubectl apply -f - apiVersion: rbac.authorization.k8s.io/v1 kind: Role metadata: namespace: openshift-config name: helm-chartrepos-tls-conf-viewer rules: - apiGroups: [\"\"] resources: [\"configmaps\"] resourceNames: [\"helm-ca-cert\"] verbs: [\"get\"] - apiGroups: [\"\"] resources: [\"secrets\"] resourceNames: [\"helm-tls-configs\"] verbs: [\"get\"] --- kind: RoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: namespace: openshift-config name: helm-chartrepos-tls-conf-viewer subjects: - kind: Group apiGroup: rbac.authorization.k8s.io name: 'system:authenticated' roleRef: apiGroup: rbac.authorization.k8s.io kind: Role name: helm-chartrepos-tls-conf-viewer EOF", "cat <<EOF \| oc apply -f - apiVersion: helm.openshift.io/v1beta1 kind: HelmChartRepository metadata: name: azure-sample-repo spec: connectionConfig: url:https://raw.githubusercontent.com/Azure-Samples/helm-charts/master/docs disabled: true EOF", "spec: connectionConfig: url: <url-of-the-repositoru-to-be-disabled> disabled: true" ]	https://docs.redhat.com/en/documentation/openshift_container_platform/4.7/html/cli_tools/helm-cli
Part I. Vulnerability reporting with Clair on Red Hat Quay overview	Part I. Vulnerability reporting with Clair on Red Hat Quay overview The content in this guide explains the key purposes and concepts of Clair on Red Hat Quay. It also contains information about Clair releases and the location of official Clair containers.	null	https://docs.redhat.com/en/documentation/red_hat_quay/3.9/html/vulnerability_reporting_with_clair_on_red_hat_quay/vulnerability-reporting-clair-quay-overview
Working with accelerators	Working with accelerators Red Hat OpenShift AI Cloud Service 1 Working with accelerators from Red Hat OpenShift AI Cloud Service	null	https://docs.redhat.com/en/documentation/red_hat_openshift_ai_cloud_service/1/html/working_with_accelerators/index