mtpti5iD/redhat-docs_dataset huggingface dataset:
Chapter 17. Configuring Queue Proxy resources The Queue Proxy is a sidecar container to each application container within a service. It improves managing Serverless workloads, ensuring efficient resource usage. You can configure the Queue Proxy. 17.1. Configuring Queue Proxy Resources for a Knative Service Apart from configuring the Queue Proxy resource requests and limits globally in the deployment configmap, you can set them at the service level using the corresponding annotations targeting CPU, memory, and ephemeral storage resource types. Prerequisites Red Hat OpenShift Pipelines must be installed on your cluster. You have installed the OpenShift ( oc ) CLI. You have installed the Knative ( kn ) CLI. Procedure Modify the configmap of your service with resource requests and limits: apiVersion: serving.knative.dev/v1 kind: Service metadata: name: example-service namespace: default spec: template: metadata: annotations: queue.sidecar.serving.knative.dev/cpu-resource-request: "1" queue.sidecar.serving.knative.dev/cpu-resource-limit: "2" queue.sidecar.serving.knative.dev/memory-resource-request: "1Gi" queue.sidecar.serving.knative.dev/memory-resource-limit: "2Gi" queue.sidecar.serving.knative.dev/ephemeral-storage-resource-request: "400Mi" queue.sidecar.serving.knative.dev/ephemeral-storage-resource-limit: "450Mi" Alternatively, you can use the special queue.sidecar.serving.knative.dev/resource-percentage annotation, which calculates the Queue Proxy resources as a percentage of the application container. When CPU and memory resource requirements are calculated from the application container requirements and they are outside the boundaries below, the values are adjusted to fit within the boundaries. In this case, the following minimum and maximum boundaries are applied to the CPU and memory resource requirements: Table 17.1. Resource requirements boundaries Resource requirements Min Max CPU request 25m 100m CPU limit 40m 500m Memory request 50Mi 200Mi Memory limit 200Mi 500Mi Note If you simultaneously set a percentage annotation and a specific resource value using the corresponding resource annotation, then the latter takes precedence. Warning The queue.sidecar.serving.knative.dev/resource-percentage annotation is now deprecated and will be removed in a future version of OpenShift Serverless.

Commands:
apiVersion: serving.knative.dev/v1 kind: Service metadata: name: example-service namespace: default spec: template: metadata: annotations: queue.sidecar.serving.knative.dev/cpu-resource-request: "1" queue.sidecar.serving.knative.dev/cpu-resource-limit: "2" queue.sidecar.serving.knative.dev/memory-resource-request: "1Gi" queue.sidecar.serving.knative.dev/memory-resource-limit: "2Gi" queue.sidecar.serving.knative.dev/ephemeral-storage-resource-request: "400Mi" queue.sidecar.serving.knative.dev/ephemeral-storage-resource-limit: "450Mi"
Chapter 14. Known issues in Red Hat Process Automation Manager 7.13.4 This section lists known issues with Red Hat Process Automation Manager 7.13.4. 14.1. Business Central Business Central EAP7 deployable zip from 7.13.4 cannot be updated by the update tool [ RHPAM-4838 ] Issue: If you use the update tool to update an Red Hat Process Automation Manager 7.13.4 rhpam-business-central-eap7-deployable installation to release 7.13.4SP1, the update fails with the following error: Workaround: Download the build.metadata file attached to RHPAM-4838 to the business-central.war/META-INF directory in your Business Central installation. Run the update tool again.

Commands:
ERROR - The patch is not valid for the distribution and version!
Chapter 3. Features This chapter provides the necessary information about Camel on EAP features. Camel Context Definitions Camel Contexts can be configured in standalone-camel.xml and domain.xml as part of the subsystem definition like this <subsystem xmlns="urn:jboss:domain:camel:1.0"> <camelContext id="system-context-1"> <![CDATA[ <route> <from uri="direct:start"/> <transform> <simple>Hello #{body}</simple> </transform> </route> ]]> </camelContext> </subsystem> Camel Context Deployments You can deploy camel contexts to JBoss EAP with a -camel-context.xml suffix as: a standalone XML file a part of another supported deployment A deployment may contain multiple -camel-context.xml files. A deployed Camel context is CDI injectable like this @Resource(lookup = "java:jboss/camel/context/mycontext") CamelContext camelContext; [discrete] ### Management Console By default, access to management consoles is secured. Therefore, you need to setup a Management User first. The Hawt.io console should show the camel context from subsystem configuration. Arquillian Test Support The Camel on EAP test suite uses the WildFly Arquillian managed container. This can connect to an already running JBoss EAP instance or alternatively start up a standalone server instance when needed. A number of test enrichers have been implemented that allow you to have these Camel on EAP specific types injected into your Arquillian test cases. @ArquillianResource CamelContextFactory contextFactory; @ArquillianResource CamelContextRegistry contextRegistry;

Commands:
<subsystem xmlns="urn:jboss:domain:camel:1.0"> <camelContext id="system-context-1"> <![CDATA[ <route> <from uri="direct:start"/> <transform> <simple>Hello #{body}</simple> </transform> </route> ]]> </camelContext> </subsystem>
@Resource(lookup = "java:jboss/camel/context/mycontext") CamelContext camelContext; ## Management Console
bin/add-user.sh What type of user do you wish to add? a) Management User (mgmt-users.properties) b) Application User (application-users.properties)
@ArquillianResource CamelContextFactory contextFactory; @ArquillianResource CamelContextRegistry contextRegistry;
4.4.4. Static Configuration with Dynamic Resource Labeling Some applications require full control over the generation of security labels but still require libvirt to take care of resource labeling. The following guest XML configuration demonstrates an example of static configuration with dynamic resource labeling:

Commands:
<seclabel type='static' model='selinux' relabel='yes'> <label>system_u:system_r:svirt_custom_t:s0:c87,c520</label> </seclabel>
Chapter 4. Configuring the remote node on the Nagios Core server Configure the Nagios Core server to be aware of the remote nodes. Prerequisites User-level access to the remote node on the Nagios Core server. Internet access. Procedure Install the check_nrpe plug-in: Create a configuration for the remote host: Replace localhost with the hostname of the remote host, and the loopback IP address with the IP address of the remote host. Finally, delete or comment out the Host Group definition. Change the file ownership to Nagios: Add a cfg_file= reference to the mon.cfg file in /usr/local/nagios/etc/nagios.cfg : Example Restart the Nagios server: Ensure that the make and install procedures worked and that there is connectivity between the Nagios Core server and the remote host containing NRPE: It should echo NRPE v3.1.0-rc1 if it is working correctly.

Commands:
cd ~ wget https://github.com/NagiosEnterprises/nrpe/releases/download/nrpe-3.1.0/nrpe-3.1.0.tar.gz tar xvfz nrpe-3.1.0.tar.gz cd nrpe-3.1.0 ./configure make check_nrpe make install-plugin
cd /usr/local/nagios/etc/objects cp localhost.cfg mon.cfg
chown nagios:nagios mon.cfg
vi /usr/local/nagios/etc/nagios.cfg
cfg_file=/usr/local/nagios/etc/objects/mon.cfg
systemctl restart nagios
/usr/local/nagios/libexec/check_nrpe -H IP_ADDRESS_OF_REMOTE_HOST
4.4. Node Types Section The Node Types section is used to create and maintain the node type definitions declared in the CND file. The node types section consists of a table containing all the declared node type definitions, and a node type name filter box which allows you to limit the number of node type definitions being displayed. The Node Type table can be used to delete a selected node type. You can copy node type definitions and paste within the same CND editor or between different CND editors. The right hand side of the Node Types section consists of a Details area, as well as, collapseable areas for property and child node definitions. When a Node Type Definition is selected, its corresponding information is used to populate the details, properties, and child nodes areas. The Details area is used to edit namespace, name, supertypes, attributes of a node type. You can also add comments for the selected node type . A Node Type Definition can contain zero or more property definitions. When the properties area is expanded, the table will show the declared property definitions for the selected node type definition. The properties table can be used to delete a selected property definition and can optionally show inherited properties. You can copy Property definitions and paste within the same CND editor or between different CND editors. You can create a new property definition or edit existing definition using the Property Definition Editor. A node type definition can contain zero or more child node definitions. When the child nodes area is expanded, the table will show the declared child node definitions for the selected node type definition. You can delete a selected child node definition using the child nodes table. The Child Nodes table optionally shows inherited child nodes. Child node definitions can be copied and pasted within the same CND editor or between different CND editors. A child node definition can be created or edited using the Child Node Definition Editor.

Commands:
3.14. Logical Networks Logical networking enables the Red Hat Virtualization environment to separate network traffic by type. For example, the ovirtmgmt network is created by default during the installation of Red Hat Virtualization to be used for management communication between the Manager and hosts. A typical use for logical networks is to group network traffic with similar requirements and usage together. In many cases, a storage network and a display network are created by an administrator to isolate traffic of each respective type for optimization and troubleshooting. Red Hat Virtualization supports the following logical network types: Logical networks that carry only host network traffic, such as storage or migration traffic Logical networks that carry host and virtual machine network traffic Logical networks that carry only virtual machine network traffic, such as OVN networks Logical networks are defined at the data center level. If necessary, the Red Hat Virtualization Manager automatically instantiates logical networks on the host, depending on the type of virtual machine network. For more information, see virtual machine networking types . Example 3.1. Example usage of a logical network. A system administrator wants to use a logical network to test a web server. There are two hosts called Red Host and White Host in a cluster called Pink Cluster in a data center called Purple Data Center. Both Red Host and White Host have been using the default logical network, ovirtmgmt , for all networking functions. The system administrator responsible for Pink Cluster decides to isolate network testing for a web server by placing the web server and some client virtual machines on a separate logical network. She decides to call the new logical network test_logical_network . She creates a new logical network, named test_logical_network , for the Purple Data Center with VLAN tagging enabled. VLAN tagging is necessary when you have two logical networks connected to the same physical NIC. She applies test_logical_network to the Pink Cluster. In Red Host, she attaches test_logical_network to a physical NIC that will be included in the bridge that RHV creates. The network is non-operational until she sets up the corresponding bridge in all hosts in the cluster by adding a physical network interface on each host in the Pink cluster to test_logical_network . She repeats this step for White Host. When both White Host and Red Host have the test_logical_network logical network bridged to a physical network interface, the test_logical_network becomes operational and is ready to be used by virtual machines. She associates the virtual machines on the Red Host and White Host with the new network.

Commands:
8.115. libnl3 8.115.1. RHEA-2014:1532 - libnl3 enhancement update Updated libnl3 packages that add one enhancement are now available for Red Hat Enterprise Linux 6. The libnl packages contain a convenience library to simplify using the Linux kernel's Netlink sockets interface for network manipulation. Enhancement BZ# 1052119 The libnl3 packages have been introduced into Red Hat Enterprise Linux 6, which allows for improved functionality of the oVirt application. In addition, this ensures compatibility with other future applications, since the version of the Netlink protocol library, libnl1, is no longer being developed. Users of libnl3 are advised to upgrade to these updated packages, which add this enhancement.

Commands:
Chapter 6. Subscribing to the Red Hat OpenShift AI Cloud Service You can subscribe to the Red Hat OpenShift AI managed cloud service in the following ways: Subscribe through Red Hat if you have a Red Hat OpenShift Dedicated cluster deployed with a Customer Cloud Subscription (CCS) on Amazon Web Services (AWS) or Google Cloud Platform (GCP). Subscribe through the AWS Marketplace if you have a Red Hat OpenShift Service on AWS (ROSA) cluster. Note You can also purchase Red Hat OpenShift AI as self-managed software. To purchase a new subscription, contact your Red Hat account manager. If you do not yet have an account manager, complete the form at https://www.redhat.com/en/contact to request one. 6.1. Subscribing to the OpenShift AI managed cloud service on AWS or GCP For a Red Hat OpenShift Dedicated cluster that is deployed on AWS or GCP, contact your Red Hat account manager to purchase a new subscription. If you do not yet have an account manager, complete the form at https://cloud.redhat.com/products/dedicated/contact/ to request one. Prerequisite You have worked with Red Hat Sales to enable a private offer of OpenShift AI, follow these steps to accept your offer and deploy the solution. Procedure Visit your Private Offer with the URL link provided by your Red Hat Sales representative. Click Accept Terms to subscribe to the AMI Private Offer named Openshift Data Science from AWS Marketplace . After accepting the offer terms, click Continue to Configuration . 6.2. Subscribing to the OpenShift AI managed cloud service on Red Hat OpenShift Service on AWS (ROSA) For a ROSA cluster, you can subscribe to the OpenShift AI managed cloud service through the Amazon Web Services (AWS) Marketplace. Prerequisites Access to a ROSA cluster, including permissions to view and install add-ons. An AWS account with permission to view and subscribe to offerings in the AWS marketplace. Procedure In the AWS Console, navigate to the AWS Marketplace. For example: Click the help icon and then select Getting Started Resource Center. Select AWS Marketplace > Browse AWS Marketplace. In the top Search field, type: Red Hat OpenShift Data Science . Select one of the two options depending on the geographical location of the billing address for your AWS account (note that this location might differ from the geographical location of the cluster): Europe, the Middle East, and Africa (EMEA region) North America and regions outside EMEA Click Continue to Subscribe . Click Continue to Configuration and then select the appropriate fulfillment options. Note that some selectors might have only one option. Click Continue to Launch . Link your AWS account with your Red Hat account to complete your registration: In the AWS Marketplace console, navigate to the Manage Subscriptions page. On the Red Hat OpenShift Data Science tile, click Set up product . On the top banner, click Set up account . This link takes you to the Red Hat Hybrid console. If you are not already logged in, log in. Review and then accept the terms and agreements. Click Connect accounts . Verification The Data Science product page opens.

Commands:
5.2.2. OProfile in Red Hat Enterprise Linux 7 OProfile 0.9.9 , which is included in Red Hat Enterprise Linux 7, contains a number of improvements over versions. The new version also supports the utilization of the Linux Performance Events subsystem using the new operf command. 5.2.2.1. New Features A new operf program is now available that allows non-root users to profile single processes. This can also be used for system-wide profiling, but in this case, root authority is required. OProfile 0.9.9 now supports the following: IBM POWER8 processors Intel Haswell processors IBM zEnterprise EC12 (zEC12) processor AMD Generic Performance Events IBM Power ISA 2.07 Architected Events

Commands:
2.8. Other means for auditing Red Hat Enterprise Linux 6 offers quite a few more tools with which to perform system auditing and analysis. Most of them can be used as a supplementary source of information in case you want to verify what you have discovered already or in case you need more in-depth information on certain parts. Many of these tools are used for performance tuning as well. They include: vmstat vmstat gives you detailed information about processes, memory, paging, block I/O, traps, and CPU activity. Use it to take a closer look at what the system overall does and where it is busy. iostat iostat is similar to vmstat , but only for I/O on block devices. It also provides more verbose output and statistics. blktrace blktrace is a very detailed block I/O trace program. It breaks down information to single blocks associated with applications. It is very useful in combination with diskdevstat .

Commands:
Chapter 3. Navigating the Streams for Apache Kafka Console When you open the Streams for Apache Kafka Console, the homepage presents a list of connected Kafka clusters. By clicking on a Kafka cluster name on the homepage or from the side menu, you can find information on the following components: Kafka clusters A group of Kafka brokers and management components. Brokers A broker contains topics and orchestrates the storage and passing of messages. Topics A topic provides a destination for the storage of data. Kafka splits each topic into one or more partitions. Partitions A subset of a topic used for data sharding and replication. The number of partitions is defined in the topic configuration. Consumer groups Kafka groups consumers with the same group ID and distributes messages across group members. Consumers within a group receive data from one or more partitions. For example, you can view the status of a Kafka cluster before navigating to view information on the cluster's brokers and topics, or the consumer groups connected to the Kafka cluster. Note If the side menu is not visible, click the hamburger menu (three horizontal lines) in the console header.

Commands:
Chapter 12. Transactions subsystem tuning If your environment uses XA distributed transactions, you can tune the transaction manager's log store for better performance. The default transaction log store uses a simple file store. For XA transactions this type of log store can be inefficient, as it creates one system file for each transaction log. Especially for XA transactions, a journal store is much more efficient as it uses a journal that consists of one file for all transactions. For better XA transaction performance, it is recommended that you use a journal log store. For Red Hat Enterprise Linux systems, you can additionally enable asynchronous I/O (AIO) on the journal store to further improve performance. Note For Red Hat Enterprise Linux systems, if you are enabling asynchronous I/O (AIO) on the journal store, ensure that the libaio package is installed. 12.1. Enable the journal log store using the management console You can use the management console to enable the journal log store. Procedure Navigate to Configuration Subsystems Transaction and click View . In the Configuration tab, click Edit . Set the Use Journal Store field to ON . Optional : For Red Hat Enterprise Linux systems, set the Journal Store Enable Async IO field to ON . Click Save . 12.2. Enable the journal log store Using the management CLI You can use the management CLI to enable the journal log store. Procedure To enable the journal log store using the management CLI, use the following command: Optional : For Red Hat Enterprise Linux systems, use the following command to enable journal log store asynchronous I/O:

Commands:
/subsystem=transactions:write-attribute(name=use-journal-store,value=true)
/subsystem=transactions:write-attribute(name=journal-store-enable-async-io, value=true)
3.5.5. Clearing/Deleting Arrays and Array Elements Sometimes, you may need to clear the associated values in array elements, or reset an entire array for re-use in another probe. Example 3.15, "cumulative-vfsreads.stp" in Section 3.5.4, "Processing Multiple Elements in an Array" allows you to track how the number of VFS reads per process grows over time, but it does not show you the number of VFS reads each process makes per 3-second period. To do that, you will need to clear the values accumulated by the array. You can accomplish this using the delete operator to delete elements in an array, or an entire array. Consider the following example: Example 3.16. noncumulative-vfsreads.stp In Example 3.16, "noncumulative-vfsreads.stp" , the second probe prints the number of VFS reads each process made within the probed 3-second period only . The delete reads statement clears the reads array within the probe. Note You can have multiple array operations within the same probe. Using the examples from Section 3.5.4, "Processing Multiple Elements in an Array" and Section 3.5.5, "Clearing/Deleting Arrays and Array Elements" , you can track the number of VFS reads each process makes per 3-second period and tally the cumulative VFS reads of those same processes. Consider the following example: In this example, the arrays reads and totalreads track the same information, and are printed out in a similar fashion. The only difference here is that reads is cleared every 3-second period, whereas totalreads keeps growing.

Commands:
global reads probe vfs.read { reads[execname()] ++ } probe timer.s(3) { foreach (count in reads) printf("%s : %d \n", count, reads[count]) delete reads }
global reads, totalreads probe vfs.read { reads[execname()] ++ totalreads[execname()] ++ } probe timer.s(3) { printf("=======\n") foreach (count in reads-) printf("%s : %d \n", count, reads[count]) delete reads } probe end { printf("TOTALS\n") foreach (total in totalreads-) printf("%s : %d \n", total, totalreads[total]) }
7.116. libica 7.116.1. RHEA-2013:0399 - libica enhancement update Updated libica packages that add one enhancement are now available for Red Hat Enterprise Linux 6. The libica library contains a set of functions and utilities for accessing the IBM eServer Cryptographic Accelerator (ICA) hardware on IBM System z. Enhancement BZ#738835 The libica library has been modified to allow usage of new algorithms that support the Message Security Assist Extension 4 instructions in the Central Processor Assist for Cryptographic Function (CPACF) feature. For the DES and 3DES block ciphers, the new feature supports the following modes of operation: Cipher Block Chaining with Ciphertext Stealing (CBC-CS) Cipher-based Message Authentication Code (CMAC) For the AES block cipher, this feature supports the following modes of operation: Cipher Block Chaining with Ciphertext Stealing (CBC-CS) Counter with Cipher Block Chaining Message Authentication Code (CCM) Galois/Counter (GCM) With this acceleration of complex cryptographic algorithms, performance of IBM System z machines significantly improves. All users of libica are advised to upgrade to these updated packages, which add this enhancement.

Commands: