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Chapter 5. BuildRequest [build.openshift.io/v1]	Chapter 5. BuildRequest [build.openshift.io/v1] Description BuildRequest is the resource used to pass parameters to build generator Compatibility level 1: Stable within a major release for a minimum of 12 months or 3 minor releases (whichever is longer). Type object 5.1. Specification Property Type Description apiVersion string APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources binary object BinaryBuildSource describes a binary file to be used for the Docker and Source build strategies, where the file will be extracted and used as the build source. dockerStrategyOptions object DockerStrategyOptions contains extra strategy options for container image builds env array (EnvVar) env contains additional environment variables you want to pass into a builder container. from ObjectReference from is the reference to the ImageStreamTag that triggered the build. kind string Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds lastVersion integer lastVersion (optional) is the LastVersion of the BuildConfig that was used to generate the build. If the BuildConfig in the generator doesn't match, a build will not be generated. metadata ObjectMeta metadata is the standard object's metadata. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata revision object SourceRevision is the revision or commit information from the source for the build sourceStrategyOptions object SourceStrategyOptions contains extra strategy options for Source builds triggeredBy array triggeredBy describes which triggers started the most recent update to the build configuration and contains information about those triggers. triggeredBy[] object BuildTriggerCause holds information about a triggered build. It is used for displaying build trigger data for each build and build configuration in oc describe. It is also used to describe which triggers led to the most recent update in the build configuration. triggeredByImage ObjectReference triggeredByImage is the Image that triggered this build. 5.1.1. .binary Description BinaryBuildSource describes a binary file to be used for the Docker and Source build strategies, where the file will be extracted and used as the build source. Type object Property Type Description asFile string asFile indicates that the provided binary input should be considered a single file within the build input. For example, specifying "webapp.war" would place the provided binary as /webapp.war for the builder. If left empty, the Docker and Source build strategies assume this file is a zip, tar, or tar.gz file and extract it as the source. The custom strategy receives this binary as standard input. This filename may not contain slashes or be '..' or '.'. 5.1.2. .dockerStrategyOptions Description DockerStrategyOptions contains extra strategy options for container image builds Type object Property Type Description buildArgs array (EnvVar) Args contains any build arguments that are to be passed to Docker. See https://docs.docker.com/engine/reference/builder/#/arg for more details noCache boolean noCache overrides the docker-strategy noCache option in the build config 5.1.3. .revision Description SourceRevision is the revision or commit information from the source for the build Type object Required type Property Type Description git object GitSourceRevision is the commit information from a git source for a build type string type of the build source, may be one of 'Source', 'Dockerfile', 'Binary', or 'Images' 5.1.4. .revision.git Description GitSourceRevision is the commit information from a git source for a build Type object Property Type Description author object SourceControlUser defines the identity of a user of source control commit string commit is the commit hash identifying a specific commit committer object SourceControlUser defines the identity of a user of source control message string message is the description of a specific commit 5.1.5. .revision.git.author Description SourceControlUser defines the identity of a user of source control Type object Property Type Description email string email of the source control user name string name of the source control user 5.1.6. .revision.git.committer Description SourceControlUser defines the identity of a user of source control Type object Property Type Description email string email of the source control user name string name of the source control user 5.1.7. .sourceStrategyOptions Description SourceStrategyOptions contains extra strategy options for Source builds Type object Property Type Description incremental boolean incremental overrides the source-strategy incremental option in the build config 5.1.8. .triggeredBy Description triggeredBy describes which triggers started the most recent update to the build configuration and contains information about those triggers. Type array 5.1.9. .triggeredBy[] Description BuildTriggerCause holds information about a triggered build. It is used for displaying build trigger data for each build and build configuration in oc describe. It is also used to describe which triggers led to the most recent update in the build configuration. Type object Property Type Description bitbucketWebHook object BitbucketWebHookCause has information about a Bitbucket webhook that triggered a build. genericWebHook object GenericWebHookCause holds information about a generic WebHook that triggered a build. githubWebHook object GitHubWebHookCause has information about a GitHub webhook that triggered a build. gitlabWebHook object GitLabWebHookCause has information about a GitLab webhook that triggered a build. imageChangeBuild object ImageChangeCause contains information about the image that triggered a build message string message is used to store a human readable message for why the build was triggered. E.g.: "Manually triggered by user", "Configuration change",etc. 5.1.10. .triggeredBy[].bitbucketWebHook Description BitbucketWebHookCause has information about a Bitbucket webhook that triggered a build. Type object Property Type Description revision object SourceRevision is the revision or commit information from the source for the build secret string Secret is the obfuscated webhook secret that triggered a build. 5.1.11. .triggeredBy[].bitbucketWebHook.revision Description SourceRevision is the revision or commit information from the source for the build Type object Required type Property Type Description git object GitSourceRevision is the commit information from a git source for a build type string type of the build source, may be one of 'Source', 'Dockerfile', 'Binary', or 'Images' 5.1.12. .triggeredBy[].bitbucketWebHook.revision.git Description GitSourceRevision is the commit information from a git source for a build Type object Property Type Description author object SourceControlUser defines the identity of a user of source control commit string commit is the commit hash identifying a specific commit committer object SourceControlUser defines the identity of a user of source control message string message is the description of a specific commit 5.1.13. .triggeredBy[].bitbucketWebHook.revision.git.author Description SourceControlUser defines the identity of a user of source control Type object Property Type Description email string email of the source control user name string name of the source control user 5.1.14. .triggeredBy[].bitbucketWebHook.revision.git.committer Description SourceControlUser defines the identity of a user of source control Type object Property Type Description email string email of the source control user name string name of the source control user 5.1.15. .triggeredBy[].genericWebHook Description GenericWebHookCause holds information about a generic WebHook that triggered a build. Type object Property Type Description revision object SourceRevision is the revision or commit information from the source for the build secret string secret is the obfuscated webhook secret that triggered a build. 5.1.16. .triggeredBy[].genericWebHook.revision Description SourceRevision is the revision or commit information from the source for the build Type object Required type Property Type Description git object GitSourceRevision is the commit information from a git source for a build type string type of the build source, may be one of 'Source', 'Dockerfile', 'Binary', or 'Images' 5.1.17. .triggeredBy[].genericWebHook.revision.git Description GitSourceRevision is the commit information from a git source for a build Type object Property Type Description author object SourceControlUser defines the identity of a user of source control commit string commit is the commit hash identifying a specific commit committer object SourceControlUser defines the identity of a user of source control message string message is the description of a specific commit 5.1.18. .triggeredBy[].genericWebHook.revision.git.author Description SourceControlUser defines the identity of a user of source control Type object Property Type Description email string email of the source control user name string name of the source control user 5.1.19. .triggeredBy[].genericWebHook.revision.git.committer Description SourceControlUser defines the identity of a user of source control Type object Property Type Description email string email of the source control user name string name of the source control user 5.1.20. .triggeredBy[].githubWebHook Description GitHubWebHookCause has information about a GitHub webhook that triggered a build. Type object Property Type Description revision object SourceRevision is the revision or commit information from the source for the build secret string secret is the obfuscated webhook secret that triggered a build. 5.1.21. .triggeredBy[].githubWebHook.revision Description SourceRevision is the revision or commit information from the source for the build Type object Required type Property Type Description git object GitSourceRevision is the commit information from a git source for a build type string type of the build source, may be one of 'Source', 'Dockerfile', 'Binary', or 'Images' 5.1.22. .triggeredBy[].githubWebHook.revision.git Description GitSourceRevision is the commit information from a git source for a build Type object Property Type Description author object SourceControlUser defines the identity of a user of source control commit string commit is the commit hash identifying a specific commit committer object SourceControlUser defines the identity of a user of source control message string message is the description of a specific commit 5.1.23. .triggeredBy[].githubWebHook.revision.git.author Description SourceControlUser defines the identity of a user of source control Type object Property Type Description email string email of the source control user name string name of the source control user 5.1.24. .triggeredBy[].githubWebHook.revision.git.committer Description SourceControlUser defines the identity of a user of source control Type object Property Type Description email string email of the source control user name string name of the source control user 5.1.25. .triggeredBy[].gitlabWebHook Description GitLabWebHookCause has information about a GitLab webhook that triggered a build. Type object Property Type Description revision object SourceRevision is the revision or commit information from the source for the build secret string Secret is the obfuscated webhook secret that triggered a build. 5.1.26. .triggeredBy[].gitlabWebHook.revision Description SourceRevision is the revision or commit information from the source for the build Type object Required type Property Type Description git object GitSourceRevision is the commit information from a git source for a build type string type of the build source, may be one of 'Source', 'Dockerfile', 'Binary', or 'Images' 5.1.27. .triggeredBy[].gitlabWebHook.revision.git Description GitSourceRevision is the commit information from a git source for a build Type object Property Type Description author object SourceControlUser defines the identity of a user of source control commit string commit is the commit hash identifying a specific commit committer object SourceControlUser defines the identity of a user of source control message string message is the description of a specific commit 5.1.28. .triggeredBy[].gitlabWebHook.revision.git.author Description SourceControlUser defines the identity of a user of source control Type object Property Type Description email string email of the source control user name string name of the source control user 5.1.29. .triggeredBy[].gitlabWebHook.revision.git.committer Description SourceControlUser defines the identity of a user of source control Type object Property Type Description email string email of the source control user name string name of the source control user 5.1.30. .triggeredBy[].imageChangeBuild Description ImageChangeCause contains information about the image that triggered a build Type object Property Type Description fromRef ObjectReference fromRef contains detailed information about an image that triggered a build. imageID string imageID is the ID of the image that triggered a new build. 5.2. API endpoints The following API endpoints are available: /apis/build.openshift.io/v1/namespaces/{namespace}/builds/{name}/clone POST : create clone of a Build /apis/build.openshift.io/v1/namespaces/{namespace}/buildconfigs/{name}/instantiate POST : create instantiate of a BuildConfig 5.2.1. /apis/build.openshift.io/v1/namespaces/{namespace}/builds/{name}/clone Table 5.1. Global path parameters Parameter Type Description name string name of the BuildRequest Table 5.2. Global query parameters Parameter Type Description dryRun string When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed fieldValidation string fieldValidation instructs the server on how to handle objects in the request (POST/PUT/PATCH) containing unknown or duplicate fields. Valid values are: - Ignore: This will ignore any unknown fields that are silently dropped from the object, and will ignore all but the last duplicate field that the decoder encounters. This is the default behavior prior to v1.23. - Warn: This will send a warning via the standard warning response header for each unknown field that is dropped from the object, and for each duplicate field that is encountered. The request will still succeed if there are no other errors, and will only persist the last of any duplicate fields. This is the default in v1.23+ - Strict: This will fail the request with a BadRequest error if any unknown fields would be dropped from the object, or if any duplicate fields are present. The error returned from the server will contain all unknown and duplicate fields encountered. HTTP method POST Description create clone of a Build Table 5.3. Body parameters Parameter Type Description body BuildRequest schema Table 5.4. HTTP responses HTTP code Reponse body 200 - OK BuildRequest schema 201 - Created BuildRequest schema 202 - Accepted BuildRequest schema 401 - Unauthorized Empty 5.2.2. /apis/build.openshift.io/v1/namespaces/{namespace}/buildconfigs/{name}/instantiate Table 5.5. Global path parameters Parameter Type Description name string name of the BuildRequest Table 5.6. Global query parameters Parameter Type Description dryRun string When present, indicates that modifications should not be persisted. An invalid or unrecognized dryRun directive will result in an error response and no further processing of the request. Valid values are: - All: all dry run stages will be processed fieldValidation string fieldValidation instructs the server on how to handle objects in the request (POST/PUT/PATCH) containing unknown or duplicate fields. Valid values are: - Ignore: This will ignore any unknown fields that are silently dropped from the object, and will ignore all but the last duplicate field that the decoder encounters. This is the default behavior prior to v1.23. - Warn: This will send a warning via the standard warning response header for each unknown field that is dropped from the object, and for each duplicate field that is encountered. The request will still succeed if there are no other errors, and will only persist the last of any duplicate fields. This is the default in v1.23+ - Strict: This will fail the request with a BadRequest error if any unknown fields would be dropped from the object, or if any duplicate fields are present. The error returned from the server will contain all unknown and duplicate fields encountered. HTTP method POST Description create instantiate of a BuildConfig Table 5.7. Body parameters Parameter Type Description body BuildRequest schema Table 5.8. HTTP responses HTTP code Reponse body 200 - OK Build schema 201 - Created Build schema 202 - Accepted Build schema 401 - Unauthorized Empty	null	https://docs.redhat.com/en/documentation/openshift_container_platform/4.15/html/workloads_apis/buildrequest-build-openshift-io-v1
18.4. Single File Cache Store	18.4. Single File Cache Store Red Hat JBoss Data Grid includes one file system based cache store: the SingleFileCacheStore . The SingleFileCacheStore is a simple, file system based implementation and a replacement to the older file system based cache store: the FileCacheStore . SingleFileCacheStore stores all key/value pairs and their corresponding metadata information in a single file. To speed up data location, it also keeps all keys and the positions of their values and metadata in memory. Hence, using the single file cache store slightly increases the memory required, depending on the key size and the amount of keys stored. Hence SingleFileCacheStore is not recommended for use cases where the keys are too big. To reduce memory consumption, the size of the cache store can be set to a fixed number of entries to store in the file. However, this works only when Infinispan is used as a cache. When Infinispan used this way, data which is not present in Infinispan can be recomputed or re-retrieved from the authoritative data store and stored in Infinispan cache. The reason for this limitation is because once the maximum number of entries is reached, older data in the cache store is removed, so if Infinispan was used as an authoritative data store, it would lead to data loss which is undesirable in this use case Due to its limitations, SingleFileCacheStore can be used in a limited capacity in production environments. It can not be used on shared file system (such as NFS and Windows shares) due to a lack of proper file locking, resulting in data corruption. Furthermore, file systems are not inherently transactional, resulting in file writing failures during the commit phase if the cache is used in a transactional context. Report a bug 18.4.1. Single File Store Configuration (Remote Client-Server Mode) The following is an example of a Single File Store configuration for Red Hat JBoss Data Grid's Remote Client-Server mode: For details about the elements and parameters used in this sample configuration, see Section 18.3, "Cache Store Configuration Details (Remote Client-Server Mode)" . Report a bug 18.4.2. Single File Store Configuration (Library Mode) In Red Hat JBoss Grid's Library mode, configure a Single File Cache Store as follows:. For details about the elements and parameters used in this sample configuration, see Section 18.2, "Cache Store Configuration Details (Library Mode)" . Report a bug 18.4.3. Upgrade JBoss Data Grid Cache Stores Red Hat JBoss Data Grid stores data in a different format than versions of JBoss Data Grid. As a result, the newer version of JBoss Data Grid cannot read data stored by older versions. Use rolling upgrades to upgrade persisted data from the format used by the old JBoss Data Grid to the new format. Additionally, the newer version of JBoss Data Grid also stores persistence configuration information in a different location. Rolling upgrades is the process by which a JBoss Data Grid installation is upgraded without a service shutdown. In Library mode, it refers to a node installation where JBoss Data Grid is running in Library mode. For JBoss Data Grid servers, it refers to the server side components. The upgrade can be due to either hardware or software change such as upgrading JBoss Data Grid. Rolling upgrades are only available in JBoss Data Grid's Remote Client-Server mode. Report a bug	[ "<local-cache name=\"default\" statistics=\"true\"> <file-store name=\"myFileStore\" passivation=\"true\" purge=\"true\" relative-to=\"{PATH}\" path=\"{DIRECTORY}\" max-entries=\"10000\" fetch-state=\"true\" preload=\"false\" /> </local-cache>", "<namedCache name=\"writeThroughToFile\"> <persistence passivation=\"false\"> <singleFile fetchPersistentState=\"true\" ignoreModifications=\"false\" purgeOnStartup=\"false\" shared=\"false\" preload=\"false\" location=\"/tmp/Another-FileCacheStore-Location\" maxEntries=\"100\" maxKeysInMemory=\"100\"> <async enabled=\"true\" threadPoolSize=\"500\" flushLockTimeout=\"1\" modificationQueueSize=\"1024\" shutdownTimeout=\"25000\"/> </singleFile> </persistence> </namedCache>" ]	https://docs.redhat.com/en/documentation/red_hat_data_grid/6.6/html/administration_and_configuration_guide/sect-single_file_cache_store
17.2. Running the Volume Top Command	17.2. Running the Volume Top Command The volume top command allows you to view the glusterFS bricks' performance metrics, including read, write, file open calls, file read calls, file write calls, directory open calls, and directory real calls. The volume top command displays up to 100 results. This section describes how to use the volume top command. 17.2.1. Viewing Open File Descriptor Count and Maximum File Descriptor Count You can view the current open file descriptor count and the list of files that are currently being accessed on the brick with the volume top command. The volume top command also displays the maximum open file descriptor count of files that are currently open, and the maximum number of files opened at any given point of time since the servers are up and running. If the brick name is not specified, then the open file descriptor metrics of all the bricks belonging to the volume displays. To view the open file descriptor count and the maximum file descriptor count, use the following command: # gluster volume top VOLNAME open [nfs \| brick BRICK-NAME ] [list-cnt cnt ] For example, to view the open file descriptor count and the maximum file descriptor count on brick server:/export on test-volume , and list the top 10 open calls: 17.2.2. Viewing Highest File Read Calls You can view a list of files with the highest file read calls on each brick with the volume top command. If the brick name is not specified, a list of 100 files are displayed by default. To view the highest read() calls, use the following command: # gluster volume top VOLNAME read [nfs \| brick BRICK-NAME ] [list-cnt cnt ] For example, to view the highest read calls on brick server:/export of test-volume : 17.2.3. Viewing Highest File Write Calls You can view a list of files with the highest file write calls on each brick with the volume top command. If the brick name is not specified, a list of 100 files displays by default. To view the highest write() calls, use the following command: # gluster volume top VOLNAME write [nfs \| brick BRICK-NAME ] [list-cnt cnt ] For example, to view the highest write calls on brick server:/export of test-volume : 17.2.4. Viewing Highest Open Calls on a Directory You can view a list of files with the highest open calls on the directories of each brick with the volume top command. If the brick name is not specified, the metrics of all bricks belonging to that volume displays. To view the highest open() calls on each directory, use the following command: # gluster volume top VOLNAME opendir [brick BRICK-NAME ] [list-cnt cnt ] For example, to view the highest open calls on brick server:/export/ of test-volume : 17.2.5. Viewing Highest Read Calls on a Directory You can view a list of files with the highest directory read calls on each brick with the volume top command. If the brick name is not specified, the metrics of all bricks belonging to that volume displays. To view the highest directory read() calls on each brick, use the following command: # gluster volume top VOLNAME readdir [nfs \| brick BRICK-NAME ] [list-cnt cnt ] For example, to view the highest directory read calls on brick server:/export/ of test-volume : 17.2.6. Viewing Read Performance You can view the read throughput of files on each brick with the volume top command. If the brick name is not specified, the metrics of all the bricks belonging to that volume is displayed. The output is the read throughput. This command initiates a read() call for the specified count and block size and measures the corresponding throughput directly on the back-end export, bypassing glusterFS processes. To view the read performance on each brick, use the command, specifying options as needed: # gluster volume top VOLNAME read-perf [bs blk-size count count ] [nfs \| brick BRICK-NAME ] [list-cnt cnt ] For example, to view the read performance on brick server:/export/ of test-volume , specifying a 256 block size, and list the top 10 results: 17.2.7. Viewing Write Performance You can view the write throughput of files on each brick or NFS server with the volume top command. If brick name is not specified, then the metrics of all the bricks belonging to that volume will be displayed. The output will be the write throughput. This command initiates a write operation for the specified count and block size and measures the corresponding throughput directly on back-end export, bypassing glusterFS processes. To view the write performance on each brick, use the following command, specifying options as needed: # gluster volume top VOLNAME write-perf [bs blk-size count count ] [nfs \| brick BRICK-NAME ] [list-cnt cnt ] For example, to view the write performance on brick server:/export/ of test-volume , specifying a 256 block size, and list the top 10 results:	[ "gluster volume top test open brick server:/bricks/brick1/test list-cnt 10 Brick: server/bricks/brick1/test Current open fds: 2, Max open fds: 4, Max openfd time: 2020-10-09 05:57:20.171038 Count filename ======================= 2 /file222 1 /file1", "gluster volume top testvol_distributed-dispersed read brick `hostname`:/bricks/brick1/testvol_distributed-dispersed_brick1/ list-cnt 10 Brick: server/bricks/brick1/testvol_distributed-dispersed_brick1 Count filename ======================= 9 /user11/dir1/dir4/testfile2.txt 9 /user11/dir1/dir1/testfile0.txt 9 /user11/dir0/dir4/testfile4.txt 9 /user11/dir0/dir3/testfile4.txt 9 /user11/dir0/dir1/testfile0.txt 9 /user11/testfile4.txt 9 /user11/testfile3.txt 5 /user11/dir2/dir1/testfile4.txt 5 /user11/dir2/dir1/testfile0.txt 5 /user11/dir2/testfile2.txt", "gluster volume top testvol_distributed-dispersed write brick `hostname`:/bricks/brick1/testvol_distributed-dispersed_brick1/ list-cnt 10 Brick: server/bricks/brick1/testvol_distributed-dispersed_brick1 Count filename ======================= 8 /user12/dir4/dir4/testfile3.txt 8 /user12/dir4/dir3/testfile3.txt 8 /user2/dir4/dir3/testfile4.txt 8 /user3/dir4/dir4/testfile1.txt 8 /user12/dir4/dir2/testfile3.txt 8 /user2/dir4/dir1/testfile0.txt 8 /user11/dir4/dir3/testfile4.txt 8 /user3/dir4/dir2/testfile2.txt 8 /user12/dir4/dir0/testfile0.txt 8 /user11/dir4/dir3/testfile3.txt", "gluster volume top testvol_distributed-dispersed opendir brick `hostname`:/bricks/brick1/testvol_distributed-dispersed_brick1/ list-cnt 10 Brick: server/bricks/brick1/testvol_distributed-dispersed_brick1 Count filename ======================= 3 /user2/dir3/dir2 3 /user2/dir3/dir1 3 /user2/dir3/dir0 3 /user2/dir3 3 /user2/dir2/dir4 3 /user2/dir2/dir3 3 /user2/dir2/dir2 3 /user2/dir2/dir1 3 /user2/dir2/dir0 3 /user2/dir2", "gluster volume top testvol_distributed-dispersed readdir brick `hostname`:/bricks/brick1/testvol_distributed-dispersed_brick1/ list-cnt 10 Brick: server/bricks/brick1/testvol_distributed-dispersed_brick1 Count filename ======================= 4 /user6/dir2/dir3 4 /user6/dir1/dir4 4 /user6/dir1/dir2 4 /user6/dir1 4 /user6/dir0 4 /user13/dir1/dir1 4 /user3/dir4/dir4 4 /user3/dir3/dir4 4 /user3/dir3/dir3 4 /user3/dir3/dir1", "gluster volume top testvol_distributed-dispersed read-perf bs 256 count 1 brick `hostname`:/bricks/brick1/testvol_distributed-dispersed_brick1/ list-cnt 10 Brick: server/bricks/brick1/testvol_distributed-dispersed_brick1 Throughput 10.67 MBps time 0.0000 secs MBps Filename Time ==== ======== ==== 0 /user2/dir3/dir2/testfile3.txt 2021-02-01 15:47:35.391234 0 /user2/dir3/dir2/testfile0.txt 2021-02-01 15:47:35.371018 0 /user2/dir3/dir1/testfile4.txt 2021-02-01 15:47:33.375333 0 /user2/dir3/dir1/testfile0.txt 2021-02-01 15:47:31.859194 0 /user2/dir3/dir0/testfile2.txt 2021-02-01 15:47:31.749105 0 /user2/dir3/dir0/testfile1.txt 2021-02-01 15:47:31.728151 0 /user2/dir3/testfile4.txt 2021-02-01 15:47:31.296924 0 /user2/dir3/testfile3.txt 2021-02-01 15:47:30.988683 0 /user2/dir3/testfile0.txt 2021-02-01 15:47:30.557743 0 /user2/dir2/dir4/testfile4.txt 2021-02-01 15:47:30.464017", "gluster volume top testvol_distributed-dispersed write-perf bs 256 count 1 brick `hostname`:/bricks/brick1/testvol_distributed-dispersed_brick1/ list-cnt 10 Brick: server/bricks/brick1/testvol_distributed-dispersed_brick1 Throughput 3.88 MBps time 0.0001 secs MBps Filename Time ==== ======== ==== 0 /user12/dir4/dir4/testfile4.txt 2021-02-01 13:30:32.225628 0 /user12/dir4/dir4/testfile3.txt 2021-02-01 13:30:31.771095 0 /user12/dir4/dir4/testfile0.txt 2021-02-01 13:30:29.655447 0 /user12/dir4/dir3/testfile4.txt 2021-02-01 13:30:29.62920 0 /user12/dir4/dir3/testfile3.txt 2021-02-01 13:30:28.995407 0 /user2/dir4/dir4/testfile2.txt 2021-02-01 13:30:28.489318 0 /user2/dir4/dir4/testfile1.txt 2021-02-01 13:30:27.956523 0 /user2/dir4/dir3/testfile4.txt 2021-02-01 13:30:27.34337 0 /user12/dir4/dir3/testfile0.txt 2021-02-01 13:30:26.699984 0 /user3/dir4/dir4/testfile2.txt 2021-02-01 13:30:26.602165" ]	https://docs.redhat.com/en/documentation/red_hat_gluster_storage/3.5/html/administration_guide/sect-running_the_volume_top_command
Chapter 1. Getting started with TuneD	Chapter 1. Getting started with TuneD As a system administrator, you can use the TuneD application to optimize the performance profile of your system for a variety of use cases. 1.1. The purpose of TuneD TuneD is a service that monitors your system and optimizes the performance under certain workloads. The core of TuneD are profiles , which tune your system for different use cases. TuneD is distributed with a number of predefined profiles for use cases such as: High throughput Low latency Saving power It is possible to modify the rules defined for each profile and customize how to tune a particular device. When you switch to another profile or deactivate TuneD , all changes made to the system settings by the profile revert back to their original state. You can also configure TuneD to react to changes in device usage and adjusts settings to improve performance of active devices and reduce power consumption of inactive devices. 1.2. TuneD profiles A detailed analysis of a system can be very time-consuming. TuneD provides a number of predefined profiles for typical use cases. You can also create, modify, and delete profiles. The profiles provided with TuneD are divided into the following categories: Power-saving profiles Performance-boosting profiles The performance-boosting profiles include profiles that focus on the following aspects: Low latency for storage and network High throughput for storage and network Virtual machine performance Virtualization host performance Syntax of profile configuration The tuned.conf file can contain one [main] section and other sections for configuring plug-in instances. However, all sections are optional. Lines starting with the hash sign ( # ) are comments. Additional resources tuned.conf(5) man page on your system 1.3. The default TuneD profile During the installation, the best profile for your system is selected automatically. Currently, the default profile is selected according to the following customizable rules: Environment Default profile Goal Compute nodes throughput-performance The best throughput performance Virtual machines virtual-guest The best performance. If you are not interested in the best performance, you can change it to the balanced or powersave profile. Other cases balanced Balanced performance and power consumption Additional resources tuned.conf(5) man page on your system 1.4. Merged TuneD profiles As an experimental feature, it is possible to select more profiles at once. TuneD will try to merge them during the load. If there are conflicts, the settings from the last specified profile takes precedence. Example 1.1. Low power consumption in a virtual guest The following example optimizes the system to run in a virtual machine for the best performance and concurrently tunes it for low power consumption, while the low power consumption is the priority: Warning Merging is done automatically without checking whether the resulting combination of parameters makes sense. Consequently, the feature might tune some parameters the opposite way, which might be counterproductive: for example, setting the disk for high throughput by using the throughput-performance profile and concurrently setting the disk spindown to the low value by the spindown-disk profile. Additional resources tuned-adm and tuned.conf(5) man pages on your system 1.5. The location of TuneD profiles TuneD stores profiles in the following directories: /usr/lib/tuned/ Distribution-specific profiles are stored in the directory. Each profile has its own directory. The profile consists of the main configuration file called tuned.conf , and optionally other files, for example helper scripts. /etc/tuned/ If you need to customize a profile, copy the profile directory into the directory, which is used for custom profiles. If there are two profiles of the same name, the custom profile located in /etc/tuned/ is used. Additional resources tuned.conf(5) man page on your system 1.6. TuneD profiles distributed with RHEL The following is a list of profiles that are installed with TuneD on Red Hat Enterprise Linux. Note There might be more product-specific or third-party TuneD profiles available. Such profiles are usually provided by separate RPM packages. balanced The default power-saving profile. It is intended to be a compromise between performance and power consumption. It uses auto-scaling and auto-tuning whenever possible. The only drawback is the increased latency. In the current TuneD release, it enables the CPU, disk, audio, and video plugins, and activates the conservative CPU governor. The radeon_powersave option uses the dpm-balanced value if it is supported, otherwise it is set to auto . It changes the energy_performance_preference attribute to the normal energy setting. It also changes the scaling_governor policy attribute to either the conservative or powersave CPU governor. powersave A profile for maximum power saving performance. It can throttle the performance in order to minimize the actual power consumption. In the current TuneD release it enables USB autosuspend, WiFi power saving, and Aggressive Link Power Management (ALPM) power savings for SATA host adapters. It also schedules multi-core power savings for systems with a low wakeup rate and activates the ondemand governor. It enables AC97 audio power saving or, depending on your system, HDA-Intel power savings with a 10 seconds timeout. If your system contains a supported Radeon graphics card with enabled KMS, the profile configures it to automatic power saving. On ASUS Eee PCs, a dynamic Super Hybrid Engine is enabled. It changes the energy_performance_preference attribute to the powersave or power energy setting. It also changes the scaling_governor policy attribute to either the ondemand or powersave CPU governor. Note In certain cases, the balanced profile is more efficient compared to the powersave profile. Consider there is a defined amount of work that needs to be done, for example a video file that needs to be transcoded. Your machine might consume less energy if the transcoding is done on the full power, because the task is finished quickly, the machine starts to idle, and it can automatically step-down to very efficient power save modes. On the other hand, if you transcode the file with a throttled machine, the machine consumes less power during the transcoding, but the process takes longer and the overall consumed energy can be higher. That is why the balanced profile can be generally a better option. throughput-performance A server profile optimized for high throughput. It disables power savings mechanisms and enables sysctl settings that improve the throughput performance of the disk and network IO. CPU governor is set to performance . It changes the energy_performance_preference and scaling_governor attribute to the performance profile. accelerator-performance The accelerator-performance profile contains the same tuning as the throughput-performance profile. Additionally, it locks the CPU to low C states so that the latency is less than 100us. This improves the performance of certain accelerators, such as GPUs. latency-performance A server profile optimized for low latency. It disables power savings mechanisms and enables sysctl settings that improve latency. CPU governor is set to performance and the CPU is locked to the low C states (by PM QoS). It changes the energy_performance_preference and scaling_governor attribute to the performance profile. network-latency A profile for low latency network tuning. It is based on the latency-performance profile. It additionally disables transparent huge pages and NUMA balancing, and tunes several other network-related sysctl parameters. It inherits the latency-performance profile which changes the energy_performance_preference and scaling_governor attribute to the performance profile. hpc-compute A profile optimized for high-performance computing. It is based on the latency-performance profile. network-throughput A profile for throughput network tuning. It is based on the throughput-performance profile. It additionally increases kernel network buffers. It inherits either the latency-performance or throughput-performance profile, and changes the energy_performance_preference and scaling_governor attribute to the performance profile. virtual-guest A profile designed for Red Hat Enterprise Linux 9 virtual machines and VMWare guests based on the throughput-performance profile that, among other tasks, decreases virtual memory swappiness and increases disk readahead values. It does not disable disk barriers. It inherits the throughput-performance profile and changes the energy_performance_preference and scaling_governor attribute to the performance profile. virtual-host A profile designed for virtual hosts based on the throughput-performance profile that, among other tasks, decreases virtual memory swappiness, increases disk readahead values, and enables a more aggressive value of dirty pages writeback. It inherits the throughput-performance profile and changes the energy_performance_preference and scaling_governor attribute to the performance profile. oracle A profile optimized for Oracle databases loads based on throughput-performance profile. It additionally disables transparent huge pages and modifies other performance-related kernel parameters. This profile is provided by the tuned-profiles-oracle package. desktop A profile optimized for desktops, based on the balanced profile. It additionally enables scheduler autogroups for better response of interactive applications. optimize-serial-console A profile that tunes down I/O activity to the serial console by reducing the printk value. This should make the serial console more responsive. This profile is intended to be used as an overlay on other profiles. For example: mssql A profile provided for Microsoft SQL Server. It is based on the throughput-performance profile. intel-sst A profile optimized for systems with user-defined Intel Speed Select Technology configurations. This profile is intended to be used as an overlay on other profiles. For example: 1.7. TuneD cpu-partitioning profile For tuning Red Hat Enterprise Linux 9 for latency-sensitive workloads, Red Hat recommends to use the cpu-partitioning TuneD profile. Prior to Red Hat Enterprise Linux 9, the low-latency Red Hat documentation described the numerous low-level steps needed to achieve low-latency tuning. In Red Hat Enterprise Linux 9, you can perform low-latency tuning more efficiently by using the cpu-partitioning TuneD profile. This profile is easily customizable according to the requirements for individual low-latency applications. The following figure is an example to demonstrate how to use the cpu-partitioning profile. This example uses the CPU and node layout. Figure 1.1. Figure cpu-partitioning You can configure the cpu-partitioning profile in the /etc/tuned/cpu-partitioning-variables.conf file using the following configuration options: Isolated CPUs with load balancing In the cpu-partitioning figure, the blocks numbered from 4 to 23, are the default isolated CPUs. The kernel scheduler's process load balancing is enabled on these CPUs. It is designed for low-latency processes with multiple threads that need the kernel scheduler load balancing. You can configure the cpu-partitioning profile in the /etc/tuned/cpu-partitioning-variables.conf file using the isolated_cores=cpu-list option, which lists CPUs to isolate that will use the kernel scheduler load balancing. The list of isolated CPUs is comma-separated or you can specify a range using a dash, such as 3-5 . This option is mandatory. Any CPU missing from this list is automatically considered a housekeeping CPU. Isolated CPUs without load balancing In the cpu-partitioning figure, the blocks numbered 2 and 3, are the isolated CPUs that do not provide any additional kernel scheduler process load balancing. You can configure the cpu-partitioning profile in the /etc/tuned/cpu-partitioning-variables.conf file using the no_balance_cores=cpu-list option, which lists CPUs to isolate that will not use the kernel scheduler load balancing. Specifying the no_balance_cores option is optional, however any CPUs in this list must be a subset of the CPUs listed in the isolated_cores list. Application threads using these CPUs need to be pinned individually to each CPU. Housekeeping CPUs Any CPU not isolated in the cpu-partitioning-variables.conf file is automatically considered a housekeeping CPU. On the housekeeping CPUs, all services, daemons, user processes, movable kernel threads, interrupt handlers, and kernel timers are permitted to execute. Additional resources tuned-profiles-cpu-partitioning(7) man page on your system 1.8. Using the TuneD cpu-partitioning profile for low-latency tuning This procedure describes how to tune a system for low-latency using the TuneD's cpu-partitioning profile. It uses the example of a low-latency application that can use cpu-partitioning and the CPU layout as mentioned in the cpu-partitioning figure. The application in this case uses: One dedicated reader thread that reads data from the network will be pinned to CPU 2. A large number of threads that process this network data will be pinned to CPUs 4-23. A dedicated writer thread that writes the processed data to the network will be pinned to CPU 3. Prerequisites You have installed the cpu-partitioning TuneD profile by using the dnf install tuned-profiles-cpu-partitioning command as root. Procedure Edit the /etc/tuned/cpu-partitioning-variables.conf file with the following changes: Comment the isolated_cores=USD{f:calc_isolated_cores:1} line: Add the following information for isolated CPUS: Set the cpu-partitioning TuneD profile: Reboot the system. After rebooting, the system is tuned for low-latency, according to the isolation in the cpu-partitioning figure. The application can use taskset to pin the reader and writer threads to CPUs 2 and 3, and the remaining application threads on CPUs 4-23. Verification Verify that the isolated CPUs are not reflected in the Cpus_allowed_list field: To see affinity of all processes, enter: Note TuneD cannot change the affinity of some processes, mostly kernel processes. In this example, processes with PID 4 and 9 remain unchanged. Additional resources tuned-profiles-cpu-partitioning(7) man page 1.9. Customizing the cpu-partitioning TuneD profile You can extend the TuneD profile to make additional tuning changes. For example, the cpu-partitioning profile sets the CPUs to use cstate=1 . In order to use the cpu-partitioning profile but to additionally change the CPU cstate from cstate1 to cstate0, the following procedure describes a new TuneD profile named my_profile , which inherits the cpu-partitioning profile and then sets C state-0. Procedure Create the /etc/tuned/my_profile directory: Create a tuned.conf file in this directory, and add the following content: Use the new profile: Note In the shared example, a reboot is not required. However, if the changes in the my_profile profile require a reboot to take effect, then reboot your machine. Additional resources tuned-profiles-cpu-partitioning(7) man page on your system 1.10. Real-time TuneD profiles distributed with RHEL Real-time profiles are intended for systems running the real-time kernel. Without a special kernel build, they do not configure the system to be real-time. On RHEL, the profiles are available from additional repositories. The following real-time profiles are available: realtime Use on bare-metal real-time systems. Provided by the tuned-profiles-realtime package, which is available from the RT or NFV repositories. realtime-virtual-host Use in a virtualization host configured for real-time. Provided by the tuned-profiles-nfv-host package, which is available from the NFV repository. realtime-virtual-guest Use in a virtualization guest configured for real-time. Provided by the tuned-profiles-nfv-guest package, which is available from the NFV repository. 1.11. Static and dynamic tuning in TuneD Understanding the difference between the two categories of system tuning that TuneD applies, static and dynamic , is important when determining which one to use for a given situation or purpose. Static tuning Mainly consists of the application of predefined sysctl and sysfs settings and one-shot activation of several configuration tools such as ethtool . Dynamic tuning Watches how various system components are used throughout the uptime of your system. TuneD adjusts system settings dynamically based on that monitoring information. For example, the hard drive is used heavily during startup and login, but is barely used later when the user might mainly work with applications such as web browsers or email clients. Similarly, the CPU and network devices are used differently at different times. TuneD monitors the activity of these components and reacts to the changes in their use. By default, dynamic tuning is disabled. To enable it, edit the /etc/tuned/tuned-main.conf file and change the dynamic_tuning option to 1 . TuneD then periodically analyzes system statistics and uses them to update your system tuning settings. To configure the time interval in seconds between these updates, use the update_interval option. Currently implemented dynamic tuning algorithms try to balance the performance and powersave, and are therefore disabled in the performance profiles. Dynamic tuning for individual plug-ins can be enabled or disabled in the TuneD profiles. Example 1.2. Static and dynamic tuning on a workstation On a typical office workstation, the Ethernet network interface is inactive most of the time. Only a few emails go in and out or some web pages might be loaded. For those kinds of loads, the network interface does not have to run at full speed all the time, as it does by default. TuneD has a monitoring and tuning plug-in for network devices that can detect this low activity and then automatically lower the speed of that interface, typically resulting in a lower power usage. If the activity on the interface increases for a longer period of time, for example because a DVD image is being downloaded or an email with a large attachment is opened, TuneD detects this and sets the interface speed to maximum to offer the best performance while the activity level is high. This principle is used for other plug-ins for CPU and disks as well. 1.12. TuneD no-daemon mode You can run TuneD in no-daemon mode, which does not require any resident memory. In this mode, TuneD applies the settings and exits. By default, no-daemon mode is disabled because a lot of TuneD functionality is missing in this mode, including: D-Bus support Hot-plug support Rollback support for settings To enable no-daemon mode, include the following line in the /etc/tuned/tuned-main.conf file: 1.13. Installing and enabling TuneD This procedure installs and enables the TuneD application, installs TuneD profiles, and presets a default TuneD profile for your system. Procedure Install the TuneD package: Enable and start the TuneD service: Optional: Install TuneD profiles for real-time systems: For the TuneD profiles for real-time systems enable rhel-9 repository. Install it. Verify that a TuneD profile is active and applied: Note The active profile TuneD automatically presets differs based on your machine type and system settings. 1.14. Listing available TuneD profiles This procedure lists all TuneD profiles that are currently available on your system. Procedure To list all available TuneD profiles on your system, use: USD tuned-adm list Available profiles: - accelerator-performance - Throughput performance based tuning with disabled higher latency STOP states - balanced - General non-specialized TuneD profile - desktop - Optimize for the desktop use-case - latency-performance - Optimize for deterministic performance at the cost of increased power consumption - network-latency - Optimize for deterministic performance at the cost of increased power consumption, focused on low latency network performance - network-throughput - Optimize for streaming network throughput, generally only necessary on older CPUs or 40G+ networks - powersave - Optimize for low power consumption - throughput-performance - Broadly applicable tuning that provides excellent performance across a variety of common server workloads - virtual-guest - Optimize for running inside a virtual guest - virtual-host - Optimize for running KVM guests Current active profile: balanced To display only the currently active profile, use: Additional resources tuned-adm(8) man page on your system 1.15. Setting a TuneD profile This procedure activates a selected TuneD profile on your system. Prerequisites The TuneD service is running. See Installing and Enabling TuneD for details. Procedure Optional: You can let TuneD recommend the most suitable profile for your system: Activate a profile: Alternatively, you can activate a combination of multiple profiles: Example 1.3. A virtual machine optimized for low power consumption The following example optimizes the system to run in a virtual machine with the best performance and concurrently tunes it for low power consumption, while the low power consumption is the priority: View the current active TuneD profile on your system: Reboot the system: Verification Verify that the TuneD profile is active and applied: Additional resources tuned-adm(8) man page on your system 1.16. Using the TuneD D-Bus interface You can directly communicate with TuneD at runtime through the TuneD D-Bus interface to control a variety of TuneD services. You can use the busctl or dbus-send commands to access the D-Bus API. Note Although you can use either the busctl or dbus-send command, the busctl command is a part of systemd and, therefore, present on most hosts already. 1.16.1. Using the TuneD D-Bus interface to show available TuneD D-Bus API methods You can see the D-Bus API methods available to use with TuneD by using the TuneD D-Bus interface. Prerequisites The TuneD service is running. See Installing and Enabling TuneD for details. Procedure To see the available TuneD API methods, run: The output should look similar to the following: You can find descriptions of the different available methods in the TuneD upstream repository . 1.16.2. Using the TuneD D-Bus interface to change the active TuneD profile You can replace the active TuneD profile with your desired TuneD profile by using the TuneD D-Bus interface. Prerequisites The TuneD service is running. See Installing and Enabling TuneD for details. Procedure To change the active TuneD profile, run: Replace profile with the name of your desired profile. Verification To view the current active TuneD profile, run: 1.17. Disabling TuneD This procedure disables TuneD and resets all affected system settings to their original state before TuneD modified them. Procedure To disable all tunings temporarily: The tunings are applied again after the TuneD service restarts. Alternatively, to stop and disable the TuneD service permanently: Additional resources tuned-adm(8) man page on your system	[ "tuned-adm profile virtual-guest powersave", "tuned-adm profile throughput-performance optimize-serial-console", "tuned-adm profile cpu-partitioning intel-sst", "isolated_cores=USD{f:calc_isolated_cores:1}", "All isolated CPUs: isolated_cores=2-23 Isolated CPUs without the kernel's scheduler load balancing: no_balance_cores=2,3", "tuned-adm profile cpu-partitioning", "cat /proc/self/status \| grep Cpu Cpus_allowed: 003 Cpus_allowed_list: 0-1", "ps -ae -o pid= \| xargs -n 1 taskset -cp pid 1's current affinity list: 0,1 pid 2's current affinity list: 0,1 pid 3's current affinity list: 0,1 pid 4's current affinity list: 0-5 pid 5's current affinity list: 0,1 pid 6's current affinity list: 0,1 pid 7's current affinity list: 0,1 pid 9's current affinity list: 0", "mkdir /etc/tuned/ my_profile", "vi /etc/tuned/ my_profile /tuned.conf [main] summary=Customized tuning on top of cpu-partitioning include=cpu-partitioning [cpu] force_latency=cstate.id:0\|1", "tuned-adm profile my_profile", "daemon = 0", "dnf install tuned", "systemctl enable --now tuned", "subscription-manager repos --enable=rhel-9-for-x86_64-nfv-beta-rpms", "dnf install tuned-profiles-realtime tuned-profiles-nfv", "tuned-adm active Current active profile: throughput-performance", "tuned-adm verify Verification succeeded, current system settings match the preset profile. See tuned log file ('/var/log/tuned/tuned.log') for details.", "tuned-adm list Available profiles: - accelerator-performance - Throughput performance based tuning with disabled higher latency STOP states - balanced - General non-specialized TuneD profile - desktop - Optimize for the desktop use-case - latency-performance - Optimize for deterministic performance at the cost of increased power consumption - network-latency - Optimize for deterministic performance at the cost of increased power consumption, focused on low latency network performance - network-throughput - Optimize for streaming network throughput, generally only necessary on older CPUs or 40G+ networks - powersave - Optimize for low power consumption - throughput-performance - Broadly applicable tuning that provides excellent performance across a variety of common server workloads - virtual-guest - Optimize for running inside a virtual guest - virtual-host - Optimize for running KVM guests Current active profile: balanced", "tuned-adm active Current active profile: throughput-performance", "tuned-adm recommend throughput-performance", "tuned-adm profile selected-profile", "tuned-adm profile selected-profile1 selected-profile2", "tuned-adm profile virtual-guest powersave", "tuned-adm active Current active profile: selected-profile", "reboot", "tuned-adm verify Verification succeeded, current system settings match the preset profile. See tuned log file ('/var/log/tuned/tuned.log') for details.", "busctl introspect com.redhat.tuned /Tuned com.redhat.tuned.control", "NAME TYPE SIGNATURE RESULT/VALUE FLAGS .active_profile method - s - .auto_profile method - (bs) - .disable method - b - .get_all_plugins method - a{sa{ss}} - .get_plugin_documentation method s s - .get_plugin_hints method s a{ss} - .instance_acquire_devices method ss (bs) - .is_running method - b - .log_capture_finish method s s - .log_capture_start method ii s - .post_loaded_profile method - s - .profile_info method s (bsss) - .profile_mode method - (ss) - .profiles method - as - .profiles2 method - a(ss) - .recommend_profile method - s - .register_socket_signal_path method s b - .reload method - b - .start method - b - .stop method - b - .switch_profile method s (bs) - .verify_profile method - b - .verify_profile_ignore_missing method - b - .profile_changed signal sbs - -", "busctl call com.redhat.tuned /Tuned com.redhat.tuned.control switch_profile s profile (bs) true \"OK\"", "busctl call com.redhat.tuned /Tuned com.redhat.tuned.control active_profile s \" profile \"", "tuned-adm off", "systemctl disable --now tuned" ]	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/9/html/monitoring_and_managing_system_status_and_performance/getting-started-with-tuned_monitoring-and-managing-system-status-and-performance
function::task_utime_tid	function::task_utime_tid Name function::task_utime_tid - User time of the given task Synopsis Arguments tid Thread id of the given task Description Returns the user time of the given task in cputime, or zero if the task doesn't exist. Does not include any time used by other tasks in this process, nor does it include any time of the children of this task.	[ "task_utime_tid:long(tid:long)" ]	https://docs.redhat.com/en/documentation/Red_Hat_Enterprise_Linux/7/html/systemtap_tapset_reference/api-task-utime-tid
Chapter 4. Dynamically provisioned OpenShift Data Foundation deployed on Google cloud	Chapter 4. Dynamically provisioned OpenShift Data Foundation deployed on Google cloud 4.1. Replacing operational or failed storage devices on Google Cloud installer-provisioned infrastructure When you need to replace a device in a dynamically created storage cluster on an Google Cloud installer-provisioned infrastructure, you must replace the storage node. For information about how to replace nodes, see: Replacing operational nodes on Google Cloud installer-provisioned infrastructure Replacing failed nodes on Google Cloud installer-provisioned infrastructures .	null	https://docs.redhat.com/en/documentation/red_hat_openshift_data_foundation/4.16/html/replacing_devices/dynamically_provisioned_openshift_data_foundation_deployed_on_google_cloud
Chapter 33. Configuring Microsoft SQL Server by using RHEL system roles	Chapter 33. Configuring Microsoft SQL Server by using RHEL system roles You can use the microsoft.sql.server Ansible system role to automate the installation and management of Microsoft SQL Server. This role also optimizes Red Hat Enterprise Linux (RHEL) to improve the performance and throughput of SQL Server by applying the mssql TuneD profile. Note During the installation, the role adds repositories for SQL Server and related packages to the managed hosts. Packages in these repositories are provided, maintained, and hosted by Microsoft. 33.1. Installing and configuring SQL Server with an existing TLS certificate by using the microsoft.sql.server Ansible system role If your application requires a Microsoft SQL Server database, you can configure SQL Server with TLS encryption to enable secure communication between the application and the database. By using the microsoft.sql.server Ansible system role, you can automate this process and remotely install and configure SQL Server with TLS encryption. In the playbook, you can use an existing private key and a TLS certificate that was issued by a certificate authority (CA). Depending on the RHEL version on the managed host, the version of SQL Server that you can install differs: RHEL 7.9: SQL Server 2017 and 2019 RHEL 8: SQL Server 2017, 2019, and 2022 RHEL 9.4 and later: SQL Server 2022 Prerequisites You have prepared the control node and the managed nodes . You are logged in to the control node as a user who can run playbooks on the managed nodes. The account you use to connect to the managed nodes has sudo permissions on them. You installed the ansible-collection-microsoft-sql package or the microsoft.sql collection on the control node. The managed node has 2 GB or more RAM installed. The managed node uses one of the following versions: RHEL 7.9, RHEL 8, RHEL 9.4 or later. You stored the certificate in the sql_crt.pem file in the same directory as the playbook. You stored the private key in the sql_cert.key file in the same directory as the playbook. SQL clients trust the CA that issued the certificate. Procedure Store your sensitive variables in an encrypted file: Create the vault: After the ansible-vault create command opens an editor, enter the sensitive data in the <key> : <value> format: sa_pwd: <sa_password> Save the changes, and close the editor. Ansible encrypts the data in the vault. Create a playbook file, for example ~/playbook.yml , with the following content: --- - name: Installing and configuring Microsoft SQL Server hosts: managed-node-01.example.com vars_files: - vault.yml tasks: - name: SQL Server with an existing private key and certificate ansible.builtin.include_role: name: microsoft.sql.server vars: mssql_accept_microsoft_odbc_driver_17_for_sql_server_eula: true mssql_accept_microsoft_cli_utilities_for_sql_server_eula: true mssql_accept_microsoft_sql_server_standard_eula: true mssql_version: 2022 mssql_password: "{{ sa_pwd }}" mssql_edition: Developer mssql_tcp_port: 1433 mssql_manage_firewall: true mssql_tls_enable: true mssql_tls_cert: sql_crt.pem mssql_tls_private_key: sql_cert.key mssql_tls_version: 1.2 mssql_tls_force: true The settings specified in the example playbook include the following: mssql_tls_enable: true Enables TLS encryption. If you enable this setting, you must also define mssql_tls_cert and mssql_tls_private_key . mssql_tls_cert: <path> Sets the path to the TLS certificate stored on the control node. The role copies this file to the /etc/pki/tls/certs/ directory on the managed node. mssql_tls_private_key: <path> Sets the path to the TLS private key on the control node. The role copies this file to the /etc/pki/tls/private/ directory on the managed node. mssql_tls_force: true Replaces the TLS certificate and private key in their destination directories if they exist. For details about all variables used in the playbook, see the /usr/share/ansible/roles/microsoft.sql-server/README.md file on the control node. Validate the playbook syntax: Note that this command only validates the syntax and does not protect against a wrong but valid configuration. Run the playbook: Verification On the SQL Server host, use the sqlcmd utility with the -N parameter to establish an encrypted connection to SQL server and run a query, for example: If the command succeeds, the connection to the server was TLS encrypted. Additional resources /usr/share/ansible/roles/microsoft.sql-server/README.md file Ansible vault 33.2. Installing and configuring SQL Server with a TLS certificate issued from IdM by using the microsoft.sql.server Ansible system role If your application requires a Microsoft SQL Server database, you can configure SQL Server with TLS encryption to enable secure communication between the application and the database. If the SQL Server host is a member in a Red Hat Enterprise Linux Identity Management (IdM) domain, the certmonger service can manage the certificate request and future renewals. By using the microsoft.sql.server Ansible system role, you can automate this process. You can remotely install and configure SQL Server with TLS encryption, and the microsoft.sql.server role uses the certificate Ansible system role to configure certmonger and request a certificate from IdM. Depending on the RHEL version on the managed host, the version of SQL Server that you can install differs: RHEL 7.9: SQL Server 2017 and 2019 RHEL 8: SQL Server 2017, 2019, and 2022 RHEL 9.4 and later: SQL Server 2022 Prerequisites You have prepared the control node and the managed nodes . You are logged in to the control node as a user who can run playbooks on the managed nodes. The account you use to connect to the managed nodes has sudo permissions on them. You installed the ansible-collection-microsoft-sql package or the microsoft.sql collection on the control node. The managed node has 2 GB or more RAM installed. The managed node uses one of the following versions: RHEL 7.9, RHEL 8, RHEL 9.4 or later. You enrolled the managed node in a Red Hat Enterprise Linux Identity Management (IdM) domain. Procedure Store your sensitive variables in an encrypted file: Create the vault: After the ansible-vault create command opens an editor, enter the sensitive data in the <key> : <value> format: sa_pwd: <sa_password> Save the changes, and close the editor. Ansible encrypts the data in the vault. Create a playbook file, for example ~/playbook.yml , with the following content: --- - name: Installing and configuring Microsoft SQL Server hosts: managed-node-01.example.com vars_files: - vault.yml tasks: - name: SQL Server with certificates issued by Red Hat IdM ansible.builtin.include_role: name: microsoft.sql.server vars: mssql_accept_microsoft_odbc_driver_17_for_sql_server_eula: true mssql_accept_microsoft_cli_utilities_for_sql_server_eula: true mssql_accept_microsoft_sql_server_standard_eula: true mssql_version: 2022 mssql_password: "{{ sa_pwd }}" mssql_edition: Developer mssql_tcp_port: 1433 mssql_manage_firewall: true mssql_tls_enable: true mssql_tls_certificates: - name: sql_cert dns: server.example.com ca: ipa The settings specified in the example playbook include the following: mssql_tls_enable: true Enables TLS encryption. If you enable this setting, you must also define mssql_tls_certificates . mssql_tls_certificates A list of YAML dictionaries with settings for the certificate role. name: <file_name> Defines the base name of the certificate and private key. The certificate role stores the certificate in the /etc/pki/tls/certs/ <file_name> .crt and the private key in the /etc/pki/tls/private/ <file_name> .key file. dns: <hostname_or_list_of_hostnames> Sets the hostnames that the Subject Alternative Names (SAN) field in the issued certificate contains. You can use a wildcard ( * ) or specify multiple names in YAML list format. ca: <ca_type> Defines how the certificate role requests the certificate. Set the variable to ipa if the host is enrolled in an IdM domain or self-sign to request a self-signed certificate. For details about all variables used in the playbook, see the /usr/share/ansible/roles/microsoft.sql-server/README.md file on the control node. Validate the playbook syntax: Note that this command only validates the syntax and does not protect against a wrong but valid configuration. Run the playbook: Verification On the SQL Server host, use the sqlcmd utility with the -N parameter to establish an encrypted connection to SQL server and run a query, for example: If the command succeeds, the connection to the server was TLS encrypted. Additional resources /usr/share/ansible/roles/microsoft.sql-server/README.md file Requesting certificates by using RHEL system roles Ansible vault 33.3. Installing and configuring SQL Server with custom storage paths by using the microsoft.sql.server Ansible system role When you use the microsoft.sql.server Ansible system role to install and configure a new SQL Server, you can customize the paths and modes of the data and log directories. For example, configure custom paths if you want to store databases and log files in a different directory with more storage. Important If you change the data or log path and re-run the playbook, the previously-used directories and all their content remains at the original path. Only new databases and logs are stored in the new location. Table 33.1. SQL Server default settings for data and log directories Type Directory Mode Owner Group Data /var/opt/mssql/data/ [a] mssql mssql Logs /var/opt/mssql/los/ [a] mssql mssql [a] If the directory exists, the role preserves the mode. If the directory does not exist, the role applies the default umask on the managed node when it creates the directory. Prerequisites You have prepared the control node and the managed nodes . You are logged in to the control node as a user who can run playbooks on the managed nodes. The account you use to connect to the managed nodes has sudo permissions on them. You installed the ansible-collection-microsoft-sql package or the microsoft.sql collection on the control node. The managed node has 2 GB or more RAM installed. The managed node uses one of the following versions: RHEL 7.9, RHEL 8, RHEL 9.4 or later. Procedure Store your sensitive variables in an encrypted file: Create the vault: After the ansible-vault create command opens an editor, enter the sensitive data in the <key> : <value> format: sa_pwd: <sa_password> Save the changes, and close the editor. Ansible encrypts the data in the vault. Edit an existing playbook file, for example ~/playbook.yml , and add the storage and log-related variables: --- - name: Installing and configuring Microsoft SQL Server hosts: managed-node-01.example.com vars_files: - vault.yml tasks: - name: SQL Server with custom storage paths ansible.builtin.include_role: name: microsoft.sql.server vars: mssql_accept_microsoft_odbc_driver_17_for_sql_server_eula: true mssql_accept_microsoft_cli_utilities_for_sql_server_eula: true mssql_accept_microsoft_sql_server_standard_eula: true mssql_version: 2022 mssql_password: "{{ sa_pwd }}" mssql_edition: Developer mssql_tcp_port: 1433 mssql_manage_firewall: true mssql_datadir: /var/lib/mssql/ mssql_datadir_mode: '0700' mssql_logdir: /var/log/mssql/ mssql_logdir_mode: '0700' The settings specified in the example playbook include the following: mssql_datadir_mode and mssql_logdir_mode Set the permission modes. Specify the value in single quotes to ensure that the role parses the value as a string and not as an octal number. For details about all variables used in the playbook, see the /usr/share/ansible/roles/microsoft.sql-server/README.md file on the control node. Validate the playbook syntax: Note that this command only validates the syntax and does not protect against a wrong but valid configuration. Run the playbook: Verification Display the mode of the data directory: Display the mode of the log directory: Additional resources /usr/share/ansible/roles/microsoft.sql-server/README.md file Ansible vault 33.4. Installing and configuring SQL Server with AD integration by using the microsoft.sql.server Ansible system role You can integrate Microsoft SQL Server into an Active Directory (AD) to enable AD users to authenticate to SQL Server. By using the microsoft.sql.server Ansible system role, you can automate this process and remotely install and configure SQL Server accordingly. Note that you must still perform manual steps in AD and SQL Server after you run the playbook. Depending on the RHEL version on the managed host, the version of SQL Server that you can install differs: RHEL 7.9: SQL Server 2017 and 2019 RHEL 8: SQL Server 2017, 2019, and 2022 RHEL 9.4 and later: SQL Server 2022 Prerequisites You have prepared the control node and the managed nodes . You are logged in to the control node as a user who can run playbooks on the managed nodes. The account you use to connect to the managed nodes has sudo permissions on them. You installed the ansible-collection-microsoft-sql package or the microsoft.sql collection on the control node. The managed node has 2 GB or more RAM installed. The managed node uses one of the following versions: RHEL 7.9, RHEL 8, RHEL 9.4 or later. An AD domain is available in the network. A reverse DNS (RDNS) zone exists in AD, and it contains Pointer (PTR) resource records for each AD domain controller (DC). The managed host's network settings use an AD DNS server. The managed host can resolve the following DNS entries: Both the hostnames and the fully-qualified domain names (FQDNs) of the AD DCs resolve to their IP addresses. The IP addresses of the AD DCs resolve to their FQDNs. Procedure Store your sensitive variables in an encrypted file: Create the vault: After the ansible-vault create command opens an editor, enter the sensitive data in the <key> : <value> format: sa_pwd: <sa_password> sql_pwd: <SQL_AD_password> ad_admin_pwd: <AD_admin_password> Save the changes, and close the editor. Ansible encrypts the data in the vault. Create a playbook file, for example ~/playbook.yml , with the following content: --- - name: Installing and configuring Microsoft SQL Server hosts: managed-node-01.example.com vars_files: - vault.yml tasks: - name: SQL Server with AD authentication ansible.builtin.include_role: name: microsoft.sql.server vars: mssql_accept_microsoft_odbc_driver_17_for_sql_server_eula: true mssql_accept_microsoft_cli_utilities_for_sql_server_eula: true mssql_accept_microsoft_sql_server_standard_eula: true mssql_version: 2022 mssql_password: "{{ sa_pwd }}" mssql_edition: Developer mssql_tcp_port: 1433 mssql_manage_firewall: true mssql_ad_configure: true mssql_ad_join: true mssql_ad_sql_user: sqluser mssql_ad_sql_password: "{{ sql_pwd }}" ad_integration_realm: ad.example.com ad_integration_user: Administrator ad_integration_password: "{{ ad_admin_pwd }}" The settings specified in the example playbook include the following: mssql_ad_configure: true Enables authentication against AD. mssql_ad_join: true Uses the ad_integration RHEL system role to join the managed node to AD. The role uses the settings from the ad_integration_realm , ad_integration_user , and ad_integration_password variables to join the domain. mssql_ad_sql_user: <username> Sets the name of an AD account that the role should create in AD and SQL Server for administration purposes. ad_integration_user: <AD_user> Sets the name of an AD user with privileges to join machines to the domain and to create the AD user specified in mssql_ad_sql_user . For details about all variables used in the playbook, see the /usr/share/ansible/roles/microsoft.sql-server/README.md file on the control node. Validate the playbook syntax: Note that this command only validates the syntax and does not protect against a wrong but valid configuration. Run the playbook: In your AD domain, enable 128 bit and 256 bit Kerberos authentication for the AD SQL user which you specified in the playbook. Use one of the following options: In the Active Directory Users and Computers application: Navigate to ad.example.com > Users > sqluser > Accounts . In the Account options list, select This account supports Kerberos AES 128 bit encryption and This account supports Kerberos AES 256 bit encryption . Click Apply . In PowerShell in admin mode, enter: Authorize AD users that should be able to authenticate to SQL Server. On the SQL Server, perform the following steps: Obtain a Kerberos ticket for the Administrator user: Authorize an AD user: Repeat this step for every AD user who should be able to access SQL Server. Verification On the managed node that runs SQL Server: Obtain a Kerberos ticket for an AD user: Use the sqlcmd utility to log in to SQL Server and run a query, for example: Additional resources /usr/share/ansible/roles/microsoft.sql-server/README.md file Ansible vault	[ "ansible-vault create vault.yml New Vault password: <vault_password> Confirm New Vault password: <vault_password>", "sa_pwd: <sa_password>", "--- - name: Installing and configuring Microsoft SQL Server hosts: managed-node-01.example.com vars_files: - vault.yml tasks: - name: SQL Server with an existing private key and certificate ansible.builtin.include_role: name: microsoft.sql.server vars: mssql_accept_microsoft_odbc_driver_17_for_sql_server_eula: true mssql_accept_microsoft_cli_utilities_for_sql_server_eula: true mssql_accept_microsoft_sql_server_standard_eula: true mssql_version: 2022 mssql_password: \"{{ sa_pwd }}\" mssql_edition: Developer mssql_tcp_port: 1433 mssql_manage_firewall: true mssql_tls_enable: true mssql_tls_cert: sql_crt.pem mssql_tls_private_key: sql_cert.key mssql_tls_version: 1.2 mssql_tls_force: true", "ansible-playbook --ask-vault-pass --syntax-check ~/playbook.yml", "ansible-playbook --ask-vault-pass ~/playbook.yml", "/opt/mssql-tools/bin/sqlcmd -N -S server.example.com -U \"sa\" -P <sa_password> -Q 'SELECT SYSTEM_USER'", "ansible-vault create vault.yml New Vault password: <vault_password> Confirm New Vault password: <vault_password>", "sa_pwd: <sa_password>", "--- - name: Installing and configuring Microsoft SQL Server hosts: managed-node-01.example.com vars_files: - vault.yml tasks: - name: SQL Server with certificates issued by Red Hat IdM ansible.builtin.include_role: name: microsoft.sql.server vars: mssql_accept_microsoft_odbc_driver_17_for_sql_server_eula: true mssql_accept_microsoft_cli_utilities_for_sql_server_eula: true mssql_accept_microsoft_sql_server_standard_eula: true mssql_version: 2022 mssql_password: \"{{ sa_pwd }}\" mssql_edition: Developer mssql_tcp_port: 1433 mssql_manage_firewall: true mssql_tls_enable: true mssql_tls_certificates: - name: sql_cert dns: server.example.com ca: ipa", "ansible-playbook --ask-vault-pass --syntax-check ~/playbook.yml", "ansible-playbook --ask-vault-pass ~/playbook.yml", "/opt/mssql-tools/bin/sqlcmd -N -S server.example.com -U \"sa\" -P <sa_password> -Q 'SELECT SYSTEM_USER'", "ansible-vault create vault.yml New Vault password: <vault_password> Confirm New Vault password: <vault_password>", "sa_pwd: <sa_password>", "--- - name: Installing and configuring Microsoft SQL Server hosts: managed-node-01.example.com vars_files: - vault.yml tasks: - name: SQL Server with custom storage paths ansible.builtin.include_role: name: microsoft.sql.server vars: mssql_accept_microsoft_odbc_driver_17_for_sql_server_eula: true mssql_accept_microsoft_cli_utilities_for_sql_server_eula: true mssql_accept_microsoft_sql_server_standard_eula: true mssql_version: 2022 mssql_password: \"{{ sa_pwd }}\" mssql_edition: Developer mssql_tcp_port: 1433 mssql_manage_firewall: true mssql_datadir: /var/lib/mssql/ mssql_datadir_mode: '0700' mssql_logdir: /var/log/mssql/ mssql_logdir_mode: '0700'", "ansible-playbook --ask-vault-pass --syntax-check ~/playbook.yml", "ansible-playbook --ask-vault-pass ~/playbook.yml", "ansible managed-node-01.example.com -m command -a 'ls -ld /var/lib/mssql/' drwx------. 12 mssql mssql 4096 Jul 3 13:53 /var/lib/mssql/", "ansible managed-node-01.example.com -m command -a 'ls -ld /var/log/mssql/' drwx------. 12 mssql mssql 4096 Jul 3 13:53 /var/log/mssql/", "ansible-vault create vault.yml New Vault password: <vault_password> Confirm New Vault password: <vault_password>", "sa_pwd: <sa_password> sql_pwd: <SQL_AD_password> ad_admin_pwd: <AD_admin_password>", "--- - name: Installing and configuring Microsoft SQL Server hosts: managed-node-01.example.com vars_files: - vault.yml tasks: - name: SQL Server with AD authentication ansible.builtin.include_role: name: microsoft.sql.server vars: mssql_accept_microsoft_odbc_driver_17_for_sql_server_eula: true mssql_accept_microsoft_cli_utilities_for_sql_server_eula: true mssql_accept_microsoft_sql_server_standard_eula: true mssql_version: 2022 mssql_password: \"{{ sa_pwd }}\" mssql_edition: Developer mssql_tcp_port: 1433 mssql_manage_firewall: true mssql_ad_configure: true mssql_ad_join: true mssql_ad_sql_user: sqluser mssql_ad_sql_password: \"{{ sql_pwd }}\" ad_integration_realm: ad.example.com ad_integration_user: Administrator ad_integration_password: \"{{ ad_admin_pwd }}\"", "ansible-playbook --ask-vault-pass --syntax-check ~/playbook.yml", "ansible-playbook --ask-vault-pass ~/playbook.yml", "C:\\> Set-ADUser -Identity sqluser -KerberosEncryptionType AES128,AES256", "kinit [email protected]", "/opt/mssql-tools/bin/sqlcmd -S. -Q 'CREATE LOGIN [AD\\<AD_user>] FROM WINDOWS;'", "kinit <AD_user> @ad.example.com", "/opt/mssql-tools/bin/sqlcmd -S. -Q 'SELECT SYSTEM_USER'" ]	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/9/html/automating_system_administration_by_using_rhel_system_roles/assembly_configuring-microsoft-sql-server-using-microsoft-sql-server-ansible-role_automating-system-administration-by-using-rhel-system-roles
Chapter 2. Preparing Red Hat Enterprise Linux for a Red Hat Quay proof of concept deployment	Chapter 2. Preparing Red Hat Enterprise Linux for a Red Hat Quay proof of concept deployment Use the following procedures to configure Red Hat Enterprise Linux (RHEL) for a Red Hat Quay proof of concept deployment. 2.1. Install and register the RHEL server Use the following procedure to configure the Red Hat Enterprise Linux (RHEL) server for a Red Hat Quay proof of concept deployment. Procedure Install the latest RHEL 9 server. You can do a minimal, shell-access only install, or Server plus GUI if you want a desktop. Register and subscribe your RHEL server system as described in How to register and subscribe a RHEL system to the Red Hat Customer Portal using Red Hat Subscription-Manager Enter the following commands to register your system and list available subscriptions. Choose an available RHEL server subscription, attach to its pool ID, and upgrade to the latest software: # subscription-manager register --username=<user_name> --password=<password> # subscription-manager refresh # subscription-manager list --available # subscription-manager attach --pool=<pool_id> # yum update -y 2.2. Registry authentication Use the following procedure to authenticate your registry for a Red Hat Quay proof of concept. Procedure Set up authentication to registry.redhat.io by following the Red Hat Container Registry Authentication procedure. Setting up authentication allows you to pull the Quay container. Note This differs from earlier versions of Red Hat Quay, when the images were hosted on Quay.io. Enter the following command to log in to the registry: USD sudo podman login registry.redhat.io You are prompted to enter your username and password . 2.3. Firewall configuration If you have a firewall running on your system, you might have to add rules that allow access to Red Hat Quay. Use the following procedure to configure your firewall for a proof of concept deployment. Procedure The commands required depend on the ports that you have mapped on your system, for example: # firewall-cmd --permanent --add-port=80/tcp \ && firewall-cmd --permanent --add-port=443/tcp \ && firewall-cmd --permanent --add-port=5432/tcp \ && firewall-cmd --permanent --add-port=5433/tcp \ && firewall-cmd --permanent --add-port=6379/tcp \ && firewall-cmd --reload 2.4. IP addressing and naming services There are several ways to configure the component containers in Red Hat Quay so that they can communicate with each other, for example: Using a naming service . If you want your deployment to survive container restarts, which typically result in changed IP addresses, you can implement a naming service. For example, the dnsname plugin is used to allow containers to resolve each other by name. Using the host network . You can use the podman run command with the --net=host option and then use container ports on the host when specifying the addresses in the configuration. This option is susceptible to port conflicts when two containers want to use the same port. This method is not recommended. Configuring port mapping . You can use port mappings to expose ports on the host and then use these ports in combination with the host IP address or host name. This document uses port mapping and assumes a static IP address for your host system. Table 2.1. Sample proof of concept port mapping Component Port mapping Address Quay -p 80:8080 -p 443:8443 http://quay-server.example.com Postgres for Quay -p 5432:5432 quay-server.example.com:5432 Redis -p 6379:6379 quay-server.example.com:6379 Postgres for Clair V4 -p 5433:5432 quay-server.example.com:5433 Clair V4 -p 8081:8080 http://quay-server.example.com:8081	[ "subscription-manager register --username=<user_name> --password=<password> subscription-manager refresh subscription-manager list --available subscription-manager attach --pool=<pool_id> yum update -y", "sudo podman login registry.redhat.io", "firewall-cmd --permanent --add-port=80/tcp && firewall-cmd --permanent --add-port=443/tcp && firewall-cmd --permanent --add-port=5432/tcp && firewall-cmd --permanent --add-port=5433/tcp && firewall-cmd --permanent --add-port=6379/tcp && firewall-cmd --reload" ]	https://docs.redhat.com/en/documentation/red_hat_quay/3.10/html/proof_of_concept_-_deploying_red_hat_quay/poc-configuring-rhel-server
Chapter 2. The pcs Command Line Interface	Chapter 2. The pcs Command Line Interface The pcs command line interface provides the ability to control and configure corosync and pacemaker . The general format of the pcs command is as follows. 2.1. The pcs Commands The pcs commands are as follows. cluster Configure cluster options and nodes. For information on the pcs cluster command, see Chapter 3, Cluster Creation and Administration . resource Create and manage cluster resources. For information on the pcs cluster command, see Chapter 5, Configuring Cluster Resources , Chapter 7, Managing Cluster Resources , and Chapter 8, Advanced Resource types . stonith Configure fence devices for use with Pacemaker. For information on the pcs stonith command, see Chapter 4, Fencing: Configuring STONITH . constraint Manage resource constraints. For information on the pcs constraint command, see Chapter 6, Resource Constraints . property Set Pacemaker properties. For information on setting properties with the pcs property command, see Chapter 10, Pacemaker Cluster Properties . status View current cluster and resource status. For information on the pcs status command, see Section 2.5, "Displaying Status" . config Display complete cluster configuration in user-readable form. For information on the pcs config command, see Section 2.6, "Displaying the Full Cluster Configuration" .	[ "pcs [-f file ] [-h] [ commands ]" ]	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/configuring_the_red_hat_high_availability_add-on_with_pacemaker/ch-pcscommand-haar
Chapter 6. Troubleshooting notification failures	Chapter 6. Troubleshooting notification failures The notifications service event log enables Notifications administrators to see when notifications are not working properly. The event log provides a list of all triggered events on the Red Hat Hybrid Cloud Console account, and actions taken (as configured in the associated behavior group) for the past 14 days. In the Action taken column, each event shows the notification method highlighted in green or red to indicate the status of the message transmission. The filterable event log is a useful troubleshooting tool to see a failed notification event and identify potential issues with endpoints. After seeing a failed action in the event log, the Notifications administrator can check the endpoint and the status of the last five connection attempts on the Integrations screen. In the integrations service, the following connection statuses are reflected by color: Green: Five transmissions were successful. Red: Five transmissions were unsuccessful (timeout, 404 error, etc). Yellow: Connection is degraded; at least two of the five transmissions were unsuccessful. Unknown: The integration has not yet been called, or is not associated with a behavior group. The event log can answer questions related to receipt of emails. By showing the email action for an event as green, the event log enables a Notifications administrator to confirm that emails were sent successfully. Even when notifications and integrations are configured properly, individual users on the Hybrid Cloud Console account must configure their user preferences to receive emails. Before users receive notifications using the webhook integration type, a Notifications administrator must configure endpoints for your organization's preferred webhook application. Prerequisites You are logged in to the Hybrid Cloud Console as a user with Notifications administrator or Organization Administrator permissions. Procedure In the Hybrid Cloud Console, navigate to Settings > Notifications > Event Log . Filter the events list by event, application, application bundle, action type, or action status. Select the time frame to show events from today, yesterday, the last seven days, the last 14 days (default), or set a custom range within the last 14 days. Sort the Date and time column in ascending or descending order. Navigate to Settings > Notifications > Configure Events , and verify or change settings by event. Ask users to check their user preferences for receiving email notifications. Even when notifications and integrations are configured properly, individual users on the Hybrid Cloud Console account must configure their user preferences to receive emails. Additional resources For more information about network and firewall configuration, see Firewall Configuration for accessing Red Hat Insights / Hybrid Cloud Console Integrations & Notifications . To configure your personal preferences for receiving notifications, see Configuring user preferences for email notifications .	null	https://docs.redhat.com/en/documentation/red_hat_hybrid_cloud_console/1-latest/html/configuring_notifications_on_the_red_hat_hybrid_cloud_console/proc-troubleshoot_notifications
Chapter 1. Understanding Red Hat Network Functions Virtualization (NFV)	Chapter 1. Understanding Red Hat Network Functions Virtualization (NFV) Network Functions Virtualization (NFV) is a software-based solution that helps the Communication Service Providers (CSPs) to move beyond the traditional, proprietary hardware to achieve greater efficiency and agility while reducing the operational costs. An NFV environment allows for IT and network convergence by providing a virtualized infrastructure using the standard virtualization technologies that run on standard hardware devices such as switches, routers, and storage to virtualize network functions (VNFs). The management and orchestration logic deploys and sustains these services. NFV also includes a Systems Administration, Automation and Life-Cycle Management thereby reducing the manual work necessary. 1.1. Advantages of NFV The main advantages of implementing network functions virtualization (NFV) are as follows: Accelerates the time-to-market by allowing you to to quickly deploy and scale new networking services to address changing demands. Supports innovation by enabling service developers to self-manage their resources and prototype using the same platform that will be used in production. Addresses customer demands in hours or minutes instead of weeks or days, without sacrificing security or performance. Reduces capital expenditure because it uses commodity-off-the-shelf hardware instead of expensive tailor-made equipment. Uses streamlined operations and automation that optimize day-to-day tasks to improve employee productivity and reduce operational costs. 1.2. Supported Configurations for NFV Deployments You can use the Red Hat OpenStack Platform director toolkit to isolate specific network types, for example, external, project, internal API, and so on. You can deploy a network on a single network interface, or distributed over a multiple-host network interface. With Open vSwitch you can create bonds by assigning multiple interfaces to a single bridge. Configure network isolation in a Red Hat OpenStack Platform installation with template files. If you do not provide template files, the service networks deploy on the provisioning network. There are two types of template configuration files: network-environment.yaml This file contains network details, such as subnets and IP address ranges, for the overcloud nodes. This file also contains the different settings that override the default parameter values for various scenarios. Host network templates, for example, compute.yaml and controller.yaml These templates define the network interface configuration for the overcloud nodes. The values of the network details are provided by the network-environment.yaml file. These heat template files are located at /usr/share/openstack-tripleo-heat-templates/ on the undercloud node. For samples of these heat template files for NFV, see Sample DPDK SR-IOV YAML files . The Hardware requirements and Software requirements sections provide more details on how to plan and configure the heat template files for NFV using the Red Hat OpenStack Platform director. You can edit YAML files to configure NFV. For an introduction to the YAML file format, see YAML in a Nutshell . Data Plane Development Kit (DPDK) and Single Root I/O Virtualization (SR-IOV) Red Hat OpenStack Platform (RHOSP) supports NFV deployments with the inclusion of automated OVS-DPDK and SR-IOV configuration. Important Red Hat does not support the use of OVS-DPDK for non-NFV workloads. If you need OVS-DPDK functionality for non-NFV workloads, contact your Technical Account Manager (TAM) or open a customer service request case to discuss a Support Exception and other options. To open a customer service request case, go to Create a case and choose Account > Customer Service Request . Hyper-converged Infrastructure (HCI) You can colocate the Compute sub-system with the Red Hat Ceph Storage nodes. This hyper-converged model delivers lower cost of entry, smaller initial deployment footprints, maximized capacity utilization, and more efficient management in NFV use cases. For more information about HCI, see Deploying a hyperconverged infrastructure . Composable roles You can use composable roles to create custom deployments. Composable roles allow you to add or remove services from each role. For more information about the Composable Roles, see Composable services and custom roles in Customizing your Red Hat OpenStack Platform deployment . Open vSwitch (OVS) with LACP As of OVS 2.9, LACP with OVS is fully supported. This is not recommended for Openstack control plane traffic, as OVS or Openstack Networking interruptions might interfere with management. For more information, see Open vSwitch (OVS) bonding options in Installing and managing Red Hat OpenStack Platform with director . OVS Hardware offload Red Hat OpenStack Platform supports, with limitations, the deployment of OVS hardware offload. For information about deploying OVS with hardware offload, see Configuring OVS hardware offload . Open Virtual Network (OVN) The following NFV OVN configurations are available in RHOSP 16.1.4: Deploying OVN with OVS-DPDK and SR-IOV Deploying OVN with OVS TC Flower offload 1.3. NFV data plane connectivity With the introduction of NFV, more networking vendors are starting to implement their traditional devices as VNFs. While the majority of networking vendors are considering virtual machines, some are also investigating a container-based approach as a design choice. An OpenStack-based solution should be rich and flexible due to two primary reasons: Application readiness - Network vendors are currently in the process of transforming their devices into VNFs. Different VNFs in the market have different maturity levels; common barriers to this readiness include enabling RESTful interfaces in their APIs, evolving their data models to become stateless, and providing automated management operations. OpenStack should provide a common platform for all. Broad use-cases - NFV includes a broad range of applications that serve different use-cases. For example, Virtual Customer Premise Equipment (vCPE) aims at providing a number of network functions such as routing, firewall, virtual private network (VPN), and network address translation (NAT) at customer premises. Virtual Evolved Packet Core (vEPC), is a cloud architecture that provides a cost-effective platform for the core components of Long-Term Evolution (LTE) network, allowing dynamic provisioning of gateways and mobile endpoints to sustain the increased volumes of data traffic from smartphones and other devices. These use cases are implemented using different network applications and protocols, and require different connectivity, isolation, and performance characteristics from the infrastructure. It is also common to separate between control plane interfaces and protocols and the actual forwarding plane. OpenStack must be flexible enough to offer different datapath connectivity options. In principle, there are two common approaches for providing data plane connectivity to virtual machines: Direct hardware access bypasses the linux kernel and provides secure direct memory access (DMA) to the physical NIC using technologies such as PCI Passthrough or single root I/O virtualization (SR-IOV) for both Virtual Function (VF) and Physical Function (PF) pass-through. Using a virtual switch (vswitch) , implemented as a software service of the hypervisor. Virtual machines are connected to the vSwitch using virtual interfaces (vNICs), and the vSwitch is capable of forwarding traffic between virtual machines, as well as between virtual machines and the physical network. Some of the fast data path options are as follows: Single Root I/O Virtualization (SR-IOV) is a standard that makes a single PCI hardware device appear as multiple virtual PCI devices. It works by introducing Physical Functions (PFs), which are the fully featured PCIe functions that represent the physical hardware ports, and Virtual Functions (VFs), which are lightweight functions that are assigned to the virtual machines. To the VM, the VF resembles a regular NIC that communicates directly with the hardware. NICs support multiple VFs. Open vSwitch (OVS) is an open source software switch that is designed to be used as a virtual switch within a virtualized server environment. OVS supports the capabilities of a regular L2-L3 switch and also offers support to the SDN protocols such as OpenFlow to create user-defined overlay networks (for example, VXLAN). OVS uses Linux kernel networking to switch packets between virtual machines and across hosts using physical NIC. OVS now supports connection tracking (Conntrack) with built-in firewall capability to avoid the overhead of Linux bridges that use iptables/ebtables. Open vSwitch for Red Hat OpenStack Platform environments offers default OpenStack Networking (neutron) integration with OVS. Data Plane Development Kit (DPDK) consists of a set of libraries and poll mode drivers (PMD) for fast packet processing. It is designed to run mostly in the user-space, enabling applications to perform their own packet processing directly from or to the NIC. DPDK reduces latency and allows more packets to be processed. DPDK Poll Mode Drivers (PMDs) run in busy loop, constantly scanning the NIC ports on host and vNIC ports in guest for arrival of packets. DPDK accelerated Open vSwitch (OVS-DPDK) is Open vSwitch bundled with DPDK for a high performance user-space solution with Linux kernel bypass and direct memory access (DMA) to physical NICs. The idea is to replace the standard OVS kernel data path with a DPDK-based data path, creating a user-space vSwitch on the host that uses DPDK internally for its packet forwarding. The advantage of this architecture is that it is mostly transparent to users. The interfaces it exposes, such as OpenFlow, OVSDB, the command line, remain mostly the same. 1.4. ETSI NFV Architecture The European Telecommunications Standards Institute (ETSI) is an independent standardization group that develops standards for information and communications technologies (ICT) in Europe. Network functions virtualization (NFV) focuses on addressing problems involved in using proprietary hardware devices. With NFV, the necessity to install network-specific equipment is reduced, depending upon the use case requirements and economic benefits. The ETSI Industry Specification Group for Network Functions Virtualization (ETSI ISG NFV) sets the requirements, reference architecture, and the infrastructure specifications necessary to ensure virtualized functions are supported. Red Hat is offering an open-source based cloud-optimized solution to help the Communication Service Providers (CSP) to achieve IT and network convergence. Red Hat adds NFV features such as single root I/O virtualization (SR-IOV) and Open vSwitch with Data Plane Development Kit (OVS-DPDK) to Red Hat OpenStack. 1.5. NFV ETSI architecture and components In general, a network functions virtualization (NFV) platform has the following components: Figure 1.1. NFV ETSI architecture and components Virtualized Network Functions (VNFs) - the software implementation of routers, firewalls, load balancers, broadband gateways, mobile packet processors, servicing nodes, signalling, location services, and other network functions. NFV Infrastructure (NFVi) - the physical resources (compute, storage, network) and the virtualization layer that make up the infrastructure. The network includes the datapath for forwarding packets between virtual machines and across hosts. This allows you to install VNFs without being concerned about the details of the underlying hardware. NFVi forms the foundation of the NFV stack. NFVi supports multi-tenancy and is managed by the Virtual Infrastructure Manager (VIM). Enhanced Platform Awareness (EPA) improves the virtual machine packet forwarding performance (throughput, latency, jitter) by exposing low-level CPU and NIC acceleration components to the VNF. NFV Management and Orchestration (MANO) - the management and orchestration layer focuses on all the service management tasks required throughout the life cycle of the VNF. The main goals of MANO is to allow service definition, automation, error-correlation, monitoring, and life-cycle management of the network functions offered by the operator to its customers, decoupled from the physical infrastructure. This decoupling requires additional layers of management, provided by the Virtual Network Function Manager (VNFM). VNFM manages the life cycle of the virtual machines and VNFs by either interacting directly with them or through the Element Management System (EMS) provided by the VNF vendor. The other important component defined by MANO is the Orchestrator, also known as NFVO. NFVO interfaces with various databases and systems including Operations/Business Support Systems (OSS/BSS) on the top and the VNFM on the bottom. If the NFVO wants to create a new service for a customer, it asks the VNFM to trigger the instantiation of a VNF, which may result in multiple virtual machines. Operations and Business Support Systems (OSS/BSS) - provides the essential business function applications, for example, operations support and billing. The OSS/BSS needs to be adapted to NFV, integrating with both legacy systems and the new MANO components. The BSS systems set policies based on service subscriptions and manage reporting and billing. Systems Administration, Automation and Life-Cycle Management - manages system administration, automation of the infrastructure components and life cycle of the NFVi platform. 1.6. Red Hat NFV components Red Hat's solution for NFV includes a range of products that can act as the different components of the NFV framework in the ETSI model. The following products from the Red Hat portfolio integrate into an NFV solution: Red Hat OpenStack Platform - Supports IT and NFV workloads. The Enhanced Platform Awareness (EPA) features deliver deterministic performance improvements through CPU Pinning, Huge pages, Non-Uniform Memory Access (NUMA) affinity and network adaptors (NICs) that support SR-IOV and OVS-DPDK. Red Hat Enterprise Linux and Red Hat Enterprise Linux Atomic Host - Create virtual machines and containers as VNFs. Red Hat Ceph Storage - Provides the the unified elastic and high-performance storage layer for all the needs of the service provider workloads. Red Hat JBoss Middleware and OpenShift Enterprise by Red Hat - Optionally provide the ability to modernize the OSS/BSS components. Red Hat CloudForms - Provides a VNF manager and presents data from multiple sources, such as the VIM and the NFVi in a unified display. Red Hat Satellite and Ansible by Red Hat - Optionally provide enhanced systems administration, automation and life-cycle management. 1.7. NFV installation summary The Red Hat OpenStack Platform director installs and manages a complete OpenStack environment. The director is based on the upstream OpenStack TripleO project, which is an abbreviation for "OpenStack-On-OpenStack". This project takes advantage of the OpenStack components to install a fully operational OpenStack environment; this includes a minimal OpenStack node called the undercloud. The undercloud provisions and controls the overcloud (a series of bare metal systems used as the production OpenStack nodes). The director provides a simple method for installing a complete Red Hat OpenStack Platform environment that is both lean and robust. For more information on installing the undercloud and overcloud, see Red Hat OpenStack Platform Installing and managing Red Hat OpenStack Platform with director . To install the NFV features, complete the following additional steps: Include SR-IOV and PCI Passthrough parameters in your network-environment.yaml file, update the post-install.yaml file for CPU tuning, modify the compute.yaml file, and run the overcloud_deploy.sh script to deploy the overcloud. Install the DPDK libraries and drivers for fast packets processing by polling data directly from the NICs. Include the DPDK parameters in your network-environment.yaml file, update the post-install.yaml files for CPU tuning, update the compute.yaml file to set the bridge with DPDK port, update the controller.yaml file to set the bridge and an interface with VLAN configured, and run the overcloud_deploy.sh script to deploy the overcloud.	null	https://docs.redhat.com/en/documentation/red_hat_openstack_platform/17.1/html/configuring_network_functions_virtualization/understanding-nfv_rhosp-nfv
Chapter 23. Using different DNS servers for different domains	Chapter 23. Using different DNS servers for different domains By default, Red Hat Enterprise Linux (RHEL) sends all DNS requests to the first DNS server specified in the /etc/resolv.conf file. If this server does not reply, RHEL tries the server in this file until it finds a working one. In environments where one DNS server cannot resolve all domains, administrators can configure RHEL to send DNS requests for a specific domain to a selected DNS server. For example, you connect a server to a Virtual Private Network (VPN), and hosts in the VPN use the example.com domain. In this case, you can configure RHEL to process DNS queries in the following way: Send only DNS requests for example.com to the DNS server in the VPN network. Send all other requests to the DNS server that is configured in the connection profile with the default gateway. 23.1. Using dnsmasq in NetworkManager to send DNS requests for a specific domain to a selected DNS server You can configure NetworkManager to start an instance of dnsmasq . This DNS caching server then listens on port 53 on the loopback device. Consequently, this service is only reachable from the local system and not from the network. With this configuration, NetworkManager adds the nameserver 127.0.0.1 entry to the /etc/resolv.conf file, and dnsmasq dynamically routes DNS requests to the corresponding DNS servers specified in the NetworkManager connection profiles. Prerequisites The system has multiple NetworkManager connections configured. A DNS server and search domain are configured in the NetworkManager connection profile that is responsible for resolving a specific domain. For example, to ensure that the DNS server specified in a VPN connection resolves queries for the example.com domain, the VPN connection profile must contain the following settings: A DNS server that can resolve example.com A search domain set to example.com in the ipv4.dns-search and ipv6.dns-search parameters The dnsmasq service is not running or configured to listen on a different interface than localhost . Procedure Install the dnsmasq package: Edit the /etc/NetworkManager/NetworkManager.conf file, and set the following entry in the [main] section: Reload the NetworkManager service: Verification Search in the systemd journal of the NetworkManager unit for which domains the service uses a different DNS server: Use the tcpdump packet sniffer to verify the correct route of DNS requests: Install the tcpdump package: On one terminal, start tcpdump to capture DNS traffic on all interfaces: On a different terminal, resolve host names for a domain for which an exception exists and another domain, for example: Verify in the tcpdump output that Red Hat Enterprise Linux sends only DNS queries for the example.com domain to the designated DNS server and through the corresponding interface: Red Hat Enterprise Linux sends the DNS query for www.example.com to the DNS server on 198.51.100.7 and the query for www.redhat.com to 192.0.2.1 . Troubleshooting Verify that the nameserver entry in the /etc/resolv.conf file refers to 127.0.0.1 : If the entry is missing, check the dns parameter in the /etc/NetworkManager/NetworkManager.conf file. Verify that the dnsmasq service listens on port 53 on the loopback device: If the service does not listen on 127.0.0.1:53 , check the journal entries of the NetworkManager unit: 23.2. Using systemd-resolved in NetworkManager to send DNS requests for a specific domain to a selected DNS server You can configure NetworkManager to start an instance of systemd-resolved . This DNS stub resolver then listens on port 53 on IP address 127.0.0.53 . Consequently, this stub resolver is only reachable from the local system and not from the network. With this configuration, NetworkManager adds the nameserver 127.0.0.53 entry to the /etc/resolv.conf file, and systemd-resolved dynamically routes DNS requests to the corresponding DNS servers specified in the NetworkManager connection profiles. Important The systemd-resolved service is provided as a Technology Preview only. Technology Preview features are not supported with Red Hat production Service Level Agreements (SLAs), might not be functionally complete, and Red Hat does not recommend using them for production. These previews provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process. See Technology Preview Features Support Scope on the Red Hat Customer Portal for information about the support scope for Technology Preview features. For a supported solution, see Using dnsmasq in NetworkManager to send DNS requests for a specific domain to a selected DNS server . Prerequisites The system has multiple NetworkManager connections configured. A DNS server and search domain are configured in the NetworkManager connection profile that is responsible for resolving a specific domain. For example, to ensure that the DNS server specified in a VPN connection resolves queries for the example.com domain, the VPN connection profile must contain the following settings: A DNS server that can resolve example.com A search domain set to example.com in the ipv4.dns-search and ipv6.dns-search parameters Procedure Enable and start the systemd-resolved service: Edit the /etc/NetworkManager/NetworkManager.conf file, and set the following entry in the [main] section: Reload the NetworkManager service: Verification Display the DNS servers systemd-resolved uses and for which domains the service uses a different DNS server: The output confirms that systemd-resolved uses different DNS servers for the example.com domain. Use the tcpdump packet sniffer to verify the correct route of DNS requests: Install the tcpdump package: On one terminal, start tcpdump to capture DNS traffic on all interfaces: On a different terminal, resolve host names for a domain for which an exception exists and another domain, for example: Verify in the tcpdump output that Red Hat Enterprise Linux sends only DNS queries for the example.com domain to the designated DNS server and through the corresponding interface: Red Hat Enterprise Linux sends the DNS query for www.example.com to the DNS server on 198.51.100.7 and the query for www.redhat.com to 192.0.2.1 . Troubleshooting Verify that the nameserver entry in the /etc/resolv.conf file refers to 127.0.0.53 : If the entry is missing, check the dns parameter in the /etc/NetworkManager/NetworkManager.conf file. Verify that the systemd-resolved service listens on port 53 on the local IP address 127.0.0.53 : If the service does not listen on 127.0.0.53:53 , check if the systemd-resolved service is running.	[ "yum install dnsmasq", "dns=dnsmasq", "systemctl reload NetworkManager", "journalctl -xeu NetworkManager Jun 02 13:30:17 <client_hostname>_ dnsmasq[5298]: using nameserver 198.51.100.7#53 for domain example.com", "yum install tcpdump", "tcpdump -i any port 53", "host -t A www.example.com host -t A www.redhat.com", "13:52:42.234533 IP server .43534 > 198.51.100.7 .domain: 50121+ [1au] A? www.example.com. (33) 13:52:57.753235 IP server .40864 > 192.0.2.1 .domain: 6906+ A? www.redhat.com. (33)", "cat /etc/resolv.conf nameserver 127.0.0.1", "ss -tulpn \| grep \"127.0.0.1:53\" udp UNCONN 0 0 127.0.0.1:53 0.0.0.0:* users:((\"dnsmasq\",pid=7340,fd=18)) tcp LISTEN 0 32 127.0.0.1:53 0.0.0.0:* users:((\"dnsmasq\",pid=7340,fd=19))", "journalctl -u NetworkManager", "systemctl --now enable systemd-resolved", "dns=systemd-resolved", "systemctl reload NetworkManager", "resolvectl Link 2 ( enp1s0 ) Current Scopes: DNS Protocols: +DefaultRoute Current DNS Server: 192.0.2.1 DNS Servers: 192.0.2.1 Link 3 ( tun0 ) Current Scopes: DNS Protocols: -DefaultRoute Current DNS Server: 198.51.100.7 DNS Servers: 198.51.100.7 203.0.113.19 DNS Domain: example.com", "yum install tcpdump", "tcpdump -i any port 53", "host -t A www.example.com host -t A www.redhat.com", "13:52:42.234533 IP server .43534 > 198.51.100.7 .domain: 50121+ [1au] A? www.example.com. (33) 13:52:57.753235 IP server .40864 > 192.0.2.1 .domain: 6906+ A? www.redhat.com. (33)", "cat /etc/resolv.conf nameserver 127.0.0.53", "ss -tulpn \| grep \"127.0.0.53\" udp UNCONN 0 0 127.0.0.53%lo:53 0.0.0.0:* users:((\"systemd-resolve\",pid=1050,fd=12)) tcp LISTEN 0 4096 127.0.0.53%lo:53 0.0.0.0:* users:((\"systemd-resolve\",pid=1050,fd=13))" ]	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/8/html/configuring_and_managing_networking/using-different-dns-servers-for-different-domains_configuring-and-managing-networking
Chapter 2. Deploying OpenShift Data Foundation on Microsoft Azure	Chapter 2. Deploying OpenShift Data Foundation on Microsoft Azure You can deploy OpenShift Data Foundation on OpenShift Container Platform using dynamic storage devices provided by Microsoft Azure installer-provisioned infrastructure (IPI) (type: managed-csi ) that enables you to create internal cluster resources. This results in internal provisioning of the base services, which helps to make additional storage classes available to applications. Also, it is possible to deploy only the Multicloud Object Gateway (MCG) component with OpenShift Data Foundation. For more information, see Deploy standalone Multicloud Object Gateway . Note Only internal OpenShift Data Foundation clusters are supported on Microsoft Azure. See Planning your deployment for more information about deployment requirements. Ensure that you have addressed the requirements in Preparing to deploy OpenShift Data Foundation chapter before proceeding with the below steps for deploying using dynamic storage devices: Install the Red Hat OpenShift Data Foundation Operator . Create the OpenShift Data Foundation Cluster 2.1. Installing Red Hat OpenShift Data Foundation Operator You can install Red Hat OpenShift Data Foundation Operator using the Red Hat OpenShift Container Platform Operator Hub. Prerequisites Access to an OpenShift Container Platform cluster using an account with cluster-admin and operator installation permissions. You must have at least three worker or infrastructure nodes in the Red Hat OpenShift Container Platform cluster. For additional resource requirements, see the Planning your deployment guide. Important When you need to override the cluster-wide default node selector for OpenShift Data Foundation, you can use the following command to specify a blank node selector for the openshift-storage namespace (create openshift-storage namespace in this case): Taint a node as infra to ensure only Red Hat OpenShift Data Foundation resources are scheduled on that node. This helps you save on subscription costs. For more information, see the How to use dedicated worker nodes for Red Hat OpenShift Data Foundation section in the Managing and Allocating Storage Resources guide. Procedure Log in to the OpenShift Web Console. Click Operators OperatorHub . Scroll or type OpenShift Data Foundation into the Filter by keyword box to find the OpenShift Data Foundation Operator. Click Install . Set the following options on the Install Operator page: Update Channel as stable-4.17 . Installation Mode as A specific namespace on the cluster . Installed Namespace as Operator recommended namespace openshift-storage . If Namespace openshift-storage does not exist, it is created during the operator installation. Select Approval Strategy as Automatic or Manual . If you select Automatic updates, then the Operator Lifecycle Manager (OLM) automatically upgrades the running instance of your Operator without any intervention. If you select Manual updates, then the OLM creates an update request. As a cluster administrator, you must then manually approve that update request to update the Operator to a newer version. Ensure that the Enable option is selected for the Console plugin . Click Install . Verification steps After the operator is successfully installed, a pop-up with a message, Web console update is available appears on the user interface. Click Refresh web console from this pop-up for the console changes to reflect. In the Web Console: Navigate to Installed Operators and verify that the OpenShift Data Foundation Operator shows a green tick indicating successful installation. Navigate to Storage and verify if the Data Foundation dashboard is available. 2.2. Enabling cluster-wide encryption with KMS using the Token authentication method You can enable the key value backend path and policy in the vault for token authentication. Prerequisites Administrator access to the vault. A valid Red Hat OpenShift Data Foundation Advanced subscription. For more information, see the knowledgebase article on OpenShift Data Foundation subscriptions . Carefully, select a unique path name as the backend path that follows the naming convention since you cannot change it later. Procedure Enable the Key/Value (KV) backend path in the vault. For vault KV secret engine API, version 1: For vault KV secret engine API, version 2: Create a policy to restrict the users to perform a write or delete operation on the secret: Create a token that matches the above policy: 2.3. Enabling cluster-wide encryption with KMS using the Kubernetes authentication method You can enable the Kubernetes authentication method for cluster-wide encryption using the Key Management System (KMS). Prerequisites Administrator access to Vault. A valid Red Hat OpenShift Data Foundation Advanced subscription. For more information, see the knowledgebase article on OpenShift Data Foundation subscriptions . The OpenShift Data Foundation operator must be installed from the Operator Hub. Select a unique path name as the backend path that follows the naming convention carefully. You cannot change this path name later. Procedure Create a service account: where, <serviceaccount_name> specifies the name of the service account. For example: Create clusterrolebindings and clusterroles : For example: Create a secret for the serviceaccount token and CA certificate. where, <serviceaccount_name> is the service account created in the earlier step. Get the token and the CA certificate from the secret. Retrieve the OCP cluster endpoint. Fetch the service account issuer: Use the information collected in the step to setup the Kubernetes authentication method in Vault: Important To configure the Kubernetes authentication method in Vault when the issuer is empty: Enable the Key/Value (KV) backend path in Vault. For Vault KV secret engine API, version 1: For Vault KV secret engine API, version 2: Create a policy to restrict the users to perform a write or delete operation on the secret: Generate the roles: The role odf-rook-ceph-op is later used while you configure the KMS connection details during the creation of the storage system. 2.3.1. Enabling key rotation when using KMS Security common practices require periodic encryption key rotation. You can enable key rotation when using KMS using this procedure. To enable key rotation, add the annotation keyrotation.csiaddons.openshift.io/schedule: <value> to either Namespace , StorageClass , or PersistentVolumeClaims (in order of precedence). <value> can be either @hourly , @daily , @weekly , @monthly , or @yearly . If <value> is empty, the default is @weekly . The below examples use @weekly . Important Key rotation is only supported for RBD backed volumes. Annotating Namespace Annotating StorageClass Annotating PersistentVolumeClaims 2.4. Creating an OpenShift Data Foundation cluster Create an OpenShift Data Foundation cluster after you install the OpenShift Data Foundation operator. Prerequisites The OpenShift Data Foundation operator must be installed from the Operator Hub. For more information, see Installing OpenShift Data Foundation Operator using the Operator Hub . If you want to use Azure Vault as the key management service provider, make sure to set up client authentication and fetch the client credentials from Azure using the following steps: Create Azure Vault. For more information, see Quickstart: Create a key vault using the Azure portal in Microsoft product documentation. Create Service Principal with certificate based authentication. For more information, see Create an Azure service principal with Azure CLI in Microsoft product documentation. Set Azure Key Vault role based access control (RBAC). For more information, see Enable Azure RBAC permissions on Key Vault Procedure In the OpenShift Web Console, click Operators Installed Operators to view all the installed operators. Ensure that the Project selected is openshift-storage . Click on the OpenShift Data Foundation operator, and then click Create StorageSystem . In the Backing storage page, select the following: Select Full Deployment for the Deployment type option. Select the Use an existing StorageClass option. Select the Storage Class . By default, it is set to managed-csi . Optional: Select Use external PostgreSQL checkbox to use an external PostgreSQL [Technology preview] . This provides high availability solution for Multicloud Object Gateway where the PostgreSQL pod is a single point of failure. Provide the following connection details: Username Password Server name and Port Database name Select Enable TLS/SSL checkbox to enable encryption for the Postgres server. Click . In the Capacity and nodes page, provide the necessary information: Select a value for Requested Capacity from the dropdown list. It is set to 2 TiB by default. Note Once you select the initial storage capacity, cluster expansion is performed only using the selected usable capacity (three times of raw storage). In the Select Nodes section, select at least three available nodes. In the Configure performance section, select one of the following performance profiles: Lean Use this in a resource constrained environment with minimum resources that are lower than the recommended. This profile minimizes resource consumption by allocating fewer CPUs and less memory. Balanced (default) Use this when recommended resources are available. This profile provides a balance between resource consumption and performance for diverse workloads. Performance Use this in an environment with sufficient resources to get the best performance. This profile is tailored for high performance by allocating ample memory and CPUs to ensure optimal execution of demanding workloads. Note You have the option to configure the performance profile even after the deployment using the Configure performance option from the options menu of the StorageSystems tab. Important Before selecting a resource profile, make sure to check the current availability of resources within the cluster. Opting for a higher resource profile in a cluster with insufficient resources might lead to installation failures. For more information about resource requirements, see Resource requirement for performance profiles . Optional: Select the Taint nodes checkbox to dedicate the selected nodes for OpenShift Data Foundation. For cloud platforms with multiple availability zones, ensure that the Nodes are spread across different Locations/availability zones. If the nodes selected do not match the OpenShift Data Foundation cluster requirements of an aggregated 30 CPUs and 72 GiB of RAM, a minimal cluster is deployed. For minimum starting node requirements, see the Resource requirements section in the Planning guide. Click . Optional: In the Security and network page, configure the following based on your requirements: To enable encryption, select Enable data encryption for block and file storage . Select either one or both the encryption levels: Cluster-wide encryption Encrypts the entire cluster (block and file). StorageClass encryption Creates encrypted persistent volume (block only) using encryption enabled storage class. Optional: Select the Connect to an external key management service checkbox. This is optional for cluster-wide encryption. From the Key Management Service Provider drop-down list, select one of the following providers and provide the necessary details: Vault Select an Authentication Method . Using Token authentication method Enter a unique Connection Name , host Address of the Vault server ('https://<hostname or ip>'), Port number and Token . Expand Advanced Settings to enter additional settings and certificate details based on your Vault configuration: Enter the Key Value secret path in Backend Path that is dedicated and unique to OpenShift Data Foundation. Optional: Enter TLS Server Name and Vault Enterprise Namespace . Upload the respective PEM encoded certificate file to provide the CA Certificate , Client Certificate and Client Private Key . Click Save . Using Kubernetes authentication method Enter a unique Vault Connection Name , host Address of the Vault server ('https://<hostname or ip>'), Port number and Role name. Expand Advanced Settings to enter additional settings and certificate details based on your Vault configuration: Enter the Key Value secret path in Backend Path that is dedicated and unique to OpenShift Data Foundation. Optional: Enter TLS Server Name and Authentication Path if applicable. Upload the respective PEM encoded certificate file to provide the CA Certificate , Client Certificate and Client Private Key . Click Save . Thales CipherTrust Manager (using KMIP) Enter a unique Connection Name for the Key Management service within the project. In the Address and Port sections, enter the IP of Thales CipherTrust Manager and the port where the KMIP interface is enabled. For example: Address : 123.34.3.2 Port : 5696 Upload the Client Certificate , CA certificate , and Client Private Key . If StorageClass encryption is enabled, enter the Unique Identifier to be used for encryption and decryption generated above. The TLS Server field is optional and used when there is no DNS entry for the KMIP endpoint. For example, kmip_all_<port>.ciphertrustmanager.local . Azure Key Vault For information about setting up client authentication and fetching the client credentials in Azure platform, see the Prerequisites section of this procedure. Enter a unique Connection name for the key management service within the project. Enter Azure Vault URL . Enter Client ID . Enter Tenant ID . Upload Certificate file in .PEM format and the certificate file must include a client certificate and a private key. To enable in-transit encryption, select In-transit encryption . Select a Network . Click . In the Data Protection page, if you are configuring Regional-DR solution for Openshift Data Foundation then select the Prepare cluster for disaster recovery (Regional-DR only) checkbox, else click . In the Review and create page, review the configuration details. To modify any configuration settings, click Back . Click Create StorageSystem . Note When your deployment has five or more nodes, racks, or rooms, and when there are five or more number of failure domains present in the deployment, you can configure Ceph monitor counts based on the number of racks or zones. An alert is displayed in the notification panel or Alert Center of the OpenShift Web Console to indicate the option to increase the number of Ceph monitor counts. You can use the Configure option in the alert to configure the Ceph monitor counts. For more information, see Resolving low Ceph monitor count alert . Verification steps To verify the final Status of the installed storage cluster: In the OpenShift Web Console, navigate to Installed Operators OpenShift Data Foundation Storage System ocs-storagecluster-storagesystem Resources . Verify that Status of StorageCluster is Ready and has a green tick mark to it. To verify that all components for OpenShift Data Foundation are successfully installed, see Verifying your OpenShift Data Foundation deployment . Additional resources To enable Overprovision Control alerts, refer to Alerts in Monitoring guide.	[ "oc annotate namespace openshift-storage openshift.io/node-selector=", "vault secrets enable -path=odf kv", "vault secrets enable -path=odf kv-v2", "echo ' path \"odf/\" { capabilities = [\"create\", \"read\", \"update\", \"delete\", \"list\"] } path \"sys/mounts\" { capabilities = [\"read\"] }'\| vault policy write odf -", "vault token create -policy=odf -format json", "oc -n openshift-storage create serviceaccount <serviceaccount_name>", "oc -n openshift-storage create serviceaccount odf-vault-auth", "oc -n openshift-storage create clusterrolebinding vault-tokenreview-binding --clusterrole=system:auth-delegator --serviceaccount=openshift-storage:_<serviceaccount_name>_", "oc -n openshift-storage create clusterrolebinding vault-tokenreview-binding --clusterrole=system:auth-delegator --serviceaccount=openshift-storage:odf-vault-auth", "cat <<EOF \| oc create -f - apiVersion: v1 kind: Secret metadata: name: odf-vault-auth-token namespace: openshift-storage annotations: kubernetes.io/service-account.name: <serviceaccount_name> type: kubernetes.io/service-account-token data: {} EOF", "SA_JWT_TOKEN=USD(oc -n openshift-storage get secret odf-vault-auth-token -o jsonpath=\"{.data['token']}\" \| base64 --decode; echo) SA_CA_CRT=USD(oc -n openshift-storage get secret odf-vault-auth-token -o jsonpath=\"{.data['ca\\.crt']}\" \| base64 --decode; echo)", "OCP_HOST=USD(oc config view --minify --flatten -o jsonpath=\"{.clusters[0].cluster.server}\")", "oc proxy & proxy_pid=USD! issuer=\"USD( curl --silent http://127.0.0.1:8001/.well-known/openid-configuration \| jq -r .issuer)\" kill USDproxy_pid", "vault auth enable kubernetes", "vault write auth/kubernetes/config token_reviewer_jwt=\"USDSA_JWT_TOKEN\" kubernetes_host=\"USDOCP_HOST\" kubernetes_ca_cert=\"USDSA_CA_CRT\" issuer=\"USDissuer\"", "vault write auth/kubernetes/config token_reviewer_jwt=\"USDSA_JWT_TOKEN\" kubernetes_host=\"USDOCP_HOST\" kubernetes_ca_cert=\"USDSA_CA_CRT\"", "vault secrets enable -path=odf kv", "vault secrets enable -path=odf kv-v2", "echo ' path \"odf/\" { capabilities = [\"create\", \"read\", \"update\", \"delete\", \"list\"] } path \"sys/mounts\" { capabilities = [\"read\"] }'\| vault policy write odf -", "vault write auth/kubernetes/role/odf-rook-ceph-op bound_service_account_names=rook-ceph-system,rook-ceph-osd,noobaa bound_service_account_namespaces=openshift-storage policies=odf ttl=1440h", "vault write auth/kubernetes/role/odf-rook-ceph-osd bound_service_account_names=rook-ceph-osd bound_service_account_namespaces=openshift-storage policies=odf ttl=1440h", "oc get namespace default NAME STATUS AGE default Active 5d2h", "oc annotate namespace default \"keyrotation.csiaddons.openshift.io/schedule=@weekly\" namespace/default annotated", "oc get storageclass rbd-sc NAME PROVISIONER RECLAIMPOLICY VOLUMEBINDINGMODE ALLOWVOLUMEEXPANSION AGE rbd-sc rbd.csi.ceph.com Delete Immediate true 5d2h", "oc annotate storageclass rbd-sc \"keyrotation.csiaddons.openshift.io/schedule=@weekly\" storageclass.storage.k8s.io/rbd-sc annotated", "oc get pvc data-pvc NAME STATUS VOLUME CAPACITY ACCESS MODES STORAGECLASS AGE data-pvc Bound pvc-f37b8582-4b04-4676-88dd-e1b95c6abf74 1Gi RWO default 20h", "oc annotate pvc data-pvc \"keyrotation.csiaddons.openshift.io/schedule=@weekly\" persistentvolumeclaim/data-pvc annotated", "oc get encryptionkeyrotationcronjobs.csiaddons.openshift.io NAME SCHEDULE SUSPEND ACTIVE LASTSCHEDULE AGE data-pvc-1642663516 @weekly 3s", "oc annotate pvc data-pvc \"keyrotation.csiaddons.openshift.io/schedule=/1 * * \" --overwrite=true persistentvolumeclaim/data-pvc annotated", "oc get encryptionkeyrotationcronjobs.csiaddons.openshift.io NAME SCHEDULE SUSPEND ACTIVE LASTSCHEDULE AGE data-pvc-1642664617 /1 * * * * 3s" ]	https://docs.redhat.com/en/documentation/red_hat_openshift_data_foundation/4.17/html/deploying_openshift_data_foundation_using_microsoft_azure/deploying-openshift-data-foundation-on-microsoft-azure_azure
Chapter 3. Using the OpenShift Container Platform dashboard to get cluster information	Chapter 3. Using the OpenShift Container Platform dashboard to get cluster information Access the OpenShift Container Platform dashboard, which captures high-level information about the cluster, by navigating to Home Dashboards Overview from the OpenShift Container Platform web console. The OpenShift Container Platform dashboard provides various cluster information, captured in individual dashboard cards. 3.1. About the OpenShift Container Platform dashboards page The OpenShift Container Platform dashboard consists of the following cards: Details provides a brief overview of informational cluster details. Status include ok , error , warning , in progress , and unknown . Resources can add custom status names. Cluster ID Provider Version Cluster Inventory details number of resources and associated statuses. It is helpful when intervention is required to resolve problems, including information about: Number of nodes Number of pods Persistent storage volume claims Bare metal hosts in the cluster, listed according to their state (only available in metal3 environment). Cluster Capacity charts help administrators understand when additional resources are required in the cluster. The charts contain an inner ring that displays current consumption, while an outer ring displays thresholds configured for the resource, including information about: CPU time Memory allocation Storage consumed Network resources consumed Cluster Utilization shows the capacity of various resources over a specified period of time, to help administrators understand the scale and frequency of high resource consumption. Events lists messages related to recent activity in the cluster, such as pod creation or virtual machine migration to another host. Top Consumers helps administrators understand how cluster resources are consumed. Click on a resource to jump to a detailed page listing pods and nodes that consume the largest amount of the specified cluster resource (CPU, memory, or storage).	null	https://docs.redhat.com/en/documentation/openshift_container_platform/4.10/html/web_console/using-dashboard-to-get-cluster-info
Chapter 3. Viewing your Insights results	Chapter 3. Viewing your Insights results You can view system and infrastructure results in the Red Hat Insights for Red Hat Enterprise Linux application dashboard. The dashboard provides links to each available Insights service. This includes advisor, vulnerability, compliance, policies and patch. From this starting point, you can proactively identify and manage issues affecting system security, performance, stability and availability. Prerequisites The insights-client package is installed on the system. You are logged in to the Red Hat Hybrid Cloud Console. Procedure Navigate to Red Hat Insights > RHEL > Inventory in the Hybrid Cloud Console. Search for your system name and confirm that it exists in the inventory.	null	https://docs.redhat.com/en/documentation/red_hat_insights/1-latest/html/deploying_red_hat_insights_on_existing_rhel_systems_managed_by_red_hat_cloud_access/viewing-insights-results_deploying-insights-with-rhca
Chapter 9. Migrating your applications	Chapter 9. Migrating your applications You can migrate your applications by using the Migration Toolkit for Containers (MTC) web console or the command line . Most cluster-scoped resources are not yet handled by MTC. If your applications require cluster-scoped resources, you might have to create them manually on the target cluster. You can use stage migration and cutover migration to migrate an application between clusters: Stage migration copies data from the source cluster to the target cluster without stopping the application. You can run a stage migration multiple times to reduce the duration of the cutover migration. Cutover migration stops the transactions on the source cluster and moves the resources to the target cluster. You can use state migration to migrate an application's state: State migration copies selected persistent volume claims (PVCs). You can use state migration to migrate a namespace within the same cluster. During migration, the MTC preserves the following namespace annotations: openshift.io/sa.scc.mcs openshift.io/sa.scc.supplemental-groups openshift.io/sa.scc.uid-range These annotations preserve the UID range, ensuring that the containers retain their file system permissions on the target cluster. There is a risk that the migrated UIDs could duplicate UIDs within an existing or future namespace on the target cluster. 9.1. Migration prerequisites You must be logged in as a user with cluster-admin privileges on all clusters. Direct image migration You must ensure that the secure OpenShift image registry of the source cluster is exposed. You must create a route to the exposed registry. Direct volume migration If your clusters use proxies, you must configure an Stunnel TCP proxy. Clusters The source cluster must be upgraded to the latest MTC z-stream release. The MTC version must be the same on all clusters. Network The clusters have unrestricted network access to each other and to the replication repository. If you copy the persistent volumes with move , the clusters must have unrestricted network access to the remote volumes. You must enable the following ports on an OpenShift Container Platform 4 cluster: 6443 (API server) 443 (routes) 53 (DNS) You must enable port 443 on the replication repository if you are using TLS. Persistent volumes (PVs) The PVs must be valid. The PVs must be bound to persistent volume claims. If you use snapshots to copy the PVs, the following additional prerequisites apply: The cloud provider must support snapshots. The PVs must have the same cloud provider. The PVs must be located in the same geographic region. The PVs must have the same storage class. 9.2. Migrating your applications by using the MTC web console You can configure clusters and a replication repository by using the MTC web console. Then, you can create and run a migration plan. 9.2.1. Launching the MTC web console You can launch the Migration Toolkit for Containers (MTC) web console in a browser. Prerequisites The MTC web console must have network access to the OpenShift Container Platform web console. The MTC web console must have network access to the OAuth authorization server. Procedure Log in to the OpenShift Container Platform cluster on which you have installed MTC. Obtain the MTC web console URL by entering the following command: USD oc get -n openshift-migration route/migration -o go-template='https://{{ .spec.host }}' The output resembles the following: https://migration-openshift-migration.apps.cluster.openshift.com . Launch a browser and navigate to the MTC web console. Note If you try to access the MTC web console immediately after installing the Migration Toolkit for Containers Operator, the console might not load because the Operator is still configuring the cluster. Wait a few minutes and retry. If you are using self-signed CA certificates, you will be prompted to accept the CA certificate of the source cluster API server. The web page guides you through the process of accepting the remaining certificates. Log in with your OpenShift Container Platform username and password . 9.2.2. Adding a cluster to the MTC web console You can add a cluster to the Migration Toolkit for Containers (MTC) web console. Prerequisites Cross-origin resource sharing must be configured on the source cluster. If you are using Azure snapshots to copy data: You must specify the Azure resource group name for the cluster. The clusters must be in the same Azure resource group. The clusters must be in the same geographic location. If you are using direct image migration, you must expose a route to the image registry of the source cluster. Procedure Log in to the cluster. Obtain the migration-controller service account token: USD oc create token migration-controller -n openshift-migration Example output eyJhbGciOiJSUzI1NiIsImtpZCI6IiJ9.eyJpc3MiOiJrdWJlcm5ldGVzL3NlcnZpY2VhY2NvdW50Iiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9uYW1lc3BhY2UiOiJtaWciLCJrdWJlcm5ldGVzLmlvL3NlcnZpY2VhY2NvdW50L3NlY3JldC5uYW1lIjoibWlnLXRva2VuLWs4dDJyIiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9zZXJ2aWNlLWFjY291bnQubmFtZSI6Im1pZyIsImt1YmVybmV0ZXMuaW8vc2VydmljZWFjY291bnQvc2VydmljZS1hY2NvdW50LnVpZCI6ImE1YjFiYWMwLWMxYmYtMTFlOS05Y2NiLTAyOWRmODYwYjMwOCIsInN1YiI6InN5c3RlbTpzZXJ2aWNlYWNjb3VudDptaWc6bWlnIn0.xqeeAINK7UXpdRqAtOj70qhBJPeMwmgLomV9iFxr5RoqUgKchZRG2J2rkqmPm6vr7K-cm7ibD1IBpdQJCcVDuoHYsFgV4mp9vgOfn9osSDp2TGikwNz4Az95e81xnjVUmzh-NjDsEpw71DH92iHV_xt2sTwtzftS49LpPW2LjrV0evtNBP_t_RfskdArt5VSv25eORl7zScqfe1CiMkcVbf2UqACQjo3LbkpfN26HAioO2oH0ECPiRzT0Xyh-KwFutJLS9Xgghyw-LD9kPKcE_xbbJ9Y4Rqajh7WdPYuB0Jd9DPVrslmzK-F6cgHHYoZEv0SvLQi-PO0rpDrcjOEQQ Log in to the MTC web console. In the MTC web console, click Clusters . Click Add cluster . Fill in the following fields: Cluster name : The cluster name can contain lower-case letters ( a-z ) and numbers ( 0-9 ). It must not contain spaces or international characters. URL : Specify the API server URL, for example, https://<www.example.com>:8443 . Service account token : Paste the migration-controller service account token. Exposed route host to image registry : If you are using direct image migration, specify the exposed route to the image registry of the source cluster. To create the route, run the following command: For OpenShift Container Platform 3: USD oc create route passthrough --service=docker-registry --port=5000 -n default For OpenShift Container Platform 4: USD oc create route passthrough --service=image-registry --port=5000 -n openshift-image-registry Azure cluster : You must select this option if you use Azure snapshots to copy your data. Azure resource group : This field is displayed if Azure cluster is selected. Specify the Azure resource group. When an OpenShift Container Platform cluster is created on Microsoft Azure, an Azure Resource Group is created to contain all resources associated with the cluster. In the Azure CLI, you can display all resource groups by issuing the following command: USD az group list ResourceGroups associated with OpenShift Container Platform clusters are tagged, where sample-rg-name is the value you would extract and supply to the UI: { "id": "/subscriptions/...//resourceGroups/sample-rg-name", "location": "centralus", "name": "...", "properties": { "provisioningState": "Succeeded" }, "tags": { "kubernetes.io_cluster.sample-ld57c": "owned", "openshift_creationDate": "2019-10-25T23:28:57.988208+00:00" }, "type": "Microsoft.Resources/resourceGroups" }, This information is also available from the Azure Portal in the Resource groups blade. Require SSL verification : Optional: Select this option to verify the Secure Socket Layer (SSL) connection to the cluster. CA bundle file : This field is displayed if Require SSL verification is selected. If you created a custom CA certificate bundle file for self-signed certificates, click Browse , select the CA bundle file, and upload it. Click Add cluster . The cluster appears in the Clusters list. 9.2.3. Adding a replication repository to the MTC web console You can add an object storage as a replication repository to the Migration Toolkit for Containers (MTC) web console. MTC supports the following storage providers: Amazon Web Services (AWS) S3 Multi-Cloud Object Gateway (MCG) Generic S3 object storage, for example, Minio or Ceph S3 Google Cloud Provider (GCP) Microsoft Azure Blob Prerequisites You must configure the object storage as a replication repository. Procedure In the MTC web console, click Replication repositories . Click Add repository . Select a Storage provider type and fill in the following fields: AWS for S3 providers, including AWS and MCG: Replication repository name : Specify the replication repository name in the MTC web console. S3 bucket name : Specify the name of the S3 bucket. S3 bucket region : Specify the S3 bucket region. Required for AWS S3. Optional for some S3 providers. Check the product documentation of your S3 provider for expected values. S3 endpoint : Specify the URL of the S3 service, not the bucket, for example, https://<s3-storage.apps.cluster.com> . Required for a generic S3 provider. You must use the https:// prefix. S3 provider access key : Specify the <AWS_SECRET_ACCESS_KEY> for AWS or the S3 provider access key for MCG and other S3 providers. S3 provider secret access key : Specify the <AWS_ACCESS_KEY_ID> for AWS or the S3 provider secret access key for MCG and other S3 providers. Require SSL verification : Clear this checkbox if you are using a generic S3 provider. If you created a custom CA certificate bundle for self-signed certificates, click Browse and browse to the Base64-encoded file. GCP : Replication repository name : Specify the replication repository name in the MTC web console. GCP bucket name : Specify the name of the GCP bucket. GCP credential JSON blob : Specify the string in the credentials-velero file. Azure : Replication repository name : Specify the replication repository name in the MTC web console. Azure resource group : Specify the resource group of the Azure Blob storage. Azure storage account name : Specify the Azure Blob storage account name. Azure credentials - INI file contents : Specify the string in the credentials-velero file. Click Add repository and wait for connection validation. Click Close . The new repository appears in the Replication repositories list. 9.2.4. Creating a migration plan in the MTC web console You can create a migration plan in the Migration Toolkit for Containers (MTC) web console. Prerequisites You must be logged in as a user with cluster-admin privileges on all clusters. You must ensure that the same MTC version is installed on all clusters. You must add the clusters and the replication repository to the MTC web console. If you want to use the move data copy method to migrate a persistent volume (PV), the source and target clusters must have uninterrupted network access to the remote volume. If you want to use direct image migration, you must specify the exposed route to the image registry of the source cluster. This can be done by using the MTC web console or by updating the MigCluster custom resource manifest. Procedure In the MTC web console, click Migration plans . Click Add migration plan . Enter the Plan name . The migration plan name must not exceed 253 lower-case alphanumeric characters ( a-z, 0-9 ) and must not contain spaces or underscores ( _ ). Select a Source cluster , a Target cluster , and a Repository . Click . Select the projects for migration. Optional: Click the edit icon beside a project to change the target namespace. Click . Select a Migration type for each PV: The Copy option copies the data from the PV of a source cluster to the replication repository and then restores the data on a newly created PV, with similar characteristics, in the target cluster. The Move option unmounts a remote volume, for example, NFS, from the source cluster, creates a PV resource on the target cluster pointing to the remote volume, and then mounts the remote volume on the target cluster. Applications running on the target cluster use the same remote volume that the source cluster was using. Click . Select a Copy method for each PV: Snapshot copy backs up and restores data using the cloud provider's snapshot functionality. It is significantly faster than Filesystem copy . Filesystem copy backs up the files on the source cluster and restores them on the target cluster. The file system copy method is required for direct volume migration. You can select Verify copy to verify data migrated with Filesystem copy . Data is verified by generating a checksum for each source file and checking the checksum after restoration. Data verification significantly reduces performance. Select a Target storage class . If you selected Filesystem copy , you can change the target storage class. Click . On the Migration options page, the Direct image migration option is selected if you specified an exposed image registry route for the source cluster. The Direct PV migration option is selected if you are migrating data with Filesystem copy . The direct migration options copy images and files directly from the source cluster to the target cluster. This option is much faster than copying images and files from the source cluster to the replication repository and then from the replication repository to the target cluster. Click . Optional: Click Add Hook to add a hook to the migration plan. A hook runs custom code. You can add up to four hooks to a single migration plan. Each hook runs during a different migration step. Enter the name of the hook to display in the web console. If the hook is an Ansible playbook, select Ansible playbook and click Browse to upload the playbook or paste the contents of the playbook in the field. Optional: Specify an Ansible runtime image if you are not using the default hook image. If the hook is not an Ansible playbook, select Custom container image and specify the image name and path. A custom container image can include Ansible playbooks. Select Source cluster or Target cluster . Enter the Service account name and the Service account namespace . Select the migration step for the hook: preBackup : Before the application workload is backed up on the source cluster postBackup : After the application workload is backed up on the source cluster preRestore : Before the application workload is restored on the target cluster postRestore : After the application workload is restored on the target cluster Click Add . Click Finish . The migration plan is displayed in the Migration plans list. Additional resources for persistent volume copy methods MTC file system copy method MTC snapshot copy method 9.2.5. Running a migration plan in the MTC web console You can migrate applications and data with the migration plan you created in the Migration Toolkit for Containers (MTC) web console. Note During migration, MTC sets the reclaim policy of migrated persistent volumes (PVs) to Retain on the target cluster. The Backup custom resource contains a PVOriginalReclaimPolicy annotation that indicates the original reclaim policy. You can manually restore the reclaim policy of the migrated PVs. Prerequisites The MTC web console must contain the following: Source cluster in a Ready state Target cluster in a Ready state Replication repository Valid migration plan Procedure Log in to the MTC web console and click Migration plans . Click the Options menu to a migration plan and select one of the following options under Migration : Stage copies data from the source cluster to the target cluster without stopping the application. Cutover stops the transactions on the source cluster and moves the resources to the target cluster. Optional: In the Cutover migration dialog, you can clear the Halt transactions on the source cluster during migration checkbox. State copies selected persistent volume claims (PVCs). Important Do not use state migration to migrate a namespace between clusters. Use stage or cutover migration instead. Select one or more PVCs in the State migration dialog and click Migrate . When the migration is complete, verify that the application migrated successfully in the OpenShift Container Platform web console: Click Home Projects . Click the migrated project to view its status. In the Routes section, click Location to verify that the application is functioning, if applicable. Click Workloads Pods to verify that the pods are running in the migrated namespace. Click Storage Persistent volumes to verify that the migrated persistent volumes are correctly provisioned.	[ "oc get -n openshift-migration route/migration -o go-template='https://{{ .spec.host }}'", "oc create token migration-controller -n openshift-migration", "eyJhbGciOiJSUzI1NiIsImtpZCI6IiJ9.eyJpc3MiOiJrdWJlcm5ldGVzL3NlcnZpY2VhY2NvdW50Iiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9uYW1lc3BhY2UiOiJtaWciLCJrdWJlcm5ldGVzLmlvL3NlcnZpY2VhY2NvdW50L3NlY3JldC5uYW1lIjoibWlnLXRva2VuLWs4dDJyIiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9zZXJ2aWNlLWFjY291bnQubmFtZSI6Im1pZyIsImt1YmVybmV0ZXMuaW8vc2VydmljZWFjY291bnQvc2VydmljZS1hY2NvdW50LnVpZCI6ImE1YjFiYWMwLWMxYmYtMTFlOS05Y2NiLTAyOWRmODYwYjMwOCIsInN1YiI6InN5c3RlbTpzZXJ2aWNlYWNjb3VudDptaWc6bWlnIn0.xqeeAINK7UXpdRqAtOj70qhBJPeMwmgLomV9iFxr5RoqUgKchZRG2J2rkqmPm6vr7K-cm7ibD1IBpdQJCcVDuoHYsFgV4mp9vgOfn9osSDp2TGikwNz4Az95e81xnjVUmzh-NjDsEpw71DH92iHV_xt2sTwtzftS49LpPW2LjrV0evtNBP_t_RfskdArt5VSv25eORl7zScqfe1CiMkcVbf2UqACQjo3LbkpfN26HAioO2oH0ECPiRzT0Xyh-KwFutJLS9Xgghyw-LD9kPKcE_xbbJ9Y4Rqajh7WdPYuB0Jd9DPVrslmzK-F6cgHHYoZEv0SvLQi-PO0rpDrcjOEQQ", "oc create route passthrough --service=docker-registry --port=5000 -n default", "oc create route passthrough --service=image-registry --port=5000 -n openshift-image-registry", "az group list", "{ \"id\": \"/subscriptions/...//resourceGroups/sample-rg-name\", \"location\": \"centralus\", \"name\": \"...\", \"properties\": { \"provisioningState\": \"Succeeded\" }, \"tags\": { \"kubernetes.io_cluster.sample-ld57c\": \"owned\", \"openshift_creationDate\": \"2019-10-25T23:28:57.988208+00:00\" }, \"type\": \"Microsoft.Resources/resourceGroups\" }," ]	https://docs.redhat.com/en/documentation/openshift_container_platform/4.14/html/migration_toolkit_for_containers/migrating-applications-with-mtc
5.9. Configuring Fencing Levels	5.9. Configuring Fencing Levels Pacemaker supports fencing nodes with multiple devices through a feature called fencing topologies. To implement topologies, create the individual devices as you normally would and then define one or more fencing levels in the fencing topology section in the configuration. Each level is attempted in ascending numeric order, starting at 1. If a device fails, processing terminates for the current level. No further devices in that level are exercised and the level is attempted instead. If all devices are successfully fenced, then that level has succeeded and no other levels are tried. The operation is finished when a level has passed (success), or all levels have been attempted (failed). Use the following command to add a fencing level to a node. The devices are given as a comma-separated list of stonith ids, which are attempted for the node at that level. The following command lists all of the fencing levels that are currently configured. In the following example, there are two fence devices configured for node rh7-2 : an ilo fence device called my_ilo and an apc fence device called my_apc . These commands sets up fence levels so that if the device my_ilo fails and is unable to fence the node, then Pacemaker will attempt to use the device my_apc . This example also shows the output of the pcs stonith level command after the levels are configured. The following command removes the fence level for the specified node and devices. If no nodes or devices are specified then the fence level you specify is removed from all nodes. The following command clears the fence levels on the specified node or stonith id. If you do not specify a node or stonith id, all fence levels are cleared. If you specify more than one stonith id, they must be separated by a comma and no spaces, as in the following example. The following command verifies that all fence devices and nodes specified in fence levels exist. As of Red Hat Enterprise Linux 7.4, you can specify nodes in fencing topology by a regular expression applied on a node name and by a node attribute and its value. For example, the following commands configure nodes node1 , node2 , and ` node3 to use fence devices apc1 and ` apc2 , and nodes ` node4 , node5 , and ` node6 to use fence devices apc3 and ` apc4 . The following commands yield the same results by using node attribute matching.	[ "pcs stonith level add level node devices", "pcs stonith level", "pcs stonith level add 1 rh7-2 my_ilo pcs stonith level add 2 rh7-2 my_apc pcs stonith level Node: rh7-2 Level 1 - my_ilo Level 2 - my_apc", "pcs stonith level remove level [ node_id ] [ stonith_id ] ... [ stonith_id ]", "pcs stonith level clear [ node \| stonith_id (s)]", "pcs stonith level clear dev_a,dev_b", "pcs stonith level verify", "pcs stonith level add 1 \"regexp%node[1-3]\" apc1,apc2 pcs stonith level add 1 \"regexp%node[4-6]\" apc3,apc4", "pcs node attribute node1 rack=1 pcs node attribute node2 rack=1 pcs node attribute node3 rack=1 pcs node attribute node4 rack=2 pcs node attribute node5 rack=2 pcs node attribute node6 rack=2 pcs stonith level add 1 attrib%rack=1 apc1,apc2 pcs stonith level add 1 attrib%rack=2 apc3,apc4" ]	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/7/html/high_availability_add-on_reference/s1-fencelevels-HAAR
4.3. Configuring Static Routes with GUI	4.3. Configuring Static Routes with GUI To set a static route, open the IPv4 or IPv6 settings window for the connection you want to configure. See Section 3.4.1, "Connecting to a Network Using the control-center GUI" for instructions on how to do that. Routes Address - Enter the IP address of a remote network, sub-net, or host. Netmask - The netmask or prefix length of the IP address entered above. Gateway - The IP address of the gateway leading to the remote network, sub-net, or host entered above. Metric - A network cost, a preference value to give to this route. Lower values will be preferred over higher values. Automatic When Automatic is ON , routes from RA or DHCP are used, but you can also add additional static routes. When OFF , only static routes you define are used. Use this connection only for resources on its network Select this check box to prevent the connection from becoming the default route. Typical examples are where a connection is a VPN tunnel or a leased line to a head office and you do not want any Internet-bound traffic to pass over the connection. Selecting this option means that only traffic specifically destined for routes learned automatically over the connection or entered here manually will be routed over the connection.	null	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/7/html/networking_guide/sec-configuring_static_routes_with_gui
Chapter 28. Ref	Chapter 28. Ref Overview The Ref expression language is really just a way to look up a custom Expression from the Registry . This is particular convenient to use in the XML DSL. The Ref language is part of camel-core . Static import To use the Ref language in your Java application code, include the following import statement in your Java source files: XML example For example, the splitter pattern can reference a custom expression using the Ref language, as follows: Java example The preceding route can also be implemented in the Java DSL, as follows:	[ "import static org.apache.camel.language.ref.RefLanguage.ref;", "<beans ...> <bean id=\" myExpression \" class=\"com.mycompany.MyCustomExpression\"/> <camelContext> <route> <from uri=\"seda:a\"/> <split> <ref> myExpression </ref> <to uri=\"mock:b\"/> </split> </route> </camelContext> </beans>", "from(\"seda:a\") .split().ref(\"myExpression\") .to(\"seda:b\");" ]	https://docs.redhat.com/en/documentation/red_hat_fuse/7.13/html/apache_camel_development_guide/ref
Chapter 3. Distributed tracing installation	Chapter 3. Distributed tracing installation 3.1. Installing distributed tracing You can install Red Hat OpenShift distributed tracing on OpenShift Container Platform in either of two ways: You can install Red Hat OpenShift distributed tracing as part of Red Hat OpenShift Service Mesh. Distributed tracing is included by default in the Service Mesh installation. To install Red Hat OpenShift distributed tracing as part of a service mesh, follow the Red Hat Service Mesh Installation instructions. You must install Red Hat OpenShift distributed tracing in the same namespace as your service mesh, that is, the ServiceMeshControlPlane and the Red Hat OpenShift distributed tracing resources must be in the same namespace. If you do not want to install a service mesh, you can use the Red Hat OpenShift distributed tracing Operators to install distributed tracing by itself. To install Red Hat OpenShift distributed tracing without a service mesh, use the following instructions. 3.1.1. Prerequisites Before you can install Red Hat OpenShift distributed tracing, review the installation activities, and ensure that you meet the prerequisites: Possess an active OpenShift Container Platform subscription on your Red Hat account. If you do not have a subscription, contact your sales representative for more information. Review the OpenShift Container Platform 4.7 overview . Install OpenShift Container Platform 4.7. Install OpenShift Container Platform 4.7 on AWS Install OpenShift Container Platform 4.7 on user-provisioned AWS Install OpenShift Container Platform 4.7 on bare metal Install OpenShift Container Platform 4.7 on vSphere Install the version of the OpenShift CLI ( oc ) that matches your OpenShift Container Platform version and add it to your path. An account with the cluster-admin role. 3.1.2. Red Hat OpenShift distributed tracing installation overview The steps for installing Red Hat OpenShift distributed tracing are as follows: Review the documentation and determine your deployment strategy. If your deployment strategy requires persistent storage, install the OpenShift Elasticsearch Operator via the OperatorHub. Install the Red Hat OpenShift distributed tracing platform Operator via the OperatorHub. Modify the custom resource YAML file to support your deployment strategy. Deploy one or more instances of Red Hat OpenShift distributed tracing platform to your OpenShift Container Platform environment. 3.1.3. Installing the OpenShift Elasticsearch Operator The default Red Hat OpenShift distributed tracing platform deployment uses in-memory storage because it is designed to be installed quickly for those evaluating Red Hat OpenShift distributed tracing, giving demonstrations, or using Red Hat OpenShift distributed tracing platform in a test environment. If you plan to use Red Hat OpenShift distributed tracing platform in production, you must install and configure a persistent storage option, in this case, Elasticsearch. Prerequisites You have access to the OpenShift Container Platform web console. You have access to the cluster as a user with the cluster-admin role. If you use Red Hat OpenShift Dedicated, you must have an account with the dedicated-admin role. Warning Do not install Community versions of the Operators. Community Operators are not supported. Note If you have already installed the OpenShift Elasticsearch Operator as part of OpenShift Logging, you do not need to install the OpenShift Elasticsearch Operator again. The Red Hat OpenShift distributed tracing platform Operator creates the Elasticsearch instance using the installed OpenShift Elasticsearch Operator. Procedure Log in to the OpenShift Container Platform web console as a user with the cluster-admin role. If you use Red Hat OpenShift Dedicated, you must have an account with the dedicated-admin role. Navigate to Operators OperatorHub . Type Elasticsearch into the filter box to locate the OpenShift Elasticsearch Operator. Click the OpenShift Elasticsearch Operator provided by Red Hat to display information about the Operator. Click Install . On the Install Operator page, select the stable Update Channel. This automatically updates your Operator as new versions are released. Accept the default All namespaces on the cluster (default) . This installs the Operator in the default openshift-operators-redhat project and makes the Operator available to all projects in the cluster. Note The Elasticsearch installation requires the openshift-operators-redhat namespace for the OpenShift Elasticsearch Operator. The other Red Hat OpenShift distributed tracing Operators are installed in the openshift-operators namespace. Accept the default Automatic approval strategy. By accepting the default, when a new version of this Operator is available, 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. Note The Manual approval strategy requires a user with appropriate credentials to approve the Operator install and subscription process. Click Install . On the Installed Operators page, select the openshift-operators-redhat project. Wait until you see that the OpenShift Elasticsearch Operator shows a status of "InstallSucceeded" before continuing. 3.1.4. Installing the Red Hat OpenShift distributed tracing platform Operator To install Red Hat OpenShift distributed tracing platform, you use the OperatorHub to install the Red Hat OpenShift distributed tracing platform Operator. By default, the Operator is installed in the openshift-operators project. Prerequisites You have access to the OpenShift Container Platform web console. You have access to the cluster as a user with the cluster-admin role. If you use Red Hat OpenShift Dedicated, you must have an account with the dedicated-admin role. If you require persistent storage, you must also install the OpenShift Elasticsearch Operator before installing the Red Hat OpenShift distributed tracing platform Operator. Warning Do not install Community versions of the Operators. Community Operators are not supported. Procedure Log in to the OpenShift Container Platform web console as a user with the cluster-admin role. If you use Red Hat OpenShift Dedicated, you must have an account with the dedicated-admin role. Navigate to Operators OperatorHub . Type distributing tracing platform into the filter to locate the Red Hat OpenShift distributed tracing platform Operator. Click the Red Hat OpenShift distributed tracing platform Operator provided by Red Hat to display information about the Operator. Click Install . On the Install Operator page, select the stable Update Channel. This automatically updates your Operator as new versions are released. Accept the default All namespaces on the cluster (default) . This installs the Operator in the default openshift-operators project and makes the Operator available to all projects in the cluster. Accept the default Automatic approval strategy. By accepting the default, when a new version of this Operator is available, 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. Note The Manual approval strategy requires a user with appropriate credentials to approve the Operator install and subscription process. Click Install . Navigate to Operators Installed Operators . On the Installed Operators page, select the openshift-operators project. Wait until you see that the Red Hat OpenShift distributed tracing platform Operator shows a status of "Succeeded" before continuing. 3.1.5. Installing the Red Hat OpenShift distributed tracing data collection Operator Important The Red Hat OpenShift distributed tracing data collection Operator is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process. For more information about the support scope of Red Hat Technology Preview features, see https://access.redhat.com/support/offerings/techpreview/ . To install Red Hat OpenShift distributed tracing data collection, you use the OperatorHub to install the Red Hat OpenShift distributed tracing data collection Operator. By default, the Operator is installed in the openshift-operators project. Prerequisites You have access to the OpenShift Container Platform web console. You have access to the cluster as a user with the cluster-admin role. If you use Red Hat OpenShift Dedicated, you must have an account with the dedicated-admin role. Warning Do not install Community versions of the Operators. Community Operators are not supported. Procedure Log in to the OpenShift Container Platform web console as a user with the cluster-admin role. If you use Red Hat OpenShift Dedicated, you must have an account with the dedicated-admin role. Navigate to Operators OperatorHub . Type distributing tracing data collection into the filter to locate the Red Hat OpenShift distributed tracing data collection Operator. Click the Red Hat OpenShift distributed tracing data collection Operator provided by Red Hat to display information about the Operator. Click Install . On the Install Operator page, accept the default stable Update channel. This automatically updates your Operator as new versions are released. Accept the default All namespaces on the cluster (default) . This installs the Operator in the default openshift-operators project and makes the Operator available to all projects in the cluster. Accept the default Automatic approval strategy. By accepting the default, when a new version of this Operator is available, 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. Note The Manual approval strategy requires a user with appropriate credentials to approve the Operator install and subscription process. Click Install . Navigate to Operators Installed Operators . On the Installed Operators page, select the openshift-operators project. Wait until you see that the Red Hat OpenShift distributed tracing data collection Operator shows a status of "Succeeded" before continuing. 3.2. Configuring and deploying distributed tracing The Red Hat OpenShift distributed tracing platform Operator uses a custom resource definition (CRD) file that defines the architecture and configuration settings to be used when creating and deploying the distributed tracing platform resources. You can either install the default configuration or modify the file to better suit your business requirements. Red Hat OpenShift distributed tracing platform has predefined deployment strategies. You specify a deployment strategy in the custom resource file. When you create a distributed tracing platform instance the Operator uses this configuration file to create the objects necessary for the deployment. Jaeger custom resource file showing deployment strategy apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: MyConfigFile spec: strategy: production 1 1 The Red Hat OpenShift distributed tracing platform Operator currently supports the following deployment strategies: allInOne (Default) - This strategy is intended for development, testing, and demo purposes; it is not intended for production use. The main backend components, Agent, Collector, and Query service, are all packaged into a single executable which is configured, by default. to use in-memory storage. Note In-memory storage is not persistent, which means that if the distributed tracing platform instance shuts down, restarts, or is replaced, that your trace data will be lost. And in-memory storage cannot be scaled, since each pod has its own memory. For persistent storage, you must use the production or streaming strategies, which use Elasticsearch as the default storage. production - The production strategy is intended for production environments, where long term storage of trace data is important, as well as a more scalable and highly available architecture is required. Each of the backend components is therefore deployed separately. The Agent can be injected as a sidecar on the instrumented application. The Query and Collector services are configured with a supported storage type - currently Elasticsearch. Multiple instances of each of these components can be provisioned as required for performance and resilience purposes. streaming - The streaming strategy is designed to augment the production strategy by providing a streaming capability that effectively sits between the Collector and the Elasticsearch backend storage. This provides the benefit of reducing the pressure on the backend storage, under high load situations, and enables other trace post-processing capabilities to tap into the real time span data directly from the streaming platform ( AMQ Streams / Kafka ). Note The streaming strategy requires an additional Red Hat subscription for AMQ Streams. Note The streaming deployment strategy is currently unsupported on IBM Z. Note There are two ways to install and use Red Hat OpenShift distributed tracing, as part of a service mesh or as a stand alone component. If you have installed distributed tracing as part of Red Hat OpenShift Service Mesh, you can perform basic configuration as part of the ServiceMeshControlPlane but for completely control you should configure a Jaeger CR and then reference your distributed tracing configuration file in the ServiceMeshControlPlane . 3.2.1. Deploying the distributed tracing default strategy from the web console The custom resource definition (CRD) defines the configuration used when you deploy an instance of Red Hat OpenShift distributed tracing. The default CR is named jaeger-all-in-one-inmemory and it is configured with minimal resources to ensure that you can successfully install it on a default OpenShift Container Platform installation. You can use this default configuration to create a Red Hat OpenShift distributed tracing platform instance that uses the AllInOne deployment strategy, or you can define your own custom resource file. Note In-memory storage is not persistent. If the Jaeger pod shuts down, restarts, or is replaced, your trace data will be lost. For persistent storage, you must use the production or streaming strategies, which use Elasticsearch as the default storage. Prerequisites The Red Hat OpenShift distributed tracing platform Operator has been installed. You have reviewed the instructions for how to customize the deployment. You have access to the cluster as a user with the cluster-admin role. Procedure Log in to the OpenShift Container Platform web console as a user with the cluster-admin role. Create a new project, for example tracing-system . Note If you are installing as part of Service Mesh, the distributed tracing resources must be installed in the same namespace as the ServiceMeshControlPlane resource, for example istio-system . Navigate to Home Projects . Click Create Project . Enter tracing-system in the Name field. Click Create . Navigate to Operators Installed Operators . If necessary, select tracing-system from the Project menu. You may have to wait a few moments for the Operators to be copied to the new project. Click the Red Hat OpenShift distributed tracing platform Operator. On the Details tab, under Provided APIs , the Operator provides a single link. Under Jaeger , click Create Instance . On the Create Jaeger page, to install using the defaults, click Create to create the distributed tracing platform instance. On the Jaegers page, click the name of the distributed tracing platform instance, for example, jaeger-all-in-one-inmemory . On the Jaeger Details page, click the Resources tab. Wait until the pod has a status of "Running" before continuing. 3.2.1.1. Deploying the distributed tracing default strategy from the CLI Follow this procedure to create an instance of distributed tracing platform from the command line. Prerequisites The Red Hat OpenShift distributed tracing platform Operator has been installed and verified. You have reviewed the instructions for how to customize the deployment. You have access to the OpenShift CLI ( oc ) that matches your OpenShift Container Platform version. You have access to the cluster as a user with the cluster-admin role. Procedure Log in to the OpenShift Container Platform CLI as a user with the cluster-admin role. USD oc login --username=<NAMEOFUSER> https://<HOSTNAME>:8443 Create a new project named tracing-system . USD oc new-project tracing-system Create a custom resource file named jaeger.yaml that contains the following text: Example jaeger-all-in-one.yaml apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: jaeger-all-in-one-inmemory Run the following command to deploy distributed tracing platform: USD oc create -n tracing-system -f jaeger.yaml Run the following command to watch the progress of the pods during the installation process: USD oc get pods -n tracing-system -w After the installation process has completed, you should see output similar to the following example: NAME READY STATUS RESTARTS AGE jaeger-all-in-one-inmemory-cdff7897b-qhfdx 2/2 Running 0 24s 3.2.2. Deploying the distributed tracing production strategy from the web console The production deployment strategy is intended for production environments that require a more scalable and highly available architecture, and where long-term storage of trace data is important. Prerequisites The OpenShift Elasticsearch Operator has been installed. The Red Hat OpenShift distributed tracing platform Operator has been installed. You have reviewed the instructions for how to customize the deployment. You have access to the cluster as a user with the cluster-admin role. Procedure Log in to the OpenShift Container Platform web console as a user with the cluster-admin role. Create a new project, for example tracing-system . Note If you are installing as part of Service Mesh, the distributed tracing resources must be installed in the same namespace as the ServiceMeshControlPlane resource, for example istio-system . Navigate to Home Projects . Click Create Project . Enter tracing-system in the Name field. Click Create . Navigate to Operators Installed Operators . If necessary, select tracing-system from the Project menu. You may have to wait a few moments for the Operators to be copied to the new project. Click the Red Hat OpenShift distributed tracing platform Operator. On the Overview tab, under Provided APIs , the Operator provides a single link. Under Jaeger , click Create Instance . On the Create Jaeger page, replace the default all-in-one YAML text with your production YAML configuration, for example: Example jaeger-production.yaml file with Elasticsearch apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: jaeger-production namespace: spec: strategy: production ingress: security: oauth-proxy storage: type: elasticsearch elasticsearch: nodeCount: 3 redundancyPolicy: SingleRedundancy esIndexCleaner: enabled: true numberOfDays: 7 schedule: 55 23 * * * esRollover: schedule: '/30 * * ' Click Create to create the distributed tracing platform instance. On the Jaegers page, click the name of the distributed tracing platform instance, for example, jaeger-prod-elasticsearch . On the Jaeger Details page, click the Resources tab. Wait until all the pods have a status of "Running" before continuing. 3.2.2.1. Deploying the distributed tracing production strategy from the CLI Follow this procedure to create an instance of distributed tracing platform from the command line. Prerequisites The OpenShift Elasticsearch Operator has been installed. The Red Hat OpenShift distributed tracing platform Operator has been installed. You have reviewed the instructions for how to customize the deployment. You have access to the OpenShift CLI ( oc ) that matches your OpenShift Container Platform version. You have access to the cluster as a user with the cluster-admin role. Procedure Log in to the OpenShift Container Platform CLI as a user with the cluster-admin role. USD oc login --username=<NAMEOFUSER> https://<HOSTNAME>:8443 Create a new project named tracing-system . USD oc new-project tracing-system Create a custom resource file named jaeger-production.yaml that contains the text of the example file in the procedure. Run the following command to deploy distributed tracing platform: USD oc create -n tracing-system -f jaeger-production.yaml Run the following command to watch the progress of the pods during the installation process: USD oc get pods -n tracing-system -w After the installation process has completed, you should see output similar to the following example: NAME READY STATUS RESTARTS AGE elasticsearch-cdm-jaegersystemjaegerproduction-1-6676cf568gwhlw 2/2 Running 0 10m elasticsearch-cdm-jaegersystemjaegerproduction-2-bcd4c8bf5l6g6w 2/2 Running 0 10m elasticsearch-cdm-jaegersystemjaegerproduction-3-844d6d9694hhst 2/2 Running 0 10m jaeger-production-collector-94cd847d-jwjlj 1/1 Running 3 8m32s jaeger-production-query-5cbfbd499d-tv8zf 3/3 Running 3 8m32s 3.2.3. Deploying the distributed tracing streaming strategy from the web console The streaming deployment strategy is intended for production environments that require a more scalable and highly available architecture, and where long-term storage of trace data is important. The streaming strategy provides a streaming capability that sits between the Collector and the Elasticsearch storage. This reduces the pressure on the storage under high load situations, and enables other trace post-processing capabilities to tap into the real-time span data directly from the Kafka streaming platform. Note The streaming strategy requires an additional Red Hat subscription for AMQ Streams. If you do not have an AMQ Streams subscription, contact your sales representative for more information. Note The streaming deployment strategy is currently unsupported on IBM Z. Prerequisites The AMQ Streams Operator has been installed. If using version 1.4.0 or higher you can use self-provisioning. Otherwise you must create the Kafka instance. The Red Hat OpenShift distributed tracing platform Operator has been installed. You have reviewed the instructions for how to customize the deployment. You have access to the cluster as a user with the cluster-admin role. Procedure Log in to the OpenShift Container Platform web console as a user with the cluster-admin role. Create a new project, for example tracing-system . Note If you are installing as part of Service Mesh, the distributed tracing resources must be installed in the same namespace as the ServiceMeshControlPlane resource, for example istio-system . Navigate to Home Projects . Click Create Project . Enter tracing-system in the Name field. Click Create . Navigate to Operators Installed Operators . If necessary, select tracing-system from the Project menu. You may have to wait a few moments for the Operators to be copied to the new project. Click the Red Hat OpenShift distributed tracing platform Operator. On the Overview tab, under Provided APIs , the Operator provides a single link. Under Jaeger , click Create Instance . On the Create Jaeger page, replace the default all-in-one YAML text with your streaming YAML configuration, for example: Example jaeger-streaming.yaml file apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: jaeger-streaming spec: strategy: streaming collector: options: kafka: producer: topic: jaeger-spans #Note: If brokers are not defined,AMQStreams 1.4.0+ will self-provision Kafka. brokers: my-cluster-kafka-brokers.kafka:9092 storage: type: elasticsearch ingester: options: kafka: consumer: topic: jaeger-spans brokers: my-cluster-kafka-brokers.kafka:9092 Click Create to create the distributed tracing platform instance. On the Jaegers page, click the name of the distributed tracing platform instance, for example, jaeger-streaming . On the Jaeger Details page, click the Resources tab. Wait until all the pods have a status of "Running" before continuing. 3.2.3.1. Deploying the distributed tracing streaming strategy from the CLI Follow this procedure to create an instance of distributed tracing platform from the command line. Prerequisites The AMQ Streams Operator has been installed. If using version 1.4.0 or higher you can use self-provisioning. Otherwise you must create the Kafka instance. The Red Hat OpenShift distributed tracing platform Operator has been installed. You have reviewed the instructions for how to customize the deployment. You have access to the OpenShift CLI ( oc ) that matches your OpenShift Container Platform version. You have access to the cluster as a user with the cluster-admin role. Procedure Log in to the OpenShift Container Platform CLI as a user with the cluster-admin role. USD oc login --username=<NAMEOFUSER> https://<HOSTNAME>:8443 Create a new project named tracing-system . USD oc new-project tracing-system Create a custom resource file named jaeger-streaming.yaml that contains the text of the example file in the procedure. Run the following command to deploy Jaeger: USD oc create -n tracing-system -f jaeger-streaming.yaml Run the following command to watch the progress of the pods during the installation process: USD oc get pods -n tracing-system -w After the installation process has completed, you should see output similar to the following example: NAME READY STATUS RESTARTS AGE elasticsearch-cdm-jaegersystemjaegerstreaming-1-697b66d6fcztcnn 2/2 Running 0 5m40s elasticsearch-cdm-jaegersystemjaegerstreaming-2-5f4b95c78b9gckz 2/2 Running 0 5m37s elasticsearch-cdm-jaegersystemjaegerstreaming-3-7b6d964576nnz97 2/2 Running 0 5m5s jaeger-streaming-collector-6f6db7f99f-rtcfm 1/1 Running 0 80s jaeger-streaming-entity-operator-6b6d67cc99-4lm9q 3/3 Running 2 2m18s jaeger-streaming-ingester-7d479847f8-5h8kc 1/1 Running 0 80s jaeger-streaming-kafka-0 2/2 Running 0 3m1s jaeger-streaming-query-65bf5bb854-ncnc7 3/3 Running 0 80s jaeger-streaming-zookeeper-0 2/2 Running 0 3m39s 3.2.4. Validating your deployment 3.2.4.1. Accessing the Jaeger console To access the Jaeger console you must have either Red Hat OpenShift Service Mesh or Red Hat OpenShift distributed tracing installed, and Red Hat OpenShift distributed tracing platform installed, configured, and deployed. The installation process creates a route to access the Jaeger console. If you know the URL for the Jaeger console, you can access it directly. If you do not know the URL, use the following directions. Procedure from OpenShift console Log in to the OpenShift Container Platform web console as a user with cluster-admin rights. If you use Red Hat OpenShift Dedicated, you must have an account with the dedicated-admin role. Navigate to Networking Routes . On the Routes page, select the control plane project, for example tracing-system , from the Namespace menu. The Location column displays the linked address for each route. If necessary, use the filter to find the jaeger route. Click the route Location to launch the console. Click Log In With OpenShift . Procedure from the CLI Log in to the OpenShift Container Platform CLI as a user with the cluster-admin role. If you use Red Hat OpenShift Dedicated, you must have an account with the dedicated-admin role. USD oc login --username=<NAMEOFUSER> https://<HOSTNAME>:6443 To query for details of the route using the command line, enter the following command. In this example, tracing-system is the control plane namespace. USD export JAEGER_URL=USD(oc get route -n tracing-system jaeger -o jsonpath='{.spec.host}') Launch a browser and navigate to https://<JAEGER_URL> , where <JAEGER_URL> is the route that you discovered in the step. Log in using the same user name and password that you use to access the OpenShift Container Platform console. If you have added services to the service mesh and have generated traces, you can use the filters and Find Traces button to search your trace data. If you are validating the console installation, there is no trace data to display. 3.2.5. Customizing your deployment 3.2.5.1. Deployment best practices Red Hat OpenShift distributed tracing instance names must be unique. If you want to have multiple Red Hat OpenShift distributed tracing platform instances and are using sidecar injected agents, then the Red Hat OpenShift distributed tracing platform instances should have unique names, and the injection annotation should explicitly specify the Red Hat OpenShift distributed tracing platform instance name the tracing data should be reported to. If you have a multitenant implementation and tenants are separated by namespaces, deploy a Red Hat OpenShift distributed tracing platform instance to each tenant namespace. Agent as a daemonset is not supported for multitenant installations or Red Hat OpenShift Dedicated. Agent as a sidecar is the only supported configuration for these use cases. If you are installing distributed tracing as part of Red Hat OpenShift Service Mesh, the distributed tracing resources must be installed in the same namespace as the ServiceMeshControlPlane resource. For information about configuring persistent storage, see Understanding persistent storage and the appropriate configuration topic for your chosen storage option. 3.2.5.2. Distributed tracing default configuration options The Jaeger custom resource (CR) defines the architecture and settings to be used when creating the distributed tracing platform resources. You can modify these parameters to customize your distributed tracing platform implementation to your business needs. Jaeger generic YAML example apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: name spec: strategy: <deployment_strategy> allInOne: options: {} resources: {} agent: options: {} resources: {} collector: options: {} resources: {} sampling: options: {} storage: type: options: {} query: options: {} resources: {} ingester: options: {} resources: {} options: {} Table 3.1. Jaeger parameters Parameter Description Values Default value apiVersion: API version to use when creating the object. jaegertracing.io/v1 jaegertracing.io/v1 kind: Defines the kind of Kubernetes object to create. jaeger metadata: Data that helps uniquely identify the object, including a name string, UID , and optional namespace . OpenShift Container Platform automatically generates the UID and completes the namespace with the name of the project where the object is created. name: Name for the object. The name of your distributed tracing platform instance. jaeger-all-in-one-inmemory spec: Specification for the object to be created. Contains all of the configuration parameters for your distributed tracing platform instance. When a common definition for all Jaeger components is required, it is defined under the spec node. When the definition relates to an individual component, it is placed under the spec/<component> node. N/A strategy: Jaeger deployment strategy allInOne , production , or streaming allInOne allInOne: Because the allInOne image deploys the Agent, Collector, Query, Ingester, and Jaeger UI in a single pod, configuration for this deployment must nest component configuration under the allInOne parameter. agent: Configuration options that define the Agent. collector: Configuration options that define the Jaeger Collector. sampling: Configuration options that define the sampling strategies for tracing. storage: Configuration options that define the storage. All storage-related options must be placed under storage , rather than under the allInOne or other component options. query: Configuration options that define the Query service. ingester: Configuration options that define the Ingester service. The following example YAML is the minimum required to create a Red Hat OpenShift distributed tracing platform deployment using the default settings. Example minimum required dist-tracing-all-in-one.yaml apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: jaeger-all-in-one-inmemory 3.2.5.3. Jaeger Collector configuration options The Jaeger Collector is the component responsible for receiving the spans that were captured by the tracer and writing them to persistent Elasticsearch storage when using the production strategy, or to AMQ Streams when using the streaming strategy. The Collectors are stateless and thus many instances of Jaeger Collector can be run in parallel. Collectors require almost no configuration, except for the location of the Elasticsearch cluster. Table 3.2. Parameters used by the Operator to define the Jaeger Collector Parameter Description Values Specifies the number of Collector replicas to create. Integer, for example, 5 Table 3.3. Configuration parameters passed to the Collector Parameter Description Values Configuration options that define the Jaeger Collector. The number of workers pulling from the queue. Integer, for example, 50 The size of the Collector queue. Integer, for example, 2000 The topic parameter identifies the Kafka configuration used by the Collector to produce the messages, and the Ingester to consume the messages. Label for the producer. Identifies the Kafka configuration used by the Collector to produce the messages. If brokers are not specified, and you have AMQ Streams 1.4.0+ installed, the Red Hat OpenShift distributed tracing platform Operator will self-provision Kafka. Logging level for the Collector. Possible values: debug , info , warn , error , fatal , panic . 3.2.5.4. Distributed tracing sampling configuration options The Red Hat OpenShift distributed tracing platform Operator can be used to define sampling strategies that will be supplied to tracers that have been configured to use a remote sampler. While all traces are generated, only a few are sampled. Sampling a trace marks the trace for further processing and storage. Note This is not relevant if a trace was started by the Envoy proxy, as the sampling decision is made there. The Jaeger sampling decision is only relevant when the trace is started by an application using the client. When a service receives a request that contains no trace context, the client starts a new trace, assigns it a random trace ID, and makes a sampling decision based on the currently installed sampling strategy. The sampling decision propagates to all subsequent requests in the trace so that other services are not making the sampling decision again. distributed tracing platform libraries support the following samplers: Probabilistic - The sampler makes a random sampling decision with the probability of sampling equal to the value of the sampling.param property. For example, using sampling.param=0.1 samples approximately 1 in 10 traces. Rate Limiting - The sampler uses a leaky bucket rate limiter to ensure that traces are sampled with a certain constant rate. For example, using sampling.param=2.0 samples requests with the rate of 2 traces per second. Table 3.4. Jaeger sampling options Parameter Description Values Default value Configuration options that define the sampling strategies for tracing. If you do not provide configuration, the Collectors will return the default probabilistic sampling policy with 0.001 (0.1%) probability for all services. Sampling strategy to use. See descriptions above. Valid values are probabilistic , and ratelimiting . probabilistic Parameters for the selected sampling strategy. Decimal and integer values (0, .1, 1, 10) 1 This example defines a default sampling strategy that is probabilistic, with a 50% chance of the trace instances being sampled. Probabilistic sampling example apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: with-sampling spec: sampling: options: default_strategy: type: probabilistic param: 0.5 service_strategies: - service: alpha type: probabilistic param: 0.8 operation_strategies: - operation: op1 type: probabilistic param: 0.2 - operation: op2 type: probabilistic param: 0.4 - service: beta type: ratelimiting param: 5 If there are no user-supplied configurations, the distributed tracing platform uses the following settings: Default sampling spec: sampling: options: default_strategy: type: probabilistic param: 1 3.2.5.5. Distributed tracing storage configuration options You configure storage for the Collector, Ingester, and Query services under spec.storage . Multiple instances of each of these components can be provisioned as required for performance and resilience purposes. Table 3.5. General storage parameters used by the Red Hat OpenShift distributed tracing platform Operator to define distributed tracing storage Parameter Description Values Default value Type of storage to use for the deployment. memory or elasticsearch . Memory storage is only appropriate for development, testing, demonstrations, and proof of concept environments as the data does not persist if the pod is shut down. For production environments distributed tracing platform supports Elasticsearch for persistent storage. memory Name of the secret, for example tracing-secret . N/A Configuration options that define the storage. Table 3.6. Elasticsearch index cleaner parameters Parameter Description Values Default value When using Elasticsearch storage, by default a job is created to clean old traces from the index. This parameter enables or disables the index cleaner job. true / false true Number of days to wait before deleting an index. Integer value 7 Defines the schedule for how often to clean the Elasticsearch index. Cron expression "55 23 * " 3.2.5.5.1. Auto-provisioning an Elasticsearch instance When you deploy a Jaeger custom resource, the Red Hat OpenShift distributed tracing platform Operator uses the OpenShift Elasticsearch Operator to create an Elasticsearch cluster based on the configuration provided in the storage section of the custom resource file. The Red Hat OpenShift distributed tracing platform Operator will provision Elasticsearch if the following configurations are set: spec.storage:type is set to elasticsearch spec.storage.elasticsearch.doNotProvision set to false spec.storage.options.es.server-urls is not defined, that is, there is no connection to an Elasticsearch instance that was not provisioned by the Red Hat Elasticsearch Operator. When provisioning Elasticsearch, the Red Hat OpenShift distributed tracing platform Operator sets the Elasticsearch custom resource name to the value of spec.storage.elasticsearch.name from the Jaeger custom resource. If you do not specify a value for spec.storage.elasticsearch.name , the Operator uses elasticsearch . Restrictions You can have only one distributed tracing platform with self-provisioned Elasticsearch instance per namespace. The Elasticsearch cluster is meant to be dedicated for a single distributed tracing platform instance. There can be only one Elasticsearch per namespace. Note If you already have installed Elasticsearch as part of OpenShift Logging, the Red Hat OpenShift distributed tracing platform Operator can use the installed OpenShift Elasticsearch Operator to provision storage. The following configuration parameters are for a self-provisioned Elasticsearch instance, that is an instance created by the Red Hat OpenShift distributed tracing platform Operator using the OpenShift Elasticsearch Operator. You specify configuration options for self-provisioned Elasticsearch under spec:storage:elasticsearch in your configuration file. Table 3.7. Elasticsearch resource configuration parameters Parameter Description Values Default value Use to specify whether or not an Elasticsearch instance should be provisioned by the Red Hat OpenShift distributed tracing platform Operator. true / false true Name of the Elasticsearch instance. The Red Hat OpenShift distributed tracing platform Operator uses the Elasticsearch instance specified in this parameter to connect to Elasticsearch. string elasticsearch Number of Elasticsearch nodes. For high availability use at least 3 nodes. Do not use 2 nodes as "split brain" problem can happen. Integer value. For example, Proof of concept = 1, Minimum deployment =3 3 Number of central processing units for requests, based on your environment's configuration. Specified in cores or millicores, for example, 200m, 0.5, 1. For example, Proof of concept = 500m, Minimum deployment =1 1 Available memory for requests, based on your environment's configuration. Specified in bytes, for example, 200Ki, 50Mi, 5Gi. For example, Proof of concept = 1Gi, Minimum deployment = 16Gi 16Gi Limit on number of central processing units, based on your environment's configuration. Specified in cores or millicores, for example, 200m, 0.5, 1. For example, Proof of concept = 500m, Minimum deployment =1 Available memory limit based on your environment's configuration. Specified in bytes, for example, 200Ki, 50Mi, 5Gi. For example, Proof of concept = 1Gi, Minimum deployment = 16Gi* Data replication policy defines how Elasticsearch shards are replicated across data nodes in the cluster. If not specified, the Red Hat OpenShift distributed tracing platform Operator automatically determines the most appropriate replication based on number of nodes. ZeroRedundancy (no replica shards), SingleRedundancy (one replica shard), MultipleRedundancy (each index is spread over half of the Data nodes), FullRedundancy (each index is fully replicated on every Data node in the cluster). Use to specify whether or not distributed tracing platform should use the certificate management feature of the Red Hat Elasticsearch Operator. This feature was added to logging subsystem for Red Hat OpenShift 5.2 in OpenShift Container Platform 4.7 and is the preferred setting for new Jaeger deployments. true / false true Each Elasticsearch node can operate with a lower memory setting though this is NOT recommended for production deployments. For production use, you should have no less than 16Gi allocated to each pod by default, but preferably allocate as much as you can, up to 64Gi per pod. Production storage example apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: simple-prod spec: strategy: production storage: type: elasticsearch elasticsearch: nodeCount: 3 resources: requests: cpu: 1 memory: 16Gi limits: memory: 16Gi Storage example with persistent storage: apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: simple-prod spec: strategy: production storage: type: elasticsearch elasticsearch: nodeCount: 1 storage: 1 storageClassName: gp2 size: 5Gi resources: requests: cpu: 200m memory: 4Gi limits: memory: 4Gi redundancyPolicy: ZeroRedundancy 1 Persistent storage configuration. In this case AWS gp2 with 5Gi size. When no value is specified, distributed tracing platform uses emptyDir . The OpenShift Elasticsearch Operator provisions PersistentVolumeClaim and PersistentVolume which are not removed with distributed tracing platform instance. You can mount the same volumes if you create a distributed tracing platform instance with the same name and namespace. 3.2.5.5.2. Connecting to an existing Elasticsearch instance You can use an existing Elasticsearch cluster for storage with distributed tracing. An existing Elasticsearch cluster, also known as an external Elasticsearch instance, is an instance that was not installed by the Red Hat OpenShift distributed tracing platform Operator or by the Red Hat Elasticsearch Operator. When you deploy a Jaeger custom resource, the Red Hat OpenShift distributed tracing platform Operator will not provision Elasticsearch if the following configurations are set: spec.storage.elasticsearch.doNotProvision set to true spec.storage.options.es.server-urls has a value spec.storage.elasticsearch.name has a value, or if the Elasticsearch instance name is elasticsearch . The Red Hat OpenShift distributed tracing platform Operator uses the Elasticsearch instance specified in spec.storage.elasticsearch.name to connect to Elasticsearch. Restrictions You cannot share or reuse a OpenShift Container Platform logging Elasticsearch instance with distributed tracing platform. The Elasticsearch cluster is meant to be dedicated for a single distributed tracing platform instance. Note Red Hat does not provide support for your external Elasticsearch instance. You can review the tested integrations matrix on the Customer Portal . The following configuration parameters are for an already existing Elasticsearch instance, also known as an external Elasticsearch instance. In this case, you specify configuration options for Elasticsearch under spec:storage:options:es in your custom resource file. Table 3.8. General ES configuration parameters Parameter Description Values Default value URL of the Elasticsearch instance. The fully-qualified domain name of the Elasticsearch server. http://elasticsearch.<namespace>.svc:9200 The maximum document count to return from an Elasticsearch query. This will also apply to aggregations. If you set both es.max-doc-count and es.max-num-spans , Elasticsearch will use the smaller value of the two. 10000 [ Deprecated - Will be removed in a future release, use es.max-doc-count instead.] The maximum number of spans to fetch at a time, per query, in Elasticsearch. If you set both es.max-num-spans and es.max-doc-count , Elasticsearch will use the smaller value of the two. 10000 The maximum lookback for spans in Elasticsearch. 72h0m0s The sniffer configuration for Elasticsearch. The client uses the sniffing process to find all nodes automatically. Disabled by default. true / false false Option to enable TLS when sniffing an Elasticsearch Cluster. The client uses the sniffing process to find all nodes automatically. Disabled by default true / false false Timeout used for queries. When set to zero there is no timeout. 0s The username required by Elasticsearch. The basic authentication also loads CA if it is specified. See also es.password . The password required by Elasticsearch. See also, es.username . The major Elasticsearch version. If not specified, the value will be auto-detected from Elasticsearch. 0 Table 3.9. ES data replication parameters Parameter Description Values Default value The number of replicas per index in Elasticsearch. 1 The number of shards per index in Elasticsearch. 5 Table 3.10. ES index configuration parameters Parameter Description Values Default value Automatically create index templates at application startup when set to true . When templates are installed manually, set to false . true / false true Optional prefix for distributed tracing platform indices. For example, setting this to "production" creates indices named "production-tracing-". Table 3.11. ES bulk processor configuration parameters Parameter Description Values Default value The number of requests that can be added to the queue before the bulk processor decides to commit updates to disk. 1000 A time.Duration after which bulk requests are committed, regardless of other thresholds. To disable the bulk processor flush interval, set this to zero. 200ms The number of bytes that the bulk requests can take up before the bulk processor decides to commit updates to disk. 5000000 The number of workers that are able to receive and commit bulk requests to Elasticsearch. 1 Table 3.12. ES TLS configuration parameters Parameter Description Values Default value Path to a TLS Certification Authority (CA) file used to verify the remote servers. Will use the system truststore by default. Path to a TLS Certificate file, used to identify this process to the remote servers. Enable transport layer security (TLS) when talking to the remote servers. Disabled by default. true / false false Path to a TLS Private Key file, used to identify this process to the remote servers. Override the expected TLS server name in the certificate of the remote servers. Path to a file containing the bearer token. This flag also loads the Certification Authority (CA) file if it is specified. Table 3.13. ES archive configuration parameters Parameter Description Values Default value The number of requests that can be added to the queue before the bulk processor decides to commit updates to disk. 0 A time.Duration after which bulk requests are committed, regardless of other thresholds. To disable the bulk processor flush interval, set this to zero. 0s The number of bytes that the bulk requests can take up before the bulk processor decides to commit updates to disk. 0 The number of workers that are able to receive and commit bulk requests to Elasticsearch. 0 Automatically create index templates at application startup when set to true . When templates are installed manually, set to false . true / false false Enable extra storage. true / false false Optional prefix for distributed tracing platform indices. For example, setting this to "production" creates indices named "production-tracing-*". The maximum document count to return from an Elasticsearch query. This will also apply to aggregations. 0 [ Deprecated - Will be removed in a future release, use es-archive.max-doc-count instead.] The maximum number of spans to fetch at a time, per query, in Elasticsearch. 0 The maximum lookback for spans in Elasticsearch. 0s The number of replicas per index in Elasticsearch. 0 The number of shards per index in Elasticsearch. 0 The password required by Elasticsearch. See also, es.username . The comma-separated list of Elasticsearch servers. Must be specified as fully qualified URLs, for example, http://localhost:9200 . The sniffer configuration for Elasticsearch. The client uses the sniffing process to find all nodes automatically. Disabled by default. true / false false Option to enable TLS when sniffing an Elasticsearch Cluster. The client uses the sniffing process to find all nodes automatically. Disabled by default. true / false false Timeout used for queries. When set to zero there is no timeout. 0s Path to a TLS Certification Authority (CA) file used to verify the remote servers. Will use the system truststore by default. Path to a TLS Certificate file, used to identify this process to the remote servers. Enable transport layer security (TLS) when talking to the remote servers. Disabled by default. true / false false Path to a TLS Private Key file, used to identify this process to the remote servers. Override the expected TLS server name in the certificate of the remote servers. Path to a file containing the bearer token. This flag also loads the Certification Authority (CA) file if it is specified. The username required by Elasticsearch. The basic authentication also loads CA if it is specified. See also es-archive.password . The major Elasticsearch version. If not specified, the value will be auto-detected from Elasticsearch. 0 Storage example with volume mounts apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: simple-prod spec: strategy: production storage: type: elasticsearch options: es: server-urls: https://quickstart-es-http.default.svc:9200 index-prefix: my-prefix tls: ca: /es/certificates/ca.crt secretName: tracing-secret volumeMounts: - name: certificates mountPath: /es/certificates/ readOnly: true volumes: - name: certificates secret: secretName: quickstart-es-http-certs-public The following example shows a Jaeger CR using an external Elasticsearch cluster with TLS CA certificate mounted from a volume and user/password stored in a secret. External Elasticsearch example: apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: simple-prod spec: strategy: production storage: type: elasticsearch options: es: server-urls: https://quickstart-es-http.default.svc:9200 1 index-prefix: my-prefix tls: 2 ca: /es/certificates/ca.crt secretName: tracing-secret 3 volumeMounts: 4 - name: certificates mountPath: /es/certificates/ readOnly: true volumes: - name: certificates secret: secretName: quickstart-es-http-certs-public 1 URL to Elasticsearch service running in default namespace. 2 TLS configuration. In this case only CA certificate, but it can also contain es.tls.key and es.tls.cert when using mutual TLS. 3 Secret which defines environment variables ES_PASSWORD and ES_USERNAME. Created by kubectl create secret generic tracing-secret --from-literal=ES_PASSWORD=changeme --from-literal=ES_USERNAME=elastic 4 Volume mounts and volumes which are mounted into all storage components. 3.2.5.6. Managing certificates with Elasticsearch You can create and manage certificates using the Red Hat Elasticsearch Operator. Managing certificates using the Red Hat Elasticsearch Operator also lets you use a single Elasticsearch cluster with multiple Jaeger Collectors. Important Managing certificates with Elasticsearch is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process. For more information about the support scope of Red Hat Technology Preview features, see https://access.redhat.com/support/offerings/techpreview/ . Starting with version 2.4, the Red Hat OpenShift distributed tracing platform Operator delegates certificate creation to the Red Hat Elasticsearch Operator by using the following annotations in the Elasticsearch custom resource: logging.openshift.io/elasticsearch-cert-management: "true" logging.openshift.io/elasticsearch-cert.jaeger-<shared-es-node-name>: "user.jaeger" logging.openshift.io/elasticsearch-cert.curator-<shared-es-node-name>: "system.logging.curator" Where the <shared-es-node-name> is the name of the Elasticsearch node. For example, if you create an Elasticsearch node named custom-es , your custom resource might look like the following example. Example Elasticsearch CR showing annotations apiVersion: logging.openshift.io/v1 kind: Elasticsearch metadata: annotations: logging.openshift.io/elasticsearch-cert-management: "true" logging.openshift.io/elasticsearch-cert.jaeger-custom-es: "user.jaeger" logging.openshift.io/elasticsearch-cert.curator-custom-es: "system.logging.curator" name: custom-es spec: managementState: Managed nodeSpec: resources: limits: memory: 16Gi requests: cpu: 1 memory: 16Gi nodes: - nodeCount: 3 proxyResources: {} resources: {} roles: - master - client - data storage: {} redundancyPolicy: ZeroRedundancy Prerequisites OpenShift Container Platform 4.7 logging subsystem for Red Hat OpenShift 5.2 The Elasticsearch node and the Jaeger instances must be deployed in the same namespace. For example, tracing-system . You enable certificate management by setting spec.storage.elasticsearch.useCertManagement to true in the Jaeger custom resource. Example showing useCertManagement apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: jaeger-prod spec: strategy: production storage: type: elasticsearch elasticsearch: name: custom-es doNotProvision: true useCertManagement: true The Red Hat OpenShift distributed tracing platform Operator sets the Elasticsearch custom resource name to the value of spec.storage.elasticsearch.name from the Jaeger custom resource when provisioning Elasticsearch. The certificates are provisioned by the Red Hat Elasticsearch Operator and the Red Hat OpenShift distributed tracing platform Operator injects the certificates. 3.2.5.7. Query configuration options Query is a service that retrieves traces from storage and hosts the user interface to display them. Table 3.14. Parameters used by the Red Hat OpenShift distributed tracing platform Operator to define Query Parameter Description Values Default value Specifies the number of Query replicas to create. Integer, for example, 2 Table 3.15. Configuration parameters passed to Query Parameter Description Values Default value Configuration options that define the Query service. Logging level for Query. Possible values: debug , info , warn , error , fatal , panic . The base path for all jaeger-query HTTP routes can be set to a non-root value, for example, /jaeger would cause all UI URLs to start with /jaeger . This can be useful when running jaeger-query behind a reverse proxy. /<path> Sample Query configuration apiVersion: jaegertracing.io/v1 kind: "Jaeger" metadata: name: "my-jaeger" spec: strategy: allInOne allInOne: options: log-level: debug query: base-path: /jaeger 3.2.5.8. Ingester configuration options Ingester is a service that reads from a Kafka topic and writes to the Elasticsearch storage backend. If you are using the allInOne or production deployment strategies, you do not need to configure the Ingester service. Table 3.16. Jaeger parameters passed to the Ingester Parameter Description Values Configuration options that define the Ingester service. Specifies the interval, in seconds or minutes, that the Ingester must wait for a message before terminating. The deadlock interval is disabled by default (set to 0 ), to avoid terminating the Ingester when no messages arrive during system initialization. Minutes and seconds, for example, 1m0s . Default value is 0 . The topic parameter identifies the Kafka configuration used by the collector to produce the messages, and the Ingester to consume the messages. Label for the consumer. For example, jaeger-spans . Identifies the Kafka configuration used by the Ingester to consume the messages. Label for the broker, for example, my-cluster-kafka-brokers.kafka:9092 . Logging level for the Ingester. Possible values: debug , info , warn , error , fatal , dpanic , panic . Streaming Collector and Ingester example apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: simple-streaming spec: strategy: streaming collector: options: kafka: producer: topic: jaeger-spans brokers: my-cluster-kafka-brokers.kafka:9092 ingester: options: kafka: consumer: topic: jaeger-spans brokers: my-cluster-kafka-brokers.kafka:9092 ingester: deadlockInterval: 5 storage: type: elasticsearch options: es: server-urls: http://elasticsearch:9200 3.2.6. Injecting sidecars Red Hat OpenShift distributed tracing platform relies on a proxy sidecar within the application's pod to provide the agent. The Red Hat OpenShift distributed tracing platform Operator can inject Agent sidecars into Deployment workloads. You can enable automatic sidecar injection or manage it manually. 3.2.6.1. Automatically injecting sidecars The Red Hat OpenShift distributed tracing platform Operator can inject Jaeger Agent sidecars into Deployment workloads. To enable automatic injection of sidecars, add the sidecar.jaegertracing.io/inject annotation set to either the string true or to the distributed tracing platform instance name that is returned by running USD oc get jaegers . When you specify true , there should be only a single distributed tracing platform instance for the same namespace as the deployment, otherwise, the Operator cannot determine which distributed tracing platform instance to use. A specific distributed tracing platform instance name on a deployment has a higher precedence than true applied on its namespace. The following snippet shows a simple application that will inject a sidecar, with the agent pointing to the single distributed tracing platform instance available in the same namespace: Automatic sidecar injection example apiVersion: apps/v1 kind: Deployment metadata: name: myapp annotations: "sidecar.jaegertracing.io/inject": "true" 1 spec: selector: matchLabels: app: myapp template: metadata: labels: app: myapp spec: containers: - name: myapp image: acme/myapp:myversion 1 Set to either the string true or to the Jaeger instance name. When the sidecar is injected, the agent can then be accessed at its default location on localhost . 3.2.6.2. Manually injecting sidecars The Red Hat OpenShift distributed tracing platform Operator can only automatically inject Jaeger Agent sidecars into Deployment workloads. For controller types other than Deployments , such as StatefulSets`and `DaemonSets , you can manually define the Jaeger agent sidecar in your specification. The following snippet shows the manual definition you can include in your containers section for a Jaeger agent sidecar: Sidecar definition example for a StatefulSet apiVersion: apps/v1 kind: StatefulSet metadata: name: example-statefulset namespace: example-ns labels: app: example-app spec: spec: containers: - name: example-app image: acme/myapp:myversion ports: - containerPort: 8080 protocol: TCP - name: jaeger-agent image: registry.redhat.io/distributed-tracing/jaeger-agent-rhel7:<version> # The agent version must match the Operator version imagePullPolicy: IfNotPresent ports: - containerPort: 5775 name: zk-compact-trft protocol: UDP - containerPort: 5778 name: config-rest protocol: TCP - containerPort: 6831 name: jg-compact-trft protocol: UDP - containerPort: 6832 name: jg-binary-trft protocol: UDP - containerPort: 14271 name: admin-http protocol: TCP args: - --reporter.grpc.host-port=dns:///jaeger-collector-headless.example-ns:14250 - --reporter.type=grpc The agent can then be accessed at its default location on localhost. 3.3. Configuring and deploying distributed tracing data collection The Red Hat OpenShift distributed tracing data collection Operator uses a custom resource definition (CRD) file that defines the architecture and configuration settings to be used when creating and deploying the Red Hat OpenShift distributed tracing data collection resources. You can either install the default configuration or modify the file to better suit your business requirements. 3.3.1. OpenTelemetry Collector configuration options Important The Red Hat OpenShift distributed tracing data collection Operator is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process. For more information about the support scope of Red Hat Technology Preview features, see https://access.redhat.com/support/offerings/techpreview/ . The OpenTelemetry Collector consists of three components that access telemetry data: Receivers - A receiver, which can be push or pull based, is how data gets into the Collector. Generally, a receiver accepts data in a specified format, translates it into the internal format and passes it to processors and exporters defined in the applicable pipelines. By default, no receivers are configured. One or more receivers must be configured. Receivers may support one or more data sources. Processors - (Optional) Processors are run on data between being received and being exported. By default, no processors are enabled. Processors must be enabled for every data source. Not all processors support all data sources. Depending on the data source, it may be recommended that multiple processors be enabled. In addition, it is important to note that the order of processors matters. Exporters - An exporter, which can be push or pull based, is how you send data to one or more backends/destinations. By default, no exporters are configured. One or more exporters must be configured. Exporters may support one or more data sources. Exporters may come with default settings, but many require configuration to specify at least the destination and security settings. You can define multiple instances of components in a custom resource YAML file. Once configured, these components must be enabled through pipelines defined in the spec.config.service section of the YAML file. As a best practice you should only enable the components that you need. sample OpenTelemetry collector custom resource file apiVersion: opentelemetry.io/v1alpha1 kind: OpenTelemetryCollector metadata: name: cluster-collector namespace: tracing-system spec: mode: deployment config: \| receivers: otlp: protocols: grpc: http: processors: exporters: jaeger: endpoint: jaeger-production-collector-headless.tracing-system.svc:14250 tls: ca_file: "/var/run/secrets/kubernetes.io/serviceaccount/service-ca.crt" service: pipelines: traces: receivers: [otlp] processors: [] exporters: [jaeger] Note If a component is configured, but not defined within the service section then it is not enabled. Table 3.17. Parameters used by the Operator to define the OpenTelemetry Collector Parameter Description Values Default A receiver is how data gets into the Collector. By default, no receivers are configured. There must be at least one enabled receiver for a configuration to be considered valid. Receivers are enabled by being added to a pipeline. otlp , jaeger None The oltp and jaeger receivers come with default settings, specifying the name of the receiver is enough to configure it. Processors run on data between being received and being exported. By default, no processors are enabled. None An exporter sends data to one or more backends/destinations. By default, no exporters are configured. There must be at least one enabled exporter for a configuration to be considered valid. Exporters are enabled by being added to a pipeline. Exporters may come with default settings, but many require configuration to specify at least the destination and security settings. logging , jaeger None The jaeger exporter's endpoint must be of the form <name>-collector-headless.<namespace>.svc , with the name and namespace of the Jaeger deployment, for a secure connection to be established. Path to the CA certificate. For a client this verifies the server certificate. For a server this verifies client certificates. If empty uses system root CA. Components are enabled by adding them to a pipeline under services.pipeline . You enable receivers for tracing by adding them under service.pipelines.traces . None You enable processors for tracing by adding them under service.pipelines.traces . None You enable exporters for tracing by adding them under service.pipelines.traces . None 3.3.2. Validating your deployment 3.3.3. Accessing the Jaeger console To access the Jaeger console you must have either Red Hat OpenShift Service Mesh or Red Hat OpenShift distributed tracing installed, and Red Hat OpenShift distributed tracing platform installed, configured, and deployed. The installation process creates a route to access the Jaeger console. If you know the URL for the Jaeger console, you can access it directly. If you do not know the URL, use the following directions. Procedure from OpenShift console Log in to the OpenShift Container Platform web console as a user with cluster-admin rights. If you use Red Hat OpenShift Dedicated, you must have an account with the dedicated-admin role. Navigate to Networking Routes . On the Routes page, select the control plane project, for example tracing-system , from the Namespace menu. The Location column displays the linked address for each route. If necessary, use the filter to find the jaeger route. Click the route Location to launch the console. Click Log In With OpenShift . Procedure from the CLI Log in to the OpenShift Container Platform CLI as a user with the cluster-admin role. If you use Red Hat OpenShift Dedicated, you must have an account with the dedicated-admin role. USD oc login --username=<NAMEOFUSER> https://<HOSTNAME>:6443 To query for details of the route using the command line, enter the following command. In this example, tracing-system is the control plane namespace. USD export JAEGER_URL=USD(oc get route -n tracing-system jaeger -o jsonpath='{.spec.host}') Launch a browser and navigate to https://<JAEGER_URL> , where <JAEGER_URL> is the route that you discovered in the step. Log in using the same user name and password that you use to access the OpenShift Container Platform console. If you have added services to the service mesh and have generated traces, you can use the filters and Find Traces button to search your trace data. If you are validating the console installation, there is no trace data to display. 3.4. Upgrading distributed tracing Operator Lifecycle Manager (OLM) controls the installation, upgrade, and role-based access control (RBAC) of Operators in a cluster. The OLM runs by default in OpenShift Container Platform. OLM queries for available Operators as well as upgrades for installed Operators. For more information about how OpenShift Container Platform handles upgrades, see the Operator Lifecycle Manager documentation. During an update, the Red Hat OpenShift distributed tracing Operators upgrade the managed distributed tracing instances to the version associated with the Operator. Whenever a new version of the Red Hat OpenShift distributed tracing platform Operator is installed, all the distributed tracing platform application instances managed by the Operator are upgraded to the Operator's version. For example, after upgrading the Operator from 1.10 installed to 1.11, the Operator scans for running distributed tracing platform instances and upgrades them to 1.11 as well. For specific instructions on how to update the OpenShift Elasticsearch Operator, see Updating OpenShift Logging . 3.4.1. Changing the Operator channel for 2.0 Red Hat OpenShift distributed tracing 2.0.0 made the following changes: Renamed the Red Hat OpenShift Jaeger Operator to the Red Hat OpenShift distributed tracing platform Operator. Stopped support for individual release channels. Going forward, the Red Hat OpenShift distributed tracing platform Operator will only support the stable Operator channel. Maintenance channels, for example 1.24-stable , will no longer be supported by future Operators. As part of the update to version 2.0, you must update your OpenShift Elasticsearch and Red Hat OpenShift distributed tracing platform Operator subscriptions. Prerequisites The OpenShift Container Platform version is 4.6 or later. You have updated the OpenShift Elasticsearch Operator. You have backed up the Jaeger custom resource file. An account with the cluster-admin role. If you use Red Hat OpenShift Dedicated, you must have an account with the dedicated-admin role. Important If you have not already updated your OpenShift Elasticsearch Operator as described in Updating OpenShift Logging complete that update before updating your Red Hat OpenShift distributed tracing platform Operator. For instructions on how to update the Operator channel, see Upgrading installed Operators . 3.5. Removing distributed tracing The steps for removing Red Hat OpenShift distributed tracing from an OpenShift Container Platform cluster are as follows: Shut down any Red Hat OpenShift distributed tracing pods. Remove any Red Hat OpenShift distributed tracing instances. Remove the Red Hat OpenShift distributed tracing platform Operator. Remove the Red Hat OpenShift distributed tracing data collection Operator. 3.5.1. Removing a Red Hat OpenShift distributed tracing platform instance using the web console Note When deleting an instance that uses the in-memory storage, all data is permanently lost. Data stored in a persistent storage such as Elasticsearch is not be deleted when a Red Hat OpenShift distributed tracing platform instance is removed. Procedure Log in to the OpenShift Container Platform web console. Navigate to Operators Installed Operators . Select the name of the project where the Operators are installed from the Project menu, for example, openshift-operators . Click the Red Hat OpenShift distributed tracing platform Operator. Click the Jaeger tab. Click the Options menu to the instance you want to delete and select Delete Jaeger . In the confirmation message, click Delete . 3.5.2. Removing a Red Hat OpenShift distributed tracing platform instance from the CLI Log in to the OpenShift Container Platform CLI. USD oc login --username=<NAMEOFUSER> To display the distributed tracing platform instances run the command: USD oc get deployments -n <jaeger-project> For example, USD oc get deployments -n openshift-operators The names of Operators have the suffix -operator . The following example shows two Red Hat OpenShift distributed tracing platform Operators and four distributed tracing platform instances: USD oc get deployments -n openshift-operators You should see output similar to the following: NAME READY UP-TO-DATE AVAILABLE AGE elasticsearch-operator 1/1 1 1 93m jaeger-operator 1/1 1 1 49m jaeger-test 1/1 1 1 7m23s jaeger-test2 1/1 1 1 6m48s tracing1 1/1 1 1 7m8s tracing2 1/1 1 1 35m To remove an instance of distributed tracing platform, run the following command: USD oc delete jaeger <deployment-name> -n <jaeger-project> For example: USD oc delete jaeger tracing2 -n openshift-operators To verify the deletion, run the oc get deployments command again: USD oc get deployments -n <jaeger-project> For example: USD oc get deployments -n openshift-operators You should see generated output that is similar to the following example: NAME READY UP-TO-DATE AVAILABLE AGE elasticsearch-operator 1/1 1 1 94m jaeger-operator 1/1 1 1 50m jaeger-test 1/1 1 1 8m14s jaeger-test2 1/1 1 1 7m39s tracing1 1/1 1 1 7m59s 3.5.3. Removing the Red Hat OpenShift distributed tracing Operators Procedure Follow the instructions for Deleting Operators from a cluster . Remove the Red Hat OpenShift distributed tracing platform Operator. After the Red Hat OpenShift distributed tracing platform Operator has been removed, if appropriate, remove the OpenShift Elasticsearch Operator.	[ "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: MyConfigFile spec: strategy: production 1", "oc login --username=<NAMEOFUSER> https://<HOSTNAME>:8443", "oc new-project tracing-system", "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: jaeger-all-in-one-inmemory", "oc create -n tracing-system -f jaeger.yaml", "oc get pods -n tracing-system -w", "NAME READY STATUS RESTARTS AGE jaeger-all-in-one-inmemory-cdff7897b-qhfdx 2/2 Running 0 24s", "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: jaeger-production namespace: spec: strategy: production ingress: security: oauth-proxy storage: type: elasticsearch elasticsearch: nodeCount: 3 redundancyPolicy: SingleRedundancy esIndexCleaner: enabled: true numberOfDays: 7 schedule: 55 23 * * * esRollover: schedule: '/30 * * *'", "oc login --username=<NAMEOFUSER> https://<HOSTNAME>:8443", "oc new-project tracing-system", "oc create -n tracing-system -f jaeger-production.yaml", "oc get pods -n tracing-system -w", "NAME READY STATUS RESTARTS AGE elasticsearch-cdm-jaegersystemjaegerproduction-1-6676cf568gwhlw 2/2 Running 0 10m elasticsearch-cdm-jaegersystemjaegerproduction-2-bcd4c8bf5l6g6w 2/2 Running 0 10m elasticsearch-cdm-jaegersystemjaegerproduction-3-844d6d9694hhst 2/2 Running 0 10m jaeger-production-collector-94cd847d-jwjlj 1/1 Running 3 8m32s jaeger-production-query-5cbfbd499d-tv8zf 3/3 Running 3 8m32s", "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: jaeger-streaming spec: strategy: streaming collector: options: kafka: producer: topic: jaeger-spans #Note: If brokers are not defined,AMQStreams 1.4.0+ will self-provision Kafka. brokers: my-cluster-kafka-brokers.kafka:9092 storage: type: elasticsearch ingester: options: kafka: consumer: topic: jaeger-spans brokers: my-cluster-kafka-brokers.kafka:9092", "oc login --username=<NAMEOFUSER> https://<HOSTNAME>:8443", "oc new-project tracing-system", "oc create -n tracing-system -f jaeger-streaming.yaml", "oc get pods -n tracing-system -w", "NAME READY STATUS RESTARTS AGE elasticsearch-cdm-jaegersystemjaegerstreaming-1-697b66d6fcztcnn 2/2 Running 0 5m40s elasticsearch-cdm-jaegersystemjaegerstreaming-2-5f4b95c78b9gckz 2/2 Running 0 5m37s elasticsearch-cdm-jaegersystemjaegerstreaming-3-7b6d964576nnz97 2/2 Running 0 5m5s jaeger-streaming-collector-6f6db7f99f-rtcfm 1/1 Running 0 80s jaeger-streaming-entity-operator-6b6d67cc99-4lm9q 3/3 Running 2 2m18s jaeger-streaming-ingester-7d479847f8-5h8kc 1/1 Running 0 80s jaeger-streaming-kafka-0 2/2 Running 0 3m1s jaeger-streaming-query-65bf5bb854-ncnc7 3/3 Running 0 80s jaeger-streaming-zookeeper-0 2/2 Running 0 3m39s", "oc login --username=<NAMEOFUSER> https://<HOSTNAME>:6443", "export JAEGER_URL=USD(oc get route -n tracing-system jaeger -o jsonpath='{.spec.host}')", "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: name spec: strategy: <deployment_strategy> allInOne: options: {} resources: {} agent: options: {} resources: {} collector: options: {} resources: {} sampling: options: {} storage: type: options: {} query: options: {} resources: {} ingester: options: {} resources: {} options: {}", "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: jaeger-all-in-one-inmemory", "collector: replicas:", "spec: collector: options: {}", "options: collector: num-workers:", "options: collector: queue-size:", "options: kafka: producer: topic: jaeger-spans", "options: kafka: producer: brokers: my-cluster-kafka-brokers.kafka:9092", "options: log-level:", "spec: sampling: options: {} default_strategy: service_strategy:", "default_strategy: type: service_strategy: type:", "default_strategy: param: service_strategy: param:", "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: with-sampling spec: sampling: options: default_strategy: type: probabilistic param: 0.5 service_strategies: - service: alpha type: probabilistic param: 0.8 operation_strategies: - operation: op1 type: probabilistic param: 0.2 - operation: op2 type: probabilistic param: 0.4 - service: beta type: ratelimiting param: 5", "spec: sampling: options: default_strategy: type: probabilistic param: 1", "spec: storage: type:", "storage: secretname:", "storage: options: {}", "storage: esIndexCleaner: enabled:", "storage: esIndexCleaner: numberOfDays:", "storage: esIndexCleaner: schedule:", "elasticsearch: properties: doNotProvision:", "elasticsearch: properties: name:", "elasticsearch: nodeCount:", "elasticsearch: resources: requests: cpu:", "elasticsearch: resources: requests: memory:", "elasticsearch: resources: limits: cpu:", "elasticsearch: resources: limits: memory:", "elasticsearch: redundancyPolicy:", "elasticsearch: useCertManagement:", "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: simple-prod spec: strategy: production storage: type: elasticsearch elasticsearch: nodeCount: 3 resources: requests: cpu: 1 memory: 16Gi limits: memory: 16Gi", "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: simple-prod spec: strategy: production storage: type: elasticsearch elasticsearch: nodeCount: 1 storage: 1 storageClassName: gp2 size: 5Gi resources: requests: cpu: 200m memory: 4Gi limits: memory: 4Gi redundancyPolicy: ZeroRedundancy", "es: server-urls:", "es: max-doc-count:", "es: max-num-spans:", "es: max-span-age:", "es: sniffer:", "es: sniffer-tls-enabled:", "es: timeout:", "es: username:", "es: password:", "es: version:", "es: num-replicas:", "es: num-shards:", "es: create-index-templates:", "es: index-prefix:", "es: bulk: actions:", "es: bulk: flush-interval:", "es: bulk: size:", "es: bulk: workers:", "es: tls: ca:", "es: tls: cert:", "es: tls: enabled:", "es: tls: key:", "es: tls: server-name:", "es: token-file:", "es-archive: bulk: actions:", "es-archive: bulk: flush-interval:", "es-archive: bulk: size:", "es-archive: bulk: workers:", "es-archive: create-index-templates:", "es-archive: enabled:", "es-archive: index-prefix:", "es-archive: max-doc-count:", "es-archive: max-num-spans:", "es-archive: max-span-age:", "es-archive: num-replicas:", "es-archive: num-shards:", "es-archive: password:", "es-archive: server-urls:", "es-archive: sniffer:", "es-archive: sniffer-tls-enabled:", "es-archive: timeout:", "es-archive: tls: ca:", "es-archive: tls: cert:", "es-archive: tls: enabled:", "es-archive: tls: key:", "es-archive: tls: server-name:", "es-archive: token-file:", "es-archive: username:", "es-archive: version:", "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: simple-prod spec: strategy: production storage: type: elasticsearch options: es: server-urls: https://quickstart-es-http.default.svc:9200 index-prefix: my-prefix tls: ca: /es/certificates/ca.crt secretName: tracing-secret volumeMounts: - name: certificates mountPath: /es/certificates/ readOnly: true volumes: - name: certificates secret: secretName: quickstart-es-http-certs-public", "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: simple-prod spec: strategy: production storage: type: elasticsearch options: es: server-urls: https://quickstart-es-http.default.svc:9200 1 index-prefix: my-prefix tls: 2 ca: /es/certificates/ca.crt secretName: tracing-secret 3 volumeMounts: 4 - name: certificates mountPath: /es/certificates/ readOnly: true volumes: - name: certificates secret: secretName: quickstart-es-http-certs-public", "apiVersion: logging.openshift.io/v1 kind: Elasticsearch metadata: annotations: logging.openshift.io/elasticsearch-cert-management: \"true\" logging.openshift.io/elasticsearch-cert.jaeger-custom-es: \"user.jaeger\" logging.openshift.io/elasticsearch-cert.curator-custom-es: \"system.logging.curator\" name: custom-es spec: managementState: Managed nodeSpec: resources: limits: memory: 16Gi requests: cpu: 1 memory: 16Gi nodes: - nodeCount: 3 proxyResources: {} resources: {} roles: - master - client - data storage: {} redundancyPolicy: ZeroRedundancy", "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: jaeger-prod spec: strategy: production storage: type: elasticsearch elasticsearch: name: custom-es doNotProvision: true useCertManagement: true", "spec: query: replicas:", "spec: query: options: {}", "options: log-level:", "options: query: base-path:", "apiVersion: jaegertracing.io/v1 kind: \"Jaeger\" metadata: name: \"my-jaeger\" spec: strategy: allInOne allInOne: options: log-level: debug query: base-path: /jaeger", "spec: ingester: options: {}", "options: deadlockInterval:", "options: kafka: consumer: topic:", "options: kafka: consumer: brokers:", "options: log-level:", "apiVersion: jaegertracing.io/v1 kind: Jaeger metadata: name: simple-streaming spec: strategy: streaming collector: options: kafka: producer: topic: jaeger-spans brokers: my-cluster-kafka-brokers.kafka:9092 ingester: options: kafka: consumer: topic: jaeger-spans brokers: my-cluster-kafka-brokers.kafka:9092 ingester: deadlockInterval: 5 storage: type: elasticsearch options: es: server-urls: http://elasticsearch:9200", "apiVersion: apps/v1 kind: Deployment metadata: name: myapp annotations: \"sidecar.jaegertracing.io/inject\": \"true\" 1 spec: selector: matchLabels: app: myapp template: metadata: labels: app: myapp spec: containers: - name: myapp image: acme/myapp:myversion", "apiVersion: apps/v1 kind: StatefulSet metadata: name: example-statefulset namespace: example-ns labels: app: example-app spec: spec: containers: - name: example-app image: acme/myapp:myversion ports: - containerPort: 8080 protocol: TCP - name: jaeger-agent image: registry.redhat.io/distributed-tracing/jaeger-agent-rhel7:<version> # The agent version must match the Operator version imagePullPolicy: IfNotPresent ports: - containerPort: 5775 name: zk-compact-trft protocol: UDP - containerPort: 5778 name: config-rest protocol: TCP - containerPort: 6831 name: jg-compact-trft protocol: UDP - containerPort: 6832 name: jg-binary-trft protocol: UDP - containerPort: 14271 name: admin-http protocol: TCP args: - --reporter.grpc.host-port=dns:///jaeger-collector-headless.example-ns:14250 - --reporter.type=grpc", "apiVersion: opentelemetry.io/v1alpha1 kind: OpenTelemetryCollector metadata: name: cluster-collector namespace: tracing-system spec: mode: deployment config: \| receivers: otlp: protocols: grpc: http: processors: exporters: jaeger: endpoint: jaeger-production-collector-headless.tracing-system.svc:14250 tls: ca_file: \"/var/run/secrets/kubernetes.io/serviceaccount/service-ca.crt\" service: pipelines: traces: receivers: [otlp] processors: [] exporters: [jaeger]", "receivers:", "receivers: otlp:", "processors:", "exporters:", "exporters: jaeger: endpoint:", "exporters: jaeger: tls: ca_file:", "service: pipelines:", "service: pipelines: traces: receivers:", "service: pipelines: traces: processors:", "service: pipelines: traces: exporters:", "oc login --username=<NAMEOFUSER> https://<HOSTNAME>:6443", "export JAEGER_URL=USD(oc get route -n tracing-system jaeger -o jsonpath='{.spec.host}')", "oc login --username=<NAMEOFUSER>", "oc get deployments -n <jaeger-project>", "oc get deployments -n openshift-operators", "oc get deployments -n openshift-operators", "NAME READY UP-TO-DATE AVAILABLE AGE elasticsearch-operator 1/1 1 1 93m jaeger-operator 1/1 1 1 49m jaeger-test 1/1 1 1 7m23s jaeger-test2 1/1 1 1 6m48s tracing1 1/1 1 1 7m8s tracing2 1/1 1 1 35m", "oc delete jaeger <deployment-name> -n <jaeger-project>", "oc delete jaeger tracing2 -n openshift-operators", "oc get deployments -n <jaeger-project>", "oc get deployments -n openshift-operators", "NAME READY UP-TO-DATE AVAILABLE AGE elasticsearch-operator 1/1 1 1 94m jaeger-operator 1/1 1 1 50m jaeger-test 1/1 1 1 8m14s jaeger-test2 1/1 1 1 7m39s tracing1 1/1 1 1 7m59s" ]	https://docs.redhat.com/en/documentation/openshift_container_platform/4.7/html/distributed_tracing/distributed-tracing-installation
2.3.2. Striped Logical Volumes	2.3.2. Striped Logical Volumes When you write data to an LVM logical volume, the file system lays the data out across the underlying physical volumes. You can control the way the data is written to the physical volumes by creating a striped logical volume. For large sequential reads and writes, this can improve the efficiency of the data I/O. Striping enhances performance by writing data to a predetermined number of physical volumes in round-round fashion. With striping, I/O can be done in parallel. In some situations, this can result in near-linear performance gain for each additional physical volume in the stripe. The following illustration shows data being striped across three physical volumes. In this figure: the first stripe of data is written to PV1 the second stripe of data is written to PV2 the third stripe of data is written to PV3 the fourth stripe of data is written to PV1 In a striped logical volume, the size of the stripe cannnot exceed the size of an extent. Figure 2.5. Striping Data Across Three PVs Striped logical volumes can be extended by concatenating another set of devices onto the end of the first set. In order extend a striped logical volume, however, there must be enough free space on the underlying physical volumes that make up the volume group to support the stripe. For example, if you have a two-way stripe that uses up an entire volume group, adding a single physical volume to the volume group will not enable you to extend the stripe. Instead, you must add at least two physical volumes to the volume group. For more information on extending a striped volume, see Section 4.4.9, "Extending a Striped Volume" .	null	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/4/html/cluster_logical_volume_manager/striped_volumes
Chapter 2. Deploy OpenShift Data Foundation using dynamic storage devices	Chapter 2. Deploy OpenShift Data Foundation using dynamic storage devices You can deploy OpenShift Data Foundation on OpenShift Container Platform using dynamic storage devices provided by Amazon Web Services (AWS) EBS (type, gp2-csi or gp3-csi ) that provides you with the option to create internal cluster resources. This results in the internal provisioning of the base services, which helps to make additional storage classes available to applications. Although, it is possible to deploy only the Multicloud Object Gateway (MCG) component with OpenShift Data Foundation, this deployment method is not supported on ROSA. Note Only internal OpenShift Data Foundation clusters are supported on ROSA. See Planning your deployment for more information about deployment requirements. Also, ensure that you have addressed the requirements in Preparing to deploy OpenShift Data Foundation chapter before proceeding with the below steps for deploying using dynamic storage devices: Install the Red Hat OpenShift Data Foundation Operator . Create the OpenShift Data Foundation Cluster . 2.1. Installing Red Hat OpenShift Data Foundation Operator You can install Red Hat OpenShift Data Foundation Operator using the Red Hat OpenShift Container Platform Operator Hub for ROSA with hosted control planes (HCP). Prerequisites Access to an OpenShift Container Platform cluster using an account with cluster-admin and operator installation permissions. You must have at least three worker or infrastructure nodes in the Red Hat OpenShift Container Platform cluster. For additional resource requirements, see the Planning your deployment guide. Important When you need to override the cluster-wide default node selector for OpenShift Data Foundation, you can use the following command to specify a blank node selector for the storage namespace: Taint a node as infra to ensure only Red Hat OpenShift Data Foundation resources are scheduled on that node. This helps you save on subscription costs. For more information, see the How to use dedicated worker nodes for Red Hat OpenShift Data Foundation section in the Managing and Allocating Storage Resources guide. Procedure Log in to the OpenShift Web Console. Click Operators OperatorHub . Scroll or type OpenShift Data Foundation into the Filter by keyword box to find the OpenShift Data Foundation Operator. Click Install . Set the following options on the Install Operator page: Fill in role ARN . For instruction to create a Amazon resource name (ARN), see Creating an AWS role using a script . Update Channel as stable-4.18 . Installation Mode as A specific namespace on the cluster . Installed Namespace as Select a Namespace . Note openshift-storage Namespace is not recommended for ROSA deployments. Use a user defined namespace for this deployment. Avoid using "redhat" or "openshift" prefixes in namespaces. Important This guide uses <storage_namespace> as an example namespace. Replace <storage_namespace> with your defined namespace in later steps. Select Approval Strategy as Automatic or Manual . If you select Automatic updates, then the Operator Lifecycle Manager (OLM) automatically upgrades the running instance of your Operator without any intervention. If you select Manual updates, then the OLM creates an update request. As a cluster administrator, you must then manually approve that update request to update the Operator to a newer version. Manual updates strategy is recommended for ROSA with hosted control planes. Ensure that the Enable option is selected for the Console plugin . Click Install . Verification steps After the operator is successfully installed, a pop-up with a message, Web console update is available appears on the user interface. Click Refresh web console from this pop-up for the console changes to reflect. In the Web Console: Navigate to Installed Operators and verify that the OpenShift Data Foundation Operator shows a green tick indicating successful installation. Navigate to Storage and verify if the Data Foundation dashboard is available. 2.2. Enabling cluster-wide encryption with KMS using the Token authentication method You can enable the key value backend path and policy in the vault for token authentication. Prerequisites Administrator access to the vault. A valid Red Hat OpenShift Data Foundation Advanced subscription. For more information, see the knowledgebase article on OpenShift Data Foundation subscriptions . Carefully, select a unique path name as the backend path that follows the naming convention since you cannot change it later. Procedure Enable the Key/Value (KV) backend path in the vault. For vault KV secret engine API, version 1: For vault KV secret engine API, version 2: Create a policy to restrict the users to perform a write or delete operation on the secret: Create a token that matches the above policy: 2.3. Enabling cluster-wide encryption with KMS using the Kubernetes authentication method You can enable the Kubernetes authentication method for cluster-wide encryption using the Key Management System (KMS). Prerequisites Administrator access to Vault. A valid Red Hat OpenShift Data Foundation Advanced subscription. For more information, see the knowledgebase article on OpenShift Data Foundation subscriptions . The OpenShift Data Foundation operator must be installed from the Operator Hub. Select a unique path name as the backend path that follows the naming convention carefully. You cannot change this path name later. Procedure Create a service account: where, <serviceaccount_name> specifies the name of the service account. For example: Create clusterrolebindings and clusterroles : For example: Create a secret for the serviceaccount token and CA certificate. where, <serviceaccount_name> is the service account created in the earlier step, and <storage_namespace> is the namespace where ODF operator and StorageSystem were created. Get the token and the CA certificate from the secret. Retrieve the OCP cluster endpoint. Fetch the service account issuer: Use the information collected in the step to setup the Kubernetes authentication method in Vault: Important To configure the Kubernetes authentication method in Vault when the issuer is empty: Enable the Key/Value (KV) backend path in Vault. For Vault KV secret engine API, version 1: For Vault KV secret engine API, version 2: Create a policy to restrict the users to perform a write or delete operation on the secret: Generate the roles: The role odf-rook-ceph-op is later used while you configure the KMS connection details during the creation of the storage system. 2.4. Creating OpenShift Data Foundation cluster Create an OpenShift Data Foundation cluster after you install the OpenShift Data Foundation operator. Procedure In the OpenShift Web Console, click Operators Installed Operators to view all the installed operators. Ensure that the Project selected is openshift-storage . Click on the OpenShift Data Foundation operator, and then click Create StorageSystem . In the Backing storage page, select the following: Select Full Deployment for the Deployment type option. Select the Use an existing StorageClass option. Optional: Select Use external PostgreSQL checkbox to use an external PostgreSQL [Technology preview] . This provides high availability solution for Multicloud Object Gateway where the PostgreSQL pod is a single point of failure. Provide the following connection details: Username Password Server name and Port Database name Select Enable TLS/SSL checkbox to enable encryption for the Postgres server. Click . In the Capacity and nodes page, provide the necessary information: Select a value for Requested Capacity from the dropdown list. It is set to 2 TiB by default. Note Once you select the initial storage capacity, cluster expansion is performed only using the selected usable capacity (three times of raw storage). In the Select Nodes section, select at least three available nodes. In the Configure performance section, select one of the following performance profiles: Lean Use this in a resource constrained environment with minimum resources that are lower than the recommended. This profile minimizes resource consumption by allocating fewer CPUs and less memory. Balanced (default) Use this when recommended resources are available. This profile provides a balance between resource consumption and performance for diverse workloads. Performance Use this in an environment with sufficient resources to get the best performance. This profile is tailored for high performance by allocating ample memory and CPUs to ensure optimal execution of demanding workloads. Note You have the option to configure the performance profile even after the deployment using the Configure performance option from the options menu of the StorageSystems tab. Important Before selecting a resource profile, make sure to check the current availability of resources within the cluster. Opting for a higher resource profile in a cluster with insufficient resources might lead to installation failures. For more information about resource requirements, see Resource requirement for performance profiles . Optional: Select the Taint nodes checkbox to dedicate the selected nodes for OpenShift Data Foundation. Click . Optional: In the Security and network page, configure the following based on your requirements: To enable encryption, select Enable data encryption for block and file storage . Select either one or both the encryption levels: Cluster-wide encryption Encrypts the entire cluster (block and file). StorageClass encryption Creates encrypted persistent volume (block only) using encryption enabled storage class. Optional: Select the Connect to an external key management service checkbox. This is optional for cluster-wide encryption. From the Key Management Service Provider drop-down list, select one of the following providers and provide the necessary details: Vault Select an Authentication Method . Using Token authentication method Enter a unique Connection Name , host Address of the Vault server ('https://<hostname or ip>'), Port number and Token . Expand Advanced Settings to enter additional settings and certificate details based on your Vault configuration: Enter the Key Value secret path in Backend Path that is dedicated and unique to OpenShift Data Foundation. Optional: Enter TLS Server Name and Vault Enterprise Namespace . Upload the respective PEM encoded certificate file to provide the CA Certificate , Client Certificate and Client Private Key . Click Save . Using Kubernetes authentication method Enter a unique Vault Connection Name , host Address of the Vault server ('https://<hostname or ip>'), Port number and Role name. Expand Advanced Settings to enter additional settings and certificate details based on your Vault configuration: Enter the Key Value secret path in Backend Path that is dedicated and unique to OpenShift Data Foundation. Optional: Enter TLS Server Name and Authentication Path if applicable. Upload the respective PEM encoded certificate file to provide the CA Certificate , Client Certificate and Client Private Key . Click Save . Note In case you need to enable key rotation for Vault KMS, run the following command in the OpenShift web console after the storage cluster is created: Thales CipherTrust Manager (using KMIP) Enter a unique Connection Name for the Key Management service within the project. In the Address and Port sections, enter the IP of Thales CipherTrust Manager and the port where the KMIP interface is enabled. For example: Address : 123.34.3.2 Port : 5696 Upload the Client Certificate , CA certificate , and Client Private Key . If StorageClass encryption is enabled, enter the Unique Identifier to be used for encryption and decryption generated above. The TLS Server field is optional and used when there is no DNS entry for the KMIP endpoint. For example, kmip_all_<port>.ciphertrustmanager.local . To enable in-transit encryption, select In-transit encryption . Select a Network . Click . In the Review and create page, review the configuration details. To modify any configuration settings, click Back . Click Create StorageSystem . Note When your deployment has five or more nodes, racks, or rooms, and when there are five or more number of failure domains present in the deployment, you can configure Ceph monitor counts based on the number of racks or zones. An alert is displayed in the notification panel or Alert Center of the OpenShift Web Console to indicate the option to increase the number of Ceph monitor counts. You can use the Configure option in the alert to configure the Ceph monitor counts. For more information, see Resolving low Ceph monitor count alert . Verification steps To verify the final Status of the installed storage cluster: In the OpenShift Web Console, navigate to Installed Operators OpenShift Data Foundation Storage System ocs-storagecluster-storagesystem Resources . Verify that Status of StorageCluster is Ready and has a green tick mark to it. Additional resources To enable Overprovision Control alerts, refer to Alerts in Monitoring guide. 2.5. Verifying OpenShift Data Foundation deployment To verify that OpenShift Data Foundation is deployed correctly: Verify the state of the pods . Verify that the OpenShift Data Foundation cluster is healthy . Verify that the OpenShift Data Foundation specific storage classes exist . 2.5.1. Verifying the state of the pods Procedure Click Workloads Pods from the OpenShift Web Console. Select openshift-storage from the Project drop-down list. Note If the Show default projects option is disabled, use the toggle button to list all the default projects. For more information on the expected number of pods for each component and how it varies depending on the number of nodes, see the following table: Set filter for Running and Completed pods to verify that the following pods are in Running and Completed state: 2.5.2. Verifying the OpenShift Data Foundation cluster is healthy Procedure In the OpenShift Web Console, click Storage Data Foundation . In the Status card of the Overview tab, click Storage System and then click the storage system link from the pop up that appears. In the Status card of the Block and File tab, verify that the Storage Cluster has a green tick. In the Details card, verify that the cluster information is displayed. For more information on the health of the OpenShift Data Foundation cluster using the Block and File dashboard, see Monitoring OpenShift Data Foundation . 2.5.3. Verifying that the specific storage classes exist Procedure Click Storage Storage Classes from the left pane of the OpenShift Web Console. Verify that the following storage classes are created with the OpenShift Data Foundation cluster creation:	[ "oc annotate namespace storage-namespace openshift.io/node-selector=", "vault secrets enable -path=odf kv", "vault secrets enable -path=odf kv-v2", "echo ' path \"odf/\" { capabilities = [\"create\", \"read\", \"update\", \"delete\", \"list\"] } path \"sys/mounts\" { capabilities = [\"read\"] }'\| vault policy write odf -", "vault token create -policy=odf -format json", "oc -n <storage-namespace> create serviceaccount <serviceaccount_name>", "oc -n <storage-namespace> create serviceaccount odf-vault-auth", "oc -n <storage-namespace> create clusterrolebinding vault-tokenreview-binding --clusterrole=system:auth-delegator --serviceaccount=openshift-storage:_<serviceaccount_name>_", "oc -n <storage-namespace> create clusterrolebinding vault-tokenreview-binding --clusterrole=system:auth-delegator --serviceaccount=openshift-storage:odf-vault-auth", "cat <<EOF \| oc create -f - apiVersion: v1 kind: Secret metadata: name: odf-vault-auth-token namespace: <storage-namespace> annotations: kubernetes.io/service-account.name: <serviceaccount_name> type: kubernetes.io/service-account-token data: {} EOF", "SA_JWT_TOKEN=USD(oc -n <storage_namespace> get secret odf-vault-auth-token -o jsonpath=\"{.data['token']}\" \| base64 --decode; echo) SA_CA_CRT=USD(oc -n <storage_namespace> get secret odf-vault-auth-token -o jsonpath=\"{.data['ca\\.crt']}\" \| base64 --decode; echo)", "OCP_HOST=USD(oc config view --minify --flatten -o jsonpath=\"{.clusters[0].cluster.server}\")", "oc proxy & proxy_pid=USD! issuer=\"USD( curl --silent http://127.0.0.1:8001/.well-known/openid-configuration \| jq -r .issuer)\" kill USDproxy_pid", "vault auth enable kubernetes", "vault write auth/kubernetes/config token_reviewer_jwt=\"USDSA_JWT_TOKEN\" kubernetes_host=\"USDOCP_HOST\" kubernetes_ca_cert=\"USDSA_CA_CRT\" issuer=\"USDissuer\"", "vault write auth/kubernetes/config token_reviewer_jwt=\"USDSA_JWT_TOKEN\" kubernetes_host=\"USDOCP_HOST\" kubernetes_ca_cert=\"USDSA_CA_CRT\"", "vault secrets enable -path=odf kv", "vault secrets enable -path=odf kv-v2", "echo ' path \"odf/\" { capabilities = [\"create\", \"read\", \"update\", \"delete\", \"list\"] } path \"sys/mounts\" { capabilities = [\"read\"] }'\| vault policy write odf -", "vault write auth/kubernetes/role/odf-rook-ceph-op bound_service_account_names=rook-ceph-system,rook-ceph-osd,noobaa bound_service_account_namespaces=<storage_namespace> policies=odf ttl=1440h", "vault write auth/kubernetes/role/odf-rook-ceph-osd bound_service_account_names=rook-ceph-osd bound_service_account_namespaces=<storage_namespace> policies=odf ttl=1440h", "patch storagecluster ocs-storagecluster -n openshift-storage --type=json -p '[{\"op\": \"add\", \"path\":\"/spec/encryption/keyRotation/enable\", \"value\": true}]'" ]	https://docs.redhat.com/en/documentation/red_hat_openshift_data_foundation/4.18/html/deploying_openshift_data_foundation_using_red_hat_openshift_service_on_aws_with_hosted_control_planes/deploy-using-dynamic-storage-devices-rosa
Chapter 5. Installer and image creation	Chapter 5. Installer and image creation 5.1. Add-ons 5.1.1. OSCAP The Open Security Content Automation Protocol (OSCAP) add-on is enabled by default in RHEL 8. 5.1.2. Kdump The Kdump add-on adds support for configuring kernel crash dumping during installation. This add-on has full support in Kickstart (using the %addon com_redhat_kdump command and its options), and is fully integrated as an additional window in the graphical and text-based user interfaces. 5.2. Installer networking A new network device naming scheme that generates network interface names based on a user-defined prefix is available in Red Hat Enterprise Linux 8. The net.ifnames.prefix boot option allows the device naming scheme to be used by the installation program and the installed system. Additional resources For more information, see RHEL-8 new custom NIC names helper or Customizing the prefix for Ethernet interfaces during installation . 5.3. Installation images and packages 5.3.1. Ability to register your system, attach RHEL subscriptions, and install from the Red Hat CDN Since Red Hat Enterprise Linux 8.2, you can register your system, attach RHEL subscriptions, and install from the Red Hat Content Delivery Network (CDN) before package installation. Interactive GUI installations, as well as automated Kickstart installations, support this feature. For more information, see the RHEL 8.2 Release Notes document. 5.3.2. Ability to register your system to Red Hat Insights during installation Red Hat Insights is a managed service that gathers and analyzes platform and application data to predict risk, recommend actions, and track costs. Insights alerts you about warnings or optimizations that are relevant to several operational areas: system availability (including potential outages), security (for example, a new CVE is discovered for your systems), and business (such as overspending). Insights is included as part of your Red Hat subscription and is accessible through the Red Hat Hybrid Cloud Console. See also the Red Hat Insights documentation . Since Red Hat Enterprise Linux 8.2, you can register your system to Red Hat Insights during installation. Interactive GUI installations, as well as automated Kickstart installations, support this feature. For more information, see the RHEL 8.2 Release Notes document. 5.3.3. Unified ISO In Red Hat Enterprise Linux 8, a unified ISO automatically loads the BaseOS and AppStream installation source repositories. This feature works for the first base repository that is loaded during installation. For example, if you boot the installation with no repository configured and have the unified ISO as the base repository in the graphical user interface (GUI), or if you boot the installation using the inst.repo= option that points to the unified ISO. As a result, the AppStream repository is enabled under the Additional Repositories section of the Installation Source GUI window. You cannot remove the AppStream repository or change its settings but you can disable it in Installation Source . This feature does not work if you boot the installation using a different base repository and then change it to the unified ISO. If you do that, the base repository is replaced. However, the AppStream repository is not replaced and points to the original file. 5.3.4. Stage2 image In Red Hat Enterprise Linux 8, multiple network locations of stage2 or Kickstart files can be specified to prevent installation failure. This update enables the specification of multiple inst.stage2 and inst.ks boot options with network locations of stage2 and a Kickstart file. This avoids the situation in which the requested files cannot be reached and the installation fails because the contacted server with the stage2 or the Kickstart file is inaccessible. With this new update, the installation failure can be avoided if multiple locations are specified. If all the defined locations are URLs, namely HTTP , HTTPS , or FTP , they will be tried sequentially until the requested file is fetched successfully. If there is a location that is not a URL, only the last specified location is tried. The remaining locations are ignored. 5.3.5. inst.addrepo parameter Previously, you could only specify a base repository from the kernel boot parameters. In Red Hat Enterprise Linux 8, a new kernel parameter, inst.addrepo=<name>,<url> , allows you to specify an additional repository during installation. This parameter has two mandatory values: the name of the repository and the URL that points to the repository. For more information, see the inst-addrepo usage . 5.3.6. Installation from an expanded ISO Red Hat Enterprise Linux 8 supports installing from a repository on a local hard drive. Previously, the only installation method from a hard drive was using an ISO image as the installation source. However, the Red Hat Enterprise Linux 8 ISO image might be too big for some file systems; for example, the FAT32 file system cannot store files larger than 4 GiB. In Red Hat Enterprise Linux 8, you can enable installation from a repository on a local hard drive; you only need to specify the directory instead of the ISO image. For example: inst.repo=hd:<device>:<path to the repository> . For more information about the Red Hat Enterprise Linux 8 BaseOS and AppStream repositories, see the Repositories section of this document. 5.4. Installer Graphical User Interface 5.4.1. The Installation Summary window The Installation Summary window of the Red Hat Enterprise Linux 8 graphical installation has been updated to a new three-column layout that provides improved organization of graphical installation settings. 5.5. System Purpose new in RHEL 5.5.1. System Purpose support in the graphical installation Previously, the Red Hat Enterprise Linux installation program did not provide system purpose information to Subscription Manager. In Red Hat Enterprise Linux 8, you can set the intended purpose of the system during a graphical installation by using the System Purpose window, or in a Kickstart configuration file by using the syspurpose command. When you set a system's purpose, the entitlement server receives information that helps auto-attach a subscription that satisfies the intended use of the system. 5.5.2. System Purpose support in Pykickstart Previously, it was not possible for the pykickstart library to provide system purpose information to Subscription Manager. In Red Hat Enterprise Linux 8, pykickstart parses the new syspurpose command and records the intended purpose of the system during automated and partially-automated installation. The information is then passed to the installation program, saved on the newly-installed system, and available for Subscription Manager when subscribing the system. 5.6. Installer module support 5.6.1. Installing modules using Kickstart In Red Hat Enterprise Linux 8, the installation program has been extended to handle all modular features. Kickstart scripts can now enable module and stream combinations, install module profiles, and install modular packages. 5.7. Kickstart changes The following sections describe the changes in Kickstart commands and options in Red Hat Enterprise Linux 8. auth or authconfig is deprecated in RHEL 8 The auth or authconfig Kickstart command is deprecated in Red Hat Enterprise Linux 8 because the authconfig tool and package have been removed. Similarly to authconfig commands issued on command line, authconfig commands in Kickstart scripts now use the authselect-compat tool to run the new authselect tool. For a description of this compatibility layer and its known issues, see the manual page authselect-migration(7) . The installation program will automatically detect use of the deprecated commands and install on the system the authselect-compat package to provide the compatibility layer. Kickstart no longer supports Btrfs The Btrfs file system is not supported from Red Hat Enterprise Linux 8. As a result, the Graphical User Interface (GUI) and the Kickstart commands no longer support Btrfs. Using Kickstart files from RHEL releases If you are using Kickstart files from RHEL releases, see the Repositories section of the Considerations in adopting RHEL 8 document for more information about the Red Hat Enterprise Linux 8 BaseOS and AppStream repositories. 5.7.1. Deprecated Kickstart commands and options The following Kickstart commands and options have been deprecated in Red Hat Enterprise Linux 8. Where only specific options are listed, the base command and its other options are still available and not deprecated. auth or authconfig - use authselect instead device deviceprobe dmraid install - use the subcommands or methods directly as commands multipath bootloader --upgrade ignoredisk --interactive partition --active reboot --kexec syspurpose - use subscription-manager syspurpose instead Except the auth or authconfig command, using the commands in Kickstart files prints a warning in the logs. You can turn the deprecated command warnings into errors with the inst.ksstrict boot option, except for the auth or authconfig command. 5.7.2. Removed Kickstart commands and options The following Kickstart commands and options have been completely removed in Red Hat Enterprise Linux 8. Using them in Kickstart files will cause an error. device deviceprobe dmraid install - use the subcommands or methods directly as commands multipath bootloader --upgrade ignoredisk --interactive partition --active harddrive --biospart upgrade (This command had already previously been deprecated.) btrfs part/partition btrfs part --fstype btrfs or partition --fstype btrfs logvol --fstype btrfs raid --fstype btrfs unsupported_hardware Where only specific options and values are listed, the base command and its other options are still available and not removed. 5.8. Image creation 5.8.1. Custom system image creation with Image Builder The Image Builder tool enables users to create customized RHEL images. As of Red Hat Enterprise Linux 8.3, Image Builder runs as a system service osbuild-composer package. With Image Builder, users can create custom system images which include additional packages. Image Builder functionality can be accessed through: a graphical user interface in the web console a command-line interface in the composer-cli tool. Image Builder output formats include, among others: TAR archive qcow2 file for direct use with a virtual machine or OpenStack QEMU QCOW2 Image cloud images for Azure, VMWare and AWS To learn more about Image Builder, see the documentation title Composing a customized RHEL system image .	null	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/8/html/considerations_in_adopting_rhel_8/installer-and-image-creation_considerations-in-adopting-rhel-8
Chapter 12. Upgrading	Chapter 12. Upgrading For version upgrades, the Red Hat build of OpenTelemetry Operator uses the Operator Lifecycle Manager (OLM), which controls installation, upgrade, and role-based access control (RBAC) of Operators in a cluster. The OLM runs in the OpenShift Container Platform by default. The OLM queries for available Operators as well as upgrades for installed Operators. When the Red Hat build of OpenTelemetry Operator is upgraded to the new version, it scans for running OpenTelemetry Collector instances that it manages and upgrades them to the version corresponding to the Operator's new version. 12.1. Additional resources Operator Lifecycle Manager concepts and resources Updating installed Operators	null	https://docs.redhat.com/en/documentation/openshift_container_platform/4.13/html/red_hat_build_of_opentelemetry/dist-tracing-otel-updating
Chapter 5. Running .NET 9.0 applications in containers	Chapter 5. Running .NET 9.0 applications in containers Use the ubi8/dotnet-90-runtime image to run a .NET application inside a Linux container. The following example uses Podman. Procedure Create a new MVC project in a directory called mvc_runtime_example : Publish the project: Run your image: View the application running in the container:	[ "dotnet new mvc --output mvc_runtime_example", "dotnet publish mvc_runtime_example -f net9.0 /p:PublishProfile=DefaultContainer /p:ContainerBaseImage=registry.access.redhat.com/ubi8/dotnet-90-runtime:latest", "podman run -rm -p8080:8080 mvc_runtime_example", "xdg-open http://127.0.0.1:8080" ]	https://docs.redhat.com/en/documentation/net/9.0/html/getting_started_with_.net_on_rhel_9/running-apps-in-containers-using-dotnet_assembly_publishing-apps-using-dotnet
Chapter 3. Installing and preparing the Operators	Chapter 3. Installing and preparing the Operators You install the Red Hat OpenStack Services on OpenShift (RHOSO) OpenStack Operator ( openstack-operator ) and create the RHOSO control plane on an operational Red Hat OpenShift Container Platform (RHOCP) cluster. You install the OpenStack Operator by using the RHOCP web console. You perform the control plane installation tasks and all data plane creation tasks on a workstation that has access to the RHOCP cluster. 3.1. Prerequisites An operational RHOCP cluster, version 4.16. For the RHOCP system requirements, see Red Hat OpenShift Container Platform cluster requirements in Planning your deployment . The oc command line tool is installed on your workstation. You are logged in to the RHOCP cluster as a user with cluster-admin privileges. 3.2. Installing the OpenStack Operator You use OperatorHub on the Red Hat OpenShift Container Platform (RHOCP) web console to install the OpenStack Operator ( openstack-operator ) on your RHOCP cluster. Procedure Log in to the RHOCP web console as a user with cluster-admin permissions. Select Operators OperatorHub . In the Filter by keyword field, type OpenStack . Click the OpenStack Operator tile with the Red Hat source label. Read the information about the Operator and click Install . On the Install Operator page, select "Operator recommended Namespace: openstack-operators" from the Installed Namespace list. Click Install to make the Operator available to the openstack-operators namespace. The Operators are deployed and ready when the Status of the OpenStack Operator is Succeeded .	null	https://docs.redhat.com/en/documentation/red_hat_openstack_services_on_openshift/18.0/html/deploying_a_dynamic_routing_environment/assembly_installing-and-preparing-the-operators
Chapter 7. Logging in to the Identity Management Web UI using one time passwords	Chapter 7. Logging in to the Identity Management Web UI using one time passwords Access to IdM Web UI can be secured using several methods. The basic one is password authentication. To increase the security of password authentication, you can add a second step and require automatically generated one-time passwords (OTPs). The most common usage is to combine password connected with the user account and a time limited one time password generated by a hardware or software token. The following sections help you to: Understand how the OTP authentication works in IdM. Configure OTP authentication on the IdM server. Configure a RADIUS server for OTP validation in IdM. Create OTP tokens and synchronize them with the FreeOTP app in your phone. Authenticate to the IdM Web UI with the combination of user password and one time password. Re-synchronize tokens in the Web UI. Retrieve an IdM ticket-granting ticket as an OTP or RADIUS user Enforce OTP usage for all LDAP clients 7.1. Prerequisites Accessing the IdM Web UI in a web browser 7.2. One time password (OTP) authentication in Identity Management One-time passwords bring an additional step to your authentication security. The authentication uses your password + an automatically generated one time password. To generate one time passwords, you can use a hardware or software token. IdM supports both software and hardware tokens. Identity Management supports the following two standard OTP mechanisms: The HMAC-Based One-Time Password (HOTP) algorithm is based on a counter. HMAC stands for Hashed Message Authentication Code. The Time-Based One-Time Password (TOTP) algorithm is an extension of HOTP to support time-based moving factor. Important IdM does not support OTP logins for Active Directory trust users. 7.3. Enabling the one-time password in the Web UI Identity Management (IdM) administrators can enable two-factor authentication (2FA) for IdM users either globally or individually. The user enters the one-time password (OTP) after their regular password on the command line or in the dedicated field in the Web UI login dialog, with no space between these passwords. Enabling 2FA is not the same as enforcing it. If you use logins based on LDAP-binds, IdM users can still authenticate by entering a password only. However, if you use krb5 -based logins, the 2FA is enforced. Note that there is an option to enforce 2FA for LDAP-binds by enforcing OTP usage for all LDAP clients. For more information, see Enforcing OTP usage for all LDAP clients . In a future release, Red Hat plans to provide a configuration option for administrators to select one of the following: Allow users to set their own tokens. In this case, LDAP-binds are still not going to enforce 2FA though krb5 -based logins are. Not allow users to set their own tokens. In this case, 2FA is going to be enforced in both LDAP-binds and krb5 -based logins. Complete this procedure to use the IdM Web UI to enable 2FA for the individual example.user IdM user. Prerequisites Administration privileges Procedure Log in to the IdM Web UI with IdM admin privileges. Open the Identity Users Active users tab. Select example.user to open the user settings. In the User authentication types , select Two factor authentication (password + OTP) . Click Save . At this point, the OTP authentication is enabled for the IdM user. Now you or example.user must assign a new token ID to the example.user account. 7.4. Configuring a RADIUS server for OTP validation in IdM To enable the migration of a large deployment from a proprietary one-time password (OTP) solution to the Identity Management (IdM)-native OTP solution, IdM offers a way to offload OTP validation to a third-party RADIUS server for a subset of users. The administrator creates a set of RADIUS proxies where each proxy can only reference a single RADIUS server. If more than one server needs to be addressed, it is recommended to create a virtual IP solution that points to multiple RADIUS servers. Such a solution must be built outside of RHEL IdM with the help of the keepalived daemon, for example. The administrator then assigns one of these proxy sets to a user. As long as the user has a RADIUS proxy set assigned, IdM bypasses all other authentication mechanisms. Note IdM does not provide any token management or synchronization support for tokens in the third-party system. Complete the procedure to configure a RADIUS server for OTP validation and to add a user to the proxy server: Prerequisites The radius user authentication method is enabled. See Enabling the one-time password in the Web UI for details. Procedure Add a RADIUS proxy: The command prompts you for inserting the required information. The configuration of the RADIUS proxy requires the use of a common secret between the client and the server to wrap credentials. Specify this secret in the --secret parameter. Assign a user to the added proxy: If required, configure the user name to be sent to RADIUS: As a result, the RADIUS proxy server starts to process the user OTP authentication. When the user is ready to be migrated to the IdM native OTP system, you can simply remove the RADIUS proxy assignment for the user. 7.4.1. Changing the timeout value of a KDC when running a RADIUS server in a slow network In certain situations, such as running a RADIUS proxy in a slow network, the Identity Management (IdM) Kerberos Distribution Center (KDC) closes the connection before the RADIUS server responds because the connection timed out while waiting for the user to enter the token. To change the timeout settings of the KDC: Change the value of the timeout parameter in the [otp] section in the /var/kerberos/krb5kdc/kdc.conf file. For example, to set the timeout to 120 seconds: Restart the krb5kdc service: Additional resources How to configure FreeRADIUS authentication in FIPS mode (Red Hat Knowledgebase) 7.5. Adding OTP tokens in the Web UI The following section helps you to add token to the IdM Web UI and to your software token generator. Prerequisites Active user account on the IdM server. Administrator has enabled OTP for the particular user account in the IdM Web UI. A software device generating OTP tokens, for example FreeOTP. Procedure Log in to the IdM Web UI with your user name and password. To create the token in your mobile phone, open the Authentication OTP Tokens tab. Click Add . In the Add OTP token dialog box, leave everything unfilled and click Add . At this stage, the IdM server creates a token with default parameters at the server and opens a page with a QR code. Copy the QR code into your mobile phone. Click OK to close the QR code. Now you can generate one time passwords and log in with them to the IdM Web UI. 7.6. Logging into the Web UI with a one time password Follow this procedure to login for the first time into the IdM Web UI using a one time password (OTP). Prerequisites OTP configuration enabled on the Identity Management server for the user account you are using for the OTP authentication. Administrators as well as users themselves can enable OTP. To enable the OTP configuration, see Enabling the one time password in the Web UI . A hardware or software device generating OTP tokens configured. Procedure In the Identity Management login screen, enter your user name or a user name of the IdM server administrator account. Add the password for the user name entered above. Generate a one time password on your device. Enter the one time password right after the password (without space). Click Log in . If the authentication fails, synchronize OTP tokens. If your CA uses a self-signed certificate, the browser issues a warning. Check the certificate and accept the security exception to proceed with the login. If the IdM Web UI does not open, verify the DNS configuration of your Identity Management server. After successful login, the IdM Web UI appears. 7.7. Synchronizing OTP tokens using the Web UI If the login with OTP (One Time Password) fails, OTP tokens are not synchronized correctly. The following text describes token re-synchronization. Prerequisites A login screen opened. A device generating OTP tokens configured. Procedure On the IdM Web UI login screen, click Sync OTP Token . In the login screen, enter your username and the Identity Management password. Generate one time password and enter it in the First OTP field. Generate another one time password and enter it in the Second OTP field. Optional: Enter the token ID. Click Sync OTP Token . After the successful synchronization, you can log in to the IdM server. 7.8. Changing expired passwords Administrators of Identity Management can enforce you having to change your password at the login. It means that you cannot successfully log in to the IdM Web UI until you change the password. Password expiration can happen during your first login to the Web UI. If the expiration password dialog appears, follow the instructions in the procedure. Prerequisites A login screen opened. Active account to the IdM server. Procedure In the password expiration login screen, enter the user name. Add the password for the user name entered above. In the OTP field, generate a one time password, if you use the one time password authentication. If you do not have enabled the OTP authentication, leave the field empty. Enter the new password twice for verification. Click Reset Password . After the successful password change, the usual login dialog displays. Log in with the new password. 7.9. Retrieving an IdM ticket-granting ticket as an OTP or RADIUS user To retrieve a Kerberos ticket-granting ticket (TGT) as an OTP user, request an anonymous Kerberos ticket and enable Flexible Authentication via Secure Tunneling (FAST) channel to provide a secure connection between the Kerberos client and Kerberos Distribution Center (KDC). Prerequisites Your IdM client and IdM servers use RHEL 8.7 or later. Your IdM client and IdM servers use SSSD 2.7.0 or later. You have enabled OTP for the required user account. Procedure Initialize the credentials cache by running the following command: Note that this command creates the armor.ccache file that you need to point to whenever you request a new Kerberos ticket. Request a Kerberos ticket by running the command: Verification Display your Kerberos ticket information: The pa_type = 141 indicates OTP/RADIUS authentication. 7.10. Enforcing OTP usage for all LDAP clients In RHEL IdM, you can set the default behavior for LDAP server authentication of user accounts with two-factor (OTP) authentication configured. If OTP is enforced, LDAP clients cannot authenticate against an LDAP server using single-factor authentication (a password) for users that have associated OTP tokens. RHEL IdM already enforces this method through the Kerberos backend by using a special LDAP control with OID 2.16.840.1.113730.3.8.10.7 without any data. Procedure To enforce OTP usage for all LDAP clients, use the following command: To change back to the OTP behavior for all LDAP clients, use the following command:	[ "ipa radiusproxy-add proxy_name --secret secret", "ipa user-mod radiususer --radius=proxy_name", "ipa user-mod radiususer --radius-username=radius_user", "[otp] DEFAULT = { timeout = 120 }", "systemctl restart krb5kdc", "kinit -n @IDM.EXAMPLE.COM -c FILE:armor.ccache", "kinit -T FILE:armor.ccache <username>@IDM.EXAMPLE.COM Enter your OTP Token Value.", "klist -C Ticket cache: KCM:0:58420 Default principal: <username>@IDM.EXAMPLE.COM Valid starting Expires Service principal 05/09/22 07:48:23 05/10/22 07:03:07 krbtgt/[email protected] config: fast_avail(krbtgt/[email protected]) = yes 08/17/2022 20:22:45 08/18/2022 20:22:43 krbtgt/[email protected] config: pa_type(krbtgt/[email protected]) = 141", "ipa config-mod --addattr ipaconfigstring=EnforceLDAPOTP", "ipa config-mod --delattr ipaconfigstring=EnforceLDAPOTP" ]	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/8/html/accessing_identity_management_services/logging-in-to-the-ipa-web-ui-using-one-time-passwords_accessing-idm-services
14.3. DHCP Relay Agent	14.3. DHCP Relay Agent The DHCP Relay Agent ( dhcrelay ) enables the relay of DHCP and BOOTP requests from a subnet with no DHCP server on it to one or more DHCP servers on other subnets. When a DHCP client requests information, the DHCP Relay Agent forwards the request to the list of DHCP servers specified when the DHCP Relay Agent is started. When a DHCP server returns a reply, the reply is broadcast or unicast on the network that sent the original request. The DHCP Relay Agent for IPv4 , dhcrelay , listens for DHCPv4 and BOOTP requests on all interfaces unless the interfaces are specified in /etc/sysconfig/dhcrelay with the INTERFACES directive. See Section 14.3.1, "Configure dhcrelay as a DHCPv4 and BOOTP relay agent" . The DHCP Relay Agent for IPv6 , dhcrelay6 , does not have this default behavior and interfaces to listen for DHCPv6 requests must be specified. See Section 14.3.2, "Configure dhcrelay as a DHCPv6 relay agent" . dhcrelay can either be run as a DHCPv4 and BOOTP relay agent (by default) or as a DHCPv6 relay agent (with -6 argument). To see the usage message, issue the command dhcrelay -h . 14.3.1. Configure dhcrelay as a DHCPv4 and BOOTP relay agent To run dhcrelay in DHCPv4 and BOOTP mode specify the servers to which the requests should be forwarded to. Copy and then edit the dhcrelay.service file as the root user: Edit the ExecStart option under section [Service] and add one or more server IPv4 addresses to the end of the line, for example: ExecStart=/usr/sbin/dhcrelay -d --no-pid 192.168.1.1 If you also want to specify interfaces where the DHCP Relay Agent listens for DHCP requests, add them to the ExecStart option with -i argument (otherwise it will listen on all interfaces), for example: ExecStart=/usr/sbin/dhcrelay -d --no-pid 192.168.1.1 -i em1 For other options see the dhcrelay(8) man page. To activate the changes made, as the root user, restart the service: 14.3.2. Configure dhcrelay as a DHCPv6 relay agent To run dhcrelay in DHCPv6 mode add the -6 argument and specify the " lower interface " (on which queries will be received from clients or from other relay agents) and the " upper interface " (to which queries from clients and other relay agents should be forwarded). Copy dhcrelay.service to dhcrelay6.service and edit it as the root user: Edit the ExecStart option under section [Service] add -6 argument and add the " lower interface " and " upper interface " interface, for example: ExecStart=/usr/sbin/dhcrelay -d --no-pid -6 -l em1 -u em2 For other options see the dhcrelay(8) man page. To activate the changes made, as the root user, restart the service:	[ "~]# cp /lib/systemd/system/dhcrelay.service /etc/systemd/system/ ~]# vi /etc/systemd/system/dhcrelay.service", "~]# systemctl --system daemon-reload ~]# systemctl restart dhcrelay", "~]# cp /lib/systemd/system/dhcrelay.service /etc/systemd/system/dhcrelay6.service ~]# vi /etc/systemd/system/dhcrelay6.service", "~]# systemctl --system daemon-reload ~]# systemctl restart dhcrelay6" ]	https://docs.redhat.com/en/documentation/Red_Hat_Enterprise_Linux/7/html/networking_guide/dhcp-relay-agent
Red Hat Ansible Automation Platform automation mesh guide for VM-based installations	Red Hat Ansible Automation Platform automation mesh guide for VM-based installations Red Hat Ansible Automation Platform 2.4 Automate at scale in a cloud-native way Red Hat Customer Content Services	null	https://docs.redhat.com/en/documentation/red_hat_ansible_automation_platform/2.4/html/red_hat_ansible_automation_platform_automation_mesh_guide_for_vm-based_installations/index
Chapter 17. complete.adoc	Chapter 17. complete.adoc This chapter describes the commands under the complete.adoc command. 17.1. complete print bash completion command Usage: Table 17.1. Command arguments Value Summary -h, --help Show this help message and exit --name <command_name> Command name to support with command completion --shell <shell> Shell being used. use none for data only (default: bash)	[ "openstack complete [-h] [--name <command_name>] [--shell <shell>]" ]	https://docs.redhat.com/en/documentation/red_hat_openstack_platform/17.0/html/command_line_interface_reference/complete_adoc
Installing and Configuring Central Authentication for the Ansible Automation Platform	Installing and Configuring Central Authentication for the Ansible Automation Platform Red Hat Ansible Automation Platform 2.3 Enable central authentication functions for your Ansible Automation Platform Red Hat Customer Content Services	null	https://docs.redhat.com/en/documentation/red_hat_ansible_automation_platform/2.3/html/installing_and_configuring_central_authentication_for_the_ansible_automation_platform/index
Chapter 18. Rebalancing clusters using Cruise Control	Chapter 18. Rebalancing clusters using Cruise Control Cruise Control is an open source system that supports the following Kafka operations: Monitoring cluster workload Rebalancing a cluster based on predefined constraints The operations help with running a more balanced Kafka cluster that uses broker pods more efficiently. A typical cluster can become unevenly loaded over time. Partitions that handle large amounts of message traffic might not be evenly distributed across the available brokers. To rebalance the cluster, administrators must monitor the load on brokers and manually reassign busy partitions to brokers with spare capacity. Cruise Control automates the cluster rebalancing process. It constructs a workload model of resource utilization for the cluster- based on CPU, disk, and network load- and generates optimization proposals (that you can approve or reject) for more balanced partition assignments. A set of configurable optimization goals is used to calculate these proposals. You can generate optimization proposals in specific modes. The default full mode rebalances partitions across all brokers. You can also use the add-brokers and remove-brokers modes to accommodate changes when scaling a cluster up or down. When you approve an optimization proposal, Cruise Control applies it to your Kafka cluster. You configure and generate optimization proposals using a KafkaRebalance resource. You can configure the resource using an annotation so that optimization proposals are approved automatically or manually. Note AMQ Streams provides example configuration files for Cruise Control . 18.1. Cruise Control components and features Cruise Control consists of four main components- the Load Monitor, the Analyzer, the Anomaly Detector, and the Executor- and a REST API for client interactions. AMQ Streams utilizes the REST API to support the following Cruise Control features: Generating optimization proposals from optimization goals. Rebalancing a Kafka cluster based on an optimization proposal. Optimization goals An optimization goal describes a specific objective to achieve from a rebalance. For example, a goal might be to distribute topic replicas across brokers more evenly. You can change what goals to include through configuration. A goal is defined as a hard goal or soft goal. You can add hard goals through Cruise Control deployment configuration. You also have main, default, and user-provided goals that fit into each of these categories. Hard goals are preset and must be satisfied for an optimization proposal to be successful. Soft goals do not need to be satisfied for an optimization proposal to be successful. They can be set aside if it means that all hard goals are met. Main goals are inherited from Cruise Control. Some are preset as hard goals. Main goals are used in optimization proposals by default. Default goals are the same as the main goals by default. You can specify your own set of default goals. User-provided goals are a subset of default goals that are configured for generating a specific optimization proposal. Optimization proposals Optimization proposals comprise the goals you want to achieve from a rebalance. You generate an optimization proposal to create a summary of proposed changes and the results that are possible with the rebalance. The goals are assessed in a specific order of priority. You can then choose to approve or reject the proposal. You can reject the proposal to run it again with an adjusted set of goals. You can generate an optimization proposal in one of three modes. full is the default mode and runs a full rebalance. add-brokers is the mode you use after adding brokers when scaling up a Kafka cluster. remove-brokers is the mode you use before removing brokers when scaling down a Kafka cluster. Other Cruise Control features are not currently supported, including self healing, notifications, write-your-own goals, and changing the topic replication factor. Additional resources Cruise Control documentation 18.2. Optimization goals overview Optimization goals are constraints on workload redistribution and resource utilization across a Kafka cluster. To rebalance a Kafka cluster, Cruise Control uses optimization goals to generate optimization proposals , which you can approve or reject. 18.2.1. Goals order of priority AMQ Streams supports most of the optimization goals developed in the Cruise Control project. The supported goals, in the default descending order of priority, are as follows: Rack-awareness Minimum number of leader replicas per broker for a set of topics Replica capacity Capacity goals Disk capacity Network inbound capacity Network outbound capacity CPU capacity Replica distribution Potential network output Resource distribution goals Disk utilization distribution Network inbound utilization distribution Network outbound utilization distribution CPU utilization distribution Leader bytes-in rate distribution Topic replica distribution Leader replica distribution Preferred leader election Intra-broker disk capacity Intra-broker disk usage distribution For more information on each optimization goal, see Goals in the Cruise Control Wiki. Note "Write your own" goals and Kafka assigner goals are not yet supported. 18.2.2. Goals configuration in AMQ Streams custom resources You configure optimization goals in Kafka and KafkaRebalance custom resources. Cruise Control has configurations for hard optimization goals that must be satisfied, as well as main, default, and user-provided optimization goals. You can specify optimization goals in the following configuration: Main goals - Kafka.spec.cruiseControl.config.goals Hard goals - Kafka.spec.cruiseControl.config.hard.goals Default goals - Kafka.spec.cruiseControl.config.default.goals User-provided goals - KafkaRebalance.spec.goals Note Resource distribution goals are subject to capacity limits on broker resources. 18.2.3. Hard and soft optimization goals Hard goals are goals that must be satisfied in optimization proposals. Goals that are not configured as hard goals are known as soft goals . You can think of soft goals as best effort goals: they do not need to be satisfied in optimization proposals, but are included in optimization calculations. An optimization proposal that violates one or more soft goals, but satisfies all hard goals, is valid. Cruise Control will calculate optimization proposals that satisfy all the hard goals and as many soft goals as possible (in their priority order). An optimization proposal that does not satisfy all the hard goals is rejected by Cruise Control and not sent to the user for approval. Note For example, you might have a soft goal to distribute a topic's replicas evenly across the cluster (the topic replica distribution goal). Cruise Control will ignore this goal if doing so enables all the configured hard goals to be met. In Cruise Control, the following main optimization goals are preset as hard goals: You configure hard goals in the Cruise Control deployment configuration, by editing the hard.goals property in Kafka.spec.cruiseControl.config . To inherit the preset hard goals from Cruise Control, do not specify the hard.goals property in Kafka.spec.cruiseControl.config To change the preset hard goals, specify the desired goals in the hard.goals property, using their fully-qualified domain names. Example Kafka configuration for hard optimization goals apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... entityOperator: topicOperator: {} userOperator: {} cruiseControl: brokerCapacity: inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s config: # Note that `default.goals` (superset) must also include all `hard.goals` (subset) default.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal hard.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal # ... Increasing the number of configured hard goals will reduce the likelihood of Cruise Control generating valid optimization proposals. If skipHardGoalCheck: true is specified in the KafkaRebalance custom resource, Cruise Control does not check that the list of user-provided optimization goals (in KafkaRebalance.spec.goals ) contains all the configured hard goals ( hard.goals ). Therefore, if some, but not all, of the user-provided optimization goals are in the hard.goals list, Cruise Control will still treat them as hard goals even if skipHardGoalCheck: true is specified. 18.2.4. Main optimization goals The main optimization goals are available to all users. Goals that are not listed in the main optimization goals are not available for use in Cruise Control operations. Unless you change the Cruise Control deployment configuration , AMQ Streams will inherit the following main optimization goals from Cruise Control, in descending priority order: Some of these goals are preset as hard goals . To reduce complexity, we recommend that you use the inherited main optimization goals, unless you need to completely exclude one or more goals from use in KafkaRebalance resources. The priority order of the main optimization goals can be modified, if desired, in the configuration for default optimization goals . You configure main optimization goals, if necessary, in the Cruise Control deployment configuration: Kafka.spec.cruiseControl.config.goals To accept the inherited main optimization goals, do not specify the goals property in Kafka.spec.cruiseControl.config . If you need to modify the inherited main optimization goals, specify a list of goals, in descending priority order, in the goals configuration option. Note To avoid errors when generating optimization proposals, make sure that any changes you make to the goals or default.goals in Kafka.spec.cruiseControl.config include all of the hard goals specified for the hard.goals property. To clarify, the hard goals must also be specified (as a subset) for the main optimization goals and default goals. 18.2.5. Default optimization goals Cruise Control uses the default optimization goals to generate the cached optimization proposal . For more information about the cached optimization proposal, see Section 18.3, "Optimization proposals overview" . You can override the default optimization goals by setting user-provided optimization goals in a KafkaRebalance custom resource. Unless you specify default.goals in the Cruise Control deployment configuration , the main optimization goals are used as the default optimization goals. In this case, the cached optimization proposal is generated using the main optimization goals. To use the main optimization goals as the default goals, do not specify the default.goals property in Kafka.spec.cruiseControl.config . To modify the default optimization goals, edit the default.goals property in Kafka.spec.cruiseControl.config . You must use a subset of the main optimization goals. Example Kafka configuration for default optimization goals apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # ... zookeeper: # ... entityOperator: topicOperator: {} userOperator: {} cruiseControl: brokerCapacity: inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s config: # Note that `default.goals` (superset) must also include all `hard.goals` (subset) default.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal hard.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal # ... If no default optimization goals are specified, the cached proposal is generated using the main optimization goals. 18.2.6. User-provided optimization goals User-provided optimization goals narrow down the configured default goals for a particular optimization proposal. You can set them, as required, in spec.goals in a KafkaRebalance custom resource: User-provided optimization goals can generate optimization proposals for different scenarios. For example, you might want to optimize leader replica distribution across the Kafka cluster without considering disk capacity or disk utilization. So, you create a KafkaRebalance custom resource containing a single user-provided goal for leader replica distribution. User-provided optimization goals must: Include all configured hard goals , or an error occurs Be a subset of the main optimization goals To ignore the configured hard goals when generating an optimization proposal, add the skipHardGoalCheck: true property to the KafkaRebalance custom resource. See Section 18.6, "Generating optimization proposals" . Additional resources Configuring and deploying Cruise Control with Kafka Configurations in the Cruise Control Wiki. 18.3. Optimization proposals overview Configure a KafkaRebalance resource to generate optimization proposals and apply the suggested changes. An optimization proposal is a summary of proposed changes that would produce a more balanced Kafka cluster, with partition workloads distributed more evenly among the brokers. Each optimization proposal is based on the set of optimization goals that was used to generate it, subject to any configured capacity limits on broker resources . All optimization proposals are estimates of the impact of a proposed rebalance. You can approve or reject a proposal. You cannot approve a cluster rebalance without first generating the optimization proposal. You can run optimization proposals in one of the following rebalancing modes: full add-brokers remove-brokers 18.3.1. Rebalancing modes You specify a rebalancing mode using the spec.mode property of the KafkaRebalance custom resource. full The full mode runs a full rebalance by moving replicas across all the brokers in the cluster. This is the default mode if the spec.mode property is not defined in the KafkaRebalance custom resource. add-brokers The add-brokers mode is used after scaling up a Kafka cluster by adding one or more brokers. Normally, after scaling up a Kafka cluster, new brokers are used to host only the partitions of newly created topics. If no new topics are created, the newly added brokers are not used and the existing brokers remain under the same load. By using the add-brokers mode immediately after adding brokers to the cluster, the rebalancing operation moves replicas from existing brokers to the newly added brokers. You specify the new brokers as a list using the spec.brokers property of the KafkaRebalance custom resource. remove-brokers The remove-brokers mode is used before scaling down a Kafka cluster by removing one or more brokers. If you scale down a Kafka cluster, brokers are shut down even if they host replicas. This can lead to under-replicated partitions and possibly result in some partitions being under their minimum ISR (in-sync replicas). To avoid this potential problem, the remove-brokers mode moves replicas off the brokers that are going to be removed. When these brokers are not hosting replicas anymore, you can safely run the scaling down operation. You specify the brokers you're removing as a list in the spec.brokers property in the KafkaRebalance custom resource. In general, use the full rebalance mode to rebalance a Kafka cluster by spreading the load across brokers. Use the add-brokers and remove-brokers modes only if you want to scale your cluster up or down and rebalance the replicas accordingly. The procedure to run a rebalance is actually the same across the three different modes. The only difference is with specifying a mode through the spec.mode property and, if needed, listing brokers that have been added or will be removed through the spec.brokers property. 18.3.2. The results of an optimization proposal When an optimization proposal is generated, a summary and broker load is returned. Summary The summary is contained in the KafkaRebalance resource. The summary provides an overview of the proposed cluster rebalance and indicates the scale of the changes involved. A summary of a successfully generated optimization proposal is contained in the Status.OptimizationResult property of the KafkaRebalance resource. The information provided is a summary of the full optimization proposal. Broker load The broker load is stored in a ConfigMap that contains data as a JSON string. The broker load shows before and after values for the proposed rebalance, so you can see the impact on each of the brokers in the cluster. 18.3.3. Manually approving or rejecting an optimization proposal An optimization proposal summary shows the proposed scope of changes. You can use the name of the KafkaRebalance resource to return a summary from the command line. Returning an optimization proposal summary oc describe kafkarebalance <kafka_rebalance_resource_name> -n <namespace> You can also use the jq command line JSON parser tool. Returning an optimization proposal summary using jq oc get kafkarebalance -o json \| jq <jq_query> . Use the summary to decide whether to approve or reject an optimization proposal. Approving an optimization proposal You approve the optimization proposal by setting the strimzi.io/rebalance annotation of the KafkaRebalance resource to approve . Cruise Control applies the proposal to the Kafka cluster and starts a cluster rebalance operation. Rejecting an optimization proposal If you choose not to approve an optimization proposal, you can change the optimization goals or update any of the rebalance performance tuning options , and then generate another proposal. You can generate a new optimization proposal for a KafkaRebalance resource by setting the strimzi.io/rebalance annotation to refresh . Use optimization proposals to assess the movements required for a rebalance. For example, a summary describes inter-broker and intra-broker movements. Inter-broker rebalancing moves data between separate brokers. Intra-broker rebalancing moves data between disks on the same broker when you are using a JBOD storage configuration. Such information can be useful even if you don't go ahead and approve the proposal. You might reject an optimization proposal, or delay its approval, because of the additional load on a Kafka cluster when rebalancing. In the following example, the proposal suggests the rebalancing of data between separate brokers. The rebalance involves the movement of 55 partition replicas, totaling 12MB of data, across the brokers. Though the inter-broker movement of partition replicas has a high impact on performance, the total amount of data is not large. If the total data was much larger, you could reject the proposal, or time when to approve the rebalance to limit the impact on the performance of the Kafka cluster. Rebalance performance tuning options can help reduce the impact of data movement. If you can extend the rebalance period, you can divide the rebalance into smaller batches. Fewer data movements at a single time reduces the load on the cluster. Example optimization proposal summary Name: my-rebalance Namespace: myproject Labels: strimzi.io/cluster=my-cluster Annotations: API Version: kafka.strimzi.io/v1alpha1 Kind: KafkaRebalance Metadata: # ... Status: Conditions: Last Transition Time: 2022-04-05T14:36:11.900Z Status: ProposalReady Type: State Observed Generation: 1 Optimization Result: Data To Move MB: 0 Excluded Brokers For Leadership: Excluded Brokers For Replica Move: Excluded Topics: Intra Broker Data To Move MB: 12 Monitored Partitions Percentage: 100 Num Intra Broker Replica Movements: 0 Num Leader Movements: 24 Num Replica Movements: 55 On Demand Balancedness Score After: 82.91290759174306 On Demand Balancedness Score Before: 78.01176356230222 Recent Windows: 5 Session Id: a4f833bd-2055-4213-bfdd-ad21f95bf184 The proposal will also move 24 partition leaders to different brokers. This requires a change to the ZooKeeper configuration, which has a low impact on performance. The balancedness scores are measurements of the overall balance of the Kafka cluster before and after the optimization proposal is approved. A balancedness score is based on optimization goals. If all goals are satisfied, the score is 100. The score is reduced for each goal that will not be met. Compare the balancedness scores to see whether the Kafka cluster is less balanced than it could be following a rebalance. 18.3.4. Automatically approving an optimization proposal To save time, you can automate the process of approving optimization proposals. With automation, when you generate an optimization proposal it goes straight into a cluster rebalance. To enable the optimization proposal auto-approval mechanism, create the KafkaRebalance resource with the strimzi.io/rebalance-auto-approval annotation set to true . If the annotation is not set or set to false , the optimization proposal requires manual approval. Example rebalance request with auto-approval mechanism enabled apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster annotations: strimzi.io/rebalance-auto-approval: "true" spec: mode: # any mode # ... You can still check the status when automatically approving an optimization proposal. The status of the KafkaRebalance resource moves to Ready when the rebalance is complete. 18.3.5. Optimization proposal summary properties The following table explains the properties contained in the optimization proposal's summary section. Table 18.1. Properties contained in an optimization proposal summary JSON property Description numIntraBrokerReplicaMovements The total number of partition replicas that will be transferred between the disks of the cluster's brokers. Performance impact during rebalance operation : Relatively high, but lower than numReplicaMovements . excludedBrokersForLeadership Not yet supported. An empty list is returned. numReplicaMovements The number of partition replicas that will be moved between separate brokers. Performance impact during rebalance operation : Relatively high. onDemandBalancednessScoreBefore, onDemandBalancednessScoreAfter A measurement of the overall balancedness of a Kafka Cluster, before and after the optimization proposal was generated. The score is calculated by subtracting the sum of the BalancednessScore of each violated soft goal from 100. Cruise Control assigns a BalancednessScore to every optimization goal based on several factors, including priority- the goal's position in the list of default.goals or user-provided goals. The Before score is based on the current configuration of the Kafka cluster. The After score is based on the generated optimization proposal. intraBrokerDataToMoveMB The sum of the size of each partition replica that will be moved between disks on the same broker (see also numIntraBrokerReplicaMovements ). Performance impact during rebalance operation : Variable. The larger the number, the longer the cluster rebalance will take to complete. Moving a large amount of data between disks on the same broker has less impact than between separate brokers (see dataToMoveMB ). recentWindows The number of metrics windows upon which the optimization proposal is based. dataToMoveMB The sum of the size of each partition replica that will be moved to a separate broker (see also numReplicaMovements ). Performance impact during rebalance operation : Variable. The larger the number, the longer the cluster rebalance will take to complete. monitoredPartitionsPercentage The percentage of partitions in the Kafka cluster covered by the optimization proposal. Affected by the number of excludedTopics . excludedTopics If you specified a regular expression in the spec.excludedTopicsRegex property in the KafkaRebalance resource, all topic names matching that expression are listed here. These topics are excluded from the calculation of partition replica/leader movements in the optimization proposal. numLeaderMovements The number of partitions whose leaders will be switched to different replicas. This involves a change to ZooKeeper configuration. Performance impact during rebalance operation : Relatively low. excludedBrokersForReplicaMove Not yet supported. An empty list is returned. 18.3.6. Broker load properties The broker load is stored in a ConfigMap (with the same name as the KafkaRebalance custom resource) as a JSON formatted string. This JSON string consists of a JSON object with keys for each broker IDs linking to a number of metrics for each broker. Each metric consist of three values. The first is the metric value before the optimization proposal is applied, the second is the expected value of the metric after the proposal is applied, and the third is the difference between the first two values (after minus before). Note The ConfigMap appears when the KafkaRebalance resource is in the ProposalReady state and remains after the rebalance is complete. You can use the name of the ConfigMap to view its data from the command line. Returning ConfigMap data oc describe configmaps <my_rebalance_configmap_name> -n <namespace> You can also use the jq command line JSON parser tool to extract the JSON string from the ConfigMap. Extracting the JSON string from the ConfigMap using jq oc get configmaps <my_rebalance_configmap_name> -o json \| jq '.["data"]["brokerLoad.json"]\|fromjson\|.' The following table explains the properties contained in the optimization proposal's broker load ConfigMap: JSON property Description leaders The number of replicas on this broker that are partition leaders. replicas The number of replicas on this broker. cpuPercentage The CPU utilization as a percentage of the defined capacity. diskUsedPercentage The disk utilization as a percentage of the defined capacity. diskUsedMB The absolute disk usage in MB. networkOutRate The total network output rate for the broker. leaderNetworkInRate The network input rate for all partition leader replicas on this broker. followerNetworkInRate The network input rate for all follower replicas on this broker. potentialMaxNetworkOutRate The hypothetical maximum network output rate that would be realized if this broker became the leader of all the replicas it currently hosts. 18.3.7. Cached optimization proposal Cruise Control maintains a cached optimization proposal based on the configured default optimization goals. Generated from the workload model, the cached optimization proposal is updated every 15 minutes to reflect the current state of the Kafka cluster. If you generate an optimization proposal using the default optimization goals, Cruise Control returns the most recent cached proposal. To change the cached optimization proposal refresh interval, edit the proposal.expiration.ms setting in the Cruise Control deployment configuration. Consider a shorter interval for fast changing clusters, although this increases the load on the Cruise Control server. Additional resources Section 18.2, "Optimization goals overview" Section 18.6, "Generating optimization proposals" Section 18.7, "Approving an optimization proposal" 18.4. Rebalance performance tuning overview You can adjust several performance tuning options for cluster rebalances. These options control how partition replica and leadership movements in a rebalance are executed, as well as the bandwidth that is allocated to a rebalance operation. 18.4.1. Partition reassignment commands Optimization proposals are comprised of separate partition reassignment commands. When you approve a proposal, the Cruise Control server applies these commands to the Kafka cluster. A partition reassignment command consists of either of the following types of operations: Partition movement: Involves transferring the partition replica and its data to a new location. Partition movements can take one of two forms: Inter-broker movement: The partition replica is moved to a log directory on a different broker. Intra-broker movement: The partition replica is moved to a different log directory on the same broker. Leadership movement: This involves switching the leader of the partition's replicas. Cruise Control issues partition reassignment commands to the Kafka cluster in batches. The performance of the cluster during the rebalance is affected by the number of each type of movement contained in each batch. 18.4.2. Replica movement strategies Cluster rebalance performance is also influenced by the replica movement strategy that is applied to the batches of partition reassignment commands. By default, Cruise Control uses the BaseReplicaMovementStrategy , which simply applies the commands in the order they were generated. However, if there are some very large partition reassignments early in the proposal, this strategy can slow down the application of the other reassignments. Cruise Control provides four alternative replica movement strategies that can be applied to optimization proposals: PrioritizeSmallReplicaMovementStrategy : Order reassignments in order of ascending size. PrioritizeLargeReplicaMovementStrategy : Order reassignments in order of descending size. PostponeUrpReplicaMovementStrategy : Prioritize reassignments for replicas of partitions which have no out-of-sync replicas. PrioritizeMinIsrWithOfflineReplicasStrategy : Prioritize reassignments with (At/Under)MinISR partitions with offline replicas. This strategy will only work if cruiseControl.config.concurrency.adjuster.min.isr.check.enabled is set to true in the Kafka custom resource's spec. These strategies can be configured as a sequence. The first strategy attempts to compare two partition reassignments using its internal logic. If the reassignments are equivalent, then it passes them to the strategy in the sequence to decide the order, and so on. 18.4.3. Intra-broker disk balancing Moving a large amount of data between disks on the same broker has less impact than between separate brokers. If you are running a Kafka deployment that uses JBOD storage with multiple disks on the same broker, Cruise Control can balance partitions between the disks. Note If you are using JBOD storage with a single disk, intra-broker disk balancing will result in a proposal with 0 partition movements since there are no disks to balance between. To perform an intra-broker disk balance, set rebalanceDisk to true under the KafkaRebalance.spec . When setting rebalanceDisk to true , do not set a goals field in the KafkaRebalance.spec , as Cruise Control will automatically set the intra-broker goals and ignore the inter-broker goals. Cruise Control does not perform inter-broker and intra-broker balancing at the same time. 18.4.4. Rebalance tuning options Cruise Control provides several configuration options for tuning the rebalance parameters discussed above. You can set these tuning options when configuring and deploying Cruise Control with Kafka or optimization proposal levels: The Cruise Control server setting can be set in the Kafka custom resource under Kafka.spec.cruiseControl.config . The individual rebalance performance configurations can be set under KafkaRebalance.spec . The relevant configurations are summarized in the following table. Table 18.2. Rebalance performance tuning configuration Cruise Control properties KafkaRebalance properties Default Description num.concurrent.partition.movements.per.broker concurrentPartitionMovementsPerBroker 5 The maximum number of inter-broker partition movements in each partition reassignment batch num.concurrent.intra.broker.partition.movements concurrentIntraBrokerPartitionMovements 2 The maximum number of intra-broker partition movements in each partition reassignment batch num.concurrent.leader.movements concurrentLeaderMovements 1000 The maximum number of partition leadership changes in each partition reassignment batch default.replication.throttle replicationThrottle Null (no limit) The bandwidth (in bytes per second) to assign to partition reassignment default.replica.movement.strategies replicaMovementStrategies BaseReplicaMovementStrategy The list of strategies (in priority order) used to determine the order in which partition reassignment commands are executed for generated proposals. For the server setting, use a comma separated string with the fully qualified names of the strategy class (add com.linkedin.kafka.cruisecontrol.executor.strategy. to the start of each class name). For the KafkaRebalance resource setting use a YAML array of strategy class names. - rebalanceDisk false Enables intra-broker disk balancing, which balances disk space utilization between disks on the same broker. Only applies to Kafka deployments that use JBOD storage with multiple disks. Changing the default settings affects the length of time that the rebalance takes to complete, as well as the load placed on the Kafka cluster during the rebalance. Using lower values reduces the load but increases the amount of time taken, and vice versa. Additional resources CruiseControlSpec schema reference KafkaRebalanceSpec schema reference 18.5. Configuring and deploying Cruise Control with Kafka Configure a Kafka resource to deploy Cruise Control alongside a Kafka cluster. You can use the cruiseControl properties of the Kafka resource to configure the deployment. Deploy one instance of Cruise Control per Kafka cluster. Use goals configuration in the Cruise Control config to specify optimization goals for generating optimization proposals. You can use brokerCapacity to change the default capacity limits for goals related to resource distribution. If brokers are running on nodes with heterogeneous network resources, you can use overrides to set network capacity limits for each broker. If an empty object ( {} ) is used for the cruiseControl configuration, all properties use their default values. For more information on the configuration options for Cruise Control, see the AMQ Streams Custom Resource API Reference . Prerequisites An OpenShift cluster A running Cluster Operator Procedure Edit the cruiseControl property for the Kafka resource. The properties you can configure are shown in this example configuration: apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: # ... cruiseControl: brokerCapacity: 1 inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s overrides: 2 - brokers: [0] inboundNetwork: 20000KiB/s outboundNetwork: 20000KiB/s - brokers: [1, 2] inboundNetwork: 30000KiB/s outboundNetwork: 30000KiB/s # ... config: 3 # Note that `default.goals` (superset) must also include all `hard.goals` (subset) default.goals: > 4 com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal # ... hard.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal # ... cpu.balance.threshold: 1.1 metadata.max.age.ms: 300000 send.buffer.bytes: 131072 webserver.http.cors.enabled: true 5 webserver.http.cors.origin: "*" webserver.http.cors.exposeheaders: "User-Task-ID,Content-Type" # ... resources: 6 requests: cpu: 1 memory: 512Mi limits: cpu: 2 memory: 2Gi logging: 7 type: inline loggers: rootLogger.level: INFO template: 8 pod: metadata: labels: label1: value1 securityContext: runAsUser: 1000001 fsGroup: 0 terminationGracePeriodSeconds: 120 readinessProbe: 9 initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 metricsConfig: 10 type: jmxPrometheusExporter valueFrom: configMapKeyRef: name: cruise-control-metrics key: metrics-config.yml # ... 1 Capacity limits for broker resources. 2 Overrides set network capacity limits for specific brokers when running on nodes with heterogeneous network resources. 3 Cruise Control configuration. Standard Cruise Control configuration may be provided, restricted to those properties not managed directly by AMQ Streams. 4 Optimization goals configuration, which can include configuration for default optimization goals ( default.goals ), main optimization goals ( goals ), and hard goals ( hard.goals ). 5 CORS enabled and configured for read-only access to the Cruise Control API. 6 Requests for reservation of supported resources, currently cpu and memory , and limits to specify the maximum resources that can be consumed. 7 Cruise Control loggers and log levels added directly ( inline ) or indirectly ( external ) through a ConfigMap. A custom Log4j configuration must be placed under the log4j.properties key in the ConfigMap. Cruise Control has a single logger named rootLogger.level . You can set the log level to INFO, ERROR, WARN, TRACE, DEBUG, FATAL or OFF. 8 Template customization. Here a pod is scheduled with additional security attributes. 9 Healthchecks to know when to restart a container (liveness) and when a container can accept traffic (readiness). 10 Prometheus metrics enabled. In this example, metrics are configured for the Prometheus JMX Exporter (the default metrics exporter). Create or update the resource: oc apply -f <kafka_configuration_file> Check the status of the deployment: oc get deployments -n <my_cluster_operator_namespace> Output shows the deployment name and readiness NAME READY UP-TO-DATE AVAILABLE my-cluster-cruise-control 1/1 1 1 my-cluster is the name of the Kafka cluster. READY shows the number of replicas that are ready/expected. The deployment is successful when the AVAILABLE output shows 1 . Auto-created topics The following table shows the three topics that are automatically created when Cruise Control is deployed. These topics are required for Cruise Control to work properly and must not be deleted or changed. You can change the name of the topic using the specified configuration option. Table 18.3. Auto-created topics Auto-created topic configuration Default topic name Created by Function metric.reporter.topic strimzi.cruisecontrol.metrics AMQ Streams Metrics Reporter Stores the raw metrics from the Metrics Reporter in each Kafka broker. partition.metric.sample.store.topic strimzi.cruisecontrol.partitionmetricsamples Cruise Control Stores the derived metrics for each partition. These are created by the Metric Sample Aggregator . broker.metric.sample.store.topic strimzi.cruisecontrol.modeltrainingsamples Cruise Control Stores the metrics samples used to create the Cluster Workload Model . To prevent the removal of records that are needed by Cruise Control, log compaction is disabled in the auto-created topics. Note If the names of the auto-created topics are changed in a Kafka cluster that already has Cruise Control enabled, the old topics will not be deleted and should be manually removed. What to do After configuring and deploying Cruise Control, you can generate optimization proposals . Additional resources Optimization goals overview 18.6. Generating optimization proposals When you create or update a KafkaRebalance resource, Cruise Control generates an optimization proposal for the Kafka cluster based on the configured optimization goals . Analyze the information in the optimization proposal and decide whether to approve it. You can use the results of the optimization proposal to rebalance your Kafka cluster. You can run the optimization proposal in one of the following modes: full (default) add-brokers remove-brokers The mode you use depends on whether you are rebalancing across all the brokers already running in the Kafka cluster; or you want to rebalance after scaling up or before scaling down your Kafka cluster. For more information, see Rebalancing modes with broker scaling . Prerequisites You have deployed Cruise Control to your AMQ Streams cluster. You have configured optimization goals and, optionally, capacity limits on broker resources. For more information on configuring Cruise Control, see Section 18.5, "Configuring and deploying Cruise Control with Kafka" . Procedure Create a KafkaRebalance resource and specify the appropriate mode. full mode (default) To use the default optimization goals defined in the Kafka resource, leave the spec property empty. Cruise Control rebalances a Kafka cluster in full mode by default. Example configuration with full rebalancing by default apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: {} You can also run a full rebalance by specifying the full mode through the spec.mode property. Example configuration specifying full mode apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: mode: full add-brokers mode If you want to rebalance a Kafka cluster after scaling up, specify the add-brokers mode. In this mode, existing replicas are moved to the newly added brokers. You need to specify the brokers as a list. Example configuration specifying add-brokers mode apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: mode: add-brokers brokers: [3, 4] 1 1 List of newly added brokers added by the scale up operation. This property is mandatory. remove-brokers mode If you want to rebalance a Kafka cluster before scaling down, specify the remove-brokers mode. In this mode, replicas are moved off the brokers that are going to be removed. You need to specify the brokers that are being removed as a list. Example configuration specifying remove-brokers mode apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: mode: remove-brokers brokers: [3, 4] 1 1 List of brokers to be removed by the scale down operation. This property is mandatory. Note The following steps and the steps to approve or stop a rebalance are the same regardless of the rebalance mode you are using. To configure user-provided optimization goals instead of using the default goals, add the goals property and enter one or more goals. In the following example, rack awareness and replica capacity are configured as user-provided optimization goals: apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: goals: - RackAwareGoal - ReplicaCapacityGoal To ignore the configured hard goals, add the skipHardGoalCheck: true property: apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: goals: - RackAwareGoal - ReplicaCapacityGoal skipHardGoalCheck: true (Optional) To approve the optimization proposal automatically, set the strimzi.io/rebalance-auto-approval annotation to true : apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster annotations: strimzi.io/rebalance-auto-approval: "true" spec: goals: - RackAwareGoal - ReplicaCapacityGoal skipHardGoalCheck: true Create or update the resource: oc apply -f <kafka_rebalance_configuration_file> The Cluster Operator requests the optimization proposal from Cruise Control. This might take a few minutes depending on the size of the Kafka cluster. If you used the automatic approval mechanism, wait for the status of the optimization proposal to change to Ready . If you haven't enabled the automatic approval mechanism, wait for the status of the optimization proposal to change to ProposalReady : oc get kafkarebalance -o wide -w -n <namespace> PendingProposal A PendingProposal status means the rebalance operator is polling the Cruise Control API to check if the optimization proposal is ready. ProposalReady A ProposalReady status means the optimization proposal is ready for review and approval. When the status changes to ProposalReady , the optimization proposal is ready to approve. Review the optimization proposal. The optimization proposal is contained in the Status.Optimization Result property of the KafkaRebalance resource. oc describe kafkarebalance <kafka_rebalance_resource_name> Example optimization proposal Status: Conditions: Last Transition Time: 2020-05-19T13:50:12.533Z Status: ProposalReady Type: State Observed Generation: 1 Optimization Result: Data To Move MB: 0 Excluded Brokers For Leadership: Excluded Brokers For Replica Move: Excluded Topics: Intra Broker Data To Move MB: 0 Monitored Partitions Percentage: 100 Num Intra Broker Replica Movements: 0 Num Leader Movements: 0 Num Replica Movements: 26 On Demand Balancedness Score After: 81.8666802863978 On Demand Balancedness Score Before: 78.01176356230222 Recent Windows: 1 Session Id: 05539377-ca7b-45ef-b359-e13564f1458c The properties in the Optimization Result section describe the pending cluster rebalance operation. For descriptions of each property, see Contents of optimization proposals . Insufficient CPU capacity If a Kafka cluster is overloaded in terms of CPU utilization, you might see an insufficient CPU capacity error in the KafkaRebalance status. It's worth noting that this utilization value is unaffected by the excludedTopics configuration. Although optimization proposals will not reassign replicas of excluded topics, their load is still considered in the utilization calculation. Example CPU utilization error com.linkedin.kafka.cruisecontrol.exception.OptimizationFailureException: [CpuCapacityGoal] Insufficient capacity for cpu (Utilization 615.21, Allowed Capacity 420.00, Threshold: 0.70). Add at least 3 brokers with the same cpu capacity (100.00) as broker-0. Add at least 3 brokers with the same cpu capacity (100.00) as broker-0. Note The error shows CPU capacity as a percentage rather than the number of CPU cores. For this reason, it does not directly map to the number of CPUs configured in the Kafka custom resource. It is like having a single virtual CPU per broker, which has the cycles of the CPUs configured in Kafka.spec.kafka.resources.limits.cpu . This has no effect on the rebalance behavior, since the ratio between CPU utilization and capacity remains the same. What to do Section 18.7, "Approving an optimization proposal" Additional resources Section 18.3, "Optimization proposals overview" 18.7. Approving an optimization proposal You can approve an optimization proposal generated by Cruise Control, if its status is ProposalReady . Cruise Control will then apply the optimization proposal to the Kafka cluster, reassigning partitions to brokers and changing partition leadership. Caution This is not a dry run. Before you approve an optimization proposal, you must: Refresh the proposal in case it has become out of date. Carefully review the contents of the proposal . Prerequisites You have generated an optimization proposal from Cruise Control. The KafkaRebalance custom resource status is ProposalReady . Procedure Perform these steps for the optimization proposal that you want to approve. Unless the optimization proposal is newly generated, check that it is based on current information about the state of the Kafka cluster. To do so, refresh the optimization proposal to make sure it uses the latest cluster metrics: Annotate the KafkaRebalance resource in OpenShift with strimzi.io/rebalance=refresh : oc annotate kafkarebalance <kafka_rebalance_resource_name> strimzi.io/rebalance=refresh Wait for the status of the optimization proposal to change to ProposalReady : oc get kafkarebalance -o wide -w -n <namespace> PendingProposal A PendingProposal status means the rebalance operator is polling the Cruise Control API to check if the optimization proposal is ready. ProposalReady A ProposalReady status means the optimization proposal is ready for review and approval. When the status changes to ProposalReady , the optimization proposal is ready to approve. Approve the optimization proposal that you want Cruise Control to apply. Annotate the KafkaRebalance resource in OpenShift with strimzi.io/rebalance=approve : oc annotate kafkarebalance <kafka_rebalance_resource_name> strimzi.io/rebalance=approve The Cluster Operator detects the annotated resource and instructs Cruise Control to rebalance the Kafka cluster. Wait for the status of the optimization proposal to change to Ready : oc get kafkarebalance -o wide -w -n <namespace> Rebalancing A Rebalancing status means the rebalancing is in progress. Ready A Ready status means the rebalance is complete. NotReady A NotReady status means an error occurred- see Fixing problems with a KafkaRebalance resource . When the status changes to Ready , the rebalance is complete. To use the same KafkaRebalance custom resource to generate another optimization proposal, apply the refresh annotation to the custom resource. This moves the custom resource to the PendingProposal or ProposalReady state. You can then review the optimization proposal and approve it, if desired. Additional resources Section 18.3, "Optimization proposals overview" Section 18.8, "Stopping a cluster rebalance" 18.8. Stopping a cluster rebalance Once started, a cluster rebalance operation might take some time to complete and affect the overall performance of the Kafka cluster. If you want to stop a cluster rebalance operation that is in progress, apply the stop annotation to the KafkaRebalance custom resource. This instructs Cruise Control to finish the current batch of partition reassignments and then stop the rebalance. When the rebalance has stopped, completed partition reassignments have already been applied; therefore, the state of the Kafka cluster is different when compared to prior to the start of the rebalance operation. If further rebalancing is required, you should generate a new optimization proposal. Note The performance of the Kafka cluster in the intermediate (stopped) state might be worse than in the initial state. Prerequisites You have approved the optimization proposal by annotating the KafkaRebalance custom resource with approve . The status of the KafkaRebalance custom resource is Rebalancing . Procedure Annotate the KafkaRebalance resource in OpenShift: oc annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=stop Check the status of the KafkaRebalance resource: oc describe kafkarebalance rebalance-cr-name Wait until the status changes to Stopped . Additional resources Section 18.3, "Optimization proposals overview" 18.9. Fixing problems with a KafkaRebalance resource If an issue occurs when creating a KafkaRebalance resource or interacting with Cruise Control, the error is reported in the resource status, along with details of how to fix it. The resource also moves to the NotReady state. To continue with the cluster rebalance operation, you must fix the problem in the KafkaRebalance resource itself or with the overall Cruise Control deployment. Problems might include the following: A misconfigured parameter in the KafkaRebalance resource. The strimzi.io/cluster label for specifying the Kafka cluster in the KafkaRebalance resource is missing. The Cruise Control server is not deployed as the cruiseControl property in the Kafka resource is missing. The Cruise Control server is not reachable. After fixing the issue, you need to add the refresh annotation to the KafkaRebalance resource. During a "refresh", a new optimization proposal is requested from the Cruise Control server. Prerequisites You have approved an optimization proposal . The status of the KafkaRebalance custom resource for the rebalance operation is NotReady . Procedure Get information about the error from the KafkaRebalance status: oc describe kafkarebalance rebalance-cr-name Attempt to resolve the issue in the KafkaRebalance resource. Annotate the KafkaRebalance resource in OpenShift: oc annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=refresh Check the status of the KafkaRebalance resource: oc describe kafkarebalance rebalance-cr-name Wait until the status changes to PendingProposal , or directly to ProposalReady . Additional resources Section 18.3, "Optimization proposals overview"	[ "RackAwareGoal; MinTopicLeadersPerBrokerGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal", "apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # zookeeper: # entityOperator: topicOperator: {} userOperator: {} cruiseControl: brokerCapacity: inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s config: # Note that `default.goals` (superset) must also include all `hard.goals` (subset) default.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal hard.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkInboundCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.NetworkOutboundCapacityGoal #", "RackAwareGoal; ReplicaCapacityGoal; DiskCapacityGoal; NetworkInboundCapacityGoal; NetworkOutboundCapacityGoal; CpuCapacityGoal; ReplicaDistributionGoal; PotentialNwOutGoal; DiskUsageDistributionGoal; NetworkInboundUsageDistributionGoal; NetworkOutboundUsageDistributionGoal; CpuUsageDistributionGoal; TopicReplicaDistributionGoal; LeaderReplicaDistributionGoal; LeaderBytesInDistributionGoal; PreferredLeaderElectionGoal", "apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: kafka: # zookeeper: # entityOperator: topicOperator: {} userOperator: {} cruiseControl: brokerCapacity: inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s config: # Note that `default.goals` (superset) must also include all `hard.goals` (subset) default.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal hard.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal #", "KafkaRebalance.spec.goals", "describe kafkarebalance <kafka_rebalance_resource_name> -n <namespace>", "get kafkarebalance -o json \| jq <jq_query> .", "Name: my-rebalance Namespace: myproject Labels: strimzi.io/cluster=my-cluster Annotations: API Version: kafka.strimzi.io/v1alpha1 Kind: KafkaRebalance Metadata: Status: Conditions: Last Transition Time: 2022-04-05T14:36:11.900Z Status: ProposalReady Type: State Observed Generation: 1 Optimization Result: Data To Move MB: 0 Excluded Brokers For Leadership: Excluded Brokers For Replica Move: Excluded Topics: Intra Broker Data To Move MB: 12 Monitored Partitions Percentage: 100 Num Intra Broker Replica Movements: 0 Num Leader Movements: 24 Num Replica Movements: 55 On Demand Balancedness Score After: 82.91290759174306 On Demand Balancedness Score Before: 78.01176356230222 Recent Windows: 5 Session Id: a4f833bd-2055-4213-bfdd-ad21f95bf184", "apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster annotations: strimzi.io/rebalance-auto-approval: \"true\" spec: mode: # any mode #", "describe configmaps <my_rebalance_configmap_name> -n <namespace>", "get configmaps <my_rebalance_configmap_name> -o json \| jq '.[\"data\"][\"brokerLoad.json\"]\|fromjson\|.'", "apiVersion: kafka.strimzi.io/v1beta2 kind: Kafka metadata: name: my-cluster spec: # cruiseControl: brokerCapacity: 1 inboundNetwork: 10000KB/s outboundNetwork: 10000KB/s overrides: 2 - brokers: [0] inboundNetwork: 20000KiB/s outboundNetwork: 20000KiB/s - brokers: [1, 2] inboundNetwork: 30000KiB/s outboundNetwork: 30000KiB/s # config: 3 # Note that `default.goals` (superset) must also include all `hard.goals` (subset) default.goals: > 4 com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.ReplicaCapacityGoal, com.linkedin.kafka.cruisecontrol.analyzer.goals.DiskCapacityGoal # hard.goals: > com.linkedin.kafka.cruisecontrol.analyzer.goals.RackAwareGoal # cpu.balance.threshold: 1.1 metadata.max.age.ms: 300000 send.buffer.bytes: 131072 webserver.http.cors.enabled: true 5 webserver.http.cors.origin: \"*\" webserver.http.cors.exposeheaders: \"User-Task-ID,Content-Type\" # resources: 6 requests: cpu: 1 memory: 512Mi limits: cpu: 2 memory: 2Gi logging: 7 type: inline loggers: rootLogger.level: INFO template: 8 pod: metadata: labels: label1: value1 securityContext: runAsUser: 1000001 fsGroup: 0 terminationGracePeriodSeconds: 120 readinessProbe: 9 initialDelaySeconds: 15 timeoutSeconds: 5 livenessProbe: initialDelaySeconds: 15 timeoutSeconds: 5 metricsConfig: 10 type: jmxPrometheusExporter valueFrom: configMapKeyRef: name: cruise-control-metrics key: metrics-config.yml", "apply -f <kafka_configuration_file>", "get deployments -n <my_cluster_operator_namespace>", "NAME READY UP-TO-DATE AVAILABLE my-cluster-cruise-control 1/1 1 1", "apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: {}", "apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: mode: full", "apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: mode: add-brokers brokers: [3, 4] 1", "apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: mode: remove-brokers brokers: [3, 4] 1", "apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: goals: - RackAwareGoal - ReplicaCapacityGoal", "apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster spec: goals: - RackAwareGoal - ReplicaCapacityGoal skipHardGoalCheck: true", "apiVersion: kafka.strimzi.io/v1beta2 kind: KafkaRebalance metadata: name: my-rebalance labels: strimzi.io/cluster: my-cluster annotations: strimzi.io/rebalance-auto-approval: \"true\" spec: goals: - RackAwareGoal - ReplicaCapacityGoal skipHardGoalCheck: true", "apply -f <kafka_rebalance_configuration_file>", "get kafkarebalance -o wide -w -n <namespace>", "describe kafkarebalance <kafka_rebalance_resource_name>", "Status: Conditions: Last Transition Time: 2020-05-19T13:50:12.533Z Status: ProposalReady Type: State Observed Generation: 1 Optimization Result: Data To Move MB: 0 Excluded Brokers For Leadership: Excluded Brokers For Replica Move: Excluded Topics: Intra Broker Data To Move MB: 0 Monitored Partitions Percentage: 100 Num Intra Broker Replica Movements: 0 Num Leader Movements: 0 Num Replica Movements: 26 On Demand Balancedness Score After: 81.8666802863978 On Demand Balancedness Score Before: 78.01176356230222 Recent Windows: 1 Session Id: 05539377-ca7b-45ef-b359-e13564f1458c", "com.linkedin.kafka.cruisecontrol.exception.OptimizationFailureException: [CpuCapacityGoal] Insufficient capacity for cpu (Utilization 615.21, Allowed Capacity 420.00, Threshold: 0.70). Add at least 3 brokers with the same cpu capacity (100.00) as broker-0. Add at least 3 brokers with the same cpu capacity (100.00) as broker-0.", "annotate kafkarebalance <kafka_rebalance_resource_name> strimzi.io/rebalance=refresh", "get kafkarebalance -o wide -w -n <namespace>", "annotate kafkarebalance <kafka_rebalance_resource_name> strimzi.io/rebalance=approve", "get kafkarebalance -o wide -w -n <namespace>", "annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=stop", "describe kafkarebalance rebalance-cr-name", "describe kafkarebalance rebalance-cr-name", "annotate kafkarebalance rebalance-cr-name strimzi.io/rebalance=refresh", "describe kafkarebalance rebalance-cr-name" ]	https://docs.redhat.com/en/documentation/red_hat_streams_for_apache_kafka/2.5/html/deploying_and_managing_amq_streams_on_openshift/cruise-control-concepts-str
Deploying the Shared File Systems service with CephFS through NFS	Deploying the Shared File Systems service with CephFS through NFS Red Hat OpenStack Platform 16.0 Understanding, using, and managing the Shared File Systems service with CephFS through NFS in Red Hat OpenStack Platform OpenStack Documentation Team [email protected]	null	https://docs.redhat.com/en/documentation/red_hat_openstack_platform/16.0/html/deploying_the_shared_file_systems_service_with_cephfs_through_nfs/index
Chapter 2. Resources for troubleshooting automation controller	Chapter 2. Resources for troubleshooting automation controller For information about troubleshooting automation controller, see Troubleshooting automation controller in the Automation Controller Administration Guide. For information about troubleshooting the performance of automation controller, see Performance troubleshooting for automation controller in the Automation Controller Administration Guide.	null	https://docs.redhat.com/en/documentation/red_hat_ansible_automation_platform/2.4/html/troubleshooting_ansible_automation_platform/troubleshoot-controller
Chapter 17. Minimizing system latency by isolating interrupts and user processes	Chapter 17. Minimizing system latency by isolating interrupts and user processes Real-time environments need to minimize or eliminate latency when responding to various events. To do this, you can isolate interrupts (IRQs) from user processes from one another on different dedicated CPUs. 17.1. Interrupt and process binding Isolating interrupts (IRQs) from user processes on different dedicated CPUs can minimize or eliminate latency in real-time environments. Interrupts are generally shared evenly between CPUs. This can delay interrupt processing when the CPU has to write new data and instruction caches. These interrupt delays can cause conflicts with other processing being performed on the same CPU. It is possible to allocate time-critical interrupts and processes to a specific CPU (or a range of CPUs). In this way, the code and data structures for processing this interrupt will most likely be in the processor and instruction caches. As a result, the dedicated process can run as quickly as possible, while all other non-time-critical processes run on the other CPUs. This can be particularly important where the speeds involved are near or at the limits of memory and available peripheral bus bandwidth. Any wait for memory to be fetched into processor caches will have a noticeable impact in overall processing time and determinism. In practice, optimal performance is entirely application-specific. For example, tuning applications with similar functions for different companies, required completely different optimal performance tunings. One firm saw optimal results when they isolated 2 out of 4 CPUs for operating system functions and interrupt handling. The remaining 2 CPUs were dedicated purely for application handling. Another firm found optimal determinism when they bound the network related application processes onto a single CPU which was handling the network device driver interrupt. Important To bind a process to a CPU, you usually need to know the CPU mask for a given CPU or range of CPUs. The CPU mask is typically represented as a 32-bit bitmask, a decimal number, or a hexadecimal number, depending on the command you are using. Table 17.1. Example of the CPU Mask for given CPUs CPUs Bitmask Decimal Hexadecimal 0 00000000000000000000000000000001 1 0x00000001 0, 1 00000000000000000000000000000011 3 0x00000011 17.2. Disabling the irqbalance daemon The irqbalance daemon is enabled by default and periodically forces interrupts to be handled by CPUs in an even manner. However in real-time deployments, irqbalance is not needed, because applications are typically bound to specific CPUs. Procedure Check the status of irqbalance . If irqbalance is running, disable it, and stop it. Verification Check that the irqbalance status is inactive. 17.3. Excluding CPUs from IRQ balancing You can use the IRQ balancing service to specify which CPUs you want to exclude from consideration for interrupt (IRQ) balancing. The IRQBALANCE_BANNED_CPUS parameter in the /etc/sysconfig/irqbalance configuration file controls these settings. The value of the parameter is a 64-bit hexadecimal bit mask, where each bit of the mask represents a CPU core. Procedure Open /etc/sysconfig/irqbalance in your preferred text editor and find the section of the file titled IRQBALANCE_BANNED_CPUS . Uncomment the IRQBALANCE_BANNED_CPUS variable. Enter the appropriate bitmask to specify the CPUs to be ignored by the IRQ balance mechanism. Save and close the file. Restart the irqbalance service for the changes to take effect: Note If you are running a system with up to 64 CPU cores, separate each group of eight hexadecimal digits with a comma. For example: IRQBALANCE_BANNED_CPUS=00000001,0000ff00 Table 17.2. Examples CPUs Bitmask 0 00000001 8 - 15 0000ff00 8 - 15, 33 00000002,0000ff00 Note In RHEL 7.2 and higher, the irqbalance utility automatically avoids IRQs on CPU cores isolated via the isolcpus kernel parameter if IRQBALANCE_BANNED_CPUS is not set in /etc/sysconfig/irqbalance . 17.4. Manually assigning CPU affinity to individual IRQs Assigning CPU affinity enables binding and unbinding processes and threads to a specified CPU or range of CPUs. This can reduce caching problems. Procedure Check the IRQs in use by each device by viewing the /proc/interrupts file. Each line shows the IRQ number, the number of interrupts that happened in each CPU, followed by the IRQ type and a description. Write the CPU mask to the smp_affinity entry of a specific IRQ. The CPU mask must be expressed as a hexadecimal number. For example, the following command instructs IRQ number 142 to run only on CPU 0. The change only takes effect when an interrupt occurs. Verification Perform an activity that will trigger the specified interrupt. Check /proc/interrupts for changes. The number of interrupts on the specified CPU for the configured IRQ increased, and the number of interrupts for the configured IRQ on CPUs outside the specified affinity did not increase. 17.5. Binding processes to CPUs with the taskset utility The taskset utility uses the process ID (PID) of a task to view or set its CPU affinity. You can use the utility to run a command with a chosen CPU affinity. To set the affinity, you need to get the CPU mask to be as a decimal or hexadecimal number. The mask argument is a bitmask that specifies which CPU cores are legal for the command or PID being modified. Important The taskset utility works on a NUMA (Non-Uniform Memory Access) system, but it does not allow the user to bind threads to CPUs and the closest NUMA memory node. On such systems, taskset is not the preferred tool, and the numactl utility should be used instead for its advanced capabilities. For more information, see the numactl(8) man page on your system. Procedure Run taskset with the necessary options and arguments. You can specify a CPU list using the -c parameter instead of a CPU mask. In this example, my_embedded_process is being instructed to run only on CPUs 0,4,7-11. This invocation is more convenient in most cases. To set the affinity of a process that is not currently running, use taskset and specify the CPU mask and the process. In this example, my_embedded_process is being instructed to use only CPU 3 (using the decimal version of the CPU mask). You can specify more than one CPU in the bitmask. In this example, my_embedded_process is being instructed to execute on processors 4, 5, 6, and 7 (using the hexadecimal version of the CPU mask). You can set the CPU affinity for processes that are already running by using the -p ( --pid ) option with the CPU mask and the PID of the process you want to change. In this example, the process with a PID of 7013 is being instructed to run only on CPU 0. Note You can combine the listed options. Additional resources taskset(1) and numactl(8) man pages on your system	[ "systemctl status irqbalance irqbalance.service - irqbalance daemon Loaded: loaded (/usr/lib/systemd/system/irqbalance.service; enabled) Active: active (running) ...", "systemctl disable irqbalance systemctl stop irqbalance", "systemctl status irqbalance", "IRQBALANCE_BANNED_CPUS 64 bit bitmask which allows you to indicate which cpu's should be skipped when reblancing irqs. Cpu numbers which have their corresponding bits set to one in this mask will not have any irq's assigned to them on rebalance # #IRQBALANCE_BANNED_CPUS=", "systemctl restart irqbalance", "cat /proc/interrupts", "CPU0 CPU1 0: 26575949 11 IO-APIC-edge timer 1: 14 7 IO-APIC-edge i8042", "echo 1 > /proc/irq/142/smp_affinity", "taskset -c 0,4,7-11 /usr/local/bin/my_embedded_process", "taskset 8 /usr/local/bin/my_embedded_process", "taskset 0xF0 /usr/local/bin/my_embedded_process", "taskset -p 1 7013" ]	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_for_real_time/9/html/optimizing_rhel_9_for_real_time_for_low_latency_operation/assembly_binding-interrupts-and-processes_optimizing-rhel9-for-real-time-for-low-latency-operation
14.8.10. Edit Domain XML Configuration Files	14.8.10. Edit Domain XML Configuration Files save-image-edit file --running --paused command edits the XML configuration file that is associated with a saved file that was created by the virsh save command. Note that the save image records whether the domain should be restored to a --running or --paused state. Without using these options the state is determined by the file itself. By selecting --running or --paused you can overwrite the state that virsh restore should use.	null	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/virtualization_administration_guide/sub-sect-Starting_suspending_resuming_saving_and_restoring_a_guest_virtual_machine-Edit_Domain_XML_configuration_files
Chapter 28. Graphics Driver and Miscellaneous Driver Updates	Chapter 28. Graphics Driver and Miscellaneous Driver Updates The hv_utils driver, which implements guest/host integration for Hyper-V guests, has been updated to the latest upstream version. The drm subsystem drivers (ast, bochs, cirrus, gma500, i915, mga200, nouveau, qxl, radeon, udl, and vmwgfx) have been updated to version 4.4. The xorg-x11-drv-intel driver has been updated to the latest upstream version.	null	https://docs.redhat.com/en/documentation/red_hat_enterprise_linux/6/html/6.8_technical_notes/misc_drivers
Chapter 4. Configuring Red Hat Quay	Chapter 4. Configuring Red Hat Quay Before running the Red Hat Quay service as a container, you need to use that same Quay container to create the configuration file ( config.yaml ) needed to deploy Red Hat Quay. To do that, you pass a config argument and a password (replace my-secret-password here) to the Quay container. Later, you use that password to log into the configuration tool as the user quayconfig . Here's an example of how to do that: Start quay in setup mode : On the first quay node, run the following: Open browser : When the quay configuration tool starts up, open a browser to the URL and port 8080 of the system you are running the configuration tool on (for example http://myquay.example.com:8080 ). You are prompted for a username and password. Log in as quayconfig : When prompted, enter the quayconfig username and password (the one from the podman run command line). Fill in the required fields : When you start the config tool without mounting an existing configuration bundle, you will be booted into an initial setup session. In a setup session, default values will be filled automatically. The following steps will walk through how to fill out the remaining required fields. Identify the database : For the initial setup, you must include the following information about the type and location of the database to be used by Red Hat Quay: Database Type : Choose MySQL or PostgreSQL. MySQL will be used in the basic example; PostgreSQL is used with the high availability Red Hat Quay on OpenShift examples. Database Server : Identify the IP address or hostname of the database, along with the port number if it is different from 3306. Username : Identify a user with full access to the database. Password : Enter the password you assigned to the selected user. Database Name : Enter the database name you assigned when you started the database server. SSL Certificate : For production environments, you should provide an SSL certificate to connect to the database. The following figure shows an example of the screen for identifying the database used by Red Hat Quay: Identify the Redis hostname, Server Configuration and add other desired settings : Other setting you can add to complete the setup are as follows. More settings for high availability Red Hat Quay deployment that for the basic deployment: For the basic, test configuration, identifying the Redis Hostname should be all you need to do. However, you can add other features, such as Clair Scanning and Repository Mirroring, as described at the end of this procedure. For the high availability and OpenShift configurations, more settings are needed (as noted below) to allow for shared storage, secure communications between systems, and other features. Here are the settings you need to consider: Custom SSL Certificates : Upload custom or self-signed SSL certificates for use by Red Hat Quay. See Using SSL to protect connections to Red Hat Quay for details. Recommended for high availability. Important Using SSL certificates is recommended for both basic and high availability deployments. If you decide to not use SSL, you must configure your container clients to use your new Red Hat Quay setup as an insecure registry as described in Test an Insecure Registry . Basic Configuration : Upload a company logo to rebrand your Red Hat Quay registry. Server Configuration : Hostname or IP address to reach the Red Hat Quay service, along with TLS indication (recommended for production installations). The Server Hostname is required for all Red Hat Quay deployments. TLS termination can be done in two different ways: On the instance itself, with all TLS traffic governed by the nginx server in the Quay container (recommended). On the load balancer. This is not recommended. Access to Red Hat Quay could be lost if the TLS setup is not done correctly on the load balancer. Data Consistency Settings : Select to relax logging consistency guarantees to improve performance and availability. Time Machine : Allow older image tags to remain in the repository for set periods of time and allow users to select their own tag expiration times. redis : Identify the hostname or IP address (and optional password) to connect to the redis service used by Red Hat Quay. Repository Mirroring : Choose the checkbox to Enable Repository Mirroring. With this enabled, you can create repositories in your Red Hat Quay cluster that mirror selected repositories from remote registries. Before you can enable repository mirroring, start the repository mirroring worker as described later in this procedure. Registry Storage : Identify the location of storage. A variety of cloud and local storage options are available. Remote storage is required for high availability. Identify the Ceph storage location if you are following the example for Red Hat Quay high availability storage. On OpenShift, the example uses Amazon S3 storage. Action Log Storage Configuration : Action logs are stored in the Red Hat Quay database by default. If you have a large amount of action logs, you can have those logs directed to Elasticsearch for later search and analysis. To do this, change the value of Action Logs Storage to Elasticsearch and configure related settings as described in Configure action log storage . Action Log Rotation and Archiving : Select to enable log rotation, which moves logs older than 30 days into storage, then indicate storage area. Security Scanner : Enable security scanning by selecting a security scanner endpoint and authentication key. To setup Clair to do image scanning, refer to Clair Setup and Configuring Clair . Recommended for high availability. Application Registry : Enable an additional application registry that includes things like Kubernetes manifests or Helm charts (see the App Registry specification ). rkt Conversion : Allow rkt fetch to be used to fetch images from Red Hat Quay registry. Public and private GPG2 keys are needed. This field is deprecated. E-mail : Enable e-mail to use for notifications and user password resets. Internal Authentication : Change default authentication for the registry from Local Database to LDAP, Keystone (OpenStack), JWT Custom Authentication, or External Application Token. External Authorization (OAuth) : Enable to allow GitHub or GitHub Enterprise to authenticate to the registry. Google Authentication : Enable to allow Google to authenticate to the registry. Access Settings : Basic username/password authentication is enabled by default. Other authentication types that can be enabled include: external application tokens (user-generated tokens used with docker or rkt commands), anonymous access (enable for public access to anyone who can get to the registry), user creation (let users create their own accounts), encrypted client password (require command-line user access to include encrypted passwords), and prefix username autocompletion (disable to require exact username matches on autocompletion). Registry Protocol Settings : Leave the Restrict V1 Push Support checkbox enabled to restrict access to Docker V1 protocol pushes. Although Red Hat recommends against enabling Docker V1 push protocol, if you do allow it, you must explicitly whitelist the namespaces for which it is enabled. Dockerfile Build Support : Enable to allow users to submit Dockerfiles to be built and pushed to Red Hat Quay. This is not recommended for multitenant environments. Validate the changes : Select Validate Configuration Changes . If validation is successful, you will be presented with the following Download Configuration modal: Download configuration : Select the Download Configuration button and save the tarball ( quay-config.tar.gz ) to a local directory to use later to start Red Hat Quay. At this point, you can shutdown the Red Hat Quay configuration tool and close your browser. , copy the tarball file to the system on which you want to install your first Red Hat Quay node. For a basic install, you might just be running Red Hat Quay on the same system.	[ "sudo podman run --rm -it --name quay_config -p 8080:8080 registry.redhat.io/quay/quay-rhel8:v3.13.3 config my-secret-password" ]	https://docs.redhat.com/en/documentation/red_hat_quay/3/html/deploy_red_hat_quay_-_high_availability/configuring_red_hat_quay
probe::udp.recvmsg	probe::udp.recvmsg Name probe::udp.recvmsg - Fires whenever a UDP message is received Synopsis udp.recvmsg Values size Number of bytes received by the process sock Network socket used by the process daddr A string representing the destination IP address family IP address family name The name of this probe dport UDP destination port saddr A string representing the source IP address sport UDP source port Context The process which received a UDP message	null	https://docs.redhat.com/en/documentation/Red_Hat_Enterprise_Linux/7/html/systemtap_tapset_reference/api-udp-recvmsg
Chapter 1. Migration from OpenShift Container Platform 3 to 4 overview	Chapter 1. Migration from OpenShift Container Platform 3 to 4 overview OpenShift Container Platform 4 clusters are different from OpenShift Container Platform 3 clusters. OpenShift Container Platform 4 clusters contain new technologies and functionality that result in a cluster that is self-managing, flexible, and automated. To learn more about migrating from OpenShift Container Platform 3 to 4 see About migrating from OpenShift Container Platform 3 to 4 . 1.1. Differences between OpenShift Container Platform 3 and 4 Before migrating from OpenShift Container Platform 3 to 4, you can check differences between OpenShift Container Platform 3 and 4 . Review the following information: Architecture Installation and update Storage , network , logging , security , and monitoring considerations 1.2. Planning network considerations Before migrating from OpenShift Container Platform 3 to 4, review the differences between OpenShift Container Platform 3 and 4 for information about the following areas: DNS considerations Isolating the DNS domain of the target cluster from the clients . Setting up the target cluster to accept the source DNS domain . You can migrate stateful application workloads from OpenShift Container Platform 3 to 4 at the granularity of a namespace. To learn more about MTC see Understanding MTC . Note If you are migrating from OpenShift Container Platform 3, see About migrating from OpenShift Container Platform 3 to 4 and Installing the legacy Migration Toolkit for Containers Operator on OpenShift Container Platform 3 . 1.3. Installing MTC Review the following tasks to install the MTC: Install the Migration Toolkit for Containers Operator on target cluster by using Operator Lifecycle Manager (OLM) . Install the legacy Migration Toolkit for Containers Operator on the source cluster manually . Configure object storage to use as a replication repository . 1.4. Upgrading MTC You upgrade the Migration Toolkit for Containers (MTC) on OpenShift Container Platform 4.7 by using OLM. You upgrade MTC on OpenShift Container Platform 3 by reinstalling the legacy Migration Toolkit for Containers Operator. 1.5. Reviewing premigration checklists Before you migrate your application workloads with the Migration Toolkit for Containers (MTC), review the premigration checklists . 1.6. Migrating applications You can migrate your applications by using the MTC web console or the command line . 1.7. Advanced migration options You can automate your migrations and modify MTC custom resources to improve the performance of large-scale migrations by using the following options: Running a state migration Creating migration hooks Editing, excluding, and mapping migrated resources Configuring the migration controller for large migrations 1.8. Troubleshooting migrations You can perform the following troubleshooting tasks: Viewing migration plan resources by using the MTC web console Viewing the migration plan aggregated log file Using the migration log reader Accessing performance metrics Using the must-gather tool Using the Velero CLI to debug Backup and Restore CRs Using MTC custom resources for troubleshooting Checking common issues and concerns 1.9. Rolling back a migration You can roll back a migration by using the MTC web console, by using the CLI, or manually. 1.10. Uninstalling MTC and deleting resources You can uninstall the MTC and delete its resources to clean up the cluster.	null	https://docs.redhat.com/en/documentation/openshift_container_platform/4.7/html/migrating_from_version_3_to_4/migration-from-version-3-to-4-overview