Logging

Red Hat OpenShift Service on AWS 4

OpenShift Logging installation, usage, and release notes

Red Hat OpenShift Documentation Team

Abstract

This document provides instructions for configuring OpenShift Logging in Red Hat OpenShift Service on AWS.

Chapter 1. Release notes for Logging

Note

Logging is provided as an installable component, with a distinct release cycle from the core Red Hat OpenShift Service on AWS. The Red Hat OpenShift Container Platform Life Cycle Policy outlines release compatibility.

Note

The stable channel only provides updates to the most recent release of logging. To continue receiving updates for prior releases, you must change your subscription channel to stable-X where X is the version of logging you have installed.

1.1. Logging 5.7.4

This release includes OpenShift Logging Bug Fix Release 5.7.4.

1.1.1. Bug fixes

  • Before this update, when forwarding logs to CloudWatch, a namespaceUUID value was not appended to the logGroupName field. With this update, the namespaceUUID value is included, so a logGroupName in CloudWatch appears as logGroupName: vectorcw.b443fb9e-bd4c-4b6a-b9d3-c0097f9ed286. (LOG-2701)
  • Before this update, when forwarding logs over HTTP to an off-cluster destination, the Vector collector was unable to authenticate to the cluster-wide HTTP proxy even though correct credentials were provided in the proxy URL. With this update, the Vector log collector can now authenticate to the cluster-wide HTTP proxy. (LOG-3381)
  • Before this update, the Operator would fail if the Fluentd collector was configured with Splunk as an output, due to this configuration being unsupported. With this update, configuration validation rejects unsupported outputs, resolving the issue. (LOG-4237)
  • Before this update, when the Vector collector was updated an enabled = true value in the TLS configuration for AWS Cloudwatch logs and the GCP Stackdriver caused a configuration error. With this update, enabled = true value will be removed for these outputs, resolving the issue. (LOG-4242)
  • Before this update, the Vector collector occasionally panicked with the following error message in its log: thread 'vector-worker' panicked at 'all branches are disabled and there is no else branch', src/kubernetes/reflector.rs:26:9. With this update, the error has been resolved. (LOG-4275)
  • Before this update, an issue in the Loki Operator caused the alert-manager configuration for the application tenant to disappear if the Operator was configured with additional options for that tenant. With this update, the generated Loki configuration now contains both the custom and the auto-generated configuration. (LOG-4361)
  • Before this update, when multiple roles were used to authenticate using STS with AWS Cloudwatch forwarding, a recent update caused the credentials to be non-unique. With this update, multiple combinations of STS roles and static credentials can once again be used to authenticate with AWS Cloudwatch. (LOG-4368)
  • Before this update, Loki filtered label values for active streams but did not remove duplicates, making Grafana’s Label Browser unusable. With this update, Loki filters out duplicate label values for active streams, resolving the issue. (LOG-4389)

1.1.2. CVEs

1.2. Logging 5.7.3

This release includes OpenShift Logging Bug Fix Release 5.7.3.

1.2.1. Bug fixes

  • Before this update, when viewing logs within the Red Hat OpenShift Service on AWS web console, cached files caused the data to not refresh. With this update the bootstrap files are not cached, resolving the issue. (LOG-4100)
  • Before this update, the Loki Operator reset errors in a way that made identifying configuration problems difficult to troubleshoot. With this update, errors persist until the configuration error is resolved. (LOG-4156)
  • Before this update, the LokiStack ruler did not restart after changes were made to the RulerConfig custom resource (CR). With this update, the Loki Operator restarts the ruler pods after the RulerConfig CR is updated. (LOG-4161)
  • Before this update, the vector collector terminated unexpectedly when input match label values contained a / character within the ClusterLogForwarder. This update resolves the issue by quoting the match label, enabling the collector to start and collect logs. (LOG-4176)
  • Before this update, the Loki Operator terminated unexpectedly when a LokiStack CR defined tenant limits, but not global limits. With this update, the Loki Operator can process LokiStack CRs without global limits, resolving the issue. (LOG-4198)
  • Before this update, Fluentd did not send logs to an Elasticsearch cluster when the private key provided was passphrase-protected. With this update, Fluentd properly handles passphrase-protected private keys when establishing a connection with Elasticsearch. (LOG-4258)
  • Before this update, clusters with more than 8,000 namespaces caused Elasticsearch to reject queries because the list of namespaces was larger than the http.max_header_size setting. With this update, the default value for header size has been increased, resolving the issue. (LOG-4277)
  • Before this update, label values containing a / character within the ClusterLogForwarder CR would cause the collector to terminate unexpectedly. With this update, slashes are replaced with underscores, resolving the issue. (LOG-4095)
  • Before this update, the Cluster Logging Operator terminated unexpectedly when set to an unmanaged state. With this update, a check to ensure that the ClusterLogging resource is in the correct Management state before initiating the reconciliation of the ClusterLogForwarder CR, resolving the issue. (LOG-4177)
  • Before this update, when viewing logs within the Red Hat OpenShift Service on AWS web console, selecting a time range by dragging over the histogram didn’t work on the aggregated logs view inside the pod detail. With this update, the time range can be selected by dragging on the histogram in this view. (LOG-4108)
  • Before this update, when viewing logs within the Red Hat OpenShift Service on AWS web console, queries longer than 30 seconds timed out. With this update, the timeout value can be configured in the configmap/logging-view-plugin. (LOG-3498)
  • Before this update, when viewing logs within the Red Hat OpenShift Service on AWS web console, clicking the more data available option loaded more log entries only the first time it was clicked. With this update, more entries are loaded with each click. (OU-188)
  • Before this update, when viewing logs within the Red Hat OpenShift Service on AWS web console, clicking the streaming option would only display the streaming logs message without showing the actual logs. With this update, both the message and the log stream are displayed correctly. (OU-166)

1.2.2. CVEs

1.3. Logging 5.7.2

This release includes OpenShift Logging Bug Fix Release 5.7.2.

1.3.1. Bug fixes

  • Before this update, it was not possible to delete the openshift-logging namespace directly due to the presence of a pending finalizer. With this update, the finalizer is no longer utilized, enabling direct deletion of the namespace. (LOG-3316)
  • Before this update, the run.sh script would display an incorrect chunk_limit_size value if it was changed according to the Red Hat OpenShift Service on AWS documentation. However, when setting the chunk_limit_size via the environment variable $BUFFER_SIZE_LIMIT, the script would show the correct value. With this update, the run.sh script now consistently displays the correct chunk_limit_size value in both scenarios. (LOG-3330)
  • Before this update, the Red Hat OpenShift Service on AWS web console’s logging view plugin did not allow for custom node placement or tolerations. This update adds the ability to define node placement and tolerations for the logging view plugin. (LOG-3749)
  • Before this update, the Cluster Logging Operator encountered an Unsupported Media Type exception when trying to send logs to DataDog via the Fluentd HTTP Plugin. With this update, users can seamlessly assign the content type for log forwarding by configuring the HTTP header Content-Type. The value provided is automatically assigned to the content_type parameter within the plugin, ensuring successful log transmission. (LOG-3784)
  • Before this update, when the detectMultilineErrors field was set to true in the ClusterLogForwarder custom resource (CR), PHP multi-line errors were recorded as separate log entries, causing the stack trace to be split across multiple messages. With this update, multi-line error detection for PHP is enabled, ensuring that the entire stack trace is included in a single log message. (LOG-3878)
  • Before this update, ClusterLogForwarder pipelines containing a space in their name caused the Vector collector pods to continuously crash. With this update, all spaces, dashes (-), and dots (.) in pipeline names are replaced with underscores (_). (LOG-3945)
  • Before this update, the log_forwarder_output metric did not include the http parameter. This update adds the missing parameter to the metric. (LOG-3997)
  • Before this update, Fluentd did not identify some multi-line JavaScript client exceptions when they ended with a colon. With this update, the Fluentd buffer name is prefixed with an underscore, resolving the issue. (LOG-4019)
  • Before this update, when configuring log forwarding to write to a Kafka output topic which matched a key in the payload, logs dropped due to an error. With this update, Fluentd’s buffer name has been prefixed with an underscore, resolving the issue.(LOG-4027)
  • Before this update, the LokiStack gateway returned label values for namespaces without applying the access rights of a user. With this update, the LokiStack gateway applies permissions to label value requests, resolving the issue. (LOG-4049)

  • Before this update, the Cluster Logging Operator API required a certificate to be provided by a secret when the tls.insecureSkipVerify option was set to true. With this update, the Cluster Logging Operator API no longer requires a certificate to be provided by a secret in such cases. The following configuration has been added to the Operator’s CR: 

    tls.verify_certificate = false
tls.verify_hostname = false

    (LOG-3445)

  • Before this update, the LokiStack route configuration caused queries running longer than 30 seconds to timeout. With this update, the LokiStack global and per-tenant queryTimeout settings affect the route timeout settings, resolving the issue. (LOG-4052)
  • Before this update, a prior fix to remove defaulting of the collection.type resulted in the Operator no longer honoring the deprecated specs for resource, node selections, and tolerations. This update modifies the Operator behavior to always prefer the collection.logs spec over those of collection. This varies from previous behavior that allowed using both the preferred fields and deprecated fields but would ignore the deprecated fields when collection.type was populated. (LOG-4185)
  • Before this update, the Vector log collector did not generate TLS configuration for forwarding logs to multiple Kafka brokers if the broker URLs were not specified in the output. With this update, TLS configuration is generated appropriately for multiple brokers. (LOG-4163)
  • Before this update, the option to enable passphrase for log forwarding to Kafka was unavailable. This limitation presented a security risk as it could potentially expose sensitive information. With this update, users now have a seamless option to enable passphrase for log forwarding to Kafka. (LOG-3314)
  • Before this update, Vector log collector did not honor the tlsSecurityProfile settings for outgoing TLS connections. After this update, Vector handles TLS connection settings appropriately. (LOG-4011)
  • Before this update, not all available output types were included in the log_forwarder_output_info metrics. With this update, metrics contain Splunk and Google Cloud Logging data which was missing previously. (LOG-4098)
  • Before this update, when follow_inodes was set to true, the Fluentd collector could crash on file rotation. With this update, the follow_inodes setting does not crash the collector. (LOG-4151)
  • Before this update, the Fluentd collector could incorrectly close files that should be watched because of how those files were tracked. With this update, the tracking parameters have been corrected. (LOG-4149)

  • Before this update, forwarding logs with the Vector collector and naming a pipeline in the ClusterLogForwarder instance audit, application or infrastructure resulted in collector pods staying in the CrashLoopBackOff state with the following error in the collector log: 

    ERROR vector::cli: Configuration error. error=redefinition of table transforms.audit for key transforms.audit

    After this update, pipeline names no longer clash with reserved input names, and pipelines can be named audit, application or infrastructure. (LOG-4218)

  • Before this update, when forwarding logs to a syslog destination with the Vector collector and setting the addLogSource flag to true, the following extra empty fields were added to the forwarded messages: namespace_name=, container_name=, and pod_name=. With this update, these fields are no longer added to journal logs. (LOG-4219)
  • Before this update, when a structuredTypeKey was not found, and a structuredTypeName was not specified, log messages were still parsed into structured object. With this update, parsing of logs is as expected. (LOG-4220)

1.3.2. CVEs

1.4. Logging 5.7.1

This release includes: OpenShift Logging Bug Fix Release 5.7.1.

1.4.1. Bug fixes

  • Before this update, the presence of numerous noisy messages within the Cluster Logging Operator pod logs caused reduced log readability, and increased difficulty in identifying important system events. With this update, the issue is resolved by significantly reducing the noisy messages within Cluster Logging Operator pod logs. (LOG-3482)
  • Before this update, the API server would reset the value for the CollectorSpec.Type field to vector, even when the custom resource used a different value. This update removes the default for the CollectorSpec.Type field to restore the previous behavior. (LOG-4086)
  • Before this update, a time range could not be selected in the Red Hat OpenShift Service on AWS web console by clicking and dragging over the logs histogram. With this update, clicking and dragging can be used to successfully select a time range. (LOG-4501)
  • Before this update, clicking on the Show Resources link in the Red Hat OpenShift Service on AWS web console did not produce any effect. With this update, the issue is resolved by fixing the functionality of the "Show Resources" link to toggle the display of resources for each log entry. (LOG-3218)

1.4.2. CVEs

1.5. Logging 5.7.0

This release includes OpenShift Logging Bug Fix Release 5.7.0.

1.5.1. Enhancements

With this update, you can enable logging to detect multi-line exceptions and reassemble them into a single log entry.

To enable logging to detect multi-line exceptions and reassemble them into a single log entry, ensure that the ClusterLogForwarder Custom Resource (CR) contains a detectMultilineErrors field, with a value of true.

1.5.2. Known Issues

None.

1.5.3. Bug fixes

  • Before this update, the nodeSelector attribute for the Gateway component of the LokiStack did not impact node scheduling. With this update, the nodeSelector attribute works as expected. (LOG-3713)

1.5.4. CVEs

1.6. Logging 5.6.5

This release includes OpenShift Logging Bug Fix Release 5.6.5.

1.6.1. Bug fixes

  • Before this update, the template definitions prevented Elasticsearch from indexing some labels and namespace_labels, causing issues with data ingestion. With this update, the fix replaces dots and slashes in labels to ensure proper ingestion, effectively resolving the issue. (LOG-3419)
  • Before this update, if the Logs page of the OpenShift Web Console failed to connect to the LokiStack, a generic error message was displayed, providing no additional context or troubleshooting suggestions. With this update, the error message has been enhanced to include more specific details and recommendations for troubleshooting. (LOG-3750)
  • Before this update, time range formats were not validated, leading to errors selecting a custom date range. With this update, time formats are now validated, enabling users to select a valid range. If an invalid time range format is selected, an error message is displayed to the user. (LOG-3583)
  • Before this update, when searching logs in Loki, even if the length of an expression did not exceed 5120 characters, the query would fail in many cases. With this update, query authorization label matchers have been optimized, resolving the issue. (LOG-3480)
  • Before this update, the Loki Operator failed to produce a memberlist configuration that was sufficient for locating all the components when using a memberlist for private IPs. With this update, the fix ensures that the generated configuration includes the advertised port, allowing for successful lookup of all components. (LOG-4008)

1.6.2. CVEs

1.7. Logging 5.6.4

This release includes OpenShift Logging Bug Fix Release 5.6.4.

1.7.1. Bug fixes

  • Before this update, when LokiStack was deployed as the log store, the logs generated by Loki pods were collected and sent to LokiStack. With this update, the logs generated by Loki are excluded from collection and will not be stored. (LOG-3280)
  • Before this update, when the query editor on the Logs page of the OpenShift Web Console was empty, the drop-down menus did not populate. With this update, if an empty query is attempted, an error message is displayed and the drop-down menus now populate as expected. (LOG-3454)
  • Before this update, when the tls.insecureSkipVerify option was set to true, the Cluster Logging Operator would generate incorrect configuration. As a result, the operator would fail to send data to Elasticsearch when attempting to skip certificate validation. With this update, the Cluster Logging Operator generates the correct TLS configuration even when tls.insecureSkipVerify is enabled. As a result, data can be sent successfully to Elasticsearch even when attempting to skip certificate validation. (LOG-3475)
  • Before this update, when structured parsing was enabled and messages were forwarded to multiple destinations, they were not deep copied. This resulted in some of the received logs including the structured message, while others did not. With this update, the configuration generation has been modified to deep copy messages before JSON parsing. As a result, all received messages now have structured messages included, even when they are forwarded to multiple destinations. (LOG-3640)
  • Before this update, if the collection field contained {} it could result in the Operator crashing. With this update, the Operator will ignore this value, allowing the operator to continue running smoothly without interruption. (LOG-3733)
  • Before this update, the nodeSelector attribute for the Gateway component of LokiStack did not have any effect. With this update, the nodeSelector attribute functions as expected. (LOG-3783)
  • Before this update, the static LokiStack memberlist configuration relied solely on private IP networks. As a result, when the Red Hat OpenShift Service on AWS cluster pod network was configured with a public IP range, the LokiStack pods would crashloop. With this update, the LokiStack administrator now has the option to use the pod network for the memberlist configuration. This resolves the issue and prevents the LokiStack pods from entering a crashloop state when the Red Hat OpenShift Service on AWS cluster pod network is configured with a public IP range. (LOG-3814)
  • Before this update, if the tls.insecureSkipVerify field was set to true, the Cluster Logging Operator would generate an incorrect configuration. As a result, the Operator would fail to send data to Elasticsearch when attempting to skip certificate validation. With this update, the Operator generates the correct TLS configuration even when tls.insecureSkipVerify is enabled. As a result, data can be sent successfully to Elasticsearch even when attempting to skip certificate validation. (LOG-3838)
  • Before this update, if the Cluster Logging Operator (CLO) was installed without the Elasticsearch Operator, the CLO pod would continuously display an error message related to the deletion of Elasticsearch. With this update, the CLO now performs additional checks before displaying any error messages. As a result, error messages related to Elasticsearch deletion are no longer displayed in the absence of the Elasticsearch Operator.(LOG-3763)

1.7.2. CVEs

1.8. Logging 5.6.3

This release includes OpenShift Logging Bug Fix Release 5.6.3.

1.8.1. Bug fixes

  • Before this update, the operator stored gateway tenant secret information in a config map. With this update, the operator stores this information in a secret. (LOG-3717)
  • Before this update, the Fluentd collector did not capture OAuth login events stored in /var/log/auth-server/audit.log. With this update, Fluentd captures these OAuth login events, resolving the issue. (LOG-3729)

1.8.2. CVEs

1.9. Logging 5.6.2

This release includes OpenShift Logging Bug Fix Release 5.6.2.

1.9.1. Bug fixes

  • Before this update, the collector did not set level fields correctly based on priority for systemd logs. With this update, level fields are set correctly. (LOG-3429)
  • Before this update, the Operator incorrectly generated incompatibility warnings on Red Hat OpenShift Service on AWS 4.12 or later. With this update, the Operator max Red Hat OpenShift Service on AWS version value has been corrected, resolving the issue. (LOG-3584)
  • Before this update, creating a ClusterLogForwarder custom resource (CR) with an output value of default did not generate any errors. With this update, an error warning that this value is invalid generates appropriately. (LOG-3437)
  • Before this update, when the ClusterLogForwarder custom resource (CR) had multiple pipelines configured with one output set as default, the collector pods restarted. With this update, the logic for output validation has been corrected, resolving the issue. (LOG-3559)
  • Before this update, collector pods restarted after being created. With this update, the deployed collector does not restart on its own. (LOG-3608)
  • Before this update, patch releases removed previous versions of the Operators from the catalog. This made installing the old versions impossible. This update changes bundle configurations so that previous releases of the same minor version stay in the catalog. (LOG-3635)

1.9.2. CVEs

1.10. Logging 5.6.1

This release includes OpenShift Logging Bug Fix Release 5.6.1.

1.10.1. Bug fixes

  • Before this update, the compactor would report TLS certificate errors from communications with the querier when retention was active. With this update, the compactor and querier no longer communicate erroneously over HTTP. (LOG-3494)
  • Before this update, the Loki Operator would not retry setting the status of the LokiStack CR, which caused stale status information. With this update, the Operator retries status information updates on conflict. (LOG-3496)
  • Before this update, the Loki Operator Webhook server caused TLS errors when the kube-apiserver-operator Operator checked the webhook validity. With this update, the Loki Operator Webhook PKI is managed by the Operator Lifecycle Manager (OLM), resolving the issue. (LOG-3510)
  • Before this update, the LokiStack Gateway Labels Enforcer generated parsing errors for valid LogQL queries when using combined label filters with boolean expressions. With this update, the LokiStack LogQL implementation supports label filters with boolean expression and resolves the issue. (LOG-3441), (LOG-3397)
  • Before this update, records written to Elasticsearch would fail if multiple label keys had the same prefix and some keys included dots. With this update, underscores replace dots in label keys, resolving the issue. (LOG-3463)
  • Before this update, the Red Hat OpenShift Logging Operator was not available for Red Hat OpenShift Service on AWS 4.10 clusters because of an incompatibility between Red Hat OpenShift Service on AWS console and the logging-view-plugin. With this update, the plugin is properly integrated with the Red Hat OpenShift Service on AWS 4.10 admin console. (LOG-3447)
  • Before this update the reconciliation of the ClusterLogForwarder custom resource would incorrectly report a degraded status of pipelines that reference the default logstore. With this update, the pipeline validates properly.(LOG-3477)

1.10.2. CVEs

1.11. Logging 5.6

This release includes OpenShift Logging Release 5.6.

1.11.1. Deprecation notice

In Logging 5.6, Fluentd is deprecated and is planned to be removed in a future release. Red Hat will provide bug fixes and support for this feature during the current release lifecycle, but this feature will no longer receive enhancements and will be removed. As an alternative to fluentd, you can use Vector instead.

1.11.2. Enhancements

  • With this update, Logging is compliant with Red Hat OpenShift Service on AWS cluster-wide cryptographic policies. (LOG-895)
  • With this update, you can declare per-tenant, per-stream, and global policies retention policies through the LokiStack custom resource, ordered by priority. (LOG-2695)
  • With this update, Splunk is an available output option for log forwarding. (LOG-2913)
  • With this update, Vector replaces Fluentd as the default Collector. (LOG-2222)
  • With this update, the Developer role can access the per-project workload logs they are assigned to within the Log Console Plugin on clusters running Red Hat OpenShift Service on AWS 4.11 and higher. (LOG-3388)
  • With this update, logs from any source contain a field openshift.cluster_id, the unique identifier of the cluster in which the Operator is deployed. You can view the clusterID value with the command below. (LOG-2715) 
$ oc get clusterversion/version -o jsonpath='{.spec.clusterID}{"\n"}'

1.11.3. Known Issues

  • Before this update, Elasticsearch would reject logs if multiple label keys had the same prefix and some keys included the . character. This fixes the limitation of Elasticsearch by replacing . in the label keys with _. As a workaround for this issue, remove the labels that cause errors, or add a namespace to the label. (LOG-3463)

1.11.4. Bug fixes

  • Before this update, if you deleted the Kibana Custom Resource, the Red Hat OpenShift Service on AWS web console continued displaying a link to Kibana. With this update, removing the Kibana Custom Resource also removes that link. (LOG-2993)
  • Before this update, a user was not able to view the application logs of namespaces they have access to. With this update, the Loki Operator automatically creates a cluster role and cluster role binding allowing users to read application logs. (LOG-3072)
  • Before this update, the Operator removed any custom outputs defined in the ClusterLogForwarder custom resource when using LokiStack as the default log storage. With this update, the Operator merges custom outputs with the default outputs when processing the ClusterLogForwarder custom resource. (LOG-3090)
  • Before this update, the CA key was used as the volume name for mounting the CA into Loki, causing error states when the CA Key included non-conforming characters, such as dots. With this update, the volume name is standardized to an internal string which resolves the issue. (LOG-3331)
  • Before this update, a default value set within the LokiStack Custom Resource Definition, caused an inability to create a LokiStack instance without a ReplicationFactor of 1. With this update, the operator sets the actual value for the size used. (LOG-3296)
  • Before this update, Vector parsed the message field when JSON parsing was enabled without also defining structuredTypeKey or structuredTypeName values. With this update, a value is required for either structuredTypeKey or structuredTypeName when writing structured logs to Elasticsearch. (LOG-3195)
  • Before this update, the secret creation component of the Elasticsearch Operator modified internal secrets constantly. With this update, the existing secret is properly handled. (LOG-3161)
  • Before this update, the Operator could enter a loop of removing and recreating the collector daemonset while the Elasticsearch or Kibana deployments changed their status. With this update, a fix in the status handling of the Operator resolves the issue. (LOG-3157)
  • Before this update, Kibana had a fixed 24h OAuth cookie expiration time, which resulted in 401 errors in Kibana whenever the accessTokenInactivityTimeout field was set to a value lower than 24h. With this update, Kibana’s OAuth cookie expiration time synchronizes to the accessTokenInactivityTimeout, with a default value of 24h. (LOG-3129)
  • Before this update, the Operators general pattern for reconciling resources was to try and create before attempting to get or update which would lead to constant HTTP 409 responses after creation. With this update, Operators first attempt to retrieve an object and only create or update it if it is either missing or not as specified. (LOG-2919)
  • Before this update, the .level and`.structure.level` fields in Fluentd could contain different values. With this update, the values are the same for each field. (LOG-2819)
  • Before this update, the Operator did not wait for the population of the trusted CA bundle and deployed the collector a second time once the bundle updated. With this update, the Operator waits briefly to see if the bundle has been populated before it continues the collector deployment. (LOG-2789)
  • Before this update, logging telemetry info appeared twice when reviewing metrics. With this update, logging telemetry info displays as expected. (LOG-2315)
  • Before this update, Fluentd pod logs contained a warning message after enabling the JSON parsing addition. With this update, that warning message does not appear. (LOG-1806)
  • Before this update, the must-gather script did not complete because oc needs a folder with write permission to build its cache. With this update, oc has write permissions to a folder, and the must-gather script completes successfully. (LOG-3446)
  • Before this update the log collector SCC could be superseded by other SCCs on the cluster, rendering the collector unusable. This update sets the priority of the log collector SCC so that it takes precedence over the others. (LOG-3235)
  • Before this update, Vector was missing the field sequence, which was added to fluentd as a way to deal with a lack of actual nanoseconds precision. With this update, the field openshift.sequence has been added to the event logs. (LOG-3106)

1.11.5. CVEs

1.12. Logging 5.5.9

This release includes OpenShift Logging Bug Fix Release 5.5.9.

1.12.1. Bug fixes

  • Before this update, a problem with the Fluentd collector caused it to not capture OAuth login events stored in /var/log/auth-server/audit.log. This led to incomplete collection of login events from the OAuth service. With this update, the Fluentd collector now resolves this issue by capturing all login events from the OAuth service, including those stored in /var/log/auth-server/audit.log, as expected.(LOG-3730)
  • Before this update, when structured parsing was enabled and messages were forwarded to multiple destinations, they were not deep copied. This resulted in some of the received logs including the structured message, while others did not. With this update, the configuration generation has been modified to deep copy messages before JSON parsing. As a result, all received logs now have structured messages included, even when they are forwarded to multiple destinations.(LOG-3767)

1.12.2. CVEs

1.13. Logging 5.5.8

This release includes OpenShift Logging Bug Fix Release 5.5.8.

1.13.1. Bug fixes

  • Before this update, the priority field was missing from systemd logs due to an error in how the collector set level fields. With this update, these fields are set correctly, resolving the issue. (LOG-3630)

1.13.2. CVEs

1.14. Logging 5.5.7

This release includes OpenShift Logging Bug Fix Release 5.5.7.

1.14.1. Bug fixes

  • Before this update, the LokiStack Gateway Labels Enforcer generated parsing errors for valid LogQL queries when using combined label filters with boolean expressions. With this update, the LokiStack LogQL implementation supports label filters with boolean expression and resolves the issue. (LOG-3534)
  • Before this update, the ClusterLogForwarder custom resource (CR) did not pass TLS credentials for syslog output to Fluentd, resulting in errors during forwarding. With this update, credentials pass correctly to Fluentd, resolving the issue. (LOG-3533)

1.14.2. CVEs

CVE-2021-46848CVE-2022-3821CVE-2022-35737CVE-2022-42010CVE-2022-42011CVE-2022-42012CVE-2022-42898CVE-2022-43680

1.15. Logging 5.5.6

This release includes OpenShift Logging Bug Fix Release 5.5.6.

1.15.1. Bug fixes

  • Before this update, the Pod Security admission controller added the label podSecurityLabelSync = true to the openshift-logging namespace. This resulted in our specified security labels being overwritten, and as a result Collector pods would not start. With this update, the label podSecurityLabelSync = false preserves security labels. Collector pods deploy as expected. (LOG-3340)
  • Before this update, the Operator installed the console view plugin, even when it was not enabled on the cluster. This caused the Operator to crash. With this update, if an account for a cluster does not have the console view enabled, the Operator functions normally and does not install the console view. (LOG-3407)
  • Before this update, a prior fix to support a regression where the status of the Elasticsearch deployment was not being updated caused the Operator to crash unless the Red Hat Elasticsearch Operator was deployed. With this update, that fix has been reverted so the Operator is now stable but re-introduces the previous issue related to the reported status. (LOG-3428)
  • Before this update, the Loki Operator only deployed one replica of the LokiStack gateway regardless of the chosen stack size. With this update, the number of replicas is correctly configured according to the selected size. (LOG-3478)
  • Before this update, records written to Elasticsearch would fail if multiple label keys had the same prefix and some keys included dots. With this update, underscores replace dots in label keys, resolving the issue. (LOG-3341)
  • Before this update, the logging view plugin contained an incompatible feature for certain versions of Red Hat OpenShift Service on AWS. With this update, the correct release stream of the plugin resolves the issue. (LOG-3467)
  • Before this update, the reconciliation of the ClusterLogForwarder custom resource would incorrectly report a degraded status of one or more pipelines causing the collector pods to restart every 8-10 seconds. With this update, reconciliation of the ClusterLogForwarder custom resource processes correctly, resolving the issue. (LOG-3469)
  • Before this change the spec for the outputDefaults field of the ClusterLogForwarder custom resource would apply the settings to every declared Elasticsearch output type. This change corrects the behavior to match the enhancement specification where the setting specifically applies to the default managed Elasticsearch store. (LOG-3342)
  • Before this update, the OpenShift CLI (oc) must-gather script did not complete because the OpenShift CLI (oc) needs a folder with write permission to build its cache. With this update, the OpenShift CLI (oc) has write permissions to a folder, and the must-gather script completes successfully. (LOG-3472)
  • Before this update, the Loki Operator webhook server caused TLS errors. With this update, the Loki Operator webhook PKI is managed by the Operator Lifecycle Manager’s dynamic webhook management resolving the issue. (LOG-3511)

1.15.2. CVEs

1.16. Logging 5.5.5

This release includes OpenShift Logging Bug Fix Release 5.5.5.

1.16.1. Bug fixes

  • Before this update, Kibana had a fixed 24h OAuth cookie expiration time, which resulted in 401 errors in Kibana whenever the accessTokenInactivityTimeout field was set to a value lower than 24h. With this update, Kibana’s OAuth cookie expiration time synchronizes to the accessTokenInactivityTimeout, with a default value of 24h. (LOG-3305)
  • Before this update, Vector parsed the message field when JSON parsing was enabled without also defining structuredTypeKey or structuredTypeName values. With this update, a value is required for either structuredTypeKey or structuredTypeName when writing structured logs to Elasticsearch. (LOG-3284)
  • Before this update, the FluentdQueueLengthIncreasing alert could fail to fire when there was a cardinality issue with the set of labels returned from this alert expression. This update reduces labels to only include those required for the alert. (LOG-3226)
  • Before this update, Loki did not have support to reach an external storage in a disconnected cluster. With this update, proxy environment variables and proxy trusted CA bundles are included in the container image to support these connections. (LOG-2860)
  • Before this update, Red Hat OpenShift Service on AWS web console users could not choose the ConfigMap object that includes the CA certificate for Loki, causing pods to operate without the CA. With this update, web console users can select the config map, resolving the issue. (LOG-3310)
  • Before this update, the CA key was used as volume name for mounting the CA into Loki, causing error states when the CA Key included non-conforming characters (such as dots). With this update, the volume name is standardized to an internal string which resolves the issue. (LOG-3332)

1.16.2. CVEs

1.17. Logging 5.5.4

This release includes RHSA-2022:7434-OpenShift Logging Bug Fix Release 5.5.4.

1.17.1. Bug fixes

  • Before this update, an error in the query parser of the logging view plugin caused parts of the logs query to disappear if the query contained curly brackets {}. This made the queries invalid, leading to errors being returned for valid queries. With this update, the parser correctly handles these queries. (LOG-3042)
  • Before this update, the Operator could enter a loop of removing and recreating the collector daemonset while the Elasticsearch or Kibana deployments changed their status. With this update, a fix in the status handling of the Operator resolves the issue. (LOG-3049)
  • Before this update, no alerts were implemented to support the collector implementation of Vector. This change adds Vector alerts and deploys separate alerts, depending upon the chosen collector implementation. (LOG-3127)
  • Before this update, the secret creation component of the Elasticsearch Operator modified internal secrets constantly. With this update, the existing secret is properly handled. (LOG-3138)
  • Before this update, a prior refactoring of the logging must-gather scripts removed the expected location for the artifacts. This update reverts that change to write artifacts to the /must-gather folder. (LOG-3213)
  • Before this update, on certain clusters, the Prometheus exporter would bind on IPv4 instead of IPv6. After this update, Fluentd detects the IP version and binds to 0.0.0.0 for IPv4 or [::] for IPv6. (LOG-3162)

1.17.2. CVEs

1.18. Logging 5.5.3

This release includes OpenShift Logging Bug Fix Release 5.5.3.

1.18.1. Bug fixes

  • Before this update, log entries that had structured messages included the original message field, which made the entry larger. This update removes the message field for structured logs to reduce the increased size. (LOG-2759)
  • Before this update, the collector configuration excluded logs from collector, default-log-store, and visualization pods, but was unable to exclude logs archived in a .gz file. With this update, archived logs stored as .gz files of collector, default-log-store, and visualization pods are also excluded. (LOG-2844)
  • Before this update, when requests to an unavailable pod were sent through the gateway, no alert would warn of the disruption. With this update, individual alerts will generate if the gateway has issues completing a write or read request. (LOG-2884)
  • Before this update, pod metadata could be altered by fluent plugins because the values passed through the pipeline by reference. This update ensures each log message receives a copy of the pod metadata so each message processes independently. (LOG-3046)
  • Before this update, selecting unknown severity in the OpenShift Console Logs view excluded logs with a level=unknown value. With this update, logs without level and with level=unknown values are visible when filtering by unknown severity. (LOG-3062)
  • Before this update, log records sent to Elasticsearch had an extra field named write-index that contained the name of the index to which the logs needed to be sent. This field is not a part of the data model. After this update, this field is no longer sent. (LOG-3075)
  • With the introduction of the new built-in Pod Security Admission Controller, Pods not configured in accordance with the enforced security standards defined globally or on the namespace level cannot run. With this update, the Operator and collectors allow privileged execution and run without security audit warnings or errors. (LOG-3077)
  • Before this update, the Operator removed any custom outputs defined in the ClusterLogForwarder custom resource when using LokiStack as the default log storage. With this update, the Operator merges custom outputs with the default outputs when processing the ClusterLogForwarder custom resource. (LOG-3095)

1.18.2. CVEs

1.19. Logging 5.5.2

This release includes OpenShift Logging Bug Fix Release 5.5.2.

1.19.1. Bug fixes

  • Before this update, alerting rules for the Fluentd collector did not adhere to the Red Hat OpenShift Service on AWS monitoring style guidelines. This update modifies those alerts to include the namespace label, resolving the issue. (LOG-1823)
  • Before this update, the index management rollover script failed to generate a new index name whenever there was more than one hyphen character in the name of the index. With this update, index names generate correctly. (LOG-2644)
  • Before this update, the Kibana route was setting a caCertificate value without a certificate present. With this update, no caCertificate value is set. (LOG-2661)
  • Before this update, a change in the collector dependencies caused it to issue a warning message for unused parameters. With this update, removing unused configuration parameters resolves the issue. (LOG-2859)
  • Before this update, pods created for deployments that Loki Operator created were mistakenly scheduled on nodes with non-Linux operating systems, if such nodes were available in the cluster the Operator was running in. With this update, the Operator attaches an additional node-selector to the pod definitions which only allows scheduling the pods on Linux-based nodes. (LOG-2895)
  • Before this update, the OpenShift Console Logs view did not filter logs by severity due to a LogQL parser issue in the LokiStack gateway. With this update, a parser fix resolves the issue and the OpenShift Console Logs view can filter by severity. (LOG-2908)
  • Before this update, a refactoring of the Fluentd collector plugins removed the timestamp field for events. This update restores the timestamp field, sourced from the event’s received time. (LOG-2923)
  • Before this update, absence of a level field in audit logs caused an error in vector logs. With this update, the addition of a level field in the audit log record resolves the issue. (LOG-2961)
  • Before this update, if you deleted the Kibana Custom Resource, the Red Hat OpenShift Service on AWS web console continued displaying a link to Kibana. With this update, removing the Kibana Custom Resource also removes that link. (LOG-3053)
  • Before this update, each rollover job created empty indices when the ClusterLogForwarder custom resource had JSON parsing defined. With this update, new indices are not empty. (LOG-3063)
  • Before this update, when the user deleted the LokiStack after an update to Loki Operator 5.5 resources originally created by Loki Operator 5.4 remained. With this update, the resources' owner-references point to the 5.5 LokiStack. (LOG-2945)
  • Before this update, a user was not able to view the application logs of namespaces they have access to. With this update, the Loki Operator automatically creates a cluster role and cluster role binding allowing users to read application logs. (LOG-2918)
  • Before this update, users with cluster-admin privileges were not able to properly view infrastructure and audit logs using the logging console. With this update, the authorization check has been extended to also recognize users in cluster-admin and dedicated-admin groups as admins. (LOG-2970)

1.19.2. CVEs

1.20. Logging 5.5.1

This release includes OpenShift Logging Bug Fix Release 5.5.1.

1.20.1. Enhancements

  • This enhancement adds an Aggregated Logs tab to the Pod Details page of the Red Hat OpenShift Service on AWS web console when the Logging Console Plugin is in use. This enhancement is only available on Red Hat OpenShift Service on AWS 4.10 and later. (LOG-2647)
  • This enhancement adds Google Cloud Logging as an output option for log forwarding. (LOG-1482)

1.20.2. Bug fixes

  • Before this update, the Operator did not ensure that the pod was ready, which caused the cluster to reach an inoperable state during a cluster restart. With this update, the Operator marks new pods as ready before continuing to a new pod during a restart, which resolves the issue. (LOG-2745)
  • Before this update, Fluentd would sometimes not recognize that the Kubernetes platform rotated the log file and would no longer read log messages. This update corrects that by setting the configuration parameter suggested by the upstream development team. (LOG-2995)
  • Before this update, the addition of multi-line error detection caused internal routing to change and forward records to the wrong destination. With this update, the internal routing is correct. (LOG-2801)
  • Before this update, changing the Red Hat OpenShift Service on AWS web console’s refresh interval created an error when the Query field was empty. With this update, changing the interval is not an available option when the Query field is empty. (LOG-2917)

1.20.3. CVEs

1.21. Logging 5.5

The following advisories are available for Logging 5.5:Release 5.5

1.21.1. Enhancements

  • With this update, you can forward structured logs from different containers within the same pod to different indices. To use this feature, you must configure the pipeline with multi-container support and annotate the pods. (LOG-1296)
Important

JSON formatting of logs varies by application. Because creating too many indices impacts performance, limit your use of this feature to creating indices for logs that have incompatible JSON formats. Use queries to separate logs from different namespaces, or applications with compatible JSON formats.

  • With this update, you can filter logs with Elasticsearch outputs by using the Kubernetes common labels, app.kubernetes.io/component, app.kubernetes.io/managed-by, app.kubernetes.io/part-of, and app.kubernetes.io/version. Non-Elasticsearch output types can use all labels included in kubernetes.labels. (LOG-2388)
  • With this update, clusters with AWS Security Token Service (STS) enabled may use STS authentication to forward logs to Amazon CloudWatch. (LOG-1976)
  • With this update, the 'Loki Operator' Operator and Vector collector move from Technical Preview to General Availability. Full feature parity with prior releases are pending, and some APIs remain Technical Previews. See the Logging with the LokiStack section for details.

1.21.2. Bug fixes

  • Before this update, clusters configured to forward logs to Amazon CloudWatch wrote rejected log files to temporary storage, causing cluster instability over time. With this update, chunk backup for all storage options has been disabled, resolving the issue. (LOG-2746)
  • Before this update, the Operator was using versions of some APIs that are deprecated and planned for removal in future versions of Red Hat OpenShift Service on AWS. This update moves dependencies to the supported API versions. (LOG-2656)

Before this update, the Operator was using versions of some APIs that are deprecated and planned for removal in future versions of Red Hat OpenShift Service on AWS. This update moves dependencies to the supported API versions. (LOG-2656)

  • Before this update, multiple ClusterLogForwarder pipelines configured for multiline error detection caused the collector to go into a crashloopbackoff error state. This update fixes the issue where multiple configuration sections had the same unique ID. (LOG-2241)
  • Before this update, the collector could not save non UTF-8 symbols to the Elasticsearch storage logs. With this update the collector encodes non UTF-8 symbols, resolving the issue. (LOG-2203)
  • Before this update, non-latin characters displayed incorrectly in Kibana. With this update, Kibana displays all valid UTF-8 symbols correctly. (LOG-2784)

1.21.3. CVEs

1.22. Logging 5.4.11

This release includes OpenShift Logging Bug Fix Release 5.4.11.

1.22.1. Bug fixes

1.22.2. CVEs

1.23. Logging 5.4.10

This release includes OpenShift Logging Bug Fix Release 5.4.10.

1.23.1. Bug fixes

None.

1.23.2. CVEs

1.24. Logging 5.4.9

This release includes OpenShift Logging Bug Fix Release 5.4.9.

1.24.1. Bug fixes

  • Before this update, the Fluentd collector would warn of unused configuration parameters. This update removes those configuration parameters and their warning messages. (LOG-3074)
  • Before this update, Kibana had a fixed 24h OAuth cookie expiration time, which resulted in 401 errors in Kibana whenever the accessTokenInactivityTimeout field was set to a value lower than 24h. With this update, Kibana’s OAuth cookie expiration time synchronizes to the accessTokenInactivityTimeout, with a default value of 24h. (LOG-3306)

1.24.2. CVEs

1.25. Logging 5.4.8

This release includes RHSA-2022:7435-OpenShift Logging Bug Fix Release 5.4.8.

1.25.1. Bug fixes

None.

1.25.2. CVEs

1.26. Logging 5.4.6

This release includes OpenShift Logging Bug Fix Release 5.4.6.

1.26.1. Bug fixes

  • Before this update, Fluentd would sometimes not recognize that the Kubernetes platform rotated the log file and would no longer read log messages. This update corrects that by setting the configuration parameter suggested by the upstream development team. (LOG-2792)
  • Before this update, each rollover job created empty indices when the ClusterLogForwarder custom resource had JSON parsing defined. With this update, new indices are not empty. (LOG-2823)
  • Before this update, if you deleted the Kibana Custom Resource, the Red Hat OpenShift Service on AWS web console continued displaying a link to Kibana. With this update, removing the Kibana Custom Resource also removes that link. (LOG-3054)

1.26.2. CVEs

1.27. Logging 5.4.5

This release includes RHSA-2022:6183-OpenShift Logging Bug Fix Release 5.4.5.

1.27.1. Bug fixes

  • Before this update, the Operator did not ensure that the pod was ready, which caused the cluster to reach an inoperable state during a cluster restart. With this update, the Operator marks new pods as ready before continuing to a new pod during a restart, which resolves the issue. (LOG-2881)
  • Before this update, the addition of multi-line error detection caused internal routing to change and forward records to the wrong destination. With this update, the internal routing is correct. (LOG-2946)
  • Before this update, the Operator could not decode index setting JSON responses with a quoted Boolean value and would result in an error. With this update, the Operator can properly decode this JSON response. (LOG-3009)
  • Before this update, Elasticsearch index templates defined the fields for labels with the wrong types. This change updates those templates to match the expected types forwarded by the log collector. (LOG-2972)

1.27.2. CVEs

1.28. Logging 5.4.4

This release includes RHBA-2022:5907-OpenShift Logging Bug Fix Release 5.4.4.

1.28.1. Bug fixes

  • Before this update, non-latin characters displayed incorrectly in Elasticsearch. With this update, Elasticsearch displays all valid UTF-8 symbols correctly. (LOG-2794)
  • Before this update, non-latin characters displayed incorrectly in Fluentd. With this update, Fluentd displays all valid UTF-8 symbols correctly. (LOG-2657)
  • Before this update, the metrics server for the collector attempted to bind to the address using a value exposed by an environment value. This change modifies the configuration to bind to any available interface. (LOG-2821)
  • Before this update, the cluster-logging Operator relied on the cluster to create a secret. This cluster behavior changed in Red Hat OpenShift Service on AWS 4.11, which caused logging deployments to fail. With this update, the cluster-logging Operator resolves the issue by creating the secret if needed. (LOG-2840)

1.28.2. CVEs

1.29. Logging 5.4.3

This release includes RHSA-2022:5556-OpenShift Logging Bug Fix Release 5.4.3.

1.29.1. Elasticsearch Operator deprecation notice

In logging subsystem 5.4.3 the Elasticsearch Operator is deprecated and is planned to be removed in a future release. Red Hat will provide bug fixes and support for this feature during the current release lifecycle, but this feature will no longer receive enhancements and will be removed. As an alternative to using the Elasticsearch Operator to manage the default log storage, you can use the Loki Operator.

1.29.2. Bug fixes

  • Before this update, the OpenShift Logging Dashboard showed the number of active primary shards instead of all active shards. With this update, the dashboard displays all active shards. (LOG-2781)
  • Before this update, a bug in a library used by elasticsearch-operator contained a denial of service attack vulnerability. With this update, the library has been updated to a version that does not contain this vulnerability. (LOG-2816)
  • Before this update, when configuring Vector to forward logs to Loki, it was not possible to set a custom bearer token or use the default token if Loki had TLS enabled. With this update, Vector can forward logs to Loki using tokens with TLS enabled. (LOG-2786
  • Before this update, the ElasticSearch Operator omitted the referencePolicy property of the ImageStream custom resource when selecting an oauth-proxy image. This omission caused the Kibana deployment to fail in specific environments. With this update, using referencePolicy resolves the issue, and the Operator can deploy Kibana successfully. (LOG-2791)
  • Before this update, alerting rules for the ClusterLogForwarder custom resource did not take multiple forward outputs into account. This update resolves the issue. (LOG-2640)
  • Before this update, clusters configured to forward logs to Amazon CloudWatch wrote rejected log files to temporary storage, causing cluster instability over time. With this update, chunk backup for CloudWatch has been disabled, resolving the issue. (LOG-2768)

1.29.3. CVEs

1.30. Logging 5.4.2

This release includes RHBA-2022:4874-OpenShift Logging Bug Fix Release 5.4.2

1.30.1. Bug fixes

  • Before this update, editing the Collector configuration using oc edit was difficult because it had inconsistent use of white-space. This change introduces logic to normalize and format the configuration prior to any updates by the Operator so that it is easy to edit using oc edit. (LOG-2319)
  • Before this update, the FluentdNodeDown alert could not provide instance labels in the message section appropriately. This update resolves the issue by fixing the alert rule to provide instance labels in cases of partial instance failures. (LOG-2607)
  • Before this update, several log levels, such as`critical`, that were documented as supported by the product were not. This update fixes the discrepancy so the documented log levels are now supported by the product. (LOG-2033)

1.30.2. CVEs

1.31. Logging 5.4.1

This release includes RHSA-2022:2216-OpenShift Logging Bug Fix Release 5.4.1.

1.31.1. Bug fixes

  • Before this update, the log file metric exporter only reported logs created while the exporter was running, which resulted in inaccurate log growth data. This update resolves this issue by monitoring /var/log/pods. (LOG-2442)
  • Before this update, the collector would be blocked because it continually tried to use a stale connection when forwarding logs to fluentd forward receivers. With this release, the keepalive_timeout value has been set to 30 seconds (30s) so that the collector recycles the connection and re-attempts to send failed messages within a reasonable amount of time. (LOG-2534)
  • Before this update, an error in the gateway component enforcing tenancy for reading logs limited access to logs with a Kubernetes namespace causing "audit" and some "infrastructure" logs to be unreadable. With this update, the proxy correctly detects users with admin access and allows access to logs without a namespace. (LOG-2448)
  • Before this update, the system:serviceaccount:openshift-monitoring:prometheus-k8s service account had cluster level privileges as a clusterrole and clusterrolebinding. This update restricts the service account` to the openshift-logging namespace with a role and rolebinding. (LOG-2437)
  • Before this update, Linux audit log time parsing relied on an ordinal position of a key/value pair. This update changes the parsing to use a regular expression to find the time entry. (LOG-2321)

1.31.2. CVEs

1.32. Logging 5.4

The following advisories are available for logging 5.4: Logging subsystem for Red Hat OpenShift Release 5.4

1.32.1. Technology Previews

Important

Vector 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 Technology Preview Features Support Scope.

1.32.2. About Vector

Vector is a log collector offered as a tech-preview alternative to the current default collector for the logging subsystem.

The following outputs are supported:

  • elasticsearch. An external Elasticsearch instance. The elasticsearch output can use a TLS connection.
  • kafka. A Kafka broker. The kafka output can use an unsecured or TLS connection.
  • loki. Loki, a horizontally scalable, highly available, multi-tenant log aggregation system.

1.32.2.1. Enabling Vector

Vector is not enabled by default. Use the following steps to enable Vector on your Red Hat OpenShift Service on AWS cluster.

Prerequisites

  • Red Hat OpenShift Service on AWS: 4.13
  • Logging subsystem for Red Hat OpenShift: 5.4

Procedure

  1. Edit the ClusterLogging custom resource (CR) in the openshift-logging project: 

    $ oc -n openshift-logging edit ClusterLogging instance
  2. Add a logging.openshift.io/preview-vector-collector: enabled annotation to the ClusterLogging custom resource (CR).
  3. Add vector as a collection type to the ClusterLogging custom resource (CR). 
  apiVersion: "logging.openshift.io/v1"
  kind: "ClusterLogging"
  metadata:
    name: "instance"
    namespace: "openshift-logging"
    annotations:
      logging.openshift.io/preview-vector-collector: enabled
  spec:
    collection:
      logs:
        type: "vector"
        vector: {}

Additional resources

Important

Loki 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 Technology Preview Features Support Scope.

1.32.3. About Loki

Loki is a horizontally scalable, highly available, multi-tenant log aggregation system currently offered as an alternative to Elasticsearch as a log store for the logging subsystem.

Additional resources

1.32.3.1. Deploying the Lokistack

You can use the Red Hat OpenShift Service on AWS web console to install the Loki Operator.

Prerequisites

  • Red Hat OpenShift Service on AWS: 4.13
  • Logging subsystem for Red Hat OpenShift: 5.4

To install the Loki Operator using the Red Hat OpenShift Service on AWS web console:

  1. Install the Loki Operator:

    1. In the Red Hat OpenShift Service on AWS web console, click Operators → OperatorHub.
    2. Choose Loki Operator from the list of available Operators, and click Install.
    3. Under Installation Mode, select All namespaces on the cluster.

    4. Under Installed Namespace, select openshift-operators-redhat.

      You must specify the openshift-operators-redhat namespace. The openshift-operators namespace might contain Community Operators, which are untrusted and could publish a metric with the same name as an Red Hat OpenShift Service on AWS metric, which would cause conflicts.

    5. Select Enable operator recommended cluster monitoring on this namespace.

      This option sets the openshift.io/cluster-monitoring: "true" label in the Namespace object. You must select this option to ensure that cluster monitoring scrapes the openshift-operators-redhat namespace.

    6. Select an Approval Strategy.

      • The Automatic strategy allows Operator Lifecycle Manager (OLM) to automatically update the Operator when a new version is available.
      • The Manual strategy requires a user with appropriate credentials to approve the Operator update.
    7. Click Install.
    8. Verify that you installed the Loki Operator. Visit the Operators → Installed Operators page and look for "Loki Operator."
    9. Ensure that Loki Operator is listed in all the projects whose Status is Succeeded.

1.32.4. Bug fixes

  • Before this update, the cluster-logging-operator used cluster scoped roles and bindings to establish permissions for the Prometheus service account to scrape metrics. These permissions were created when deploying the Operator using the console interface but were missing when deploying from the command line. This update fixes the issue by making the roles and bindings namespace-scoped. (LOG-2286)
  • Before this update, a prior change to fix dashboard reconciliation introduced a ownerReferences field to the resource across namespaces. As a result, both the config map and dashboard were not created in the namespace. With this update, the removal of the ownerReferences field resolves the issue, and the OpenShift Logging dashboard is available in the console. (LOG-2163)
  • Before this update, changes to the metrics dashboards did not deploy because the cluster-logging-operator did not correctly compare existing and modified config maps that contain the dashboard. With this update, the addition of a unique hash value to object labels resolves the issue. (LOG-2071)
  • Before this update, the OpenShift Logging dashboard did not correctly display the pods and namespaces in the table, which displays the top producing containers collected over the last 24 hours. With this update, the pods and namespaces are displayed correctly. (LOG-2069)
  • Before this update, when the ClusterLogForwarder was set up with Elasticsearch OutputDefault and Elasticsearch outputs did not have structured keys, the generated configuration contained the incorrect values for authentication. This update corrects the secret and certificates used. (LOG-2056)
  • Before this update, the OpenShift Logging dashboard displayed an empty CPU graph because of a reference to an invalid metric. With this update, the correct data point has been selected, resolving the issue. (LOG-2026)
  • Before this update, the Fluentd container image included builder tools that were unnecessary at run time. This update removes those tools from the image.(LOG-1927)
  • Before this update, a name change of the deployed collector in the 5.3 release caused the logging collector to generate the FluentdNodeDown alert. This update resolves the issue by fixing the job name for the Prometheus alert. (LOG-1918)
  • Before this update, the log collector was collecting its own logs due to a refactoring of the component name change. This lead to a potential feedback loop of the collector processing its own log that might result in memory and log message size issues. This update resolves the issue by excluding the collector logs from the collection. (LOG-1774)
  • Before this update, Elasticsearch generated the error Unable to create PersistentVolumeClaim due to forbidden: exceeded quota: infra-storage-quota. if the PVC already existed. With this update, Elasticsearch checks for existing PVCs, resolving the issue. (LOG-2131)
  • Before this update, Elasticsearch was unable to return to the ready state when the elasticsearch-signing secret was removed. With this update, Elasticsearch is able to go back to the ready state after that secret is removed. (LOG-2171)
  • Before this update, the change of the path from which the collector reads container logs caused the collector to forward some records to the wrong indices. With this update, the collector now uses the correct configuration to resolve the issue. (LOG-2160)
  • Before this update, clusters with a large number of namespaces caused Elasticsearch to stop serving requests because the list of namespaces reached the maximum header size limit. With this update, headers only include a list of namespace names, resolving the issue. (LOG-1899)
  • Before this update, the Red Hat OpenShift Service on AWS Logging dashboard showed the number of shards 'x' times larger than the actual value when Elasticsearch had 'x' nodes. This issue occurred because it was printing all primary shards for each Elasticsearch pod and calculating a sum on it, although the output was always for the whole Elasticsearch cluster. With this update, the number of shards is now correctly calculated. (LOG-2156)
  • Before this update, the secrets kibana and kibana-proxy were not recreated if they were deleted manually. With this update, the elasticsearch-operator will watch the resources and automatically recreate them if deleted. (LOG-2250)
  • Before this update, tuning the buffer chunk size could cause the collector to generate a warning about the chunk size exceeding the byte limit for the event stream. With this update, you can also tune the read line limit, resolving the issue. (LOG-2379)
  • Before this update, the logging console link in OpenShift web console was not removed with the ClusterLogging CR. With this update, deleting the CR or uninstalling the Cluster Logging Operator removes the link. (LOG-2373)
  • Before this update, a change to the container logs path caused the collection metric to always be zero with older releases configured with the original path. With this update, the plugin which exposes metrics about collected logs supports reading from either path to resolve the issue. (LOG-2462)

1.32.5. CVEs

1.33. Logging 5.3.14

This release includes OpenShift Logging Bug Fix Release 5.3.14.

1.33.1. Bug fixes

  • Before this update, the log file size map generated by the log-file-metrics-exporter component did not remove entries for deleted files, resulting in increased file size, and process memory. With this update, the log file size map does not contain entries for deleted files. (LOG-3293)

1.33.2. CVEs

1.34. Logging 5.3.13

This release includes RHSA-2022:68828-OpenShift Logging Bug Fix Release 5.3.13.

1.34.1. Bug fixes

None.

1.34.2. CVEs

1.35. Logging 5.3.12

This release includes OpenShift Logging Bug Fix Release 5.3.12.

1.35.1. Bug fixes

None.

1.35.2. CVEs

1.36. Logging 5.3.11

This release includes OpenShift Logging Bug Fix Release 5.3.11.

1.36.1. Bug fixes

  • Before this update, the Operator did not ensure that the pod was ready, which caused the cluster to reach an inoperable state during a cluster restart. With this update, the Operator marks new pods as ready before continuing to a new pod during a restart, which resolves the issue. (LOG-2871)

1.36.2. CVEs

1.37. Logging 5.3.10

This release includes RHSA-2022:5908-OpenShift Logging Bug Fix Release 5.3.10.

1.37.1. Bug fixes

1.37.2. CVEs

1.38. Logging 5.3.9

This release includes RHBA-2022:5557-OpenShift Logging Bug Fix Release 5.3.9.

1.38.1. Bug fixes

  • Before this update, the logging collector included a path as a label for the metrics it produced. This path changed frequently and contributed to significant storage changes for the Prometheus server. With this update, the label has been dropped to resolve the issue and reduce storage consumption. (LOG-2682)

1.38.2. CVEs

1.39. Logging 5.3.8

This release includes RHBA-2022:5010-OpenShift Logging Bug Fix Release 5.3.8

1.39.1. Bug fixes

(None.)

1.39.2. CVEs

1.40. OpenShift Logging 5.3.7

This release includes RHSA-2022:2217 OpenShift Logging Bug Fix Release 5.3.7

1.40.1. Bug fixes

  • Before this update, Linux audit log time parsing relied on an ordinal position of key/value pair. This update changes the parsing to utilize a regex to find the time entry. (LOG-2322)
  • Before this update, some log forwarder outputs could re-order logs with the same time-stamp. With this update, a sequence number has been added to the log record to order entries that have matching timestamps. (LOG-2334)
  • Before this update, clusters with a large number of namespaces caused Elasticsearch to stop serving requests because the list of namespaces reached the maximum header size limit. With this update, headers only include a list of namespace names, resolving the issue. (LOG-2450)
  • Before this update, system:serviceaccount:openshift-monitoring:prometheus-k8s had cluster level privileges as a clusterrole and clusterrolebinding. This update restricts the serviceaccount to the openshift-logging namespace with a role and rolebinding. (LOG-2481))

1.40.2. CVEs

1.41. OpenShift Logging 5.3.6

This release includes RHBA-2022:1377 OpenShift Logging Bug Fix Release 5.3.6

1.41.1. Bug fixes

  • Before this update, defining a toleration with no key and the existing Operator caused the Operator to be unable to complete an upgrade. With this update, this toleration no longer blocks the upgrade from completing. (LOG-2126)
  • Before this change, it was possible for the collector to generate a warning where the chunk byte limit was exceeding an emitted event. With this change, you can tune the readline limit to resolve the issue as advised by the upstream documentation. (LOG-2380)

1.42. OpenShift Logging 5.3.5

This release includes RHSA-2022:0721 OpenShift Logging Bug Fix Release 5.3.5

1.42.1. Bug fixes

  • Before this update, if you removed OpenShift Logging from Red Hat OpenShift Service on AWS, the web console continued displaying a link to the Logging page. With this update, removing or uninstalling OpenShift Logging also removes that link. (LOG-2182)

1.42.2. CVEs

1.43. OpenShift Logging 5.3.4

This release includes RHBA-2022:0411 OpenShift Logging Bug Fix Release 5.3.4

1.43.1. Bug fixes

  • Before this update, changes to the metrics dashboards had not yet been deployed because the cluster-logging-operator did not correctly compare existing and desired config maps that contained the dashboard. This update fixes the logic by adding a unique hash value to the object labels. (LOG-2066)
  • Before this update, Elasticsearch pods failed to start after updating with FIPS enabled. With this update, Elasticsearch pods start successfully. (LOG-1974)
  • Before this update, elasticsearch generated the error "Unable to create PersistentVolumeClaim due to forbidden: exceeded quota: infra-storage-quota." if the PVC already existed. With this update, elasticsearch checks for existing PVCs, resolving the issue. (LOG-2127)

1.43.2. CVEs

1.44. OpenShift Logging 5.3.3

This release includes RHSA-2022:0227 OpenShift Logging Bug Fix Release 5.3.3

1.44.1. Bug fixes

  • Before this update, changes to the metrics dashboards had not yet been deployed because the cluster-logging-operator did not correctly compare existing and desired configmaps containing the dashboard. This update fixes the logic by adding a dashboard unique hash value to the object labels.(LOG-2066)
  • This update changes the log4j dependency to 2.17.1 to resolve CVE-2021-44832.(LOG-2102)

1.44.2. CVEs

Example 1.11. Click to expand CVEs

1.45. OpenShift Logging 5.3.2

This release includes RHSA-2022:0044 OpenShift Logging Bug Fix Release 5.3.2

1.45.1. Bug fixes

  • Before this update, Elasticsearch rejected logs from the Event Router due to a parsing error. This update changes the data model to resolve the parsing error. However, as a result, previous indices might cause warnings or errors within Kibana. The kubernetes.event.metadata.resourceVersion field causes errors until existing indices are removed or reindexed. If this field is not used in Kibana, you can ignore the error messages. If you have a retention policy that deletes old indices, the policy eventually removes the old indices and stops the error messages. Otherwise, manually reindex to stop the error messages. (LOG-2087)
  • Before this update, the OpenShift Logging Dashboard displayed the wrong pod namespace in the table that displays top producing and collected containers over the last 24 hours. With this update, the OpenShift Logging Dashboard displays the correct pod namespace. (LOG-2051)
  • Before this update, if outputDefaults.elasticsearch.structuredTypeKey in the ClusterLogForwarder custom resource (CR) instance did not have a structured key, the CR replaced the output secret with the default secret used to communicate to the default log store. With this update, the defined output secret is correctly used. (LOG-2046)

1.45.2. CVEs

1.46. OpenShift Logging 5.3.1

This release includes RHSA-2021:5129 OpenShift Logging Bug Fix Release 5.3.1

1.46.1. Bug fixes

  • Before this update, the Fluentd container image included builder tools that were unnecessary at run time. This update removes those tools from the image. (LOG-1998)
  • Before this update, the Logging dashboard displayed an empty CPU graph because of a reference to an invalid metric. With this update, the Logging dashboard displays CPU graphs correctly. (LOG-1925)
  • Before this update, the Elasticsearch Prometheus exporter plugin compiled index-level metrics using a high-cost query that impacted the Elasticsearch node performance. This update implements a lower-cost query that improves performance. (LOG-1897)

1.46.2. CVEs

1.47. OpenShift Logging 5.3.0

This release includes RHSA-2021:4627 OpenShift Logging Bug Fix Release 5.3.0

1.47.1. New features and enhancements

  • With this update, authorization options for Log Forwarding have been expanded. Outputs may now be configured with SASL, username/password, or TLS.

1.47.2. Bug fixes

  • Before this update, if you forwarded logs using the syslog protocol, serializing a ruby hash encoded key/value pairs to contain a '⇒' character and replaced tabs with "#11". This update fixes the issue so that log messages are correctly serialized as valid JSON. (LOG-1494)
  • Before this update, application logs were not correctly configured to forward to the proper Cloudwatch stream with multi-line error detection enabled. (LOG-1939)
  • Before this update, a name change of the deployed collector in the 5.3 release caused the alert 'fluentnodedown' to generate. (LOG-1918)
  • Before this update, a regression introduced in a prior release configuration caused the collector to flush its buffered messages before shutdown, creating a delay the termination and restart of collector Pods. With this update, fluentd no longer flushes buffers at shutdown, resolving the issue. (LOG-1735)
  • Before this update, a regression introduced in a prior release intentionally disabled JSON message parsing. This update re-enables JSON parsing. It also sets the log entry "level" based on the "level" field in parsed JSON message or by using regex to extract a match from a message field. (LOG-1199)
  • Before this update, the ClusterLogging custom resource (CR) applied the value of the totalLimitSize field to the Fluentd total_limit_size field, even if the required buffer space was not available. With this update, the CR applies the lesser of the two totalLimitSize or 'default' values to the Fluentd total_limit_size field, resolving the issue. (LOG-1776)

1.47.3. Known issues

  • If you forward logs to an external Elasticsearch server and then change a configured value in the pipeline secret, such as the username and password, the Fluentd forwarder loads the new secret but uses the old value to connect to an external Elasticsearch server. This issue happens because the Red Hat OpenShift Logging Operator does not currently monitor secrets for content changes. (LOG-1652)

    As a workaround, if you change the secret, you can force the Fluentd pods to redeploy by entering: 

    $ oc delete pod -l component=collector

1.47.4. Deprecated and removed features

Some features available in previous releases have been deprecated or removed.

Deprecated functionality is still included in OpenShift Logging and continues to be supported; however, it will be removed in a future release of this product and is not recommended for new deployments.

1.47.4.1. Forwarding logs using the legacy Fluentd and legacy syslog methods have been removed

In OpenShift Logging 5.3, the legacy methods of forwarding logs to Syslog and Fluentd are removed. Bug fixes and support are provided through the end of the OpenShift Logging 5.2 life cycle. After which, no new feature enhancements are made.

Instead, use the following non-legacy methods:

1.47.4.2. Configuration mechanisms for legacy forwarding methods have been removed

In OpenShift Logging 5.3, the legacy configuration mechanism for log forwarding is removed: You cannot forward logs using the legacy Fluentd method and legacy Syslog method. Use the standard log forwarding methods instead.

1.47.5. CVEs

Example 1.14. Click to expand CVEs

1.48. Logging 5.2.13

This release includes RHSA-2022:5909-OpenShift Logging Bug Fix Release 5.2.13.

1.48.1. Bug fixes

1.48.2. CVEs

1.49. Logging 5.2.12

This release includes RHBA-2022:5558-OpenShift Logging Bug Fix Release 5.2.12.

1.49.1. Bug fixes

None.

1.49.2. CVEs

1.50. Logging 5.2.11

This release includes RHBA-2022:5012-OpenShift Logging Bug Fix Release 5.2.11

1.50.1. Bug fixes

  • Before this update, clusters configured to perform CloudWatch forwarding wrote rejected log files to temporary storage, causing cluster instability over time. With this update, chunk backup for CloudWatch has been disabled, resolving the issue. (LOG-2635)

1.50.2. CVEs

1.51. OpenShift Logging 5.2.10

This release includes OpenShift Logging Bug Fix Release 5.2.10]

1.51.1. Bug fixes

  • Before this update some log forwarder outputs could re-order logs with the same time-stamp. With this update, a sequence number has been added to the log record to order entries that have matching timestamps.(LOG-2335)
  • Before this update, clusters with a large number of namespaces caused Elasticsearch to stop serving requests because the list of namespaces reached the maximum header size limit. With this update, headers only include a list of namespace names, resolving the issue. (LOG-2475)
  • Before this update, system:serviceaccount:openshift-monitoring:prometheus-k8s had cluster level privileges as a clusterrole and clusterrolebinding. This update restricts the serviceaccount to the openshift-logging namespace with a role and rolebinding. (LOG-2480)
  • Before this update, the cluster-logging-operator utilized cluster scoped roles and bindings to establish permissions for the Prometheus service account to scrape metrics. These permissions were only created when deploying the Operator using the console interface and were missing when the Operator was deployed from the command line. This fixes the issue by making this role and binding namespace scoped. (LOG-1972)

1.51.2. CVEs

1.52. OpenShift Logging 5.2.9

This release includes RHBA-2022:1375 OpenShift Logging Bug Fix Release 5.2.9]

1.52.1. Bug fixes

  • Before this update, defining a toleration with no key and the existing Operator caused the Operator to be unable to complete an upgrade. With this update, this toleration no longer blocks the upgrade from completing. (LOG-2304)

1.53. OpenShift Logging 5.2.8

This release includes RHSA-2022:0728 OpenShift Logging Bug Fix Release 5.2.8

1.53.1. Bug fixes

  • Before this update, if you removed OpenShift Logging from Red Hat OpenShift Service on AWS, the web console continued displaying a link to the Logging page. With this update, removing or uninstalling OpenShift Logging also removes that link. (LOG-2180)

1.53.2. CVEs

Example 1.19. Click to expand CVEs

1.54. OpenShift Logging 5.2.7

This release includes RHBA-2022:0478 OpenShift Logging Bug Fix Release 5.2.7

1.54.1. Bug fixes

  • Before this update, Elasticsearch pods with FIPS enabled failed to start after updating. With this update, Elasticsearch pods start successfully. (LOG-2000)
  • Before this update, if a persistent volume claim (PVC) already existed, Elasticsearch generated an error, "Unable to create PersistentVolumeClaim due to forbidden: exceeded quota: infra-storage-quota." With this update, Elasticsearch checks for existing PVCs, resolving the issue. (LOG-2118)

1.54.2. CVEs

1.55. OpenShift Logging 5.2.6

This release includes RHSA-2022:0230 OpenShift Logging Bug Fix Release 5.2.6

1.55.1. Bug fixes

  • Before this update, the release did not include a filter change which caused Fluentd to crash. With this update, the missing filter has been corrected. (LOG-2104)
  • This update changes the log4j dependency to 2.17.1 to resolve CVE-2021-44832.(LOG-2101)

1.55.2. CVEs

Example 1.21. Click to expand CVEs

1.56. OpenShift Logging 5.2.5

This release includes RHSA-2022:0043 OpenShift Logging Bug Fix Release 5.2.5

1.56.1. Bug fixes

  • Before this update, Elasticsearch rejected logs from the Event Router due to a parsing error. This update changes the data model to resolve the parsing error. However, as a result, previous indices might cause warnings or errors within Kibana. The kubernetes.event.metadata.resourceVersion field causes errors until existing indices are removed or reindexed. If this field is not used in Kibana, you can ignore the error messages. If you have a retention policy that deletes old indices, the policy eventually removes the old indices and stops the error messages. Otherwise, manually reindex to stop the error messages. LOG-2087)

1.56.2. CVEs

Example 1.22. Click to expand CVEs

1.57. OpenShift Logging 5.2.4

This release includes RHSA-2021:5127 OpenShift Logging Bug Fix Release 5.2.4

1.57.1. Bug fixes

  • Before this update, records shipped via syslog would serialize a ruby hash encoding key/value pairs to contain a '⇒' character, as well as replace tabs with "#11". This update serializes the message correctly as proper JSON. (LOG-1775)
  • Before this update, the Elasticsearch Prometheus exporter plugin compiled index-level metrics using a high-cost query that impacted the Elasticsearch node performance. This update implements a lower-cost query that improves performance. (LOG-1970)
  • Before this update, Elasticsearch sometimes rejected messages when Log Forwarding was configured with multiple outputs. This happened because configuring one of the outputs modified message content to be a single message. With this update, Log Forwarding duplicates the messages for each output so that output-specific processing does not affect the other outputs. (LOG-1824)

1.57.2. CVEs

1.58. OpenShift Logging 5.2.3

This release includes RHSA-2021:4032 OpenShift Logging Bug Fix Release 5.2.3

1.58.1. Bug fixes

  • Before this update, some alerts did not include a namespace label. This omission does not comply with the OpenShift Monitoring Team’s guidelines for writing alerting rules in Red Hat OpenShift Service on AWS. With this update, all the alerts in Elasticsearch Operator include a namespace label and follow all the guidelines for writing alerting rules in Red Hat OpenShift Service on AWS. (LOG-1857)
  • Before this update, a regression introduced in a prior release intentionally disabled JSON message parsing. This update re-enables JSON parsing. It also sets the log entry level based on the level field in parsed JSON message or by using regex to extract a match from a message field. (LOG-1759)

1.58.2. CVEs

1.59. OpenShift Logging 5.2.2

This release includes RHBA-2021:3747 OpenShift Logging Bug Fix Release 5.2.2

1.59.1. Bug fixes

  • Before this update, the ClusterLogging custom resource (CR) applied the value of the totalLimitSize field to the Fluentd total_limit_size field, even if the required buffer space was not available. With this update, the CR applies the lesser of the two totalLimitSize or 'default' values to the Fluentd total_limit_size field, resolving the issue.(LOG-1738)
  • Before this update, a regression introduced in a prior release configuration caused the collector to flush its buffered messages before shutdown, creating a delay to the termination and restart of collector pods. With this update, Fluentd no longer flushes buffers at shutdown, resolving the issue. (LOG-1739)
  • Before this update, an issue in the bundle manifests prevented installation of the Elasticsearch Operator through OLM on Red Hat OpenShift Service on AWS 4.9. With this update, a correction to bundle manifests re-enables installation and upgrade in 4.9.(LOG-1780)

1.59.2. CVEs

1.60. OpenShift Logging 5.2.1

This release includes RHBA-2021:3550 OpenShift Logging Bug Fix Release 5.2.1

1.60.1. Bug fixes

  • Before this update, due to an issue in the release pipeline scripts, the value of the olm.skipRange field remained unchanged at 5.2.0 instead of reflecting the current release number. This update fixes the pipeline scripts to update the value of this field when the release numbers change. (LOG-1743)

1.60.2. CVEs

(None)

1.61. OpenShift Logging 5.2.0

This release includes RHBA-2021:3393 OpenShift Logging Bug Fix Release 5.2.0

1.61.1. New features and enhancements

  • With this update, you can forward log data to Amazon CloudWatch, which provides application and infrastructure monitoring. For more information, see Forwarding logs to Amazon CloudWatch. (LOG-1173)
  • With this update, you can forward log data to Loki, a horizontally scalable, highly available, multi-tenant log aggregation system. For more information, see Forwarding logs to Loki. (LOG-684)
  • With this update, if you use the Fluentd forward protocol to forward log data over a TLS-encrypted connection, now you can use a password-encrypted private key file and specify the passphrase in the Cluster Log Forwarder configuration. For more information, see Forwarding logs using the Fluentd forward protocol. (LOG-1525)
  • This enhancement enables you to use a username and password to authenticate a log forwarding connection to an external Elasticsearch instance. For example, if you cannot use mutual TLS (mTLS) because a third-party operates the Elasticsearch instance, you can use HTTP or HTTPS and set a secret that contains the username and password. For more information, see Forwarding logs to an external Elasticsearch instance. (LOG-1022)
  • With this update, you can collect OVN network policy audit logs for forwarding to a logging server. (LOG-1526)

  • By default, the data model introduced in Red Hat OpenShift Service on AWS 4.5 gave logs from different namespaces a single index in common. This change made it harder to see which namespaces produced the most logs.

    The current release adds namespace metrics to the Logging dashboard in the Red Hat OpenShift Service on AWS console. With these metrics, you can see which namespaces produce logs and how many logs each namespace produces for a given timestamp.

    To see these metrics, open the Administrator perspective in the Red Hat OpenShift Service on AWS web console, and navigate to Observe → Dashboards → Logging/Elasticsearch. (LOG-1680)

  • The current release, OpenShift Logging 5.2, enables two new metrics: For a given timestamp or duration, you can see the total logs produced or logged by individual containers, and the total logs collected by the collector. These metrics are labeled by namespace, pod, and container name so that you can see how many logs each namespace and pod collects and produces. (LOG-1213)

1.61.2. Bug fixes

  • Before this update, when the OpenShift Elasticsearch Operator created index management cronjobs, it added the POLICY_MAPPING environment variable twice, which caused the apiserver to report the duplication. This update fixes the issue so that the POLICY_MAPPING environment variable is set only once per cronjob, and there is no duplication for the apiserver to report. (LOG-1130)
  • Before this update, suspending an Elasticsearch cluster to zero nodes did not suspend the index-management cronjobs, which put these cronjobs into maximum backoff. Then, after unsuspending the Elasticsearch cluster, these cronjobs stayed halted due to maximum backoff reached. This update resolves the issue by suspending the cronjobs and the cluster. (LOG-1268)
  • Before this update, in the Logging dashboard in the Red Hat OpenShift Service on AWS console, the list of top 10 log-producing containers was missing the "chart namespace" label and provided the incorrect metric name, fluentd_input_status_total_bytes_logged. With this update, the chart shows the namespace label and the correct metric name, log_logged_bytes_total. (LOG-1271)
  • Before this update, if an index management cronjob terminated with an error, it did not report the error exit code: instead, its job status was "complete." This update resolves the issue by reporting the error exit codes of index management cronjobs that terminate with errors. (LOG-1273)
  • The priorityclasses.v1beta1.scheduling.k8s.io was removed in 1.22 and replaced by priorityclasses.v1.scheduling.k8s.io (v1beta1 was replaced by v1). Before this update, APIRemovedInNextReleaseInUse alerts were generated for priorityclasses because v1beta1 was still present . This update resolves the issue by replacing v1beta1 with v1. The alert is no longer generated. (LOG-1385)
  • Previously, the OpenShift Elasticsearch Operator and Red Hat OpenShift Logging Operator did not have the annotation that was required for them to appear in the Red Hat OpenShift Service on AWS web console list of Operators that can run in a disconnected environment. This update adds the operators.openshift.io/infrastructure-features: '["Disconnected"]' annotation to these two Operators so that they appear in the list of Operators that run in disconnected environments. (LOG-1420)
  • Before this update, Red Hat OpenShift Logging Operator pods were scheduled on CPU cores that were reserved for customer workloads on performance-optimized single-node clusters. With this update, cluster logging Operator pods are scheduled on the correct CPU cores. (LOG-1440)
  • Before this update, some log entries had unrecognized UTF-8 bytes, which caused Elasticsearch to reject the messages and block the entire buffered payload. With this update, rejected payloads drop the invalid log entries and resubmit the remaining entries to resolve the issue. (LOG-1499)
  • Before this update, the kibana-proxy pod sometimes entered the CrashLoopBackoff state and logged the following message Invalid configuration: cookie_secret must be 16, 24, or 32 bytes to create an AES cipher when pass_access_token == true or cookie_refresh != 0, but is 29 bytes. The exact actual number of bytes could vary. With this update, the generation of the Kibana session secret has been corrected, and the kibana-proxy pod no longer enters a CrashLoopBackoff state due to this error. (LOG-1446)
  • Before this update, the AWS CloudWatch Fluentd plugin logged its AWS API calls to the Fluentd log at all log levels, consuming additional Red Hat OpenShift Service on AWS node resources. With this update, the AWS CloudWatch Fluentd plugin logs AWS API calls only at the "debug" and "trace" log levels. This way, at the default "warn" log level, Fluentd does not consume extra node resources. (LOG-1071)
  • Before this update, the Elasticsearch OpenDistro security plugin caused user index migrations to fail. This update resolves the issue by providing a newer version of the plugin. Now, index migrations proceed without errors. (LOG-1276)
  • Before this update, in the Logging dashboard in the Red Hat OpenShift Service on AWS console, the list of top 10 log-producing containers lacked data points. This update resolves the issue, and the dashboard displays all data points. (LOG-1353)
  • Before this update, if you were tuning the performance of the Fluentd log forwarder by adjusting the chunkLimitSize and totalLimitSize values, the Setting queued_chunks_limit_size for each buffer to message reported values that were too low. The current update fixes this issue so that this message reports the correct values. (LOG-1411)
  • Before this update, the Kibana OpenDistro security plugin caused user index migrations to fail. This update resolves the issue by providing a newer version of the plugin. Now, index migrations proceed without errors. (LOG-1558)
  • Before this update, using a namespace input filter prevented logs in that namespace from appearing in other inputs. With this update, logs are sent to all inputs that can accept them. (LOG-1570)
  • Before this update, a missing license file for the viaq/logerr dependency caused license scanners to abort without success. With this update, the viaq/logerr dependency is licensed under Apache 2.0 and the license scanners run successfully. (LOG-1590)
  • Before this update, an incorrect brew tag for curator5 within the elasticsearch-operator-bundle build pipeline caused the pull of an image pinned to a dummy SHA1. With this update, the build pipeline uses the logging-curator5-rhel8 reference for curator5, enabling index management cronjobs to pull the correct image from registry.redhat.io. (LOG-1624)
  • Before this update, an issue with the ServiceAccount permissions caused errors such as no permissions for [indices:admin/aliases/get]. With this update, a permission fix resolves the issue. (LOG-1657)
  • Before this update, the Custom Resource Definition (CRD) for the Red Hat OpenShift Logging Operator was missing the Loki output type, which caused the admission controller to reject the ClusterLogForwarder custom resource object. With this update, the CRD includes Loki as an output type so that administrators can configure ClusterLogForwarder to send logs to a Loki server. (LOG-1683)
  • Before this update, OpenShift Elasticsearch Operator reconciliation of the ServiceAccounts overwrote third-party-owned fields that contained secrets. This issue caused memory and CPU spikes due to frequent recreation of secrets. This update resolves the issue. Now, the OpenShift Elasticsearch Operator does not overwrite third-party-owned fields. (LOG-1714)
  • Before this update, in the ClusterLogging custom resource (CR) definition, if you specified a flush_interval value but did not set flush_mode to interval, the Red Hat OpenShift Logging Operator generated a Fluentd configuration. However, the Fluentd collector generated an error at runtime. With this update, the Red Hat OpenShift Logging Operator validates the ClusterLogging CR definition and only generates the Fluentd configuration if both fields are specified. (LOG-1723)

1.61.3. Known issues

  • If you forward logs to an external Elasticsearch server and then change a configured value in the pipeline secret, such as the username and password, the Fluentd forwarder loads the new secret but uses the old value to connect to an external Elasticsearch server. This issue happens because the Red Hat OpenShift Logging Operator does not currently monitor secrets for content changes. (LOG-1652)

    As a workaround, if you change the secret, you can force the Fluentd pods to redeploy by entering: 

    $ oc delete pod -l component=collector

1.61.4. Deprecated and removed features

Some features available in previous releases have been deprecated or removed.

Deprecated functionality is still included in OpenShift Logging and continues to be supported; however, it will be removed in a future release of this product and is not recommended for new deployments.

1.61.5. Forwarding logs using the legacy Fluentd and legacy syslog methods have been deprecated

From Red Hat OpenShift Service on AWS 4.6 to the present, forwarding logs by using the following legacy methods have been deprecated and will be removed in a future release:

  • Forwarding logs using the legacy Fluentd method
  • Forwarding logs using the legacy syslog method

Instead, use the following non-legacy methods:

1.61.6. CVEs

Chapter 2. Understanding the logging subsystem for Red Hat OpenShift

As a cluster administrator, you can deploy the logging subsystem to aggregate all the logs from your Red Hat OpenShift Service on AWS cluster, such as node system audit logs, application container logs, and infrastructure logs. The logging subsystem aggregates these logs from throughout your cluster and stores them in a default log store. You can use the Kibana web console to visualize log data.

The logging subsystem aggregates the following types of logs:

  • application - Container logs generated by user applications running in the cluster, except infrastructure container applications.
  • infrastructure - Logs generated by infrastructure components running in the cluster and Red Hat OpenShift Service on AWS nodes, such as journal logs. Infrastructure components are pods that run in the openshift*, kube*, or default projects.
  • audit - Logs generated by auditd, the node audit system, which are stored in the /var/log/audit/audit.log file, and the audit logs from the Kubernetes apiserver and the OpenShift apiserver.
Note

Because the internal Red Hat OpenShift Service on AWS Elasticsearch log store does not provide secure storage for audit logs, audit logs are not stored in the internal Elasticsearch instance by default. If you want to send the audit logs to the default internal Elasticsearch log store, for example to view the audit logs in Kibana, you must use the Log Forwarding API as described in Forward audit logs to the log store.

2.1. Support considerations for logging

Note

Logging is provided as an installable component, with a distinct release cycle from the core Red Hat OpenShift Service on AWS. The Red Hat OpenShift Container Platform Life Cycle Policy outlines release compatibility.

The supported way of configuring the logging subsystem for Red Hat OpenShift is by configuring it using the options described in this documentation. Do not use other configurations, as they are unsupported. Configuration paradigms might change across OpenShift Container Platform releases, and such cases can only be handled gracefully if all configuration possibilities are controlled. If you use configurations other than those described in this documentation, your changes will disappear because the Operators reconcile any differences. The Operators reverse everything to the defined state by default and by design.

Note

If you must perform configurations not described in the OpenShift Container Platform documentation, you must set your Red Hat OpenShift Logging Operator to Unmanaged. An unmanaged OpenShift Logging environment is not supported and does not receive updates until you return OpenShift Logging to Managed.

The following modifications are explicitly not supported:

  • Deploying logging to namespaces not specified in the documentation.
  • Installing custom Elasticsearch, Kibana, Fluentd, or Loki instances on Red Hat OpenShift Service on AWS.
  • Changes to the Kibana Custom Resource (CR) or Elasticsearch CR.
  • Changes to secrets or config maps not specified in the documentation.

The logging subsystem for Red Hat OpenShift is an opinionated collector and normalizer of application, infrastructure, and audit logs. It is intended to be used for forwarding logs to various supported systems.

The logging subsystem for Red Hat OpenShift is not:

  • A high scale log collection system
  • Security Information and Event Monitoring (SIEM) compliant
  • Historical or long term log retention or storage
  • A guaranteed log sink
  • Secure storage - audit logs are not stored by default

2.2. CloudWatch recommendation for Red Hat OpenShift Service on AWS

Red Hat recommends that you use the AWS CloudWatch solution for your logging needs.

2.2.1. Logging requirements

Hosting your own logging stack requires a large amount of compute resources and storage, which might be dependent on your cloud service quota. The compute resource requirements can start at 48 GB or more, while the storage requirement can be as large as 1600 GB or more. The logging stack runs on your worker nodes, which reduces your available workload resource. With these considerations, hosting your own logging stack increases your cluster operating costs.

Next steps

2.3. Glossary of common terms for Red Hat OpenShift Service on AWS Logging

This glossary defines common terms that are used in the Red Hat OpenShift Service on AWS Logging content.

annotation
You can use annotations to attach metadata to objects.
Cluster Logging Operator (CLO)
The Cluster Logging Operator provides a set of APIs to control the collection and forwarding of application, infrastructure, and audit logs.
Custom Resource (CR)
A CR is an extension of the Kubernetes API. To configure Red Hat OpenShift Service on AWS Logging and log forwarding, you can customize the ClusterLogging and the ClusterLogForwarder custom resources.
event router
The event router is a pod that watches Red Hat OpenShift Service on AWS events. It collects logs by using Red Hat OpenShift Service on AWS Logging.
Fluentd
Fluentd is a log collector that resides on each Red Hat OpenShift Service on AWS node. It gathers application, infrastructure, and audit logs and forwards them to different outputs.
garbage collection
Garbage collection is the process of cleaning up cluster resources, such as terminated containers and images that are not referenced by any running pods.
Elasticsearch
Elasticsearch is a distributed search and analytics engine. Red Hat OpenShift Service on AWS uses ELasticsearch as a default log store for Red Hat OpenShift Service on AWS Logging.
Elasticsearch Operator
Elasticsearch operator is used to run Elasticsearch cluster on top of Red Hat OpenShift Service on AWS. The Elasticsearch Operator provides self-service for the Elasticsearch cluster operations and is used by Red Hat OpenShift Service on AWS Logging.
indexing
Indexing is a data structure technique that is used to quickly locate and access data. Indexing optimizes the performance by minimizing the amount of disk access required when a query is processed.
JSON logging
Red Hat OpenShift Service on AWS Logging Log Forwarding API enables you to parse JSON logs into a structured object and forward them to either Red Hat OpenShift Service on AWS Logging-managed Elasticsearch or any other third-party system supported by the Log Forwarding API.
Kibana
Kibana is a browser-based console interface to query, discover, and visualize your Elasticsearch data through histograms, line graphs, and pie charts.
Kubernetes API server
Kubernetes API server validates and configures data for the API objects.
Labels
Labels are key-value pairs that you can use to organize and select subsets of objects, such as a pod.
Logging
With Red Hat OpenShift Service on AWS Logging you can aggregate application, infrastructure, and audit logs throughout your cluster. You can also store them to a default log store, forward them to third party systems, and query and visualize the stored logs in the default log store.
logging collector
A logging collector collects logs from the cluster, formats them, and forwards them to the log store or third party systems.
log store
A log store is used to store aggregated logs. You can use the default Elasticsearch log store or forward logs to external log stores. The default log store is optimized and tested for short-term storage.
log visualizer
Log visualizer is the user interface (UI) component you can use to view information such as logs, graphs, charts, and other metrics. The current implementation is Kibana.
node
A node is a worker machine in the Red Hat OpenShift Service on AWS cluster. A node is either a virtual machine (VM) or a physical machine.
Operators
Operators are the preferred method of packaging, deploying, and managing a Kubernetes application in an Red Hat OpenShift Service on AWS cluster. An Operator takes human operational knowledge and encodes it into software that is packaged and shared with customers.
pod
A pod is the smallest logical unit in Kubernetes. A pod consists of one or more containers and runs on a worker node..
Role-based access control (RBAC)
RBAC is a key security control to ensure that cluster users and workloads have access only to resources required to execute their roles.
shards
Elasticsearch organizes the log data from Fluentd into datastores, or indices, then subdivides each index into multiple pieces called shards.
taint
Taints ensure that pods are scheduled onto appropriate nodes. You can apply one or more taints on a node.
toleration
You can apply tolerations to pods. Tolerations allow the scheduler to schedule pods with matching taints.
web console
A user interface (UI) to manage Red Hat OpenShift Service on AWS. The web console for Red Hat OpenShift Service on AWS can be found at https://console.redhat.com/openshift.

2.4. About deploying the logging subsystem for Red Hat OpenShift

The ClusterLogging CR defines a complete logging subsystem environment that includes all the components of the logging stack to collect, store and visualize logs. The Red Hat OpenShift Logging Operator watches the logging subsystem CR and adjusts the logging deployment accordingly.

Administrators and application developers can view the logs of the projects for which they have view access.

For information, see Installing the logging subsystem for Red Hat OpenShift.

2.4.1. About JSON Red Hat OpenShift Service on AWS Logging

You can use JSON logging to configure the Log Forwarding API to parse JSON strings into a structured object. You can perform the following tasks:

  • Parse JSON logs
  • Configure JSON log data for Elasticsearch
  • Forward JSON logs to the Elasticsearch log store

2.4.2. About collecting and storing Kubernetes events

The Red Hat OpenShift Service on AWS Event Router is a pod that watches Kubernetes events and logs them for collection by Red Hat OpenShift Service on AWS Logging. You must manually deploy the Event Router.

For information, see About collecting and storing Kubernetes events.

2.4.3. About updating Red Hat OpenShift Service on AWS Logging

Red Hat OpenShift Service on AWS allows you to update Red Hat OpenShift Service on AWS logging. You must update the following operators while updating Red Hat OpenShift Service on AWS Logging:

  • Elasticsearch Operator
  • Cluster Logging Operator

For information, see Updating OpenShift Logging.

2.4.4. About viewing the cluster dashboard

The Red Hat OpenShift Service on AWS Logging dashboard contains charts that show details about your Elasticsearch instance at the cluster level. These charts help you diagnose and anticipate problems.

For information, see About viewing the cluster dashboard.

2.4.5. About troubleshooting Red Hat OpenShift Service on AWS Logging

You can troubleshoot the logging issues by performing the following tasks:

  • Viewing logging status
  • Viewing the status of the log store
  • Understanding logging alerts
  • Collecting logging data for Red Hat Support
  • Troubleshooting for critical alerts

2.4.6. About uninstalling Red Hat OpenShift Service on AWS Logging

You can stop log aggregation by deleting the ClusterLogging custom resource (CR). After deleting the CR, there are other cluster logging components that remain, which you can optionally remove.

For information, see Uninstalling OpenShift Logging.

2.4.7. About exporting fields

The logging system exports fields. Exported fields are present in the log records and are available for searching from Elasticsearch and Kibana.

For information, see About exporting fields.

2.4.8. About logging subsystem components

The logging subsystem components include a collector deployed to each node in the Red Hat OpenShift Service on AWS cluster that collects all node and container logs and writes them to a log store. You can use a centralized web UI to create rich visualizations and dashboards with the aggregated data.

The major components of the logging subsystem are:

  • collection - This is the component that collects logs from the cluster, formats them, and forwards them to the log store. The current implementation is Fluentd.
  • log store - This is where the logs are stored. The default implementation is Elasticsearch. You can use the default Elasticsearch log store or forward logs to external log stores. The default log store is optimized and tested for short-term storage.
  • visualization - This is the UI component you can use to view logs, graphs, charts, and so forth. The current implementation is Kibana.

2.4.9. About the logging collector

The logging subsystem for Red Hat OpenShift collects container and node logs.

By default, the log collector uses the following sources:

  • journald for all system logs
  • /var/log/containers/*.log for all container logs

If you configure the log collector to collect audit logs, it gets them from /var/log/audit/audit.log.

The logging collector is a daemon set that deploys pods to each Red Hat OpenShift Service on AWS node. System and infrastructure logs are generated by journald log messages from the operating system, the container runtime, and Red Hat OpenShift Service on AWS. Application logs are generated by the CRI-O container engine. Fluentd collects the logs from these sources and forwards them internally or externally as you configure in Red Hat OpenShift Service on AWS.

The container runtimes provide minimal information to identify the source of log messages: project, pod name, and container ID. This information is not sufficient to uniquely identify the source of the logs. If a pod with a given name and project is deleted before the log collector begins processing its logs, information from the API server, such as labels and annotations, might not be available. There might not be a way to distinguish the log messages from a similarly named pod and project or trace the logs to their source. This limitation means that log collection and normalization are considered best effort.

Important

The available container runtimes provide minimal information to identify the source of log messages and do not guarantee unique individual log messages or that these messages can be traced to their source.

For information, see Configuring the logging collector.

2.4.10. About the log store

By default, Red Hat OpenShift Service on AWS uses Elasticsearch (ES) to store log data. Optionally you can use the Log Forwarder API to forward logs to an external store. Several types of store are supported, including fluentd, rsyslog, kafka and others.

The logging subsystem Elasticsearch instance is optimized and tested for short term storage, approximately seven days. If you want to retain your logs over a longer term, it is recommended you move the data to a third-party storage system.

Elasticsearch organizes the log data from Fluentd into datastores, or indices, then subdivides each index into multiple pieces called shards, which it spreads across a set of Elasticsearch nodes in an Elasticsearch cluster. You can configure Elasticsearch to make copies of the shards, called replicas, which Elasticsearch also spreads across the Elasticsearch nodes. The ClusterLogging custom resource (CR) allows you to specify how the shards are replicated to provide data redundancy and resilience to failure. You can also specify how long the different types of logs are retained using a retention policy in the ClusterLogging CR.

Note

The number of primary shards for the index templates is equal to the number of Elasticsearch data nodes.

The Red Hat OpenShift Logging Operator and companion OpenShift Elasticsearch Operator ensure that each Elasticsearch node is deployed using a unique deployment that includes its own storage volume. You can use a ClusterLogging custom resource (CR) to increase the number of Elasticsearch nodes, as needed. See the Elasticsearch documentation for considerations involved in configuring storage.

Note

A highly-available Elasticsearch environment requires at least three Elasticsearch nodes, each on a different host.

Role-based access control (RBAC) applied on the Elasticsearch indices enables the controlled access of the logs to the developers. Administrators can access all logs and developers can access only the logs in their projects.

For information, see Configuring the log store.

2.4.11. About logging visualization

Red Hat OpenShift Service on AWS uses Kibana to display the log data collected by Fluentd and indexed by Elasticsearch.

Kibana is a browser-based console interface to query, discover, and visualize your Elasticsearch data through histograms, line graphs, pie charts, and other visualizations.

For information, see Configuring the log visualizer.

2.4.12. About event routing

The Event Router is a pod that watches Red Hat OpenShift Service on AWS events so they can be collected by the logging subsystem for Red Hat OpenShift. The Event Router collects events from all projects and writes them to STDOUT. Fluentd collects those events and forwards them into the Red Hat OpenShift Service on AWS Elasticsearch instance. Elasticsearch indexes the events to the infra index.

You must manually deploy the Event Router.

For information, see Collecting and storing Kubernetes events.

2.4.13. About log forwarding

By default, the logging subsystem for Red Hat OpenShift sends logs to the default internal Elasticsearch log store, defined in the ClusterLogging custom resource (CR). If you want to forward logs to other log aggregators, you can use the log forwarding features to send logs to specific endpoints within or outside your cluster.

For information, see Forwarding logs to third-party systems.

2.5. About Vector

Vector is a log collector offered as an alternative to Fluentd for the logging subsystem.

The following outputs are supported:

  • elasticsearch. An external Elasticsearch instance. The elasticsearch output can use a TLS connection.
  • kafka. A Kafka broker. The kafka output can use an unsecured or TLS connection.
  • loki. Loki, a horizontally scalable, highly available, multitenant log aggregation system.

2.5.1. Enabling Vector

Vector is not enabled by default. Use the following steps to enable Vector on your Red Hat OpenShift Service on AWS cluster.

Prerequisites

  • Red Hat OpenShift Service on AWS: 4.13
  • Logging subsystem for Red Hat OpenShift: 5.4

Procedure

  1. Edit the ClusterLogging custom resource (CR) in the openshift-logging project: 

    $ oc -n openshift-logging edit ClusterLogging instance
  2. Add a logging.openshift.io/preview-vector-collector: enabled annotation to the ClusterLogging custom resource (CR).
  3. Add vector as a collection type to the ClusterLogging custom resource (CR). 
  apiVersion: "logging.openshift.io/v1"
  kind: "ClusterLogging"
  metadata:
    name: "instance"
    namespace: "openshift-logging"
    annotations:
      logging.openshift.io/preview-vector-collector: enabled
  spec:
    collection:
    logs:
      type: "vector"
      vector: {}

Additional resources

2.5.2. Collector features

Table 2.1. Log Sources

FeatureFluentdVector

App container logs

✓

✓

App-specific routing

✓

✓

App-specific routing by namespace

✓

✓

Infra container logs

✓

✓

Infra journal logs

✓

✓

Kube API audit logs

✓

✓

OpenShift API audit logs

✓

✓

Open Virtual Network (OVN) audit logs

✓

✓

Table 2.2. Outputs

FeatureFluentdVector

Elasticsearch v5-v7

✓

✓

Fluent forward

✓

 

Syslog RFC3164

✓

✓ (Logging 5.7+)

Syslog RFC5424

✓

✓ (Logging 5.7+)

Kafka

✓

✓

Cloudwatch

✓

✓

Loki

✓

✓

HTTP

✓

✓ (Logging 5.7+)

Table 2.3. Authorization and Authentication

FeatureFluentdVector

Elasticsearch certificates

✓

✓

Elasticsearch username / password

✓

✓

Cloudwatch keys

✓

✓

Cloudwatch STS

✓

✓

Kafka certificates

✓

✓

Kafka username / password

✓

✓

Kafka SASL

✓

✓

Loki bearer token

✓

✓

Table 2.4. Normalizations and Transformations

FeatureFluentdVector

Viaq data model - app

✓

✓

Viaq data model - infra

✓

✓

Viaq data model - infra(journal)

✓

✓

Viaq data model - Linux audit

✓

✓

Viaq data model - kube-apiserver audit

✓

✓

Viaq data model - OpenShift API audit

✓

✓

Viaq data model - OVN

✓

✓

Loglevel Normalization

✓

✓

JSON parsing

✓

✓

Structured Index

✓

✓

Multiline error detection

✓

✓

Multicontainer / split indices

✓

✓

Flatten labels

✓

✓

CLF static labels

✓

✓

Table 2.5. Tuning

FeatureFluentdVector

Fluentd readlinelimit

✓

 

Fluentd buffer

✓

 

- chunklimitsize

✓

 

- totallimitsize

✓

 

- overflowaction

✓

 

- flushthreadcount

✓

 

- flushmode

✓

 

- flushinterval

✓

 

- retrywait

✓

 

- retrytype

✓

 

- retrymaxinterval

✓

 

- retrytimeout

✓

 

Table 2.6. Visibility

FeatureFluentdVector

Metrics

✓

✓

Dashboard

✓

✓

Alerts

✓

 

Table 2.7. Miscellaneous

FeatureFluentdVector

Global proxy support

✓

✓

x86 support

✓

✓

ARM support

✓

✓

IPv6 support

✓

✓

Log event buffering

✓

 

Disconnected Cluster

✓

✓

Chapter 3. Installing the logging subsystem for Red Hat OpenShift

You can install the logging subsystem for Red Hat OpenShift by deploying the OpenShift Elasticsearch and Red Hat OpenShift Logging Operators. The OpenShift Elasticsearch Operator creates and manages the Elasticsearch cluster used by OpenShift Logging. The logging subsystem Operator creates and manages the components of the logging stack.

The process for deploying the logging subsystem to Red Hat OpenShift Service on AWS (ROSA) involves:

3.1. Installing the logging subsystem for Red Hat OpenShift using the web console

You can install the OpenShift Elasticsearch and Red Hat OpenShift Logging Operators by using the Red Hat OpenShift Service on AWS OpenShift Cluster Manager Hybrid Cloud Console.

Note

If you do not want to use the default Elasticsearch log store, you can remove the internal Elasticsearch logStore and Kibana visualization components from the ClusterLogging custom resource (CR). Removing these components is optional but saves resources. For more information, see the additional resources of this section.

Prerequisites

  • Ensure that you have the necessary persistent storage for Elasticsearch. Note that each Elasticsearch node requires its own storage volume.

    Note

    If you use a local volume for persistent storage, do not use a raw block volume, which is described with volumeMode: block in the LocalVolume object. Elasticsearch cannot use raw block volumes.

    Elasticsearch is a memory-intensive application. By default, Red Hat OpenShift Service on AWS installs three Elasticsearch nodes with memory requests and limits of 16 GB. This initial set of three Red Hat OpenShift Service on AWS nodes might not have enough memory to run Elasticsearch within your cluster. If you experience memory issues that are related to Elasticsearch, add more Elasticsearch nodes to your cluster rather than increasing the memory on existing nodes.

Procedure

To install the OpenShift Elasticsearch Operator and Red Hat OpenShift Logging Operator by using the Red Hat OpenShift Service on AWS OpenShift Cluster Manager Hybrid Cloud Console:

  1. Install the OpenShift Elasticsearch Operator:

    1. In the Red Hat Hybrid Cloud Console, click Operators → OperatorHub.
    2. Choose OpenShift Elasticsearch Operator from the list of available Operators, and click Install.
    3. Ensure that the All namespaces on the cluster is selected under Installation Mode.

    4. Ensure that openshift-operators-redhat is selected under Installed Namespace.

      You must specify the openshift-operators-redhat namespace. The openshift-operators namespace might contain Community Operators, which are untrusted and could publish a metric with the same name as a ROSA metric, which would cause conflicts.

    5. Select Enable operator recommended cluster monitoring on this namespace.

      This option sets the openshift.io/cluster-monitoring: "true" label in the Namespace object. You must select this option to ensure that cluster monitoring scrapes the openshift-operators-redhat namespace.

    6. Select stable-5.x as the Update Channel.

    7. Select an Approval Strategy.

      • The Automatic strategy allows Operator Lifecycle Manager (OLM) to automatically update the Operator when a new version is available.
      • The Manual strategy requires a user with appropriate credentials to approve the Operator update.
    8. Click Install.
    9. Verify that the OpenShift Elasticsearch Operator installed by switching to the Operators → Installed Operators page.
    10. Ensure that OpenShift Elasticsearch Operator is listed in all projects with a Status of Succeeded.

  2. Install the Red Hat OpenShift Logging Operator:

    1. In the Red Hat OpenShift Service on AWS web console, click Operators → OperatorHub.
    2. Choose Red Hat OpenShift Logging from the list of available Operators, and click Install.
    3. Ensure that A specific namespace on the cluster is selected under Installation Mode.
    4. Ensure that Operator recommended namespace is openshift-logging under Installed Namespace.

    5. Select Enable operator recommended cluster monitoring on this namespace.

      This option sets the openshift.io/cluster-monitoring: "true" label in the Namespace object. You must select this option to ensure that cluster monitoring scrapes the openshift-logging namespace.

    6. Select stable-5.x as the Update Channel.

    7. Select an Approval Strategy.

      • The Automatic strategy allows Operator Lifecycle Manager (OLM) to automatically update the Operator when a new version is available.
      • The Manual strategy requires a user with appropriate credentials to approve the Operator update.
    8. Click Install.
    9. Verify that the Red Hat OpenShift Logging Operator installed by switching to the Operators → Installed Operators page.

    10. Ensure that Red Hat OpenShift Logging is listed in the openshift-logging project with a Status of Succeeded.

      If the Operator does not appear as installed, to troubleshoot further:

      • Switch to the Operators → Installed Operators page and inspect the Status column for any errors or failures.
      • Switch to the Workloads → Pods page and check the logs in any pods in the openshift-logging project that are reporting issues.

  3. Create an OpenShift Logging instance:

    1. Switch to the Administration → Custom Resource Definitions page.
    2. On the Custom Resource Definitions page, click ClusterLogging.
    3. On the Custom Resource Definition details page, select View Instances from the Actions menu.

    4. On the ClusterLoggings page, click Create ClusterLogging.

      You might have to refresh the page to load the data.

    5. In the YAML field, replace the code with the following:

      Note

      This default OpenShift Logging configuration should support a wide array of environments. Review the topics on tuning and configuring logging subsystem components for information on modifications you can make to your OpenShift Logging cluster. 

      apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance" 1
  namespace: "openshift-logging"
spec:
  managementState: "Managed"  2
  logStore:
    type: "elasticsearch"  3
    retentionPolicy: 4
      application:
        maxAge: 1d
      infra:
        maxAge: 7d
      audit:
        maxAge: 7d
    elasticsearch:
      nodeCount: 3 5
      storage:
        storageClassName: "<storage_class_name>" 6
        size: 200G
      resources: 7
          limits:
            memory: "16Gi"
          requests:
            memory: "16Gi"
      proxy: 8
        resources:
          limits:
            memory: 256Mi
          requests:
            memory: 256Mi
      redundancyPolicy: "SingleRedundancy"
  visualization:
    type: "kibana"  9
    kibana:
      replicas: 1
  collection:
    logs:
      type: "fluentd"  10
      fluentd: {}
      1
      The name must be instance.
      2
      The OpenShift Logging management state. In some cases, if you change the OpenShift Logging defaults, you must set this to Unmanaged. However, an unmanaged deployment does not receive updates until OpenShift Logging is placed back into a managed state.
      3
      Settings for configuring Elasticsearch. Using the CR, you can configure shard replication policy and persistent storage.
      4
      Specify the length of time that Elasticsearch should retain each log source. Enter an integer and a time designation: weeks(w), hours(h/H), minutes(m) and seconds(s). For example, 7d for seven days. Logs older than the maxAge are deleted. You must specify a retention policy for each log source or the Elasticsearch indices will not be created for that source.
      5
      Specify the number of Elasticsearch nodes. See the note that follows this list.
      6
      Enter the name of an existing storage class for Elasticsearch storage. For best performance, specify a storage class that allocates block storage. If you do not specify a storage class, OpenShift Logging uses ephemeral storage.
      7
      Specify the CPU and memory requests for Elasticsearch as needed. If you leave these values blank, the OpenShift Elasticsearch Operator sets default values that should be sufficient for most deployments. The default values are 16Gi for the memory request and 1 for the CPU request.
      8
      Specify the CPU and memory requests for the Elasticsearch proxy as needed. If you leave these values blank, the OpenShift Elasticsearch Operator sets default values that should be sufficient for most deployments. The default values are 256Mi for the memory request and 100m for the CPU request.
      9
      Settings for configuring Kibana. Using the CR, you can scale Kibana for redundancy and configure the CPU and memory for your Kibana nodes. For more information, see Configuring the log visualizer.
      10
      Settings for configuring Fluentd. Using the CR, you can configure Fluentd CPU and memory limits. For more information, see Configuring Fluentd.
      Note

      The maximum number of Elasticsearch control plane nodes is three. If you specify a nodeCount greater than 3, Red Hat OpenShift Service on AWS creates three Elasticsearch nodes that are Master-eligible nodes, with the master, client, and data roles. The additional Elasticsearch nodes are created as Data-only nodes, using client and data roles. Control plane nodes perform cluster-wide actions such as creating or deleting an index, shard allocation, and tracking nodes. Data nodes hold the shards and perform data-related operations such as CRUD, search, and aggregations. Data-related operations are I/O-, memory-, and CPU-intensive. It is important to monitor these resources and to add more Data nodes if the current nodes are overloaded.

      For example, if nodeCount=4, the following nodes are created: 

      $ oc get deployment

      Example output

      cluster-logging-operator       1/1     1            1           18h
elasticsearch-cd-x6kdekli-1    0/1     1            0           6m54s
elasticsearch-cdm-x6kdekli-1   1/1     1            1           18h
elasticsearch-cdm-x6kdekli-2   0/1     1            0           6m49s
elasticsearch-cdm-x6kdekli-3   0/1     1            0           6m44s

      The number of primary shards for the index templates is equal to the number of Elasticsearch data nodes.

    6. Click Create. This creates the logging subsystem components, the Elasticsearch custom resource and components, and the Kibana interface.

  4. Verify the install:

    1. Switch to the Workloads → Pods page.

    2. Select the openshift-logging project.

      You should see several pods for OpenShift Logging, Elasticsearch, Fluentd, and Kibana similar to the following list:

      • cluster-logging-operator-cb795f8dc-xkckc
      • collector-pb2f8
      • elasticsearch-cdm-b3nqzchd-1-5c6797-67kfz
      • elasticsearch-cdm-b3nqzchd-2-6657f4-wtprv
      • elasticsearch-cdm-b3nqzchd-3-588c65-clg7g
      • fluentd-2c7dg
      • fluentd-9z7kk
      • fluentd-br7r2
      • fluentd-fn2sb
      • fluentd-zqgqx
      • kibana-7fb4fd4cc9-bvt4p

Troubleshooting

Additional resources

3.2. Post-installation tasks

If you plan to use Kibana, you must manually create your Kibana index patterns and visualizations to explore and visualize data in Kibana.

If your network plugin enforces network isolation, allow network traffic between the projects that contain the logging subsystem Operators.

3.3. Installing the logging subsystem for Red Hat OpenShift using the CLI

You can use the Red Hat OpenShift Service on AWS CLI to install the OpenShift Elasticsearch and Red Hat OpenShift Logging Operators.

Prerequisites

  • Ensure that you have the necessary persistent storage for Elasticsearch. Note that each Elasticsearch node requires its own storage volume.

    Note

    If you use a local volume for persistent storage, do not use a raw block volume, which is described with volumeMode: block in the LocalVolume object. Elasticsearch cannot use raw block volumes.

    Elasticsearch is a memory-intensive application. By default, Red Hat OpenShift Service on AWS installs three Elasticsearch nodes with memory requests and limits of 16 GB. This initial set of three Red Hat OpenShift Service on AWS nodes might not have enough memory to run Elasticsearch within your cluster. If you experience memory issues that are related to Elasticsearch, add more Elasticsearch nodes to your cluster rather than increasing the memory on existing nodes.

Procedure

To install the OpenShift Elasticsearch Operator and Red Hat OpenShift Logging Operator using the CLI:

  1. Create a namespace for the OpenShift Elasticsearch Operator.

    1. Create a namespace object YAML file (for example, eo-namespace.yaml) for the OpenShift Elasticsearch Operator: 

      apiVersion: v1
kind: Namespace
metadata:
  name: openshift-operators-redhat 1
  annotations:
    openshift.io/node-selector: ""
  labels:
    openshift.io/cluster-monitoring: "true" 2
      1
      You must specify the openshift-operators-redhat namespace. To prevent possible conflicts with metrics, you should configure the Prometheus Cluster Monitoring stack to scrape metrics from the openshift-operators-redhat namespace and not the openshift-operators namespace. The openshift-operators namespace might contain community Operators, which are untrusted and could publish a metric with the same name as a ROSA metric, which would cause conflicts.
      2
      String. You must specify this label as shown to ensure that cluster monitoring scrapes the openshift-operators-redhat namespace.

    2. Create the namespace: 

      $ oc create -f <file-name>.yaml

      For example: 

      $ oc create -f eo-namespace.yaml

  2. Create a namespace for the Red Hat OpenShift Logging Operator:

    1. Create a namespace object YAML file (for example, olo-namespace.yaml) for the Red Hat OpenShift Logging Operator: 

      apiVersion: v1
kind: Namespace
metadata:
  name: openshift-logging
  annotations:
    openshift.io/node-selector: ""
  labels:
    openshift.io/cluster-monitoring: "true"

    2. Create the namespace: 

      $ oc create -f <file-name>.yaml

      For example: 

      $ oc create -f olo-namespace.yaml

  3. Install the OpenShift Elasticsearch Operator by creating the following objects:

    1. Create an Operator Group object YAML file (for example, eo-og.yaml) for the OpenShift Elasticsearch Operator: 

      apiVersion: operators.coreos.com/v1
kind: OperatorGroup
metadata:
  name: openshift-operators-redhat
  namespace: openshift-operators-redhat 1
spec: {}
      1
      You must specify the openshift-operators-redhat namespace.

    2. Create an Operator Group object: 

      $ oc create -f <file-name>.yaml

      For example: 

      $ oc create -f eo-og.yaml

    3. Create a Subscription object YAML file (for example, eo-sub.yaml) to subscribe a namespace to the OpenShift Elasticsearch Operator.

      Example Subscription

      apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: "elasticsearch-operator"
  namespace: "openshift-operators-redhat" 1
spec:
  channel: "stable-5.5" 2
  installPlanApproval: "Automatic" 3
  source: "redhat-operators" 4
  sourceNamespace: "openshift-marketplace"
  name: "elasticsearch-operator"

      1
      You must specify the openshift-operators-redhat namespace.
      2
      Specify stable, or stable-5.<x> as the channel. See the following note.
      3
      Automatic allows the Operator Lifecycle Manager (OLM) to automatically update the Operator when a new version is available. Manual requires a user with appropriate credentials to approve the Operator update.
      4
      Specify redhat-operators. If your Red Hat OpenShift Service on AWS cluster is installed on a restricted network, also known as a disconnected cluster, specify the name of the CatalogSource object created when you configured the Operator Lifecycle Manager (OLM).
      Note

      Specifying stable installs the current version of the latest stable release. Using stable with installPlanApproval: "Automatic", will automatically upgrade your operators to the latest stable major and minor release.

      Specifying stable-5.<x> installs the current minor version of a specific major release. Using stable-5.<x> with installPlanApproval: "Automatic", will automatically upgrade your operators to the latest stable minor release within the major release you specify with x.

    4. Create the Subscription object: 

      $ oc create -f <file-name>.yaml

      For example: 

      $ oc create -f eo-sub.yaml

      The OpenShift Elasticsearch Operator is installed to the openshift-operators-redhat namespace and copied to each project in the cluster.

    5. Verify the Operator installation: 

      $ oc get csv --all-namespaces

      Example output

      NAMESPACE                                               NAME                                            DISPLAY                  VERSION               REPLACES   PHASE
default                                                 elasticsearch-operator.5.1.0-202007012112.p0    OpenShift Elasticsearch Operator   5.5.0-202007012112.p0               Succeeded
kube-node-lease                                         elasticsearch-operator.5.5.0-202007012112.p0    OpenShift Elasticsearch Operator   5.5.0-202007012112.p0               Succeeded
kube-public                                             elasticsearch-operator.5.5.0-202007012112.p0    OpenShift Elasticsearch Operator   5.5.0-202007012112.p0               Succeeded
kube-system                                             elasticsearch-operator.5.5.0-202007012112.p0    OpenShift Elasticsearch Operator   5.5.0-202007012112.p0               Succeeded
openshift-apiserver-operator                            elasticsearch-operator.5.5.0-202007012112.p0    OpenShift Elasticsearch Operator   5.5.0-202007012112.p0               Succeeded
openshift-apiserver                                     elasticsearch-operator.5.5.0-202007012112.p0    OpenShift Elasticsearch Operator   5.5.0-202007012112.p0               Succeeded
openshift-authentication-operator                       elasticsearch-operator.5.5.0-202007012112.p0    OpenShift Elasticsearch Operator   5.5.0-202007012112.p0               Succeeded
openshift-authentication                                elasticsearch-operator.5.5.0-202007012112.p0    OpenShift Elasticsearch Operator   5.5.0-202007012112.p0               Succeeded
...

      There should be an OpenShift Elasticsearch Operator in each namespace. The version number might be different than shown.

  4. Install the Red Hat OpenShift Logging Operator by creating the following objects:

    1. Create an Operator Group object YAML file (for example, olo-og.yaml) for the Red Hat OpenShift Logging Operator: 

      apiVersion: operators.coreos.com/v1
kind: OperatorGroup
metadata:
  name: cluster-logging
  namespace: openshift-logging 1
spec:
  targetNamespaces:
  - openshift-logging 2
      1 2
      You must specify the openshift-logging namespace.

    2. Create the OperatorGroup object: 

      $ oc create -f <file-name>.yaml

      For example: 

      $ oc create -f olo-og.yaml

    3. Create a Subscription object YAML file (for example, olo-sub.yaml) to subscribe a namespace to the Red Hat OpenShift Logging Operator. 

      apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: cluster-logging
  namespace: openshift-logging 1
spec:
  channel: "stable" 2
  name: cluster-logging
  source: redhat-operators 3
  sourceNamespace: openshift-marketplace
      1
      You must specify the openshift-logging namespace.
      2
      Specify stable, or stable-5.<x> as the channel.
      3
      Specify redhat-operators. If your Red Hat OpenShift Service on AWS cluster is installed on a restricted network, also known as a disconnected cluster, specify the name of the CatalogSource object you created when you configured the Operator Lifecycle Manager (OLM). 
      $ oc create -f <file-name>.yaml

      For example: 

      $ oc create -f olo-sub.yaml

      The Red Hat OpenShift Logging Operator is installed to the openshift-logging namespace.

    4. Verify the Operator installation.

      There should be a Red Hat OpenShift Logging Operator in the openshift-logging namespace. The Version number might be different than shown. 

      $ oc get csv -n openshift-logging

      Example output

      NAMESPACE                                               NAME                                         DISPLAY                  VERSION               REPLACES   PHASE
...
openshift-logging                                       clusterlogging.5.1.0-202007012112.p0         OpenShift Logging          5.1.0-202007012112.p0              Succeeded
...

  5. Create an OpenShift Logging instance:

    1. Create an instance object YAML file (for example, olo-instance.yaml) for the Red Hat OpenShift Logging Operator:

      Note

      This default OpenShift Logging configuration should support a wide array of environments. Review the topics on tuning and configuring logging subsystem components for information about modifications you can make to your OpenShift Logging cluster. 

      apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance" 1
  namespace: "openshift-logging"
spec:
  managementState: "Managed"  2
  logStore:
    type: "elasticsearch"  3
    retentionPolicy: 4
      application:
        maxAge: 1d
      infra:
        maxAge: 7d
      audit:
        maxAge: 7d
    elasticsearch:
      nodeCount: 3 5
      storage:
        storageClassName: "<storage-class-name>" 6
        size: 200G
      resources: 7
        limits:
          memory: "16Gi"
        requests:
          memory: "16Gi"
      proxy: 8
        resources:
          limits:
            memory: 256Mi
          requests:
             memory: 256Mi
      redundancyPolicy: "SingleRedundancy"
  visualization:
    type: "kibana"  9
    kibana:
      replicas: 1
  collection:
    logs:
      type: "fluentd"  10
      fluentd: {}
      1
      The name must be instance.
      2
      The OpenShift Logging management state. In some cases, if you change the OpenShift Logging defaults, you must set this to Unmanaged. However, an unmanaged deployment does not receive updates until OpenShift Logging is placed back into a managed state. Placing a deployment back into a managed state might revert any modifications you made.
      3
      Settings for configuring Elasticsearch. Using the custom resource (CR), you can configure shard replication policy and persistent storage.
      4
      Specify the length of time that Elasticsearch should retain each log source. Enter an integer and a time designation: weeks(w), hours(h/H), minutes(m) and seconds(s). For example, 7d for seven days. Logs older than the maxAge are deleted. You must specify a retention policy for each log source or the Elasticsearch indices will not be created for that source.
      5
      Specify the number of Elasticsearch nodes. See the note that follows this list.
      6
      Enter the name of an existing storage class for Elasticsearch storage. For best performance, specify a storage class that allocates block storage. If you do not specify a storage class, Red Hat OpenShift Service on AWS deploys OpenShift Logging with ephemeral storage only.
      7
      Specify the CPU and memory requests for Elasticsearch as needed. If you leave these values blank, the OpenShift Elasticsearch Operator sets default values that are sufficient for most deployments. The default values are 16Gi for the memory request and 1 for the CPU request.
      8
      Specify the CPU and memory requests for the Elasticsearch proxy as needed. If you leave these values blank, the OpenShift Elasticsearch Operator sets default values that should be sufficient for most deployments. The default values are 256Mi for the memory request and 100m for the CPU request.
      9
      Settings for configuring Kibana. Using the CR, you can scale Kibana for redundancy and configure the CPU and memory for your Kibana pods. For more information, see Configuring the log visualizer.
      10
      Settings for configuring Fluentd. Using the CR, you can configure Fluentd CPU and memory limits. For more information, see Configuring Fluentd.
      Note

      The maximum number of Elasticsearch control plane nodes is three. If you specify a nodeCount greater than 3, Red Hat OpenShift Service on AWS creates three Elasticsearch nodes that are Master-eligible nodes, with the master, client, and data roles. The additional Elasticsearch nodes are created as Data-only nodes, using client and data roles. Control plane nodes perform cluster-wide actions such as creating or deleting an index, shard allocation, and tracking nodes. Data nodes hold the shards and perform data-related operations such as CRUD, search, and aggregations. Data-related operations are I/O-, memory-, and CPU-intensive. It is important to monitor these resources and to add more Data nodes if the current nodes are overloaded.

      For example, if nodeCount=4, the following nodes are created: 

      $ oc get deployment

      Example output

      cluster-logging-operator       1/1     1            1           18h
elasticsearch-cd-x6kdekli-1    1/1     1            0           6m54s
elasticsearch-cdm-x6kdekli-1   1/1     1            1           18h
elasticsearch-cdm-x6kdekli-2   1/1     1            0           6m49s
elasticsearch-cdm-x6kdekli-3   1/1     1            0           6m44s

      The number of primary shards for the index templates is equal to the number of Elasticsearch data nodes.

    2. Create the instance: 

      $ oc create -f <file-name>.yaml

      For example: 

      $ oc create -f olo-instance.yaml

      This creates the logging subsystem components, the Elasticsearch custom resource and components, and the Kibana interface.

  6. Verify the installation by listing the pods in the openshift-logging project.

    You should see several pods for components of the Logging subsystem, similar to the following list: 

    $ oc get pods -n openshift-logging

    Example output

    NAME                                            READY   STATUS    RESTARTS   AGE
cluster-logging-operator-66f77ffccb-ppzbg       1/1     Running   0          7m
elasticsearch-cdm-ftuhduuw-1-ffc4b9566-q6bhp    2/2     Running   0          2m40s
elasticsearch-cdm-ftuhduuw-2-7b4994dbfc-rd2gc   2/2     Running   0          2m36s
elasticsearch-cdm-ftuhduuw-3-84b5ff7ff8-gqnm2   2/2     Running   0          2m4s
collector-587vb                                   1/1     Running   0          2m26s
collector-7mpb9                                   1/1     Running   0          2m30s
collector-flm6j                                   1/1     Running   0          2m33s
collector-gn4rn                                   1/1     Running   0          2m26s
collector-nlgb6                                   1/1     Running   0          2m30s
collector-snpkt                                   1/1     Running   0          2m28s
kibana-d6d5668c5-rppqm                          2/2     Running   0          2m39s

3.4. Post-installation tasks

If you plan to use Kibana, you must manually create your Kibana index patterns and visualizations to explore and visualize data in Kibana.

If your network plugin enforces network isolation, allow network traffic between the projects that contain the logging subsystem Operators.

3.4.1. Defining Kibana index patterns

An index pattern defines the Elasticsearch indices that you want to visualize. To explore and visualize data in Kibana, you must create an index pattern.

Prerequisites

  • A user must have the cluster-admin role, the cluster-reader role, or both roles to view the infra and audit indices in Kibana. The default kubeadmin user has proper permissions to view these indices.

    If you can view the pods and logs in the default, kube- and openshift- projects, you should be able to access these indices. You can use the following command to check if the current user has appropriate permissions: 

    $ oc auth can-i get pods/log -n <project>

    Example output

    yes

    Note

    The audit logs are not stored in the internal Red Hat OpenShift Service on AWS Elasticsearch instance by default. To view the audit logs in Kibana, you must use the Log Forwarding API to configure a pipeline that uses the default output for audit logs.

  • Elasticsearch documents must be indexed before you can create index patterns. This is done automatically, but it might take a few minutes in a new or updated cluster.

Procedure

To define index patterns and create visualizations in Kibana:

  1. In the Red Hat OpenShift Service on AWS console, click the Application Launcher app launcher and select Logging.

  2. Create your Kibana index patterns by clicking Management → Index Patterns → Create index pattern:

    • Each user must manually create index patterns when logging into Kibana the first time to see logs for their projects. Users must create an index pattern named app and use the @timestamp time field to view their container logs.
    • Each admin user must create index patterns when logged into Kibana the first time for the app, infra, and audit indices using the @timestamp time field.
  3. Create Kibana Visualizations from the new index patterns.

3.4.2. Allowing traffic between projects when network isolation is enabled

Your cluster network plugin might enforce network isolation. If so, you must allow network traffic between the projects that contain the operators deployed by OpenShift Logging.

Network isolation blocks network traffic between pods or services that are in different projects. The logging subsystem installs the OpenShift Elasticsearch Operator in the openshift-operators-redhat project and the Red Hat OpenShift Logging Operator in the openshift-logging project. Therefore, you must allow traffic between these two projects.

Red Hat OpenShift Service on AWS offers two supported choices for the network plugin, OpenShift SDN and OVN-Kubernetes. These two providers implement various network isolation policies.

OpenShift SDN has three modes:

network policy
This is the default mode. If no policy is defined, it allows all traffic. However, if a user defines a policy, they typically start by denying all traffic and then adding exceptions. This process might break applications that are running in different projects. Therefore, explicitly configure the policy to allow traffic to egress from one logging-related project to the other.
subnet
This mode allows all traffic. It does not enforce network isolation. No action is needed.

OVN-Kubernetes always uses a network policy. Therefore, as with OpenShift SDN, you must configure the policy to allow traffic to egress from one logging-related project to the other.

Procedure

  • If you are using OpenShift SDN in multitenant mode, join the two projects. For example: 

    $ oc adm pod-network join-projects --to=openshift-operators-redhat openshift-logging

  • Otherwise, for OpenShift SDN in network policy mode and OVN-Kubernetes, perform the following actions:

    1. Set a label on the openshift-operators-redhat namespace. For example: 

      $ oc label namespace openshift-operators-redhat project=openshift-operators-redhat

    2. Create a network policy object in the openshift-logging namespace that allows ingress from the openshift-operators-redhat, openshift-monitoring and openshift-ingress projects to the openshift-logging project. For example: 

      apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: allow-from-openshift-monitoring-ingress-operators-redhat
spec:
  ingress:
  - from:
    - podSelector: {}
  - from:
    - namespaceSelector:
        matchLabels:
          project: "openshift-operators-redhat"
  - from:
    - namespaceSelector:
        matchLabels:
          name: "openshift-monitoring"
  - from:
    - namespaceSelector:
        matchLabels:
          network.openshift.io/policy-group: ingress
  podSelector: {}
  policyTypes:
  - Ingress

Additional resources

Chapter 4. Accessing the service logs for Red Hat OpenShift Service on AWS clusters

You can view the service logs for your Red Hat OpenShift Service on AWS (ROSA) clusters by using the Red Hat OpenShift Cluster Manager. The service logs detail cluster events such as load balancer quota updates and scheduled maintenance upgrades. The logs also show cluster resource changes such as the addition or deletion of users, groups, and identity providers.

Additionally, you can add notification contacts for a ROSA cluster. Subscribed users receive emails about cluster events that require customer action, known cluster incidents, upgrade maintenance, and other topics.

4.1. Viewing the service logs by using OpenShift Cluster Manager

You can view the service logs for a Red Hat OpenShift Service on AWS (ROSA) cluster by using Red Hat OpenShift Cluster Manager.

Prerequisites

  • You have installed a ROSA cluster.

Procedure

  1. Navigate to OpenShift Cluster Manager Hybrid Cloud Console and select your cluster.
  2. In the Overview page for your cluster, view the service logs in the Cluster history section.
  3. Optional: Filter the cluster service logs by Description or Severity from the drop-down menu. You can filter further by entering a specific item in the search bar.
  4. Optional: Click Download history to download the service logs for your cluster in JSON or CSV format.

4.2. Adding cluster notification contacts

You can add notification contacts for your Red Hat OpenShift Service on AWS (ROSA) cluster. When an event occurs that triggers a cluster notification email, subscribed users are notified.

Procedure

  1. Navigate to OpenShift Cluster Manager Hybrid Cloud Console and select your cluster.
  2. On the Support tab, under the Notification contacts heading, click Add notification contact.

  3. Enter the Red Hat username or email of the contact you want to add.

    Note

    The username or email address must relate to a user account in the Red Hat organization where the cluster is deployed.

  4. Click Add contact.

Verification

  • You see a confirmation message when you have successfully added the contact. The user appears under the Notification contacts heading on the Support tab.

Chapter 5. Viewing cluster logs in the AWS Console

You can view forwarded cluster logs in the AWS console.

5.1. Viewing forwarded logs

Logs that are being forwarded from Red Hat OpenShift Service on AWS are viewed in the Amazon Web Services (AWS) console.

Prerequisites

  • The cluster-logging-operator add-on service is installed and Cloudwatch is enabled.

Procedure

  1. Log in to the AWS console.
  2. Select the region the cluster is deployed in.
  3. Select the CloudWatch service.
  4. Select Logs from the left column, and select Log Groups.
  5. Select a log group to explore. You can view application, infrastructure, or audit logs, depending on which types were enabled during the add-on service installation. See the Amazon CloudWatch User Guide for more information.

Chapter 6. Configuring your Logging deployment

6.1. About the Cluster Logging custom resource

To configure logging subsystem for Red Hat OpenShift you customize the ClusterLogging custom resource (CR).

6.1.1. About the ClusterLogging custom resource

To make changes to your logging subsystem environment, create and modify the ClusterLogging custom resource (CR).

Instructions for creating or modifying a CR are provided in this documentation as appropriate.

The following example shows a typical custom resource for the logging subsystem.

Sample ClusterLogging custom resource (CR)

apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance" 1
  namespace: "openshift-logging" 2
spec:
  managementState: "Managed" 3
  logStore:
    type: "elasticsearch" 4
    retentionPolicy:
      application:
        maxAge: 1d
      infra:
        maxAge: 7d
      audit:
        maxAge: 7d
    elasticsearch:
      nodeCount: 3
      resources:
        limits:
          memory: 16Gi
        requests:
          cpu: 500m
          memory: 16Gi
      storage:
        storageClassName: "gp2"
        size: "200G"
      redundancyPolicy: "SingleRedundancy"
  visualization: 5
    type: "kibana"
    kibana:
      resources:
        limits:
          memory: 736Mi
        requests:
          cpu: 100m
          memory: 736Mi
      replicas: 1
  collection: 6
    logs:
      type: "fluentd"
      fluentd:
        resources:
          limits:
            memory: 736Mi
          requests:
            cpu: 100m
            memory: 736Mi

1
The CR name must be instance.
2
The CR must be installed to the openshift-logging namespace.
3
The Red Hat OpenShift Logging Operator management state. When set to unmanaged the operator is in an unsupported state and will not get updates.
4
Settings for the log store, including retention policy, the number of nodes, the resource requests and limits, and the storage class.
5
Settings for the visualizer, including the resource requests and limits, and the number of pod replicas.
6
Settings for the log collector, including the resource requests and limits.

6.2. Configuring the logging collector

Logging subsystem for Red Hat OpenShift collects operations and application logs from your cluster and enriches the data with Kubernetes pod and project metadata.

You can configure the CPU and memory limits for the log collector and move the log collector pods to specific nodes. All supported modifications to the log collector can be performed though the spec.collection.log.fluentd stanza in the ClusterLogging custom resource (CR).

6.2.1. About unsupported configurations

The supported way of configuring the logging subsystem for Red Hat OpenShift is by configuring it using the options described in this documentation. Do not use other configurations, as they are unsupported. Configuration paradigms might change across Red Hat OpenShift Service on AWS releases, and such cases can only be handled gracefully if all configuration possibilities are controlled. If you use configurations other than those described in this documentation, your changes will disappear because the OpenShift Elasticsearch Operator and Red Hat OpenShift Logging Operator reconcile any differences. The Operators reverse everything to the defined state by default and by design.

Note

If you must perform configurations not described in the Red Hat OpenShift Service on AWS documentation, you must set your Red Hat OpenShift Logging Operator or OpenShift Elasticsearch Operator to Unmanaged. An unmanaged OpenShift Logging environment is not supported and does not receive updates until you return OpenShift Logging to Managed.

6.2.2. Viewing logging collector pods

You can view the Fluentd logging collector pods and the corresponding nodes that they are running on. The Fluentd logging collector pods run only in the openshift-logging project.

Procedure

  • Run the following command in the openshift-logging project to view the Fluentd logging collector pods and their details: 
$ oc get pods --selector component=collector -o wide -n openshift-logging

Example output

NAME           READY  STATUS    RESTARTS   AGE     IP            NODE                  NOMINATED NODE   READINESS GATES
fluentd-8d69v  1/1    Running   0          134m    10.130.2.30   master1.example.com   <none>           <none>
fluentd-bd225  1/1    Running   0          134m    10.131.1.11   master2.example.com   <none>           <none>
fluentd-cvrzs  1/1    Running   0          134m    10.130.0.21   master3.example.com   <none>           <none>
fluentd-gpqg2  1/1    Running   0          134m    10.128.2.27   worker1.example.com   <none>           <none>
fluentd-l9j7j  1/1    Running   0          134m    10.129.2.31   worker2.example.com   <none>           <none>

6.2.3. Configure log collector CPU and memory limits

The log collector allows for adjustments to both the CPU and memory limits.

Procedure

  1. Edit the ClusterLogging custom resource (CR) in the openshift-logging project: 

    $ oc -n openshift-logging edit ClusterLogging instance
    apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance"
  namespace: openshift-logging

...

spec:
  collection:
    logs:
      fluentd:
        resources:
          limits: 1
            memory: 736Mi
          requests:
            cpu: 100m
            memory: 736Mi
    1
    Specify the CPU and memory limits and requests as needed. The values shown are the default values.

6.2.4. Advanced configuration for the log forwarder

The logging subsystem for Red Hat OpenShift includes multiple Fluentd parameters that you can use for tuning the performance of the Fluentd log forwarder. With these parameters, you can change the following Fluentd behaviors:

  • Chunk and chunk buffer sizes
  • Chunk flushing behavior
  • Chunk forwarding retry behavior

Fluentd collects log data in a single blob called a chunk. When Fluentd creates a chunk, the chunk is considered to be in the stage, where the chunk gets filled with data. When the chunk is full, Fluentd moves the chunk to the queue, where chunks are held before being flushed, or written out to their destination. Fluentd can fail to flush a chunk for a number of reasons, such as network issues or capacity issues at the destination. If a chunk cannot be flushed, Fluentd retries flushing as configured.

By default in Red Hat OpenShift Service on AWS, Fluentd uses the exponential backoff method to retry flushing, where Fluentd doubles the time it waits between attempts to retry flushing again, which helps reduce connection requests to the destination. You can disable exponential backoff and use the periodic retry method instead, which retries flushing the chunks at a specified interval.

These parameters can help you determine the trade-offs between latency and throughput.

  • To optimize Fluentd for throughput, you could use these parameters to reduce network packet count by configuring larger buffers and queues, delaying flushes, and setting longer times between retries. Be aware that larger buffers require more space on the node file system.
  • To optimize for low latency, you could use the parameters to send data as soon as possible, avoid the build-up of batches, have shorter queues and buffers, and use more frequent flush and retries.

You can configure the chunking and flushing behavior using the following parameters in the ClusterLogging custom resource (CR). The parameters are then automatically added to the Fluentd config map for use by Fluentd.

Note

These parameters are:

  • Not relevant to most users. The default settings should give good general performance.
  • Only for advanced users with detailed knowledge of Fluentd configuration and performance.
  • Only for performance tuning. They have no effect on functional aspects of logging.

Table 6.1. Advanced Fluentd Configuration Parameters

ParameterDescriptionDefault

chunkLimitSize

The maximum size of each chunk. Fluentd stops writing data to a chunk when it reaches this size. Then, Fluentd sends the chunk to the queue and opens a new chunk.

8m

totalLimitSize

The maximum size of the buffer, which is the total size of the stage and the queue. If the buffer size exceeds this value, Fluentd stops adding data to chunks and fails with an error. All data not in chunks is lost.

8G

flushInterval

The interval between chunk flushes. You can use s (seconds), m (minutes), h (hours), or d (days).

1s

flushMode

The method to perform flushes:

  • lazy: Flush chunks based on the timekey parameter. You cannot modify the timekey parameter.
  • interval: Flush chunks based on the flushInterval parameter.
  • immediate: Flush chunks immediately after data is added to a chunk.

interval

flushThreadCount

The number of threads that perform chunk flushing. Increasing the number of threads improves the flush throughput, which hides network latency.

2

overflowAction

The chunking behavior when the queue is full:

  • throw_exception: Raise an exception to show in the log.
  • block: Stop data chunking until the full buffer issue is resolved.
  • drop_oldest_chunk: Drop the oldest chunk to accept new incoming chunks. Older chunks have less value than newer chunks.

block

retryMaxInterval

The maximum time in seconds for the exponential_backoff retry method.

300s

retryType

The retry method when flushing fails:

  • exponential_backoff: Increase the time between flush retries. Fluentd doubles the time it waits until the next retry until the retry_max_interval parameter is reached.
  • periodic: Retries flushes periodically, based on the retryWait parameter.

exponential_backoff

retryTimeOut

The maximum time interval to attempt retries before the record is discarded.

60m

retryWait

The time in seconds before the next chunk flush.

1s

For more information on the Fluentd chunk lifecycle, see Buffer Plugins in the Fluentd documentation.

Procedure

  1. Edit the ClusterLogging custom resource (CR) in the openshift-logging project: 

    $ oc edit ClusterLogging instance

  2. Add or modify any of the following parameters: 

    apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name: instance
  namespace: openshift-logging
spec:
  forwarder:
    fluentd:
      buffer:
        chunkLimitSize: 8m 1
        flushInterval: 5s 2
        flushMode: interval 3
        flushThreadCount: 3 4
        overflowAction: throw_exception 5
        retryMaxInterval: "300s" 6
        retryType: periodic 7
        retryWait: 1s 8
        totalLimitSize: 32m 9
...
    1
    Specify the maximum size of each chunk before it is queued for flushing.
    2
    Specify the interval between chunk flushes.
    3
    Specify the method to perform chunk flushes: lazy, interval, or immediate.
    4
    Specify the number of threads to use for chunk flushes.
    5
    Specify the chunking behavior when the queue is full: throw_exception, block, or drop_oldest_chunk.
    6
    Specify the maximum interval in seconds for the exponential_backoff chunk flushing method.
    7
    Specify the retry type when chunk flushing fails: exponential_backoff or periodic.
    8
    Specify the time in seconds before the next chunk flush.
    9
    Specify the maximum size of the chunk buffer.

  3. Verify that the Fluentd pods are redeployed: 

    $ oc get pods -l component=collector -n openshift-logging

  4. Check that the new values are in the fluentd config map: 

    $ oc extract configmap/fluentd --confirm

    Example fluentd.conf

    <buffer>
 @type file
 path '/var/lib/fluentd/default'
 flush_mode interval
 flush_interval 5s
 flush_thread_count 3
 retry_type periodic
 retry_wait 1s
 retry_max_interval 300s
 retry_timeout 60m
 queued_chunks_limit_size "#{ENV['BUFFER_QUEUE_LIMIT'] || '32'}"
 total_limit_size 32m
 chunk_limit_size 8m
 overflow_action throw_exception
</buffer>

6.2.5. Removing unused components if you do not use the default Elasticsearch log store

As an administrator, in the rare case that you forward logs to a third-party log store and do not use the default Elasticsearch log store, you can remove several unused components from your logging cluster.

In other words, if you do not use the default Elasticsearch log store, you can remove the internal Elasticsearch logStore and Kibana visualization components from the ClusterLogging custom resource (CR). Removing these components is optional but saves resources.

Prerequisites

  • Verify that your log forwarder does not send log data to the default internal Elasticsearch cluster. Inspect the ClusterLogForwarder CR YAML file that you used to configure log forwarding. Verify that it does not have an outputRefs element that specifies default. For example: 

    outputRefs:
- default
Warning

Suppose the ClusterLogForwarder CR forwards log data to the internal Elasticsearch cluster, and you remove the logStore component from the ClusterLogging CR. In that case, the internal Elasticsearch cluster will not be present to store the log data. This absence can cause data loss.

Procedure

  1. Edit the ClusterLogging custom resource (CR) in the openshift-logging project: 

    $ oc edit ClusterLogging instance
  2. If they are present, remove the logStore and visualization stanzas from the ClusterLogging CR.

  3. Preserve the collection stanza of the ClusterLogging CR. The result should look similar to the following example: 

    apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance"
  namespace: "openshift-logging"
spec:
  managementState: "Managed"
  collection:
    logs:
      type: "fluentd"
      fluentd: {}

  4. Verify that the collector pods are redeployed: 

    $ oc get pods -l component=collector -n openshift-logging

Additional resources

6.3. Configuring the log store

Logging subsystem for Red Hat OpenShift uses Elasticsearch 6 (ES) to store and organize the log data.

You can make modifications to your log store, including:

  • storage for your Elasticsearch cluster
  • shard replication across data nodes in the cluster, from full replication to no replication
  • external access to Elasticsearch data

Elasticsearch is a memory-intensive application. Each Elasticsearch node needs at least 16G of memory for both memory requests and limits, unless you specify otherwise in the ClusterLogging custom resource. The initial set of Red Hat OpenShift Service on AWS nodes might not be large enough to support the Elasticsearch cluster. You must add additional nodes to the Red Hat OpenShift Service on AWS cluster to run with the recommended or higher memory, up to a maximum of 64G for each Elasticsearch node.

Each Elasticsearch node can operate with a lower memory setting, though this is not recommended for production environments.

6.3.1. Forwarding audit logs to the log store

By default, OpenShift Logging does not store audit logs in the internal Red Hat OpenShift Service on AWS Elasticsearch log store. You can send audit logs to this log store so, for example, you can view them in Kibana.

To send the audit logs to the default internal Elasticsearch log store, for example to view the audit logs in Kibana, you must use the Log Forwarding API.

Important

The internal Red Hat OpenShift Service on AWS Elasticsearch log store does not provide secure storage for audit logs. Verify that the system to which you forward audit logs complies with your organizational and governmental regulations and is properly secured. The logging subsystem for Red Hat OpenShift does not comply with those regulations.

Procedure

To use the Log Forward API to forward audit logs to the internal Elasticsearch instance:

  1. Create or edit a YAML file that defines the ClusterLogForwarder CR object:

    • Create a CR to send all log types to the internal Elasticsearch instance. You can use the following example without making any changes: 

      apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: instance
  namespace: openshift-logging
spec:
  pipelines: 1
  - name: all-to-default
    inputRefs:
    - infrastructure
    - application
    - audit
    outputRefs:
    - default
      1
      A pipeline defines the type of logs to forward using the specified output. The default output forwards logs to the internal Elasticsearch instance.
      Note

      You must specify all three types of logs in the pipeline: application, infrastructure, and audit. If you do not specify a log type, those logs are not stored and will be lost.

    • If you have an existing ClusterLogForwarder CR, add a pipeline to the default output for the audit logs. You do not need to define the default output. For example: 

      apiVersion: "logging.openshift.io/v1"
kind: ClusterLogForwarder
metadata:
  name: instance
  namespace: openshift-logging
spec:
  outputs:
   - name: elasticsearch-insecure
     type: "elasticsearch"
     url: http://elasticsearch-insecure.messaging.svc.cluster.local
     insecure: true
   - name: elasticsearch-secure
     type: "elasticsearch"
     url: https://elasticsearch-secure.messaging.svc.cluster.local
     secret:
       name: es-audit
   - name: secureforward-offcluster
     type: "fluentdForward"
     url: https://secureforward.offcluster.com:24224
     secret:
       name: secureforward
  pipelines:
   - name: container-logs
     inputRefs:
     - application
     outputRefs:
     - secureforward-offcluster
   - name: infra-logs
     inputRefs:
     - infrastructure
     outputRefs:
     - elasticsearch-insecure
   - name: audit-logs
     inputRefs:
     - audit
     outputRefs:
     - elasticsearch-secure
     - default 1
      1
      This pipeline sends the audit logs to the internal Elasticsearch instance in addition to an external instance.

Additional resources

6.3.2. Configuring log retention time

You can configure a retention policy that specifies how long the default Elasticsearch log store keeps indices for each of the three log sources: infrastructure logs, application logs, and audit logs.

To configure the retention policy, you set a maxAge parameter for each log source in the ClusterLogging custom resource (CR). The CR applies these values to the Elasticsearch rollover schedule, which determines when Elasticsearch deletes the rolled-over indices.

Elasticsearch rolls over an index, moving the current index and creating a new index, when an index matches any of the following conditions:

  • The index is older than the rollover.maxAge value in the Elasticsearch CR.
  • The index size is greater than 40 GB × the number of primary shards.
  • The index doc count is greater than 40960 KB × the number of primary shards.

Elasticsearch deletes the rolled-over indices based on the retention policy you configure. If you do not create a retention policy for any log sources, logs are deleted after seven days by default.

Prerequisites

  • The logging subsystem for Red Hat OpenShift and the OpenShift Elasticsearch Operator must be installed.

Procedure

To configure the log retention time:

  1. Edit the ClusterLogging CR to add or modify the retentionPolicy parameter: 

    apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
...
spec:
  managementState: "Managed"
  logStore:
    type: "elasticsearch"
    retentionPolicy: 1
      application:
        maxAge: 1d
      infra:
        maxAge: 7d
      audit:
        maxAge: 7d
    elasticsearch:
      nodeCount: 3
...
    1
    Specify the time that Elasticsearch should retain each log source. Enter an integer and a time designation: weeks(w), hours(h/H), minutes(m) and seconds(s). For example, 1d for one day. Logs older than the maxAge are deleted. By default, logs are retained for seven days.

  2. You can verify the settings in the Elasticsearch custom resource (CR).

    For example, the Red Hat OpenShift Logging Operator updated the following Elasticsearch CR to configure a retention policy that includes settings to roll over active indices for the infrastructure logs every eight hours and the rolled-over indices are deleted seven days after rollover. Red Hat OpenShift Service on AWS checks every 15 minutes to determine if the indices need to be rolled over. 

    apiVersion: "logging.openshift.io/v1"
kind: "Elasticsearch"
metadata:
  name: "elasticsearch"
spec:
...
  indexManagement:
    policies: 1
      - name: infra-policy
        phases:
          delete:
            minAge: 7d 2
          hot:
            actions:
              rollover:
                maxAge: 8h 3
        pollInterval: 15m 4
...
    1
    For each log source, the retention policy indicates when to delete and roll over logs for that source.
    2
    When Red Hat OpenShift Service on AWS deletes the rolled-over indices. This setting is the maxAge you set in the ClusterLogging CR.
    3
    The index age for Red Hat OpenShift Service on AWS to consider when rolling over the indices. This value is determined from the maxAge you set in the ClusterLogging CR.
    4
    When Red Hat OpenShift Service on AWS checks if the indices should be rolled over. This setting is the default and cannot be changed.
    Note

    Modifying the Elasticsearch CR is not supported. All changes to the retention policies must be made in the ClusterLogging CR.

    The OpenShift Elasticsearch Operator deploys a cron job to roll over indices for each mapping using the defined policy, scheduled using the pollInterval. 

    $ oc get cronjob

    Example output

    NAME                     SCHEDULE       SUSPEND   ACTIVE   LAST SCHEDULE   AGE
elasticsearch-im-app     */15 * * * *   False     0        <none>          4s
elasticsearch-im-audit   */15 * * * *   False     0        <none>          4s
elasticsearch-im-infra   */15 * * * *   False     0        <none>          4s

6.3.3. Configuring CPU and memory requests for the log store

Each component specification allows for adjustments to both the CPU and memory requests. You should not have to manually adjust these values as the OpenShift Elasticsearch Operator sets values sufficient for your environment.

Note

In large-scale clusters, the default memory limit for the Elasticsearch proxy container might not be sufficient, causing the proxy container to be OOMKilled. If you experience this issue, increase the memory requests and limits for the Elasticsearch proxy.

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 the default 16Gi allocated to each pod. Preferably you should allocate as much as possible, up to 64Gi per pod.

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.

Procedure

  1. Edit the ClusterLogging custom resource (CR) in the openshift-logging project: 

    $ oc edit ClusterLogging instance
    apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance"
....
spec:
    logStore:
      type: "elasticsearch"
      elasticsearch:1
        resources:
          limits: 2
            memory: "32Gi"
          requests: 3
            cpu: "1"
            memory: "16Gi"
        proxy: 4
          resources:
            limits:
              memory: 100Mi
            requests:
              memory: 100Mi
    1
    Specify the CPU and memory requests for Elasticsearch as needed. If you leave these values blank, the OpenShift Elasticsearch Operator sets default values that should be sufficient for most deployments. The default values are 16Gi for the memory request and 1 for the CPU request.
    2
    The maximum amount of resources a pod can use.
    3
    The minimum resources required to schedule a pod.
    4
    Specify the CPU and memory requests for the Elasticsearch proxy as needed. If you leave these values blank, the OpenShift Elasticsearch Operator sets default values that are sufficient for most deployments. The default values are 256Mi for the memory request and 100m for the CPU request.

When adjusting the amount of Elasticsearch memory, the same value should be used for both requests and limits.

For example: 

      resources:
        limits: 1
          memory: "32Gi"
        requests: 2
          cpu: "8"
          memory: "32Gi"
1
The maximum amount of the resource.
2
The minimum amount required.

Kubernetes generally adheres the node configuration and does not allow Elasticsearch to use the specified limits. Setting the same value for the requests and limits ensures that Elasticsearch can use the memory you want, assuming the node has the memory available.

6.3.4. Configuring replication policy for the log store

You can define how Elasticsearch shards are replicated across data nodes in the cluster.

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.

Procedure

  1. Edit the ClusterLogging custom resource (CR) in the openshift-logging project: 

    $ oc edit clusterlogging instance
    apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance"

....

spec:
  logStore:
    type: "elasticsearch"
    elasticsearch:
      redundancyPolicy: "SingleRedundancy" 1
    1
    Specify a redundancy policy for the shards. The change is applied upon saving the changes.
    • FullRedundancy. Elasticsearch fully replicates the primary shards for each index to every data node. This provides the highest safety, but at the cost of the highest amount of disk required and the poorest performance.
    • MultipleRedundancy. Elasticsearch fully replicates the primary shards for each index to half of the data nodes. This provides a good tradeoff between safety and performance.
    • SingleRedundancy. Elasticsearch makes one copy of the primary shards for each index. Logs are always available and recoverable as long as at least two data nodes exist. Better performance than MultipleRedundancy, when using 5 or more nodes. You cannot apply this policy on deployments of single Elasticsearch node.
    • ZeroRedundancy. Elasticsearch does not make copies of the primary shards. Logs might be unavailable or lost in the event a node is down or fails. Use this mode when you are more concerned with performance than safety, or have implemented your own disk/PVC backup/restore strategy.
Note

The number of primary shards for the index templates is equal to the number of Elasticsearch data nodes.

6.3.5. Scaling down Elasticsearch pods

Reducing the number of Elasticsearch pods in your cluster can result in data loss or Elasticsearch performance degradation.

If you scale down, you should scale down by one pod at a time and allow the cluster to re-balance the shards and replicas. After the Elasticsearch health status returns to green, you can scale down by another pod.

Note

If your Elasticsearch cluster is set to ZeroRedundancy, you should not scale down your Elasticsearch pods.

6.3.6. Configuring persistent storage for the log store

Elasticsearch requires persistent storage. The faster the storage, the faster the Elasticsearch performance.

Warning

Using NFS storage as a volume or a persistent volume (or via NAS such as Gluster) is not supported for Elasticsearch storage, as Lucene relies on file system behavior that NFS does not supply. Data corruption and other problems can occur.

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.

Procedure

  1. Edit the ClusterLogging CR to specify that each data node in the cluster is bound to a Persistent Volume Claim. 

    apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance"
# ...
spec:
  logStore:
    type: "elasticsearch"
    elasticsearch:
      nodeCount: 3
      storage:
        storageClassName: "gp2"
        size: "200G"

This example specifies each data node in the cluster is bound to a Persistent Volume Claim that requests "200G" of AWS General Purpose SSD (gp2) storage.

Note

If you use a local volume for persistent storage, do not use a raw block volume, which is described with volumeMode: block in the LocalVolume object. Elasticsearch cannot use raw block volumes.

6.3.7. Configuring the log store for emptyDir storage

You can use emptyDir with your log store, which creates an ephemeral deployment in which all of a pod’s data is lost upon restart.

Note

When using emptyDir, if log storage is restarted or redeployed, you will lose data.

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.

Procedure

  1. Edit the ClusterLogging CR to specify emptyDir: 

     spec:
    logStore:
      type: "elasticsearch"
      elasticsearch:
        nodeCount: 3
        storage: {}

6.3.8. Performing an Elasticsearch rolling cluster restart

Perform a rolling restart when you change the elasticsearch config map or any of the elasticsearch-* deployment configurations.

Also, a rolling restart is recommended if the nodes on which an Elasticsearch pod runs requires a reboot.

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.

Procedure

To perform a rolling cluster restart:

  1. Change to the openshift-logging project: 

    $ oc project openshift-logging

  2. Get the names of the Elasticsearch pods: 

    $ oc get pods -l component=elasticsearch-

  3. Scale down the collector pods so they stop sending new logs to Elasticsearch: 

    $ oc -n openshift-logging patch daemonset/collector -p '{"spec":{"template":{"spec":{"nodeSelector":{"logging-infra-collector": "false"}}}}}'

  4. Perform a shard synced flush using the Red Hat OpenShift Service on AWS es_util tool to ensure there are no pending operations waiting to be written to disk prior to shutting down: 

    $ oc exec <any_es_pod_in_the_cluster> -c elasticsearch -- es_util --query="_flush/synced" -XPOST

    For example: 

    $ oc exec -c elasticsearch-cdm-5ceex6ts-1-dcd6c4c7c-jpw6  -c elasticsearch -- es_util --query="_flush/synced" -XPOST

    Example output

    {"_shards":{"total":4,"successful":4,"failed":0},".security":{"total":2,"successful":2,"failed":0},".kibana_1":{"total":2,"successful":2,"failed":0}}

  5. Prevent shard balancing when purposely bringing down nodes using the Red Hat OpenShift Service on AWS es_util tool: 

    $ oc exec <any_es_pod_in_the_cluster> -c elasticsearch -- es_util --query="_cluster/settings" -XPUT -d '{ "persistent": { "cluster.routing.allocation.enable" : "primaries" } }'

    For example: 

    $ oc exec elasticsearch-cdm-5ceex6ts-1-dcd6c4c7c-jpw6 -c elasticsearch -- es_util --query="_cluster/settings" -XPUT -d '{ "persistent": { "cluster.routing.allocation.enable" : "primaries" } }'

    Example output

    {"acknowledged":true,"persistent":{"cluster":{"routing":{"allocation":{"enable":"primaries"}}}},"transient":

  6. After the command is complete, for each deployment you have for an ES cluster:

    1. By default, the Red Hat OpenShift Service on AWS Elasticsearch cluster blocks rollouts to their nodes. Use the following command to allow rollouts and allow the pod to pick up the changes: 

      $ oc rollout resume deployment/<deployment-name>

      For example: 

      $ oc rollout resume deployment/elasticsearch-cdm-0-1

      Example output

      deployment.extensions/elasticsearch-cdm-0-1 resumed

      A new pod is deployed. After the pod has a ready container, you can move on to the next deployment. 

      $ oc get pods -l component=elasticsearch-

      Example output

      NAME                                            READY   STATUS    RESTARTS   AGE
elasticsearch-cdm-5ceex6ts-1-dcd6c4c7c-jpw6k    2/2     Running   0          22h
elasticsearch-cdm-5ceex6ts-2-f799564cb-l9mj7    2/2     Running   0          22h
elasticsearch-cdm-5ceex6ts-3-585968dc68-k7kjr   2/2     Running   0          22h

    2. After the deployments are complete, reset the pod to disallow rollouts: 

      $ oc rollout pause deployment/<deployment-name>

      For example: 

      $ oc rollout pause deployment/elasticsearch-cdm-0-1

      Example output

      deployment.extensions/elasticsearch-cdm-0-1 paused

    3. Check that the Elasticsearch cluster is in a green or yellow state: 

      $ oc exec <any_es_pod_in_the_cluster> -c elasticsearch -- es_util --query=_cluster/health?pretty=true
      Note

      If you performed a rollout on the Elasticsearch pod you used in the previous commands, the pod no longer exists and you need a new pod name here.

      For example: 

      $ oc exec elasticsearch-cdm-5ceex6ts-1-dcd6c4c7c-jpw6 -c elasticsearch -- es_util --query=_cluster/health?pretty=true
      {
  "cluster_name" : "elasticsearch",
  "status" : "yellow", 1
  "timed_out" : false,
  "number_of_nodes" : 3,
  "number_of_data_nodes" : 3,
  "active_primary_shards" : 8,
  "active_shards" : 16,
  "relocating_shards" : 0,
  "initializing_shards" : 0,
  "unassigned_shards" : 1,
  "delayed_unassigned_shards" : 0,
  "number_of_pending_tasks" : 0,
  "number_of_in_flight_fetch" : 0,
  "task_max_waiting_in_queue_millis" : 0,
  "active_shards_percent_as_number" : 100.0
}
      1
      Make sure this parameter value is green or yellow before proceeding.
  7. If you changed the Elasticsearch configuration map, repeat these steps for each Elasticsearch pod.

  8. After all the deployments for the cluster have been rolled out, re-enable shard balancing: 

    $ oc exec <any_es_pod_in_the_cluster> -c elasticsearch -- es_util --query="_cluster/settings" -XPUT -d '{ "persistent": { "cluster.routing.allocation.enable" : "all" } }'

    For example: 

    $ oc exec elasticsearch-cdm-5ceex6ts-1-dcd6c4c7c-jpw6 -c elasticsearch -- es_util --query="_cluster/settings" -XPUT -d '{ "persistent": { "cluster.routing.allocation.enable" : "all" } }'

    Example output

    {
  "acknowledged" : true,
  "persistent" : { },
  "transient" : {
    "cluster" : {
      "routing" : {
        "allocation" : {
          "enable" : "all"
        }
      }
    }
  }
}

  9. Scale up the collector pods so they send new logs to Elasticsearch. 

    $ oc -n openshift-logging patch daemonset/collector -p '{"spec":{"template":{"spec":{"nodeSelector":{"logging-infra-collector": "true"}}}}}'

6.3.9. Exposing the log store service as a route

By default, the log store that is deployed with the logging subsystem for Red Hat OpenShift is not accessible from outside the logging cluster. You can enable a route with re-encryption termination for external access to the log store service for those tools that access its data.

Externally, you can access the log store by creating a reencrypt route, your Red Hat OpenShift Service on AWS token and the installed log store CA certificate. Then, access a node that hosts the log store service with a cURL request that contains:

Internally, you can access the log store service using the log store cluster IP, which you can get by using either of the following commands: 

$ oc get service elasticsearch -o jsonpath={.spec.clusterIP} -n openshift-logging

Example output

172.30.183.229

$ oc get service elasticsearch -n openshift-logging

Example output

NAME            TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)    AGE
elasticsearch   ClusterIP   172.30.183.229   <none>        9200/TCP   22h

You can check the cluster IP address with a command similar to the following: 

$ oc exec elasticsearch-cdm-oplnhinv-1-5746475887-fj2f8 -n openshift-logging -- curl -tlsv1.2 --insecure -H "Authorization: Bearer ${token}" "https://172.30.183.229:9200/_cat/health"

Example output

  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100    29  100    29    0     0    108      0 --:--:-- --:--:-- --:--:--   108

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.
  • You must have access to the project to be able to access to the logs.

Procedure

To expose the log store externally:

  1. Change to the openshift-logging project: 

    $ oc project openshift-logging

  2. Extract the CA certificate from the log store and write to the admin-ca file: 

    $ oc extract secret/elasticsearch --to=. --keys=admin-ca

    Example output

    admin-ca

  3. Create the route for the log store service as a YAML file:

    1. Create a YAML file with the following: 

      apiVersion: route.openshift.io/v1
kind: Route
metadata:
  name: elasticsearch
  namespace: openshift-logging
spec:
  host:
  to:
    kind: Service
    name: elasticsearch
  tls:
    termination: reencrypt
    destinationCACertificate: | 1
      1
      Add the log store CA certifcate or use the command in the next step. You do not have to set the spec.tls.key, spec.tls.certificate, and spec.tls.caCertificate parameters required by some reencrypt routes.

    2. Run the following command to add the log store CA certificate to the route YAML you created in the previous step: 

      $ cat ./admin-ca | sed -e "s/^/      /" >> <file-name>.yaml

    3. Create the route: 

      $ oc create -f <file-name>.yaml

      Example output

      route.route.openshift.io/elasticsearch created

  4. Check that the Elasticsearch service is exposed:

    1. Get the token of this service account to be used in the request: 

      $ token=$(oc whoami -t)

    2. Set the elasticsearch route you created as an environment variable. 

      $ routeES=`oc get route elasticsearch -o jsonpath={.spec.host}`

    3. To verify the route was successfully created, run the following command that accesses Elasticsearch through the exposed route: 

      curl -tlsv1.2 --insecure -H "Authorization: Bearer ${token}" "https://${routeES}"

      The response appears similar to the following:

      Example output

      {
  "name" : "elasticsearch-cdm-i40ktba0-1",
  "cluster_name" : "elasticsearch",
  "cluster_uuid" : "0eY-tJzcR3KOdpgeMJo-MQ",
  "version" : {
  "number" : "6.8.1",
  "build_flavor" : "oss",
  "build_type" : "zip",
  "build_hash" : "Unknown",
  "build_date" : "Unknown",
  "build_snapshot" : true,
  "lucene_version" : "7.7.0",
  "minimum_wire_compatibility_version" : "5.6.0",
  "minimum_index_compatibility_version" : "5.0.0"
},
  "<tagline>" : "<for search>"
}

6.4. Configuring the log visualizer

Red Hat OpenShift Service on AWS uses Kibana to display the log data collected by the logging subsystem.

You can scale Kibana for redundancy and configure the CPU and memory for your Kibana nodes.

6.4.1. Configuring CPU and memory limits

The logging subsystem components allow for adjustments to both the CPU and memory limits.

Procedure

  1. Edit the ClusterLogging custom resource (CR) in the openshift-logging project: 

    $ oc -n openshift-logging edit ClusterLogging instance
    apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance"
  namespace: openshift-logging

...

spec:
  managementState: "Managed"
  logStore:
    type: "elasticsearch"
    elasticsearch:
      nodeCount: 3
      resources: 1
        limits:
          memory: 16Gi
        requests:
          cpu: 200m
          memory: 16Gi
      storage:
        storageClassName: "gp2"
        size: "200G"
      redundancyPolicy: "SingleRedundancy"
  visualization:
    type: "kibana"
    kibana:
      resources: 2
        limits:
          memory: 1Gi
        requests:
          cpu: 500m
          memory: 1Gi
      proxy:
        resources: 3
          limits:
            memory: 100Mi
          requests:
            cpu: 100m
            memory: 100Mi
      replicas: 2
  collection:
    logs:
      type: "fluentd"
      fluentd:
        resources: 4
          limits:
            memory: 736Mi
          requests:
            cpu: 200m
            memory: 736Mi
    1
    Specify the CPU and memory limits and requests for the log store as needed. For Elasticsearch, you must adjust both the request value and the limit value.
    2 3
    Specify the CPU and memory limits and requests for the log visualizer as needed.
    4
    Specify the CPU and memory limits and requests for the log collector as needed.

6.4.2. Scaling redundancy for the log visualizer nodes

You can scale the pod that hosts the log visualizer for redundancy.

Procedure

  1. Edit the ClusterLogging custom resource (CR) in the openshift-logging project: 

    $ oc edit ClusterLogging instance
    $ oc edit ClusterLogging instance

apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance"

....

spec:
    visualization:
      type: "kibana"
      kibana:
        replicas: 1 1
    1
    Specify the number of Kibana nodes.

6.5. Configuring logging subsystem storage

Elasticsearch is a memory-intensive application. The default logging subsystem installation deploys 16G of memory for both memory requests and memory limits. The initial set of Red Hat OpenShift Service on AWS nodes might not be large enough to support the Elasticsearch cluster. You must add additional nodes to the Red Hat OpenShift Service on AWS cluster to run with the recommended or higher memory. Each Elasticsearch node can operate with a lower memory setting, though this is not recommended for production environments.

6.5.1. Storage considerations for the logging subsystem for Red Hat OpenShift

A persistent volume is required for each Elasticsearch deployment configuration. On Red Hat OpenShift Service on AWS this is achieved using persistent volume claims.

Note

If you use a local volume for persistent storage, do not use a raw block volume, which is described with volumeMode: block in the LocalVolume object. Elasticsearch cannot use raw block volumes.

The OpenShift Elasticsearch Operator names the PVCs using the Elasticsearch resource name.

Fluentd ships any logs from systemd journal and /var/log/containers/ to Elasticsearch.

Elasticsearch requires sufficient memory to perform large merge operations. If it does not have enough memory, it becomes unresponsive. To avoid this problem, evaluate how much application log data you need, and allocate approximately double that amount of free storage capacity.

By default, when storage capacity is 85% full, Elasticsearch stops allocating new data to the node. At 90%, Elasticsearch attempts to relocate existing shards from that node to other nodes if possible. But if no nodes have a free capacity below 85%, Elasticsearch effectively rejects creating new indices and becomes RED.

Note

These low and high watermark values are Elasticsearch defaults in the current release. You can modify these default values. Although the alerts use the same default values, you cannot change these values in the alerts.

6.5.2. Additional resources

6.6. Configuring CPU and memory limits for logging subsystem components

You can configure both the CPU and memory limits for each of the logging subsystem components as needed.

6.6.1. Configuring CPU and memory limits

The logging subsystem components allow for adjustments to both the CPU and memory limits.

Procedure

  1. Edit the ClusterLogging custom resource (CR) in the openshift-logging project: 

    $ oc -n openshift-logging edit ClusterLogging instance
    apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance"
  namespace: openshift-logging

...

spec:
  managementState: "Managed"
  logStore:
    type: "elasticsearch"
    elasticsearch:
      nodeCount: 3
      resources: 1
        limits:
          memory: 16Gi
        requests:
          cpu: 200m
          memory: 16Gi
      storage:
        storageClassName: "gp2"
        size: "200G"
      redundancyPolicy: "SingleRedundancy"
  visualization:
    type: "kibana"
    kibana:
      resources: 2
        limits:
          memory: 1Gi
        requests:
          cpu: 500m
          memory: 1Gi
      proxy:
        resources: 3
          limits:
            memory: 100Mi
          requests:
            cpu: 100m
            memory: 100Mi
      replicas: 2
  collection:
    logs:
      type: "fluentd"
      fluentd:
        resources: 4
          limits:
            memory: 736Mi
          requests:
            cpu: 200m
            memory: 736Mi
    1
    Specify the CPU and memory limits and requests for the log store as needed. For Elasticsearch, you must adjust both the request value and the limit value.
    2 3
    Specify the CPU and memory limits and requests for the log visualizer as needed.
    4
    Specify the CPU and memory limits and requests for the log collector as needed.

6.7. Using tolerations to control OpenShift Logging pod placement

You can use taints and tolerations to ensure that logging subsystem pods run on specific nodes and that no other workload can run on those nodes.

Taints and tolerations are simple key:value pair. A taint on a node instructs the node to repel all pods that do not tolerate the taint.

The key is any string, up to 253 characters and the value is any string up to 63 characters. The string must begin with a letter or number, and may contain letters, numbers, hyphens, dots, and underscores.

Sample logging subsystem CR with tolerations

apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogging"
metadata:
  name: "instance"
  namespace: openshift-logging

...

spec:
  managementState: "Managed"
  logStore:
    type: "elasticsearch"
    elasticsearch:
      nodeCount: 3
      tolerations: 1
      - key: "logging"
        operator: "Exists"
        effect: "NoExecute"
        tolerationSeconds: 6000
      resources:
        limits:
          memory: 16Gi
        requests:
          cpu: 200m
          memory: 16Gi
      storage: {}
      redundancyPolicy: "ZeroRedundancy"
  visualization:
    type: "kibana"
    kibana:
      tolerations: 2
      - key: "logging"
        operator: "Exists"
        effect: "NoExecute"
        tolerationSeconds: 6000
      resources:
        limits:
          memory: 2Gi
        requests:
          cpu: 100m
          memory: 1Gi
      replicas: 1
  collection:
    logs:
      type: "fluentd"
      fluentd:
        tolerations: 3
        - key: "logging"
          operator: "Exists"
          effect: "NoExecute"
          tolerationSeconds: 6000
        resources:
          limits:
            memory: 2Gi
          requests:
            cpu: 100m
            memory: 1Gi

1
This toleration is added to the Elasticsearch pods.
2
This toleration is added to the Kibana pod.
3
This toleration is added to the logging collector pods.

6.7.1. Using tolerations to control the log store pod placement

You can control which nodes the log store pods runs on and prevent other workloads from using those nodes by using tolerations on the pods.

You apply tolerations to the log store pods through the ClusterLogging custom resource (CR) and apply taints to a node through the node specification. A taint on a node is a key:value pair that instructs the node to repel all pods that do not tolerate the taint. Using a specific key:value pair that is not on other pods ensures only the log store pods can run on that node.

By default, the log store pods have the following toleration: 

tolerations:
- effect: "NoExecute"
  key: "node.kubernetes.io/disk-pressure"
  operator: "Exists"

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.

Procedure

  1. Use the following command to add a taint to a node where you want to schedule the OpenShift Logging pods: 

    $ oc adm taint nodes <node-name> <key>=<value>:<effect>

    For example: 

    $ oc adm taint nodes node1 elasticsearch=node:NoExecute

    This example places a taint on node1 that has key elasticsearch, value node, and taint effect NoExecute. Nodes with the NoExecute effect schedule only pods that match the taint and remove existing pods that do not match.

  2. Edit the logstore section of the ClusterLogging CR to configure a toleration for the Elasticsearch pods: 

      logStore:
    type: "elasticsearch"
    elasticsearch:
      nodeCount: 1
      tolerations:
      - key: "elasticsearch"  1
        operator: "Exists"  2
        effect: "NoExecute"  3
        tolerationSeconds: 6000  4
    1
    Specify the key that you added to the node.
    2
    Specify the Exists operator to require a taint with the key elasticsearch to be present on the Node.
    3
    Specify the NoExecute effect.
    4
    Optionally, specify the tolerationSeconds parameter to set how long a pod can remain bound to a node before being evicted.

This toleration matches the taint created by the oc adm taint command. A pod with this toleration could be scheduled onto node1.

6.7.2. Using tolerations to control the log visualizer pod placement

You can control the node where the log visualizer pod runs and prevent other workloads from using those nodes by using tolerations on the pods.

You apply tolerations to the log visualizer pod through the ClusterLogging custom resource (CR) and apply taints to a node through the node specification. A taint on a node is a key:value pair that instructs the node to repel all pods that do not tolerate the taint. Using a specific key:value pair that is not on other pods ensures only the Kibana pod can run on that node.

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.

Procedure

  1. Use the following command to add a taint to a node where you want to schedule the log visualizer pod: 

    $ oc adm taint nodes <node-name> <key>=<value>:<effect>

    For example: 

    $ oc adm taint nodes node1 kibana=node:NoExecute

    This example places a taint on node1 that has key kibana, value node, and taint effect NoExecute. You must use the NoExecute taint effect. NoExecute schedules only pods that match the taint and remove existing pods that do not match.

  2. Edit the visualization section of the ClusterLogging CR to configure a toleration for the Kibana pod: 

      visualization:
    type: "kibana"
    kibana:
      tolerations:
      - key: "kibana"  1
        operator: "Exists"  2
        effect: "NoExecute"  3
        tolerationSeconds: 6000 4
    1
    Specify the key that you added to the node.
    2
    Specify the Exists operator to require the key/value/effect parameters to match.
    3
    Specify the NoExecute effect.
    4
    Optionally, specify the tolerationSeconds parameter to set how long a pod can remain bound to a node before being evicted.

This toleration matches the taint created by the oc adm taint command. A pod with this toleration would be able to schedule onto node1.

6.7.3. Using tolerations to control the log collector pod placement

You can ensure which nodes the logging collector pods run on and prevent other workloads from using those nodes by using tolerations on the pods.

You apply tolerations to logging collector pods through the ClusterLogging custom resource (CR) and apply taints to a node through the node specification. You can use taints and tolerations to ensure the pod does not get evicted for things like memory and CPU issues.

By default, the logging collector pods have the following toleration: 

tolerations:
- key: "node-role.kubernetes.io/master"
  operator: "Exists"
  effect: "NoExecute"

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.

Procedure

  1. Use the following command to add a taint to a node where you want logging collector pods to schedule logging collector pods: 

    $ oc adm taint nodes <node-name> <key>=<value>:<effect>

    For example: 

    $ oc adm taint nodes node1 collector=node:NoExecute

    This example places a taint on node1 that has key collector, value node, and taint effect NoExecute. You must use the NoExecute taint effect. NoExecute schedules only pods that match the taint and removes existing pods that do not match.

  2. Edit the collection stanza of the ClusterLogging custom resource (CR) to configure a toleration for the logging collector pods: 

      collection:
    logs:
      type: "fluentd"
      fluentd:
        tolerations:
        - key: "collector"  1
          operator: "Exists"  2
          effect: "NoExecute"  3
          tolerationSeconds: 6000  4
    1
    Specify the key that you added to the node.
    2
    Specify the Exists operator to require the key/value/effect parameters to match.
    3
    Specify the NoExecute effect.
    4
    Optionally, specify the tolerationSeconds parameter to set how long a pod can remain bound to a node before being evicted.

This toleration matches the taint created by the oc adm taint command. A pod with this toleration would be able to schedule onto node1.

6.7.4. Additional resources

6.8. Moving logging subsystem resources with node selectors

You can use node selectors to deploy the Elasticsearch and Kibana pods to different nodes.

6.8.1. Moving OpenShift Logging resources

You can configure the Cluster Logging Operator to deploy the pods for logging subsystem components, such as Elasticsearch and Kibana, to different nodes. You cannot move the Cluster Logging Operator pod from its installed location.

For example, you can move the Elasticsearch pods to a separate node because of high CPU, memory, and disk requirements.

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed. These features are not installed by default.

Procedure

  1. Edit the ClusterLogging custom resource (CR) in the openshift-logging project: 

    $ oc edit ClusterLogging instance
    apiVersion: logging.openshift.io/v1
kind: ClusterLogging

...

spec:
  collection:
    logs:
      fluentd:
        resources: null
      type: fluentd
  logStore:
    elasticsearch:
      nodeCount: 3
      nodeSelector: 1
        node-role.kubernetes.io/infra: ''
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
      redundancyPolicy: SingleRedundancy
      resources:
        limits:
          cpu: 500m
          memory: 16Gi
        requests:
          cpu: 500m
          memory: 16Gi
      storage: {}
    type: elasticsearch
  managementState: Managed
  visualization:
    kibana:
      nodeSelector: 2
        node-role.kubernetes.io/infra: ''
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
      proxy:
        resources: null
      replicas: 1
      resources: null
    type: kibana

...
    1 2
    Add a nodeSelector parameter with the appropriate value to the component you want to move. You can use a nodeSelector in the format shown or use <key>: <value> pairs, based on the value specified for the node. If you added a taint to the infrasructure node, also add a matching toleration.

Verification

To verify that a component has moved, you can use the oc get pod -o wide command.

For example:

  • You want to move the Kibana pod from the ip-10-0-147-79.us-east-2.compute.internal node: 

    $ oc get pod kibana-5b8bdf44f9-ccpq9 -o wide

    Example output

    NAME                      READY   STATUS    RESTARTS   AGE   IP            NODE                                        NOMINATED NODE   READINESS GATES
kibana-5b8bdf44f9-ccpq9   2/2     Running   0          27s   10.129.2.18   ip-10-0-147-79.us-east-2.compute.internal   <none>           <none>

  • You want to move the Kibana pod to the ip-10-0-139-48.us-east-2.compute.internal node, a dedicated infrastructure node: 

    $ oc get nodes

    Example output

    NAME                                         STATUS   ROLES          AGE   VERSION
ip-10-0-133-216.us-east-2.compute.internal   Ready    master         60m   v1.26.0
ip-10-0-139-146.us-east-2.compute.internal   Ready    master         60m   v1.26.0
ip-10-0-139-192.us-east-2.compute.internal   Ready    worker         51m   v1.26.0
ip-10-0-139-241.us-east-2.compute.internal   Ready    worker         51m   v1.26.0
ip-10-0-147-79.us-east-2.compute.internal    Ready    worker         51m   v1.26.0
ip-10-0-152-241.us-east-2.compute.internal   Ready    master         60m   v1.26.0
ip-10-0-139-48.us-east-2.compute.internal    Ready    infra          51m   v1.26.0

    Note that the node has a node-role.kubernetes.io/infra: '' label: 

    $ oc get node ip-10-0-139-48.us-east-2.compute.internal -o yaml

    Example output

    kind: Node
apiVersion: v1
metadata:
  name: ip-10-0-139-48.us-east-2.compute.internal
  selfLink: /api/v1/nodes/ip-10-0-139-48.us-east-2.compute.internal
  uid: 62038aa9-661f-41d7-ba93-b5f1b6ef8751
  resourceVersion: '39083'
  creationTimestamp: '2020-04-13T19:07:55Z'
  labels:
    node-role.kubernetes.io/infra: ''
...

  • To move the Kibana pod, edit the ClusterLogging CR to add a node selector: 

    apiVersion: logging.openshift.io/v1
kind: ClusterLogging

...

spec:

...

  visualization:
    kibana:
      nodeSelector: 1
        node-role.kubernetes.io/infra: ''
      proxy:
        resources: null
      replicas: 1
      resources: null
    type: kibana
    1
    Add a node selector to match the label in the node specification.

  • After you save the CR, the current Kibana pod is terminated and new pod is deployed: 

    $ oc get pods

    Example output

    NAME                                            READY   STATUS        RESTARTS   AGE
cluster-logging-operator-84d98649c4-zb9g7       1/1     Running       0          29m
elasticsearch-cdm-hwv01pf7-1-56588f554f-kpmlg   2/2     Running       0          28m
elasticsearch-cdm-hwv01pf7-2-84c877d75d-75wqj   2/2     Running       0          28m
elasticsearch-cdm-hwv01pf7-3-f5d95b87b-4nx78    2/2     Running       0          28m
fluentd-42dzz                                   1/1     Running       0          28m
fluentd-d74rq                                   1/1     Running       0          28m
fluentd-m5vr9                                   1/1     Running       0          28m
fluentd-nkxl7                                   1/1     Running       0          28m
fluentd-pdvqb                                   1/1     Running       0          28m
fluentd-tflh6                                   1/1     Running       0          28m
kibana-5b8bdf44f9-ccpq9                         2/2     Terminating   0          4m11s
kibana-7d85dcffc8-bfpfp                         2/2     Running       0          33s

  • The new pod is on the ip-10-0-139-48.us-east-2.compute.internal node: 

    $ oc get pod kibana-7d85dcffc8-bfpfp -o wide

    Example output

    NAME                      READY   STATUS        RESTARTS   AGE   IP            NODE                                        NOMINATED NODE   READINESS GATES
kibana-7d85dcffc8-bfpfp   2/2     Running       0          43s   10.131.0.22   ip-10-0-139-48.us-east-2.compute.internal   <none>           <none>

  • After a few moments, the original Kibana pod is removed. 

    $ oc get pods

    Example output

    NAME                                            READY   STATUS    RESTARTS   AGE
cluster-logging-operator-84d98649c4-zb9g7       1/1     Running   0          30m
elasticsearch-cdm-hwv01pf7-1-56588f554f-kpmlg   2/2     Running   0          29m
elasticsearch-cdm-hwv01pf7-2-84c877d75d-75wqj   2/2     Running   0          29m
elasticsearch-cdm-hwv01pf7-3-f5d95b87b-4nx78    2/2     Running   0          29m
fluentd-42dzz                                   1/1     Running   0          29m
fluentd-d74rq                                   1/1     Running   0          29m
fluentd-m5vr9                                   1/1     Running   0          29m
fluentd-nkxl7                                   1/1     Running   0          29m
fluentd-pdvqb                                   1/1     Running   0          29m
fluentd-tflh6                                   1/1     Running   0          29m
kibana-7d85dcffc8-bfpfp                         2/2     Running   0          62s

6.9. Maintenance and support

6.9.1. About unsupported configurations

The supported way of configuring the logging subsystem for Red Hat OpenShift is by configuring it using the options described in this documentation. Do not use other configurations, as they are unsupported. Configuration paradigms might change across Red Hat OpenShift Service on AWS releases, and such cases can only be handled gracefully if all configuration possibilities are controlled. If you use configurations other than those described in this documentation, your changes will disappear because the OpenShift Elasticsearch Operator and Red Hat OpenShift Logging Operator reconcile any differences. The Operators reverse everything to the defined state by default and by design.

Note

If you must perform configurations not described in the Red Hat OpenShift Service on AWS documentation, you must set your Red Hat OpenShift Logging Operator or OpenShift Elasticsearch Operator to Unmanaged. An unmanaged OpenShift Logging environment is not supported and does not receive updates until you return OpenShift Logging to Managed.

6.9.2. Unsupported configurations

You must set the Red Hat OpenShift Logging Operator to the unmanaged state to modify the following components:

  • The Elasticsearch CR
  • The Kibana deployment
  • The fluent.conf file
  • The Fluentd daemon set

You must set the OpenShift Elasticsearch Operator to the unmanaged state to modify the following component:

  • the Elasticsearch deployment files.

Explicitly unsupported cases include:

  • Configuring default log rotation. You cannot modify the default log rotation configuration.
  • Configuring the collected log location. You cannot change the location of the log collector output file, which by default is /var/log/fluentd/fluentd.log.
  • Throttling log collection. You cannot throttle down the rate at which the logs are read in by the log collector.
  • Configuring the logging collector using environment variables. You cannot use environment variables to modify the log collector.
  • Configuring how the log collector normalizes logs. You cannot modify default log normalization.

6.9.3. Support policy for unmanaged Operators

The management state of an Operator determines whether an Operator is actively managing the resources for its related component in the cluster as designed. If an Operator is set to an unmanaged state, it does not respond to changes in configuration nor does it receive updates.

While this can be helpful in non-production clusters or during debugging, Operators in an unmanaged state are unsupported and the cluster administrator assumes full control of the individual component configurations and upgrades.

An Operator can be set to an unmanaged state using the following methods:

  • Individual Operator configuration

    Individual Operators have a managementState parameter in their configuration. This can be accessed in different ways, depending on the Operator. For example, the Red Hat OpenShift Logging Operator accomplishes this by modifying a custom resource (CR) that it manages, while the Cluster Samples Operator uses a cluster-wide configuration resource.

    Changing the managementState parameter to Unmanaged means that the Operator is not actively managing its resources and will take no action related to the related component. Some Operators might not support this management state as it might damage the cluster and require manual recovery.

    Warning

    Changing individual Operators to the Unmanaged state renders that particular component and functionality unsupported. Reported issues must be reproduced in Managed state for support to proceed.

  • Cluster Version Operator (CVO) overrides

    The spec.overrides parameter can be added to the CVO’s configuration to allow administrators to provide a list of overrides to the CVO’s behavior for a component. Setting the spec.overrides[].unmanaged parameter to true for a component blocks cluster upgrades and alerts the administrator after a CVO override has been set: 

    Disabling ownership via cluster version overrides prevents upgrades. Please remove overrides before continuing.
    Warning

    Setting a CVO override puts the entire cluster in an unsupported state. Reported issues must be reproduced after removing any overrides for support to proceed.

Chapter 7. Loki

7.1. About the LokiStack

In logging subsystem documentation, LokiStack refers to the logging subsystem supported combination of Loki, and web proxy with Red Hat OpenShift Service on AWS authentication integration. LokiStack’s proxy uses Red Hat OpenShift Service on AWS authentication to enforce multi-tenancy. Loki refers to the log store as either the individual component or an external store.

Loki is a horizontally scalable, highly available, multi-tenant log aggregation system currently offered as an alternative to Elasticsearch as a log store for the logging subsystem. Elasticsearch indexes incoming log records completely during ingestion. Loki only indexes a few fixed labels during ingestion, and defers more complex parsing until after the logs have been stored. This means Loki can collect logs more quickly. As with Elasticsearch, you can query Loki using JSON paths or regular expressions.

7.1.1. Deployment Sizing

Sizing for Loki follows the format of N<x>.<size> where the value <N> is number of instances and <size> specifies performance capabilities.

Note

1x.extra-small is for demo purposes only, and is not supported.

Table 7.1. Loki Sizing

 1x.extra-small1x.small1x.medium

Data transfer

Demo use only.

500GB/day

2TB/day

Queries per second (QPS)

Demo use only.

25-50 QPS at 200ms

25-75 QPS at 200ms

Replication factor

None

2

3

Total CPU requests

5 vCPUs

36 vCPUs

54 vCPUs

Total Memory requests

7.5Gi

63Gi

139Gi

Total Disk requests

150Gi

300Gi

450Gi

7.1.2. Supported API Custom Resource Definitions

LokiStack development is ongoing, not all APIs are supported currently supported.

CustomResourceDefinition (CRD)ApiVersionSupport state

LokiStack

lokistack.loki.grafana.com/v1

Supported in 5.5

RulerConfig

rulerconfig.loki.grafana/v1beta1

Technology Preview

AlertingRule

alertingrule.loki.grafana/v1beta1

Technology Preview

RecordingRule

recordingrule.loki.grafana/v1beta1

Technology Preview

Important

Usage of RulerConfig, AlertingRule and RecordingRule custom resource definitions (CRDs). 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 Technology Preview Features Support Scope.

7.2. Deploying the LokiStack

You can deploy the LokiStack by using the Red Hat OpenShift Service on AWS OpenShift Cluster Manager Hybrid Cloud Console.

Prerequisites

  • Logging subsystem for Red Hat OpenShift Operator 5.5 and later
  • Supported Log Store (AWS S3, Google Cloud Storage, Azure, Swift, Minio, OpenShift Data Foundation)

Procedure

  1. Install the Loki Operator Operator:

    1. In the Red Hat Hybrid Cloud Console, click Operators → OperatorHub.
    2. Choose Loki Operator from the list of available Operators, and click Install.
    3. Under Installation Mode, select All namespaces on the cluster.

    4. Under Installed Namespace, select openshift-operators-redhat.

      You must specify the openshift-operators-redhat namespace. The openshift-operators namespace might contain Community Operators, which are untrusted and might publish a metric with the same name as a Red Hat OpenShift Service on AWS metric, which would cause conflicts.

    5. Select Enable operator recommended cluster monitoring on this namespace.

      This option sets the openshift.io/cluster-monitoring: "true" label in the Namespace object. You must select this option to ensure that cluster monitoring scrapes the openshift-operators-redhat namespace.

    6. Select an Approval Strategy.

      • The Automatic strategy allows Operator Lifecycle Manager (OLM) to automatically update the Operator when a new version is available.
      • The Manual strategy requires a user with appropriate credentials to approve the Operator update.
    7. Click Install.
    8. Verify that you installed the Loki Operator. Visit the Operators → Installed Operators page and look for Loki Operator.
    9. Ensure that Loki Operator is listed with Status as Succeeded in all the projects.

  2. Create a Secret YAML file that uses the access_key_id and access_key_secret fields to specify your AWS credentials and bucketnames, endpoint and region to define the object storage location. For example: 

    apiVersion: v1
kind: Secret
metadata:
  name: logging-loki-s3
  namespace: openshift-logging
stringData:
  access_key_id: AKIAIOSFODNN7EXAMPLE
  access_key_secret: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
  bucketnames: s3-bucket-name
  endpoint: https://s3.eu-central-1.amazonaws.com
  region: eu-central-1

  3. Create the LokiStack custom resource: 

    apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
  size: 1x.small
  storage:
    schemas:
    - version: v12
      effectiveDate: "2022-06-01"
    secret:
      name: logging-loki-s3
      type: s3
  storageClassName: gp3-csi 1
  tenants:
    mode: openshift-logging
    1
    Or gp2-csi.

    1. Apply the configuration: 

      oc apply -f logging-loki.yaml

  4. Create or edit a ClusterLogging CR: 

    apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name: instance
  namespace: openshift-logging
spec:
  managementState: Managed
  logStore:
    type: lokistack
    lokistack:
      name: logging-loki
  collection:
    type: vector

    1. Apply the configuration: 

      oc apply -f cr-lokistack.yaml

  5. Enable the RedHat OpenShift Logging Console Plugin:

    1. In the Red Hat Hybrid Cloud Console, click Operators → Installed Operators.
    2. Select the RedHat OpenShift Logging Operator.
    3. Under Console plugin, click Disabled.
    4. Select Enable and then Save. This change will restart the 'openshift-console' pods.
    5. After the pods restart, you will receive a notification that a web console update is available, prompting you to refresh.
    6. After refreshing the web console, click Observe from the left main menu. A new option for Logs will be available to you.
Note

This plugin is only available on Red Hat OpenShift Service on AWS 4.10 and later.

7.3. Enabling stream-based retention with Loki

With Logging version 5.6 and higher, you can configure retention policies based on log streams. Rules for these may be set globally, per tenant, or both. If you configure both, tenant rules apply before global rules.

  1. To enable stream-based retention, create or edit the LokiStack custom resource (CR): 
oc create -f <file-name>.yaml
  1. You can refer to the examples below to configure your LokiStack CR.

Example global stream-based retention

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
  limits:
    global: 1
      retention: 2
        days: 20
        streams:
        - days: 4
          priority: 1
          selector: '{kubernetes_namespace_name=~"test.+"}' 3
        - days: 1
          priority: 1
          selector: '{log_type="infrastructure"}'
  managementState: Managed
  replicationFactor: 1
  size: 1x.small
  storage:
    schemas:
    - effectiveDate: "2020-10-11"
      version: v11
    secret:
      name: logging-loki-s3
      type: aws
  storageClassName: standard
  tenants:
    mode: openshift-logging

1
Sets retention policy for all log streams. Note: This field does not impact the retention period for stored logs in object storage.
2
Retention is enabled in the cluster when this block is added to the CR.
3
Contains the LogQL query used to define the log stream.

Example per-tenant stream-based retention

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
  limits:
    global:
      retention:
        days: 20
    tenants: 1
      application:
        retention:
          days: 1
          streams:
            - days: 4
              selector: '{kubernetes_namespace_name=~"test.+"}' 2
      infrastructure:
        retention:
          days: 5
          streams:
            - days: 1
              selector: '{kubernetes_namespace_name=~"openshift-cluster.+"}'
  managementState: Managed
  replicationFactor: 1
  size: 1x.small
  storage:
    schemas:
    - effectiveDate: "2020-10-11"
      version: v11
    secret:
      name: logging-loki-s3
      type: aws
  storageClassName: standard
  tenants:
    mode: openshift-logging

1
Sets retention policy by tenant. Valid tenant types are application, audit, and infrastructure.
2
Contains the LogQL query used to define the log stream.
  1. Then apply your configuration: 
oc apply -f <file-name>.yaml
Note

This is not for managing the retention for stored logs. Global retention periods for stored logs to a supported maximum of 30 days is configured with your object storage.

7.4. Forwarding logs to LokiStack

To configure log forwarding to the LokiStack gateway, you must create a ClusterLogging custom resource (CR).

Prerequisites

  • Logging subsystem for Red Hat OpenShift: 5.5 and later
  • Loki Operator Operator

Procedure

  1. Create or edit a YAML file that defines the ClusterLogging custom resource (CR): 
apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name: instance
  namespace: openshift-logging
spec:
  managementState: Managed
  logStore:
    type: lokistack
    lokistack:
      name: logging-loki
  collection:
    type: vector

7.4.1. Troubleshooting Loki "entry out of order" errors

If your Fluentd forwards a large block of messages to a Loki logging system that exceeds the rate limit, Loki to generates "entry out of order" errors. To fix this issue, you update some values in the Loki server configuration file, loki.yaml.

Note

loki.yaml is not available on Grafana-hosted Loki. This topic does not apply to Grafana-hosted Loki servers.

Conditions

  • The ClusterLogForwarder custom resource is configured to forward logs to Loki.

  • Your system sends a block of messages that is larger than 2 MB to Loki, such as: 

    "values":[["1630410392689800468","{\"kind\":\"Event\",\"apiVersion\":\
.......
......
......
......
\"received_at\":\"2021-08-31T11:46:32.800278+00:00\",\"version\":\"1.7.4 1.6.0\"}},\"@timestamp\":\"2021-08-31T11:46:32.799692+00:00\",\"viaq_index_name\":\"audit-write\",\"viaq_msg_id\":\"MzFjYjJkZjItNjY0MC00YWU4LWIwMTEtNGNmM2E5ZmViMGU4\",\"log_type\":\"audit\"}"]]}]}

  • When you enter oc logs -c fluentd, the Fluentd logs in your OpenShift Logging cluster show the following messages: 

    429 Too Many Requests Ingestion rate limit exceeded (limit: 8388608 bytes/sec) while attempting to ingest '2140' lines totaling '3285284' bytes

429 Too Many Requests Ingestion rate limit exceeded' or '500 Internal Server Error rpc error: code = ResourceExhausted desc = grpc: received message larger than max (5277702 vs. 4194304)'

  • When you open the logs on the Loki server, they display entry out of order messages like these: 

    ,\nentry with timestamp 2021-08-18 05:58:55.061936 +0000 UTC ignored, reason: 'entry out of order' for stream:

{fluentd_thread=\"flush_thread_0\", log_type=\"audit\"},\nentry with timestamp 2021-08-18 06:01:18.290229 +0000 UTC ignored, reason: 'entry out of order' for stream: {fluentd_thread="flush_thread_0", log_type="audit"}

Procedure

  1. Update the following fields in the loki.yaml configuration file on the Loki server with the values shown here:

    • grpc_server_max_recv_msg_size: 8388608
    • chunk_target_size: 8388608
    • ingestion_rate_mb: 8
    • ingestion_burst_size_mb: 16
  2. Apply the changes in loki.yaml to the Loki server.

Example loki.yaml file

auth_enabled: false

server:
  http_listen_port: 3100
  grpc_listen_port: 9096
  grpc_server_max_recv_msg_size: 8388608

ingester:
  wal:
    enabled: true
    dir: /tmp/wal
  lifecycler:
    address: 127.0.0.1
    ring:
      kvstore:
        store: inmemory
      replication_factor: 1
    final_sleep: 0s
  chunk_idle_period: 1h       # Any chunk not receiving new logs in this time will be flushed
  chunk_target_size: 8388608
  max_chunk_age: 1h           # All chunks will be flushed when they hit this age, default is 1h
  chunk_retain_period: 30s    # Must be greater than index read cache TTL if using an index cache (Default index read cache TTL is 5m)
  max_transfer_retries: 0     # Chunk transfers disabled

schema_config:
  configs:
    - from: 2020-10-24
      store: boltdb-shipper
      object_store: filesystem
      schema: v11
      index:
        prefix: index_
        period: 24h

storage_config:
  boltdb_shipper:
    active_index_directory: /tmp/loki/boltdb-shipper-active
    cache_location: /tmp/loki/boltdb-shipper-cache
    cache_ttl: 24h         # Can be increased for faster performance over longer query periods, uses more disk space
    shared_store: filesystem
  filesystem:
    directory: /tmp/loki/chunks

compactor:
  working_directory: /tmp/loki/boltdb-shipper-compactor
  shared_store: filesystem

limits_config:
  reject_old_samples: true
  reject_old_samples_max_age: 12h
  ingestion_rate_mb: 8
  ingestion_burst_size_mb: 16

chunk_store_config:
  max_look_back_period: 0s

table_manager:
  retention_deletes_enabled: false
  retention_period: 0s

ruler:
  storage:
    type: local
    local:
      directory: /tmp/loki/rules
  rule_path: /tmp/loki/rules-temp
  alertmanager_url: http://localhost:9093
  ring:
    kvstore:
      store: inmemory
  enable_api: true

Additional resources

7.5. Additional Resources

Chapter 8. Viewing logs for a resource

You can view the logs for various resources, such as builds, deployments, and pods by using the OpenShift CLI (oc) and the web console.

Note

Resource logs are a default feature that provides limited log viewing capability. To enhance your log retrieving and viewing experience, it is recommended that you install OpenShift Logging. The logging subsystem aggregates all the logs from your Red Hat OpenShift Service on AWS cluster, such as node system audit logs, application container logs, and infrastructure logs, into a dedicated log store. You can then query, discover, and visualize your log data through the Kibana interface. Resource logs do not access the logging subsystem log store.

8.1. Viewing resource logs

You can view the log for various resources in the OpenShift CLI (oc) and web console. Logs read from the tail, or end, of the log.

Prerequisites

  • Access to the OpenShift CLI (oc).

Procedure (UI)

  1. In the Red Hat OpenShift Service on AWS console, navigate to Workloads → Pods or navigate to the pod through the resource you want to investigate.

    Note

    Some resources, such as builds, do not have pods to query directly. In such instances, you can locate the Logs link on the Details page for the resource.

  2. Select a project from the drop-down menu.
  3. Click the name of the pod you want to investigate.
  4. Click Logs.

Procedure (CLI)

  • View the log for a specific pod: 

    $ oc logs -f <pod_name> -c <container_name>

    where:

    -f
    Optional: Specifies that the output follows what is being written into the logs.
    <pod_name>
    Specifies the name of the pod.
    <container_name>
    Optional: Specifies the name of a container. When a pod has more than one container, you must specify the container name.

    For example: 

    $ oc logs ruby-58cd97df55-mww7r
    $ oc logs -f ruby-57f7f4855b-znl92 -c ruby

    The contents of log files are printed out.

  • View the log for a specific resource: 

    $ oc logs <object_type>/<resource_name> 1
    1
    Specifies the resource type and name.

    For example: 

    $ oc logs deployment/ruby

    The contents of log files are printed out.

Chapter 9. Viewing cluster logs by using Kibana

The logging subsystem includes a web console for visualizing collected log data. Currently, Red Hat OpenShift Service on AWS deploys the Kibana console for visualization.

Using the log visualizer, you can do the following with your data:

  • search and browse the data using the Discover tab.
  • chart and map the data using the Visualize tab.
  • create and view custom dashboards using the Dashboard tab.

Use and configuration of the Kibana interface is beyond the scope of this documentation. For more information, on using the interface, see the Kibana documentation.

Note

The audit logs are not stored in the internal Red Hat OpenShift Service on AWS Elasticsearch instance by default. To view the audit logs in Kibana, you must use the Log Forwarding API to configure a pipeline that uses the default output for audit logs.

9.1. Defining Kibana index patterns

An index pattern defines the Elasticsearch indices that you want to visualize. To explore and visualize data in Kibana, you must create an index pattern.

Prerequisites

  • A user must have the cluster-admin role, the cluster-reader role, or both roles to view the infra and audit indices in Kibana. The default kubeadmin user has proper permissions to view these indices.

    If you can view the pods and logs in the default, kube- and openshift- projects, you should be able to access these indices. You can use the following command to check if the current user has appropriate permissions: 

    $ oc auth can-i get pods/log -n <project>

    Example output

    yes

    Note

    The audit logs are not stored in the internal Red Hat OpenShift Service on AWS Elasticsearch instance by default. To view the audit logs in Kibana, you must use the Log Forwarding API to configure a pipeline that uses the default output for audit logs.

  • Elasticsearch documents must be indexed before you can create index patterns. This is done automatically, but it might take a few minutes in a new or updated cluster.

Procedure

To define index patterns and create visualizations in Kibana:

  1. In the Red Hat OpenShift Service on AWS console, click the Application Launcher app launcher and select Logging.

  2. Create your Kibana index patterns by clicking Management → Index Patterns → Create index pattern:

    • Each user must manually create index patterns when logging into Kibana the first time to see logs for their projects. Users must create an index pattern named app and use the @timestamp time field to view their container logs.
    • Each admin user must create index patterns when logged into Kibana the first time for the app, infra, and audit indices using the @timestamp time field.
  3. Create Kibana Visualizations from the new index patterns.

9.2. Viewing cluster logs in Kibana

You view cluster logs in the Kibana web console. The methods for viewing and visualizing your data in Kibana that are beyond the scope of this documentation. For more information, refer to the Kibana documentation.

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.
  • Kibana index patterns must exist.

  • A user must have the cluster-admin role, the cluster-reader role, or both roles to view the infra and audit indices in Kibana. The default kubeadmin user has proper permissions to view these indices.

    If you can view the pods and logs in the default, kube- and openshift- projects, you should be able to access these indices. You can use the following command to check if the current user has appropriate permissions: 

    $ oc auth can-i get pods/log -n <project>

    Example output

    yes

    Note

    The audit logs are not stored in the internal Red Hat OpenShift Service on AWS Elasticsearch instance by default. To view the audit logs in Kibana, you must use the Log Forwarding API to configure a pipeline that uses the default output for audit logs.

Procedure

To view logs in Kibana:

  1. In the Red Hat OpenShift Service on AWS console, click the Application Launcher app launcher and select Logging.

  2. Log in using the same credentials you use to log in to the Red Hat OpenShift Service on AWS console.

    The Kibana interface launches.

  3. In Kibana, click Discover.

  4. Select the index pattern you created from the drop-down menu in the top-left corner: app, audit, or infra.

    The log data displays as time-stamped documents.

  5. Expand one of the time-stamped documents.

  6. Click the JSON tab to display the log entry for that document.

    Example 9.1. Sample infrastructure log entry in Kibana

    {
  "_index": "infra-000001",
  "_type": "_doc",
  "_id": "YmJmYTBlNDkZTRmLTliMGQtMjE3NmFiOGUyOWM3",
  "_version": 1,
  "_score": null,
  "_source": {
    "docker": {
      "container_id": "f85fa55bbef7bb783f041066be1e7c267a6b88c4603dfce213e32c1"
    },
    "kubernetes": {
      "container_name": "registry-server",
      "namespace_name": "openshift-marketplace",
      "pod_name": "redhat-marketplace-n64gc",
      "container_image": "registry.redhat.io/redhat/redhat-marketplace-index:v4.7",
      "container_image_id": "registry.redhat.io/redhat/redhat-marketplace-index@sha256:65fc0c45aabb95809e376feb065771ecda9e5e59cc8b3024c4545c168f",
      "pod_id": "8f594ea2-c866-4b5c-a1c8-a50756704b2a",
      "host": "ip-10-0-182-28.us-east-2.compute.internal",
      "master_url": "https://kubernetes.default.svc",
      "namespace_id": "3abab127-7669-4eb3-b9ef-44c04ad68d38",
      "namespace_labels": {
        "openshift_io/cluster-monitoring": "true"
      },
      "flat_labels": [
        "catalogsource_operators_coreos_com/update=redhat-marketplace"
      ]
    },
    "message": "time=\"2020-09-23T20:47:03Z\" level=info msg=\"serving registry\" database=/database/index.db port=50051",
    "level": "unknown",
    "hostname": "ip-10-0-182-28.internal",
    "pipeline_metadata": {
      "collector": {
        "ipaddr4": "10.0.182.28",
        "inputname": "fluent-plugin-systemd",
        "name": "fluentd",
        "received_at": "2020-09-23T20:47:15.007583+00:00",
        "version": "1.7.4 1.6.0"
      }
    },
    "@timestamp": "2020-09-23T20:47:03.422465+00:00",
    "viaq_msg_id": "YmJmYTBlNDktMDMGQtMjE3NmFiOGUyOWM3",
    "openshift": {
      "labels": {
        "logging": "infra"
      }
    }
  },
  "fields": {
    "@timestamp": [
      "2020-09-23T20:47:03.422Z"
    ],
    "pipeline_metadata.collector.received_at": [
      "2020-09-23T20:47:15.007Z"
    ]
  },
  "sort": [
    1600894023422
  ]
}

Chapter 10. Forwarding logs to external third-party logging systems

By default, the logging subsystem sends container and infrastructure logs to the default internal log store defined in the ClusterLogging custom resource. However, it does not send audit logs to the internal store because it does not provide secure storage. If this default configuration meets your needs, you do not need to configure the Cluster Log Forwarder.

To send logs to other log aggregators, you use the Red Hat OpenShift Service on AWS Cluster Log Forwarder. This API enables you to send container, infrastructure, and audit logs to specific endpoints within or outside your cluster. In addition, you can send different types of logs to various systems so that various individuals can access each type. You can also enable Transport Layer Security (TLS) support to send logs securely, as required by your organization.

Note

To send audit logs to the default internal Elasticsearch log store, use the Cluster Log Forwarder as described in Forward audit logs to the log store.

When you forward logs externally, the logging subsystem creates or modifies a Fluentd config map to send logs using your desired protocols. You are responsible for configuring the protocol on the external log aggregator.

Important

You cannot use the config map methods and the Cluster Log Forwarder in the same cluster.

10.1. About forwarding logs to third-party systems

To send logs to specific endpoints inside and outside your Red Hat OpenShift Service on AWS cluster, you specify a combination of outputs and pipelines in a ClusterLogForwarder custom resource (CR). You can also use inputs to forward the application logs associated with a specific project to an endpoint. Authentication is provided by a Kubernetes Secret object.

output

The destination for log data that you define, or where you want the logs sent. An output can be one of the following types:

  • elasticsearch. An external Elasticsearch instance. The elasticsearch output can use a TLS connection.
  • fluentdForward. An external log aggregation solution that supports Fluentd. This option uses the Fluentd forward protocols. The fluentForward output can use a TCP or TLS connection and supports shared-key authentication by providing a shared_key field in a secret. Shared-key authentication can be used with or without TLS.
  • syslog. An external log aggregation solution that supports the syslog RFC3164 or RFC5424 protocols. The syslog output can use a UDP, TCP, or TLS connection.
  • cloudwatch. Amazon CloudWatch, a monitoring and log storage service hosted by Amazon Web Services (AWS).
  • loki. Loki, a horizontally scalable, highly available, multi-tenant log aggregation system.
  • kafka. A Kafka broker. The kafka output can use a TCP or TLS connection.
  • default. The internal Red Hat OpenShift Service on AWS Elasticsearch instance. You are not required to configure the default output. If you do configure a default output, you receive an error message because the default output is reserved for the Red Hat OpenShift Logging Operator.
pipeline

Defines simple routing from one log type to one or more outputs, or which logs you want to send. The log types are one of the following:

  • application. Container logs generated by user applications running in the cluster, except infrastructure container applications.
  • infrastructure. Container logs from pods that run in the openshift*, kube*, or default projects and journal logs sourced from node file system.
  • audit. Audit logs generated by the node audit system, auditd, Kubernetes API server, OpenShift API server, and OVN network.

You can add labels to outbound log messages by using key:value pairs in the pipeline. For example, you might add a label to messages that are forwarded to other data centers or label the logs by type. Labels that are added to objects are also forwarded with the log message.

input

Forwards the application logs associated with a specific project to a pipeline.

In the pipeline, you define which log types to forward using an inputRef parameter and where to forward the logs to using an outputRef parameter.

Secret
A key:value map that contains confidential data such as user credentials.

Note the following:

  • If a ClusterLogForwarder CR object exists, logs are not forwarded to the default Elasticsearch instance, unless there is a pipeline with the default output.
  • By default, the logging subsystem sends container and infrastructure logs to the default internal Elasticsearch log store defined in the ClusterLogging custom resource. However, it does not send audit logs to the internal store because it does not provide secure storage. If this default configuration meets your needs, do not configure the Log Forwarding API.
  • If you do not define a pipeline for a log type, the logs of the undefined types are dropped. For example, if you specify a pipeline for the application and audit types, but do not specify a pipeline for the infrastructure type, infrastructure logs are dropped.
  • You can use multiple types of outputs in the ClusterLogForwarder custom resource (CR) to send logs to servers that support different protocols.
  • The internal Red Hat OpenShift Service on AWS Elasticsearch instance does not provide secure storage for audit logs. We recommend you ensure that the system to which you forward audit logs is compliant with your organizational and governmental regulations and is properly secured. The logging subsystem does not comply with those regulations.

The following example forwards the audit logs to a secure external Elasticsearch instance, the infrastructure logs to an insecure external Elasticsearch instance, the application logs to a Kafka broker, and the application logs from the my-apps-logs project to the internal Elasticsearch instance.

Sample log forwarding outputs and pipelines

apiVersion: "logging.openshift.io/v1"
kind: ClusterLogForwarder
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  outputs:
   - name: elasticsearch-secure 3
     type: "elasticsearch"
     url: https://elasticsearch.secure.com:9200
     secret:
        name: elasticsearch
   - name: elasticsearch-insecure 4
     type: "elasticsearch"
     url: http://elasticsearch.insecure.com:9200
   - name: kafka-app 5
     type: "kafka"
     url: tls://kafka.secure.com:9093/app-topic
  inputs: 6
   - name: my-app-logs
     application:
        namespaces:
        - my-project
  pipelines:
   - name: audit-logs 7
     inputRefs:
      - audit
     outputRefs:
      - elasticsearch-secure
      - default
     parse: json 8
     labels:
       secure: "true" 9
       datacenter: "east"
   - name: infrastructure-logs 10
     inputRefs:
      - infrastructure
     outputRefs:
      - elasticsearch-insecure
     labels:
       datacenter: "west"
   - name: my-app 11
     inputRefs:
      - my-app-logs
     outputRefs:
      - default
   - inputRefs: 12
      - application
     outputRefs:
      - kafka-app
     labels:
       datacenter: "south"

1
The name of the ClusterLogForwarder CR must be instance.
2
The namespace for the ClusterLogForwarder CR must be openshift-logging.
3
Configuration for an secure Elasticsearch output using a secret with a secure URL.
  • A name to describe the output.
  • The type of output: elasticsearch.
  • The secure URL and port of the Elasticsearch instance as a valid absolute URL, including the prefix.
  • The secret required by the endpoint for TLS communication. The secret must exist in the openshift-logging project.
4
Configuration for an insecure Elasticsearch output:
  • A name to describe the output.
  • The type of output: elasticsearch.
  • The insecure URL and port of the Elasticsearch instance as a valid absolute URL, including the prefix.
5
Configuration for a Kafka output using a client-authenticated TLS communication over a secure URL
  • A name to describe the output.
  • The type of output: kafka.
  • Specify the URL and port of the Kafka broker as a valid absolute URL, including the prefix.
6
Configuration for an input to filter application logs from the my-project namespace.
7
Configuration for a pipeline to send audit logs to the secure external Elasticsearch instance:
  • A name to describe the pipeline.
  • The inputRefs is the log type, in this example audit.
  • The outputRefs is the name of the output to use, in this example elasticsearch-secure to forward to the secure Elasticsearch instance and default to forward to the internal Elasticsearch instance.
  • Optional: Labels to add to the logs.
8
Optional: Specify whether to forward structured JSON log entries as JSON objects in the structured field. The log entry must contain valid structured JSON; otherwise, OpenShift Logging removes the structured field and instead sends the log entry to the default index, app-00000x.
9
Optional: String. One or more labels to add to the logs. Quote values like "true" so they are recognized as string values, not as a boolean.
10
Configuration for a pipeline to send infrastructure logs to the insecure external Elasticsearch instance.
11
Configuration for a pipeline to send logs from the my-project project to the internal Elasticsearch instance.
  • A name to describe the pipeline.
  • The inputRefs is a specific input: my-app-logs.
  • The outputRefs is default.
  • Optional: String. One or more labels to add to the logs.
12
Configuration for a pipeline to send logs to the Kafka broker, with no pipeline name:
  • The inputRefs is the log type, in this example application.
  • The outputRefs is the name of the output to use.
  • Optional: String. One or more labels to add to the logs.

Fluentd log handling when the external log aggregator is unavailable

If your external logging aggregator becomes unavailable and cannot receive logs, Fluentd continues to collect logs and stores them in a buffer. When the log aggregator becomes available, log forwarding resumes, including the buffered logs. If the buffer fills completely, Fluentd stops collecting logs. Red Hat OpenShift Service on AWS rotates the logs and deletes them. You cannot adjust the buffer size or add a persistent volume claim (PVC) to the Fluentd daemon set or pods.

Supported Authorization Keys

Common key types are provided here. Some output types support additional specialized keys, documented with the output-specific configuration field. All secret keys are optional. Enable the security features you want by setting the relevant keys. You are responsible for creating and maintaining any additional configurations that external destinations might require, such as keys and secrets, service accounts, port openings, or global proxy configuration. Open Shift Logging will not attempt to verify a mismatch between authorization combinations.

Transport Layer Security (TLS)

Using a TLS URL ('http://…​' or 'ssl://…​') without a Secret enables basic TLS server-side authentication. Additional TLS features are enabled by including a Secret and setting the following optional fields:

  • tls.crt: (string) File name containing a client certificate. Enables mutual authentication. Requires tls.key.
  • tls.key: (string) File name containing the private key to unlock the client certificate. Requires tls.crt.
  • passphrase: (string) Passphrase to decode an encoded TLS private key. Requires tls.key.
  • ca-bundle.crt: (string) File name of a customer CA for server authentication.
Username and Password
  • username: (string) Authentication user name. Requires password.
  • password: (string) Authentication password. Requires username.
Simple Authentication Security Layer (SASL)
  • sasl.enable (boolean) Explicitly enable or disable SASL. If missing, SASL is automatically enabled when any of the other sasl. keys are set.
  • sasl.mechanisms: (array) List of allowed SASL mechanism names. If missing or empty, the system defaults are used.
  • sasl.allow-insecure: (boolean) Allow mechanisms that send clear-text passwords. Defaults to false.

10.1.1. Creating a Secret

You can create a secret in the directory that contains your certificate and key files by using the following command: 

$ oc create secret generic -n openshift-logging <my-secret> \
 --from-file=tls.key=<your_key_file>
 --from-file=tls.crt=<your_crt_file>
 --from-file=ca-bundle.crt=<your_bundle_file>
 --from-literal=username=<your_username>
 --from-literal=password=<your_password>
Note

Generic or opaque secrets are recommended for best results.

10.2. Forwarding JSON logs from containers in the same pod to separate indices

You can forward structured logs from different containers within the same pod to different indices. To use this feature, you must configure the pipeline with multi-container support and annotate the pods. Logs are written to indices with a prefix of app-. It is recommended that Elasticsearch be configured with aliases to accommodate this.

Important

JSON formatting of logs varies by application. Because creating too many indices impacts performance, limit your use of this feature to creating indices for logs that have incompatible JSON formats. Use queries to separate logs from different namespaces, or applications with compatible JSON formats.

Prerequisites

  • Logging subsystem for Red Hat OpenShift: 5.5

Procedure

  1. Create or edit a YAML file that defines the ClusterLogForwarder CR object: 

    apiVersion: "logging.openshift.io/v1"
kind: ClusterLogForwarder
metadata:
  name: instance
  namespace: openshift-logging
spec:
  outputDefaults:
    elasticsearch:
      enableStructuredContainerLogs: true 1
  pipelines:
  - inputRefs:
    - application
    name: application-logs
    outputRefs:
    - default
    parse: json
    1
    Enables multi-container outputs.

  2. Create or edit a YAML file that defines the Pod CR object: 

        apiVersion: v1
    kind: Pod
    metadata:
      annotations:
        containerType.logging.openshift.io/heavy: heavy 1
        containerType.logging.openshift.io/low: low
    spec:
      containers:
      - name: heavy 2
        image: heavyimage
      - name: low
        image: lowimage
    1
    Format: containerType.logging.openshift.io/<container-name>: <index>
    2
    Annotation names must match container names
Warning

This configuration might significantly increase the number of shards on the cluster.

Additional Resources

10.3. Supported log data output types in OpenShift Logging 5.1

Red Hat OpenShift Logging 5.1 provides the following output types and protocols for sending log data to target log collectors.

Red Hat tests each of the combinations shown in the following table. However, you should be able to send log data to a wider range target log collectors that ingest these protocols.

Output typesProtocolsTested with

elasticsearch

elasticsearch

Elasticsearch 6.8.1

Elasticsearch 6.8.4

Elasticsearch 7.12.2

fluentdForward

fluentd forward v1

fluentd 1.7.4

logstash 7.10.1

kafka

kafka 0.11

kafka 2.4.1

kafka 2.7.0

syslog

RFC-3164, RFC-5424

rsyslog-8.39.0

Note

Previously, the syslog output supported only RFC-3164. The current syslog output adds support for RFC-5424.

10.4. Supported log data output types in OpenShift Logging 5.2

Red Hat OpenShift Logging 5.2 provides the following output types and protocols for sending log data to target log collectors.

Red Hat tests each of the combinations shown in the following table. However, you should be able to send log data to a wider range target log collectors that ingest these protocols.

Output typesProtocolsTested with

Amazon CloudWatch

REST over HTTPS

The current version of Amazon CloudWatch

elasticsearch

elasticsearch

Elasticsearch 6.8.1

Elasticsearch 6.8.4

Elasticsearch 7.12.2

fluentdForward

fluentd forward v1

fluentd 1.7.4

logstash 7.10.1

Loki

REST over HTTP and HTTPS

Loki 2.3.0 deployed on OCP and Grafana labs

kafka

kafka 0.11

kafka 2.4.1

kafka 2.7.0

syslog

RFC-3164, RFC-5424

rsyslog-8.39.0

10.5. Supported log data output types in OpenShift Logging 5.3

Red Hat OpenShift Logging 5.3 provides the following output types and protocols for sending log data to target log collectors.

Red Hat tests each of the combinations shown in the following table. However, you should be able to send log data to a wider range target log collectors that ingest these protocols.

Output typesProtocolsTested with

Amazon CloudWatch

REST over HTTPS

The current version of Amazon CloudWatch

elasticsearch

elasticsearch

Elasticsearch 7.10.1

fluentdForward

fluentd forward v1

fluentd 1.7.4

logstash 7.10.1

Loki

REST over HTTP and HTTPS

Loki 2.2.1 deployed on OCP

kafka

kafka 0.11

kafka 2.7.0

syslog

RFC-3164, RFC-5424

rsyslog-8.39.0

10.6. Supported log data output types in OpenShift Logging 5.4

Red Hat OpenShift Logging 5.4 provides the following output types and protocols for sending log data to target log collectors.

Red Hat tests each of the combinations shown in the following table. However, you should be able to send log data to a wider range target log collectors that ingest these protocols.

Output typesProtocolsTested with

Amazon CloudWatch

REST over HTTPS

The current version of Amazon CloudWatch

elasticsearch

elasticsearch

Elasticsearch 7.10.1

fluentdForward

fluentd forward v1

fluentd 1.14.5

logstash 7.10.1

Loki

REST over HTTP and HTTPS

Loki 2.2.1 deployed on OCP

kafka

kafka 0.11

kafka 2.7.0

syslog

RFC-3164, RFC-5424

rsyslog-8.39.0

10.7. Supported log data output types in OpenShift Logging 5.5

Red Hat OpenShift Logging 5.5 provides the following output types and protocols for sending log data to target log collectors.

Red Hat tests each of the combinations shown in the following table. However, you should be able to send log data to a wider range target log collectors that ingest these protocols.

Output typesProtocolsTested with

Amazon CloudWatch

REST over HTTPS

The current version of Amazon CloudWatch

elasticsearch

elasticsearch

Elasticsearch 7.10.1

fluentdForward

fluentd forward v1

fluentd 1.14.6

logstash 7.10.1

Loki

REST over HTTP and HTTPS

Loki 2.5.0 deployed on OCP

kafka

kafka 0.11

kafka 2.7.0

syslog

RFC-3164, RFC-5424

rsyslog-8.39.0

10.8. Supported log data output types in OpenShift Logging 5.6

Red Hat OpenShift Logging 5.6 provides the following output types and protocols for sending log data to target log collectors.

Red Hat tests each of the combinations shown in the following table. However, you should be able to send log data to a wider range target log collectors that ingest these protocols.

Output typesProtocolsTested with

Amazon CloudWatch

REST over HTTPS

The current version of Amazon CloudWatch

elasticsearch

elasticsearch

Elasticsearch 6.8.23

Elasticsearch 7.10.1

Elasticsearch 8.6.1

fluentdForward

fluentd forward v1

fluentd 1.14.6

logstash 7.10.1

Loki

REST over HTTP and HTTPS

Loki 2.5.0 deployed on OCP

kafka

kafka 0.11

kafka 2.7.0

syslog

RFC-3164, RFC-5424

rsyslog-8.39.0

Important

Fluentd doesn’t support Elasticsearch 8 as of 5.6.2. Vector doesn’t support fluentd/logstash/rsyslog before 5.7.0.

10.9. Forwarding logs to an external Elasticsearch instance

You can optionally forward logs to an external Elasticsearch instance in addition to, or instead of, the internal Red Hat OpenShift Service on AWS Elasticsearch instance. You are responsible for configuring the external log aggregator to receive log data from Red Hat OpenShift Service on AWS.

To configure log forwarding to an external Elasticsearch instance, you must create a ClusterLogForwarder custom resource (CR) with an output to that instance, and a pipeline that uses the output. The external Elasticsearch output can use the HTTP (insecure) or HTTPS (secure HTTP) connection.

To forward logs to both an external and the internal Elasticsearch instance, create outputs and pipelines to the external instance and a pipeline that uses the default output to forward logs to the internal instance. You do not need to create a default output. If you do configure a default output, you receive an error message because the default output is reserved for the Red Hat OpenShift Logging Operator.

Note

If you want to forward logs to only the internal Red Hat OpenShift Service on AWS Elasticsearch instance, you do not need to create a ClusterLogForwarder CR.

Prerequisites

  • You must have a logging server that is configured to receive the logging data using the specified protocol or format.

Procedure

  1. Create or edit a YAML file that defines the ClusterLogForwarder CR object: 

    apiVersion: "logging.openshift.io/v1"
kind: ClusterLogForwarder
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  outputs:
   - name: elasticsearch-insecure 3
     type: "elasticsearch" 4
     url: http://elasticsearch.insecure.com:9200 5
   - name: elasticsearch-secure
     type: "elasticsearch"
     url: https://elasticsearch.secure.com:9200 6
     secret:
        name: es-secret 7
  pipelines:
   - name: application-logs 8
     inputRefs: 9
     - application
     - audit
     outputRefs:
     - elasticsearch-secure 10
     - default 11
     parse: json 12
     labels:
       myLabel: "myValue" 13
   - name: infrastructure-audit-logs 14
     inputRefs:
     - infrastructure
     outputRefs:
     - elasticsearch-insecure
     labels:
       logs: "audit-infra"
    1
    The name of the ClusterLogForwarder CR must be instance.
    2
    The namespace for the ClusterLogForwarder CR must be openshift-logging.
    3
    Specify a name for the output.
    4
    Specify the elasticsearch type.
    5
    Specify the URL and port of the external Elasticsearch instance as a valid absolute URL. You can use the http (insecure) or https (secure HTTP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP Address.
    6
    For a secure connection, you can specify an https or http URL that you authenticate by specifying a secret.
    7
    For an https prefix, specify the name of the secret required by the endpoint for TLS communication. The secret must exist in the openshift-logging project, and must have keys of: tls.crt, tls.key, and ca-bundle.crt that point to the respective certificates that they represent. Otherwise, for http and https prefixes, you can specify a secret that contains a username and password. For more information, see the following "Example: Setting secret that contains a username and password."
    8
    Optional: Specify a name for the pipeline.
    9
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    10
    Specify the name of the output to use when forwarding logs with this pipeline.
    11
    Optional: Specify the default output to send the logs to the internal Elasticsearch instance.
    12
    Optional: Specify whether to forward structured JSON log entries as JSON objects in the structured field. The log entry must contain valid structured JSON; otherwise, OpenShift Logging removes the structured field and instead sends the log entry to the default index, app-00000x.
    13
    Optional: String. One or more labels to add to the logs.
    14
    Optional: Configure multiple outputs to forward logs to other external log aggregators of any supported type:
    • A name to describe the pipeline.
    • The inputRefs is the log type to forward by using the pipeline: application, infrastructure, or audit.
    • The outputRefs is the name of the output to use.
    • Optional: String. One or more labels to add to the logs.

  2. Create the CR object: 

    $ oc create -f <file-name>.yaml

Example: Setting a secret that contains a username and password

You can use a secret that contains a username and password to authenticate a secure connection to an external Elasticsearch instance.

For example, if you cannot use mutual TLS (mTLS) keys because a third party operates the Elasticsearch instance, you can use HTTP or HTTPS and set a secret that contains the username and password.

  1. Create a Secret YAML file similar to the following example. Use base64-encoded values for the username and password fields. The secret type is opaque by default. 

    apiVersion: v1
kind: Secret
metadata:
  name: openshift-test-secret
data:
  username: <username>
  password: <password>

  2. Create the secret: 

    $ oc create secret -n openshift-logging openshift-test-secret.yaml

  3. Specify the name of the secret in the ClusterLogForwarder CR: 

    kind: ClusterLogForwarder
metadata:
  name: instance
  namespace: openshift-logging
spec:
  outputs:
   - name: elasticsearch
     type: "elasticsearch"
     url: https://elasticsearch.secure.com:9200
     secret:
        name: openshift-test-secret
    Note

    In the value of the url field, the prefix can be http or https.

  4. Create the CR object: 

    $ oc create -f <file-name>.yaml

10.10. Forwarding logs using the Fluentd forward protocol

You can use the Fluentd forward protocol to send a copy of your logs to an external log aggregator that is configured to accept the protocol instead of, or in addition to, the default Elasticsearch log store. You are responsible for configuring the external log aggregator to receive the logs from Red Hat OpenShift Service on AWS.

To configure log forwarding using the forward protocol, you must create a ClusterLogForwarder custom resource (CR) with one or more outputs to the Fluentd servers, and pipelines that use those outputs. The Fluentd output can use a TCP (insecure) or TLS (secure TCP) connection.

Note

Alternately, you can use a config map to forward logs using the forward protocols. However, this method is deprecated in Red Hat OpenShift Service on AWS and will be removed in a future release.

Prerequisites

  • You must have a logging server that is configured to receive the logging data using the specified protocol or format.

Procedure

  1. Create or edit a YAML file that defines the ClusterLogForwarder CR object: 

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  outputs:
   - name: fluentd-server-secure 3
     type: fluentdForward 4
     url: 'tls://fluentdserver.security.example.com:24224' 5
     secret: 6
        name: fluentd-secret
   - name: fluentd-server-insecure
     type: fluentdForward
     url: 'tcp://fluentdserver.home.example.com:24224'
  pipelines:
   - name: forward-to-fluentd-secure 7
     inputRefs:  8
     - application
     - audit
     outputRefs:
     - fluentd-server-secure 9
     - default 10
     parse: json 11
     labels:
       clusterId: "C1234" 12
   - name: forward-to-fluentd-insecure 13
     inputRefs:
     - infrastructure
     outputRefs:
     - fluentd-server-insecure
     labels:
       clusterId: "C1234"
    1
    The name of the ClusterLogForwarder CR must be instance.
    2
    The namespace for the ClusterLogForwarder CR must be openshift-logging.
    3
    Specify a name for the output.
    4
    Specify the fluentdForward type.
    5
    Specify the URL and port of the external Fluentd instance as a valid absolute URL. You can use the tcp (insecure) or tls (secure TCP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP address.
    6
    If using a tls prefix, you must specify the name of the secret required by the endpoint for TLS communication. The secret must exist in the openshift-logging project, and must have keys of: tls.crt, tls.key, and ca-bundle.crt that point to the respective certificates that they represent. Otherwise, for http and https prefixes, you can specify a secret that contains a username and password. For more information, see the following "Example: Setting secret that contains a username and password."
    7
    Optional: Specify a name for the pipeline.
    8
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    9
    Specify the name of the output to use when forwarding logs with this pipeline.
    10
    Optional: Specify the default output to forward logs to the internal Elasticsearch instance.
    11
    Optional: Specify whether to forward structured JSON log entries as JSON objects in the structured field. The log entry must contain valid structured JSON; otherwise, OpenShift Logging removes the structured field and instead sends the log entry to the default index, app-00000x.
    12
    Optional: String. One or more labels to add to the logs.
    13
    Optional: Configure multiple outputs to forward logs to other external log aggregators of any supported type:
    • A name to describe the pipeline.
    • The inputRefs is the log type to forward by using the pipeline: application, infrastructure, or audit.
    • The outputRefs is the name of the output to use.
    • Optional: String. One or more labels to add to the logs.

  2. Create the CR object: 

    $ oc create -f <file-name>.yaml

10.10.1. Enabling nanosecond precision for Logstash to ingest data from fluentd

For Logstash to ingest log data from fluentd, you must enable nanosecond precision in the Logstash configuration file.

Procedure

  • In the Logstash configuration file, set nanosecond_precision to true.

Example Logstash configuration file

input { tcp { codec => fluent { nanosecond_precision => true } port => 24114 } }
filter { }
output { stdout { codec => rubydebug } }

10.11. Forwarding logs using the syslog protocol

You can use the syslog RFC3164 or RFC5424 protocol to send a copy of your logs to an external log aggregator that is configured to accept the protocol instead of, or in addition to, the default Elasticsearch log store. You are responsible for configuring the external log aggregator, such as a syslog server, to receive the logs from Red Hat OpenShift Service on AWS.

To configure log forwarding using the syslog protocol, you must create a ClusterLogForwarder custom resource (CR) with one or more outputs to the syslog servers, and pipelines that use those outputs. The syslog output can use a UDP, TCP, or TLS connection.

Note

Alternately, you can use a config map to forward logs using the syslog RFC3164 protocols. However, this method is deprecated in Red Hat OpenShift Service on AWS and will be removed in a future release.

Prerequisites

  • You must have a logging server that is configured to receive the logging data using the specified protocol or format.

Procedure

  1. Create or edit a YAML file that defines the ClusterLogForwarder CR object: 

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  outputs:
   - name: rsyslog-east 3
     type: syslog 4
     syslog: 5
       facility: local0
       rfc: RFC3164
       payloadKey: message
       severity: informational
     url: 'tls://rsyslogserver.east.example.com:514' 6
     secret: 7
        name: syslog-secret
   - name: rsyslog-west
     type: syslog
     syslog:
      appName: myapp
      facility: user
      msgID: mymsg
      procID: myproc
      rfc: RFC5424
      severity: debug
     url: 'udp://rsyslogserver.west.example.com:514'
  pipelines:
   - name: syslog-east 8
     inputRefs: 9
     - audit
     - application
     outputRefs: 10
     - rsyslog-east
     - default 11
     parse: json 12
     labels:
       secure: "true" 13
       syslog: "east"
   - name: syslog-west 14
     inputRefs:
     - infrastructure
     outputRefs:
     - rsyslog-west
     - default
     labels:
       syslog: "west"
    1
    The name of the ClusterLogForwarder CR must be instance.
    2
    The namespace for the ClusterLogForwarder CR must be openshift-logging.
    3
    Specify a name for the output.
    4
    Specify the syslog type.
    5
    Optional: Specify the syslog parameters, listed below.
    6
    Specify the URL and port of the external syslog instance. You can use the udp (insecure), tcp (insecure) or tls (secure TCP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP address.
    7
    If using a tls prefix, you must specify the name of the secret required by the endpoint for TLS communication. The secret must exist in the openshift-logging project, and must have keys of: tls.crt, tls.key, and ca-bundle.crt that point to the respective certificates that they represent.
    8
    Optional: Specify a name for the pipeline.
    9
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    10
    Specify the name of the output to use when forwarding logs with this pipeline.
    11
    Optional: Specify the default output to forward logs to the internal Elasticsearch instance.
    12
    Optional: Specify whether to forward structured JSON log entries as JSON objects in the structured field. The log entry must contain valid structured JSON; otherwise, OpenShift Logging removes the structured field and instead sends the log entry to the default index, app-00000x.
    13
    Optional: String. One or more labels to add to the logs. Quote values like "true" so they are recognized as string values, not as a boolean.
    14
    Optional: Configure multiple outputs to forward logs to other external log aggregators of any supported type:
    • A name to describe the pipeline.
    • The inputRefs is the log type to forward by using the pipeline: application, infrastructure, or audit.
    • The outputRefs is the name of the output to use.
    • Optional: String. One or more labels to add to the logs.

  2. Create the CR object: 

    $ oc create -f <file-name>.yaml

10.11.1. Adding log source information to message output

You can add namespace_name, pod_name, and container_name elements to the message field of the record by adding the AddLogSource field to your ClusterLogForwarder custom resource (CR). 

  spec:
    outputs:
    - name: syslogout
      syslog:
        addLogSource: true
        facility: user
        payloadKey: message
        rfc: RFC3164
        severity: debug
        tag: mytag
      type: syslog
      url: tls://syslog-receiver.openshift-logging.svc:24224
    pipelines:
    - inputRefs:
      - application
      name: test-app
      outputRefs:
      - syslogout
Note

This configuration is compatible with both RFC3164 and RFC5424.

Example syslog message output without AddLogSource

<15>1 2020-11-15T17:06:14+00:00 fluentd-9hkb4 mytag - - -  {"msgcontent"=>"Message Contents", "timestamp"=>"2020-11-15 17:06:09", "tag_key"=>"rec_tag", "index"=>56}

Example syslog message output with AddLogSource

<15>1 2020-11-16T10:49:37+00:00 crc-j55b9-master-0 mytag - - -  namespace_name=clo-test-6327,pod_name=log-generator-ff9746c49-qxm7l,container_name=log-generator,message={"msgcontent":"My life is my message", "timestamp":"2020-11-16 10:49:36", "tag_key":"rec_tag", "index":76}

10.11.2. Syslog parameters

You can configure the following for the syslog outputs. For more information, see the syslog RFC3164 or RFC5424 RFC.

  • facility: The syslog facility. The value can be a decimal integer or a case-insensitive keyword:

    • 0 or kern for kernel messages
    • 1 or user for user-level messages, the default.
    • 2 or mail for the mail system
    • 3 or daemon for system daemons
    • 4 or auth for security/authentication messages
    • 5 or syslog for messages generated internally by syslogd
    • 6 or lpr for the line printer subsystem
    • 7 or news for the network news subsystem
    • 8 or uucp for the UUCP subsystem
    • 9 or cron for the clock daemon
    • 10 or authpriv for security authentication messages
    • 11 or ftp for the FTP daemon
    • 12 or ntp for the NTP subsystem
    • 13 or security for the syslog audit log
    • 14 or console for the syslog alert log
    • 15 or solaris-cron for the scheduling daemon
    • 16–23 or local0 – local7 for locally used facilities

  • Optional: payloadKey: The record field to use as payload for the syslog message.

    Note

    Configuring the payloadKey parameter prevents other parameters from being forwarded to the syslog.

  • rfc: The RFC to be used for sending logs using syslog. The default is RFC5424.

  • severity: The syslog severity to set on outgoing syslog records. The value can be a decimal integer or a case-insensitive keyword:

    • 0 or Emergency for messages indicating the system is unusable
    • 1 or Alert for messages indicating action must be taken immediately
    • 2 or Critical for messages indicating critical conditions
    • 3 or Error for messages indicating error conditions
    • 4 or Warning for messages indicating warning conditions
    • 5 or Notice for messages indicating normal but significant conditions
    • 6 or Informational for messages indicating informational messages
    • 7 or Debug for messages indicating debug-level messages, the default
  • tag: Tag specifies a record field to use as a tag on the syslog message.
  • trimPrefix: Remove the specified prefix from the tag.

10.11.3. Additional RFC5424 syslog parameters

The following parameters apply to RFC5424:

  • appName: The APP-NAME is a free-text string that identifies the application that sent the log. Must be specified for RFC5424.
  • msgID: The MSGID is a free-text string that identifies the type of message. Must be specified for RFC5424.
  • procID: The PROCID is a free-text string. A change in the value indicates a discontinuity in syslog reporting. Must be specified for RFC5424.

10.12. Forwarding logs to Amazon CloudWatch

You can forward logs to Amazon CloudWatch, a monitoring and log storage service hosted by Amazon Web Services (AWS). You can forward logs to CloudWatch in addition to, or instead of, the default log store.

To configure log forwarding to CloudWatch, you must create a ClusterLogForwarder custom resource (CR) with an output for CloudWatch, and a pipeline that uses the output.

Procedure

  1. Create a Secret YAML file that uses the aws_access_key_id and aws_secret_access_key fields to specify your base64-encoded AWS credentials. For example: 

    apiVersion: v1
kind: Secret
metadata:
  name: cw-secret
  namespace: openshift-logging
data:
  aws_access_key_id: QUtJQUlPU0ZPRE5ON0VYQU1QTEUK
  aws_secret_access_key: d0phbHJYVXRuRkVNSS9LN01ERU5HL2JQeFJmaUNZRVhBTVBMRUtFWQo=

  2. Create the secret. For example: 

    $ oc apply -f cw-secret.yaml

  3. Create or edit a YAML file that defines the ClusterLogForwarder CR object. In the file, specify the name of the secret. For example: 

    apiVersion: "logging.openshift.io/v1"
kind: ClusterLogForwarder
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  outputs:
   - name: cw 3
     type: cloudwatch 4
     cloudwatch:
       groupBy: logType 5
       groupPrefix: <group prefix> 6
       region: us-east-2 7
     secret:
        name: cw-secret 8
  pipelines:
    - name: infra-logs 9
      inputRefs: 10
        - infrastructure
        - audit
        - application
      outputRefs:
        - cw 11
    1
    The name of the ClusterLogForwarder CR must be instance.
    2
    The namespace for the ClusterLogForwarder CR must be openshift-logging.
    3
    Specify a name for the output.
    4
    Specify the cloudwatch type.
    5
    Optional: Specify how to group the logs:
    • logType creates log groups for each log type
    • namespaceName creates a log group for each application name space. It also creates separate log groups for infrastructure and audit logs.
    • namespaceUUID creates a new log groups for each application namespace UUID. It also creates separate log groups for infrastructure and audit logs.
    6
    Optional: Specify a string to replace the default infrastructureName prefix in the names of the log groups.
    7
    Specify the AWS region.
    8
    Specify the name of the secret that contains your AWS credentials.
    9
    Optional: Specify a name for the pipeline.
    10
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    11
    Specify the name of the output to use when forwarding logs with this pipeline.

  4. Create the CR object: 

    $ oc create -f <file-name>.yaml

Example: Using ClusterLogForwarder with Amazon CloudWatch

Here, you see an example ClusterLogForwarder custom resource (CR) and the log data that it outputs to Amazon CloudWatch.

Suppose that you are running a ROSA cluster named mycluster. The following command returns the cluster’s infrastructureName, which you will use to compose aws commands later on: 

$ oc get Infrastructure/cluster -ojson | jq .status.infrastructureName
"mycluster-7977k"

To generate log data for this example, you run a busybox pod in a namespace called app. The busybox pod writes a message to stdout every three seconds: 

$ oc run busybox --image=busybox -- sh -c 'while true; do echo "My life is my message"; sleep 3; done'
$ oc logs -f busybox
My life is my message
My life is my message
My life is my message
...

You can look up the UUID of the app namespace where the busybox pod runs: 

$ oc get ns/app -ojson | jq .metadata.uid
"794e1e1a-b9f5-4958-a190-e76a9b53d7bf"

In your ClusterLogForwarder custom resource (CR), you configure the infrastructure, audit, and application log types as inputs to the all-logs pipeline. You also connect this pipeline to cw output, which forwards the logs to a CloudWatch instance in the us-east-2 region: 

apiVersion: "logging.openshift.io/v1"
kind: ClusterLogForwarder
metadata:
  name: instance
  namespace: openshift-logging
spec:
  outputs:
   - name: cw
     type: cloudwatch
     cloudwatch:
       groupBy: logType
       region: us-east-2
     secret:
        name: cw-secret
  pipelines:
    - name: all-logs
      inputRefs:
        - infrastructure
        - audit
        - application
      outputRefs:
        - cw

Each region in CloudWatch contains three levels of objects:

  • log group

    • log stream

      • log event

With groupBy: logType in the ClusterLogForwarding CR, the three log types in the inputRefs produce three log groups in Amazon Cloudwatch: 

$ aws --output json logs describe-log-groups | jq .logGroups[].logGroupName
"mycluster-7977k.application"
"mycluster-7977k.audit"
"mycluster-7977k.infrastructure"

Each of the log groups contains log streams: 

$ aws --output json logs describe-log-streams --log-group-name mycluster-7977k.application | jq .logStreams[].logStreamName
"kubernetes.var.log.containers.busybox_app_busybox-da085893053e20beddd6747acdbaf98e77c37718f85a7f6a4facf09ca195ad76.log"
$ aws --output json logs describe-log-streams --log-group-name mycluster-7977k.audit | jq .logStreams[].logStreamName
"ip-10-0-131-228.us-east-2.compute.internal.k8s-audit.log"
"ip-10-0-131-228.us-east-2.compute.internal.linux-audit.log"
"ip-10-0-131-228.us-east-2.compute.internal.openshift-audit.log"
...
$ aws --output json logs describe-log-streams --log-group-name mycluster-7977k.infrastructure | jq .logStreams[].logStreamName
"ip-10-0-131-228.us-east-2.compute.internal.kubernetes.var.log.containers.apiserver-69f9fd9b58-zqzw5_openshift-oauth-apiserver_oauth-apiserver-453c5c4ee026fe20a6139ba6b1cdd1bed25989c905bf5ac5ca211b7cbb5c3d7b.log"
"ip-10-0-131-228.us-east-2.compute.internal.kubernetes.var.log.containers.apiserver-797774f7c5-lftrx_openshift-apiserver_openshift-apiserver-ce51532df7d4e4d5f21c4f4be05f6575b93196336be0027067fd7d93d70f66a4.log"
"ip-10-0-131-228.us-east-2.compute.internal.kubernetes.var.log.containers.apiserver-797774f7c5-lftrx_openshift-apiserver_openshift-apiserver-check-endpoints-82a9096b5931b5c3b1d6dc4b66113252da4a6472c9fff48623baee761911a9ef.log"
...

Each log stream contains log events. To see a log event from the busybox Pod, you specify its log stream from the application log group: 

$ aws logs get-log-events --log-group-name mycluster-7977k.application --log-stream-name kubernetes.var.log.containers.busybox_app_busybox-da085893053e20beddd6747acdbaf98e77c37718f85a7f6a4facf09ca195ad76.log
{
    "events": [
        {
            "timestamp": 1629422704178,
            "message": "{\"docker\":{\"container_id\":\"da085893053e20beddd6747acdbaf98e77c37718f85a7f6a4facf09ca195ad76\"},\"kubernetes\":{\"container_name\":\"busybox\",\"namespace_name\":\"app\",\"pod_name\":\"busybox\",\"container_image\":\"docker.io/library/busybox:latest\",\"container_image_id\":\"docker.io/library/busybox@sha256:0f354ec1728d9ff32edcd7d1b8bbdfc798277ad36120dc3dc683be44524c8b60\",\"pod_id\":\"870be234-90a3-4258-b73f-4f4d6e2777c7\",\"host\":\"ip-10-0-216-3.us-east-2.compute.internal\",\"labels\":{\"run\":\"busybox\"},\"master_url\":\"https://kubernetes.default.svc\",\"namespace_id\":\"794e1e1a-b9f5-4958-a190-e76a9b53d7bf\",\"namespace_labels\":{\"kubernetes_io/metadata_name\":\"app\"}},\"message\":\"My life is my message\",\"level\":\"unknown\",\"hostname\":\"ip-10-0-216-3.us-east-2.compute.internal\",\"pipeline_metadata\":{\"collector\":{\"ipaddr4\":\"10.0.216.3\",\"inputname\":\"fluent-plugin-systemd\",\"name\":\"fluentd\",\"received_at\":\"2021-08-20T01:25:08.085760+00:00\",\"version\":\"1.7.4 1.6.0\"}},\"@timestamp\":\"2021-08-20T01:25:04.178986+00:00\",\"viaq_index_name\":\"app-write\",\"viaq_msg_id\":\"NWRjZmUyMWQtZjgzNC00MjI4LTk3MjMtNTk3NmY3ZjU4NDk1\",\"log_type\":\"application\",\"time\":\"2021-08-20T01:25:04+00:00\"}",
            "ingestionTime": 1629422744016
        },
...

Example: Customizing the prefix in log group names

In the log group names, you can replace the default infrastructureName prefix, mycluster-7977k, with an arbitrary string like demo-group-prefix. To make this change, you update the groupPrefix field in the ClusterLogForwarding CR: 

cloudwatch:
    groupBy: logType
    groupPrefix: demo-group-prefix
    region: us-east-2

The value of groupPrefix replaces the default infrastructureName prefix: 

$ aws --output json logs describe-log-groups | jq .logGroups[].logGroupName
"demo-group-prefix.application"
"demo-group-prefix.audit"
"demo-group-prefix.infrastructure"

Example: Naming log groups after application namespace names

For each application namespace in your cluster, you can create a log group in CloudWatch whose name is based on the name of the application namespace.

If you delete an application namespace object and create a new one that has the same name, CloudWatch continues using the same log group as before.

If you consider successive application namespace objects that have the same name as equivalent to each other, use the approach described in this example. Otherwise, if you need to distinguish the resulting log groups from each other, see the following "Naming log groups for application namespace UUIDs" section instead.

To create application log groups whose names are based on the names of the application namespaces, you set the value of the groupBy field to namespaceName in the ClusterLogForwarder CR: 

cloudwatch:
    groupBy: namespaceName
    region: us-east-2

Setting groupBy to namespaceName affects the application log group only. It does not affect the audit and infrastructure log groups.

In Amazon Cloudwatch, the namespace name appears at the end of each log group name. Because there is a single application namespace, "app", the following output shows a new mycluster-7977k.app log group instead of mycluster-7977k.application: 

$ aws --output json logs describe-log-groups | jq .logGroups[].logGroupName
"mycluster-7977k.app"
"mycluster-7977k.audit"
"mycluster-7977k.infrastructure"

If the cluster in this example had contained multiple application namespaces, the output would show multiple log groups, one for each namespace.

The groupBy field affects the application log group only. It does not affect the audit and infrastructure log groups.

Example: Naming log groups after application namespace UUIDs

For each application namespace in your cluster, you can create a log group in CloudWatch whose name is based on the UUID of the application namespace.

If you delete an application namespace object and create a new one, CloudWatch creates a new log group.

If you consider successive application namespace objects with the same name as different from each other, use the approach described in this example. Otherwise, see the preceding "Example: Naming log groups for application namespace names" section instead.

To name log groups after application namespace UUIDs, you set the value of the groupBy field to namespaceUUID in the ClusterLogForwarder CR: 

cloudwatch:
    groupBy: namespaceUUID
    region: us-east-2

In Amazon Cloudwatch, the namespace UUID appears at the end of each log group name. Because there is a single application namespace, "app", the following output shows a new mycluster-7977k.794e1e1a-b9f5-4958-a190-e76a9b53d7bf log group instead of mycluster-7977k.application: 

$ aws --output json logs describe-log-groups | jq .logGroups[].logGroupName
"mycluster-7977k.794e1e1a-b9f5-4958-a190-e76a9b53d7bf" // uid of the "app" namespace
"mycluster-7977k.audit"
"mycluster-7977k.infrastructure"

The groupBy field affects the application log group only. It does not affect the audit and infrastructure log groups.

10.12.1. Forwarding logs to Amazon CloudWatch from STS enabled clusters

For clusters with AWS Security Token Service (STS) enabled, create the AWS IAM roles and policies that will allow the log forwarding, and a ClusterLogForwarder custom resource (CR) with an output for CloudWatch.

Note

This feature is not supported by the vector collector.

Prerequisites

  • Logging subsystem for Red Hat OpenShift: 5.5 and later

Procedure

  1. Prepare the AWS account:

    1. Create an IAM policy JSON file with the following content: 

      {
"Version": "2012-10-17",
"Statement": [
    {
    "Effect": "Allow",
    "Action": [
      "logs:CreateLogGroup",
      "logs:CreateLogStream",
      "logs:DescribeLogGroups",
      "logs:DescribeLogStreams",
      "logs:PutLogEvents",
      "logs:PutRetentionPolicy"
    ],
    "Resource": "arn:aws:logs:*:*:*"
    }
  ]
}

    2. Create an IAM trust JSON file with the following content: 

      {
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Federated": "arn:aws:iam::<your_aws_account_id>:oidc-provider/<openshift_oidc_provider>" 1
      },
      "Action": "sts:AssumeRoleWithWebIdentity",
      "Condition": {
        "StringEquals": {
          "<openshift_oidc_provider>:sub": "system:serviceaccount:openshift-logging:logcollector" 2
        }
      }
    }
  ]
}
      1
      Specify your AWS account ID and the OpenShift OIDC provider endpoint. Obtain the endpoint by running the the following command: 
      $ rosa describe cluster \
  -c $(oc get clusterversion -o jsonpath='{.items[].spec.clusterID}{"\n"}') \
  -o yaml | awk '/oidc_endpoint_url/ {print $2}' | cut -d '/' -f 3,4
      2
      Specify the OpenShift OIDC endpoint again.

    3. Create the IAM role: 

      $ aws iam create-role
  --role-name “<your_rosa_cluster_name>-RosaCloudWatch” \
  --assume-role-policy-document file://<your_trust_file_name>.json \
  --query Role.Arn \
  --output text

      Save the output. You will use it in the next steps.

    4. Create the IAM policy: 

      $ aws iam create-policy \
--policy-name "RosaCloudWatch" \
--policy-document file:///<your_policy_file_name>.json \
--query Policy.Arn \
--output text

      Save the output. You will use it in the next steps.

    5. Attach the IAM policy to the IAM role: 

      $ aws iam attach-role-policy \
 --role-name “<your_rosa_cluster_name>-RosaCloudWatch” \
 --policy-arn <policy_ARN> 1
      1
      Replace policy_ARN with the output you saved while creating the policy.

  2. Create a Secret YAML file for the logging operator: 

    apiVersion: v1
kind: Secret
metadata:
  name: cloudwatch-credentials
  namespace: openshift-logging
stringData:
  credentials: |-
    [default]
    sts_regional_endpoints = regional
    role_arn: <role_ARN>  1
    web_identity_token_file = /var/run/secrets/openshift/serviceaccount/token
    1
    Replace role_ARN with the output you saved while creating the role.

  3. Create the secret: 

    $ oc apply -f cloudwatch-credentials.yaml

  4. Create or edit a ClusterLogForwarder custom resource: 

    apiVersion: "logging.openshift.io/v1"
kind: ClusterLogForwarder
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  outputs:
   - name: cw 3
     type: cloudwatch 4
     cloudwatch:
       groupBy: logType 5
       groupPrefix: <group prefix> 6
       region: us-east-2 7
     secret:
        name: <your_secret_name> 8
  pipelines:
    - name: to-cloudwatch 9
      inputRefs: 10
        - infrastructure
        - audit
        - application
      outputRefs:
        - cw 11
    1
    The name of the ClusterLogForwarder CR must be instance.
    2
    The namespace for the ClusterLogForwarder CR must be openshift-logging.
    3
    Specify a name for the output.
    4
    Specify the cloudwatch type.
    5
    Optional: Specify how to group the logs:
    • logType creates log groups for each log type
    • namespaceName creates a log group for each application name space. Infrastructure and audit logs are unaffected, remaining grouped by logType.
    • namespaceUUID creates a new log groups for each application namespace UUID. It also creates separate log groups for infrastructure and audit logs.
    6
    Optional: Specify a string to replace the default infrastructureName prefix in the names of the log groups.
    7
    Specify the AWS region.
    8
    Specify the name of the secret you created previously.
    9
    Optional: Specify a name for the pipeline.
    10
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    11
    Specify the name of the output to use when forwarding logs with this pipeline.

Additional resources

10.12.2. Creating a secret for AWS CloudWatch with an existing AWS role

If you have an existing role for AWS, you can create a secret for AWS with STS using the oc create secret --from-literal command.

Procedure

  • In the CLI, enter the following to generate a secret for AWS: 

    $ oc create secret generic cw-sts-secret -n openshift-logging --from-literal=role_arn=arn:aws:iam::123456789012:role/my-role_with-permissions

    Example Secret

    apiVersion: v1
kind: Secret
metadata:
  namespace: openshift-logging
  name: my-secret-name
stringData:
  role_arn: arn:aws:iam::123456789012:role/my-role_with-permissions

10.13. Forwarding logs to Loki

You can forward logs to an external Loki logging system in addition to, or instead of, the internal default Red Hat OpenShift Service on AWS Elasticsearch instance.

To configure log forwarding to Loki, you must create a ClusterLogForwarder custom resource (CR) with an output to Loki, and a pipeline that uses the output. The output to Loki can use the HTTP (insecure) or HTTPS (secure HTTP) connection.

Prerequisites

  • You must have a Loki logging system running at the URL you specify with the url field in the CR.

Procedure

  1. Create or edit a YAML file that defines the ClusterLogForwarder CR object: 

      apiVersion: "logging.openshift.io/v1"
  kind: ClusterLogForwarder
  metadata:
    name: instance 1
    namespace: openshift-logging 2
  spec:
    outputs:
     - name: loki-insecure 3
       type: "loki" 4
       url: http://loki.insecure.com:3100 5
       loki:
          tenantKey: kubernetes.namespace_name
          labelKeys: kubernetes.labels.foo
     - name: loki-secure 6
       type: "loki"
       url: https://loki.secure.com:3100
       secret:
          name: loki-secret 7
       loki:
          tenantKey: kubernetes.namespace_name 8
          labelKeys: kubernetes.labels.foo 9
    pipelines:
     - name: application-logs 10
       inputRefs:  11
       - application
       - audit
       outputRefs: 12
       - loki-secure
    1
    The name of the ClusterLogForwarder CR must be instance.
    2
    The namespace for the ClusterLogForwarder CR must be openshift-logging.
    3
    Specify a name for the output.
    4
    Specify the type as "loki".
    5
    Specify the URL and port of the Loki system as a valid absolute URL. You can use the http (insecure) or https (secure HTTP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP Address. Loki’s default port for HTTP(S) communication is 3100.
    6
    For a secure connection, you can specify an https or http URL that you authenticate by specifying a secret.
    7
    For an https prefix, specify the name of the secret required by the endpoint for TLS communication. The secret must exist in the openshift-logging project, and must have keys of: tls.crt, tls.key, and ca-bundle.crt that point to the respective certificates that they represent. Otherwise, for http and https prefixes, you can specify a secret that contains a username and password. For more information, see the following "Example: Setting secret that contains a username and password."
    8
    Optional: Specify a meta-data key field to generate values for the TenantID field in Loki. For example, setting tenantKey: kubernetes.namespace_name uses the names of the Kubernetes namespaces as values for tenant IDs in Loki. To see which other log record fields you can specify, see the "Log Record Fields" link in the following "Additional resources" section.
    9
    Optional: Specify a list of meta-data field keys to replace the default Loki labels. Loki label names must match the regular expression [a-zA-Z_:][a-zA-Z0-9_:]*. Illegal characters in meta-data keys are replaced with _ to form the label name. For example, the kubernetes.labels.foo meta-data key becomes Loki label kubernetes_labels_foo. If you do not set labelKeys, the default value is: [log_type, kubernetes.namespace_name, kubernetes.pod_name, kubernetes_host]. Keep the set of labels small because Loki limits the size and number of labels allowed. See Configuring Loki, limits_config. You can still query based on any log record field using query filters.
    10
    Optional: Specify a name for the pipeline.
    11
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    12
    Specify the name of the output to use when forwarding logs with this pipeline.
    Note

    Because Loki requires log streams to be correctly ordered by timestamp, labelKeys always includes the kubernetes_host label set, even if you do not specify it. This inclusion ensures that each stream originates from a single host, which prevents timestamps from becoming disordered due to clock differences on different hosts.

  2. Create the CR object: 

    $ oc create -f <file-name>.yaml

10.13.1. Troubleshooting Loki "entry out of order" errors

If your Fluentd forwards a large block of messages to a Loki logging system that exceeds the rate limit, Loki to generates "entry out of order" errors. To fix this issue, you update some values in the Loki server configuration file, loki.yaml.

Note

loki.yaml is not available on Grafana-hosted Loki. This topic does not apply to Grafana-hosted Loki servers.

Conditions

  • The ClusterLogForwarder custom resource is configured to forward logs to Loki.

  • Your system sends a block of messages that is larger than 2 MB to Loki, such as: 

    "values":[["1630410392689800468","{\"kind\":\"Event\",\"apiVersion\":\
.......
......
......
......
\"received_at\":\"2021-08-31T11:46:32.800278+00:00\",\"version\":\"1.7.4 1.6.0\"}},\"@timestamp\":\"2021-08-31T11:46:32.799692+00:00\",\"viaq_index_name\":\"audit-write\",\"viaq_msg_id\":\"MzFjYjJkZjItNjY0MC00YWU4LWIwMTEtNGNmM2E5ZmViMGU4\",\"log_type\":\"audit\"}"]]}]}

  • When you enter oc logs -c fluentd, the Fluentd logs in your OpenShift Logging cluster show the following messages: 

    429 Too Many Requests Ingestion rate limit exceeded (limit: 8388608 bytes/sec) while attempting to ingest '2140' lines totaling '3285284' bytes

429 Too Many Requests Ingestion rate limit exceeded' or '500 Internal Server Error rpc error: code = ResourceExhausted desc = grpc: received message larger than max (5277702 vs. 4194304)'

  • When you open the logs on the Loki server, they display entry out of order messages like these: 

    ,\nentry with timestamp 2021-08-18 05:58:55.061936 +0000 UTC ignored, reason: 'entry out of order' for stream:

{fluentd_thread=\"flush_thread_0\", log_type=\"audit\"},\nentry with timestamp 2021-08-18 06:01:18.290229 +0000 UTC ignored, reason: 'entry out of order' for stream: {fluentd_thread="flush_thread_0", log_type="audit"}

Procedure

  1. Update the following fields in the loki.yaml configuration file on the Loki server with the values shown here:

    • grpc_server_max_recv_msg_size: 8388608
    • chunk_target_size: 8388608
    • ingestion_rate_mb: 8
    • ingestion_burst_size_mb: 16
  2. Apply the changes in loki.yaml to the Loki server.

Example loki.yaml file

auth_enabled: false

server:
  http_listen_port: 3100
  grpc_listen_port: 9096
  grpc_server_max_recv_msg_size: 8388608

ingester:
  wal:
    enabled: true
    dir: /tmp/wal
  lifecycler:
    address: 127.0.0.1
    ring:
      kvstore:
        store: inmemory
      replication_factor: 1
    final_sleep: 0s
  chunk_idle_period: 1h       # Any chunk not receiving new logs in this time will be flushed
  chunk_target_size: 8388608
  max_chunk_age: 1h           # All chunks will be flushed when they hit this age, default is 1h
  chunk_retain_period: 30s    # Must be greater than index read cache TTL if using an index cache (Default index read cache TTL is 5m)
  max_transfer_retries: 0     # Chunk transfers disabled

schema_config:
  configs:
    - from: 2020-10-24
      store: boltdb-shipper
      object_store: filesystem
      schema: v11
      index:
        prefix: index_
        period: 24h

storage_config:
  boltdb_shipper:
    active_index_directory: /tmp/loki/boltdb-shipper-active
    cache_location: /tmp/loki/boltdb-shipper-cache
    cache_ttl: 24h         # Can be increased for faster performance over longer query periods, uses more disk space
    shared_store: filesystem
  filesystem:
    directory: /tmp/loki/chunks

compactor:
  working_directory: /tmp/loki/boltdb-shipper-compactor
  shared_store: filesystem

limits_config:
  reject_old_samples: true
  reject_old_samples_max_age: 12h
  ingestion_rate_mb: 8
  ingestion_burst_size_mb: 16

chunk_store_config:
  max_look_back_period: 0s

table_manager:
  retention_deletes_enabled: false
  retention_period: 0s

ruler:
  storage:
    type: local
    local:
      directory: /tmp/loki/rules
  rule_path: /tmp/loki/rules-temp
  alertmanager_url: http://localhost:9093
  ring:
    kvstore:
      store: inmemory
  enable_api: true

Additional resources

Additional resources

10.14. Forwarding logs to Splunk

You can forward logs to the Splunk HTTP Event Collector (HEC) in addition to, or instead of, the internal default Red Hat OpenShift Service on AWS log store.

Note

Using this feature with Fluentd is not supported.

Prerequisites

  • Red Hat OpenShift Logging Operator 5.6 and higher
  • ClusterLogging instance with vector specified as collector
  • Base64 encoded Splunk HEC token

Procedure

  1. Create a secret using your Base64 encoded Splunk HEC token. 

    $ oc -n openshift-logging create secret generic vector-splunk-secret --from-literal hecToken=<HEC_Token>

  2. Create or edit the ClusterLogForwarder Custom Resource (CR) using the template below: 

      apiVersion: "logging.openshift.io/v1"
  kind: "ClusterLogForwarder"
  metadata:
    name: "instance" 1
    namespace: "openshift-logging" 2
  spec:
    outputs:
      - name: splunk-receiver 3
        secret:
          name: vector-splunk-secret 4
        type: splunk 5
        url: <http://your.splunk.hec.url:8088> 6
    pipelines: 7
      - inputRefs:
          - application
          - infrastructure
        name: 8
        outputRefs:
          - splunk-receiver 9
    1
    The name of the ClusterLogForwarder CR must be instance.
    2
    The namespace for the ClusterLogForwarder CR must be openshift-logging.
    3
    Specify a name for the output.
    4
    Specify the name of the secret that contains your HEC token.
    5
    Specify the output type as splunk.
    6
    Specify the URL (including port) of your Splunk HEC.
    7
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    8
    Optional: Specify a name for the pipeline.
    9
    Specify the name of the output to use when forwarding logs with this pipeline.

10.15. Forwarding logs over HTTP

Forwarding logs over HTTP is supported for both fluentd and vector collectors. To enable, specify http as the output type in the ClusterLogForwarder custom resource (CR).

Procedure

  • Create or edit the ClusterLogForwarder Custom Resource (CR) using the template below:

Example ClusterLogForwarder CR

apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogForwarder"
metadata:
  name: "instance"
  namespace: "openshift-logging"
spec:
  outputs:
    - name: httpout-app
      type: http
      url: 1
      http:
        headers: 2
          h1: v1
          h2: v2
        method: POST
      secret:
        name: 3
      tls:
        insecureSkipVerify: 4
  pipelines:
    - name:
      inputRefs:
        - application
      outputRefs:
        - 5

1
Destination address for logs.
2
Additional headers to send with the log record.
3
Secret name for destination credentials.
4
Values are either true or false.
5
This value should be the same as the output name.

10.16. Forwarding application logs from specific projects

You can use the Cluster Log Forwarder to send a copy of the application logs from specific projects to an external log aggregator. You can do this in addition to, or instead of, using the default Elasticsearch log store. You must also configure the external log aggregator to receive log data from Red Hat OpenShift Service on AWS.

To configure forwarding application logs from a project, you must create a ClusterLogForwarder custom resource (CR) with at least one input from a project, optional outputs for other log aggregators, and pipelines that use those inputs and outputs.

Prerequisites

  • You must have a logging server that is configured to receive the logging data using the specified protocol or format.

Procedure

  1. Create or edit a YAML file that defines the ClusterLogForwarder CR object: 

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  outputs:
   - name: fluentd-server-secure 3
     type: fluentdForward 4
     url: 'tls://fluentdserver.security.example.com:24224' 5
     secret: 6
        name: fluentd-secret
   - name: fluentd-server-insecure
     type: fluentdForward
     url: 'tcp://fluentdserver.home.example.com:24224'
  inputs: 7
   - name: my-app-logs
     application:
        namespaces:
        - my-project
  pipelines:
   - name: forward-to-fluentd-insecure 8
     inputRefs: 9
     - my-app-logs
     outputRefs: 10
     - fluentd-server-insecure
     parse: json 11
     labels:
       project: "my-project" 12
   - name: forward-to-fluentd-secure 13
     inputRefs:
     - application
     - audit
     - infrastructure
     outputRefs:
     - fluentd-server-secure
     - default
     labels:
       clusterId: "C1234"
    1
    The name of the ClusterLogForwarder CR must be instance.
    2
    The namespace for the ClusterLogForwarder CR must be openshift-logging.
    3
    Specify a name for the output.
    4
    Specify the output type: elasticsearch, fluentdForward, syslog, or kafka.
    5
    Specify the URL and port of the external log aggregator as a valid absolute URL. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP address.
    6
    If using a tls prefix, you must specify the name of the secret required by the endpoint for TLS communication. The secret must exist in the openshift-logging project and have tls.crt, tls.key, and ca-bundle.crt keys that each point to the certificates they represent.
    7
    Configuration for an input to filter application logs from the specified projects.
    8
    Configuration for a pipeline to use the input to send project application logs to an external Fluentd instance.
    9
    The my-app-logs input.
    10
    The name of the output to use.
    11
    Optional: Specify whether to forward structured JSON log entries as JSON objects in the structured field. The log entry must contain valid structured JSON; otherwise, OpenShift Logging removes the structured field and instead sends the log entry to the default index, app-00000x.
    12
    Optional: String. One or more labels to add to the logs.
    13
    Configuration for a pipeline to send logs to other log aggregators.
    • Optional: Specify a name for the pipeline.
    • Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    • Specify the name of the output to use when forwarding logs with this pipeline.
    • Optional: Specify the default output to forward logs to the internal Elasticsearch instance.
    • Optional: String. One or more labels to add to the logs.

  2. Create the CR object: 

    $ oc create -f <file-name>.yaml

10.17. Forwarding application logs from specific pods

As a cluster administrator, you can use Kubernetes pod labels to gather log data from specific pods and forward it to a log collector.

Suppose that you have an application composed of pods running alongside other pods in various namespaces. If those pods have labels that identify the application, you can gather and output their log data to a specific log collector.

To specify the pod labels, you use one or more matchLabels key-value pairs. If you specify multiple key-value pairs, the pods must match all of them to be selected.

Procedure

  1. Create or edit a YAML file that defines the ClusterLogForwarder CR object. In the file, specify the pod labels using simple equality-based selectors under inputs[].name.application.selector.matchLabels, as shown in the following example.

    Example ClusterLogForwarder CR YAML file

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  pipelines:
    - inputRefs: [ myAppLogData ] 3
      outputRefs: [ default ] 4
      parse: json 5
  inputs: 6
    - name: myAppLogData
      application:
        selector:
          matchLabels: 7
            environment: production
            app: nginx
        namespaces: 8
        - app1
        - app2
  outputs: 9
    - default
    ...

    1
    The name of the ClusterLogForwarder CR must be instance.
    2
    The namespace for the ClusterLogForwarder CR must be openshift-logging.
    3
    Specify one or more comma-separated values from inputs[].name.
    4
    Specify one or more comma-separated values from outputs[].
    5
    Optional: Specify whether to forward structured JSON log entries as JSON objects in the structured field. The log entry must contain valid structured JSON; otherwise, OpenShift Logging removes the structured field and instead sends the log entry to the default index, app-00000x.
    6
    Define a unique inputs[].name for each application that has a unique set of pod labels.
    7
    Specify the key-value pairs of pod labels whose log data you want to gather. You must specify both a key and value, not just a key. To be selected, the pods must match all the key-value pairs.
    8
    Optional: Specify one or more namespaces.
    9
    Specify one or more outputs to forward your log data to. The optional default output shown here sends log data to the internal Elasticsearch instance.
  2. Optional: To restrict the gathering of log data to specific namespaces, use inputs[].name.application.namespaces, as shown in the preceding example.

  3. Optional: You can send log data from additional applications that have different pod labels to the same pipeline.

    1. For each unique combination of pod labels, create an additional inputs[].name section similar to the one shown.
    2. Update the selectors to match the pod labels of this application.

    3. Add the new inputs[].name value to inputRefs. For example: 

      - inputRefs: [ myAppLogData, myOtherAppLogData ]

  4. Create the CR object: 

    $ oc create -f <file-name>.yaml

Additional resources

Additional resources

10.18. Troubleshooting log forwarding

When you create a ClusterLogForwarder custom resource (CR), if the Red Hat OpenShift Logging Operator does not redeploy the Fluentd pods automatically, you can delete the Fluentd pods to force them to redeploy.

Prerequisites

  • You have created a ClusterLogForwarder custom resource (CR) object.

Procedure

  • Delete the Fluentd pods to force them to redeploy. 

    $ oc delete pod --selector logging-infra=collector

Chapter 11. Enabling JSON logging

You can configure the Log Forwarding API to parse JSON strings into a structured object.

11.1. Parsing JSON logs

Logs including JSON logs are usually represented as a string inside the message field. That makes it hard for users to query specific fields inside a JSON document. OpenShift Logging’s Log Forwarding API enables you to parse JSON logs into a structured object and forward them to either OpenShift Logging-managed Elasticsearch or any other third-party system supported by the Log Forwarding API.

To illustrate how this works, suppose that you have the following structured JSON log entry.

Example structured JSON log entry

{"level":"info","name":"fred","home":"bedrock"}

Normally, the ClusterLogForwarder custom resource (CR) forwards that log entry in the message field. The message field contains the JSON-quoted string equivalent of the JSON log entry, as shown in the following example.

Example message field

{"message":"{\"level\":\"info\",\"name\":\"fred\",\"home\":\"bedrock\"",
 "more fields..."}

To enable parsing JSON log, you add parse: json to a pipeline in the ClusterLogForwarder CR, as shown in the following example.

Example snippet showing parse: json

pipelines:
- inputRefs: [ application ]
  outputRefs: myFluentd
  parse: json

When you enable parsing JSON logs by using parse: json, the CR copies the JSON-structured log entry in a structured field, as shown in the following example. This does not modify the original message field.

Example structured output containing the structured JSON log entry

{"structured": { "level": "info", "name": "fred", "home": "bedrock" },
 "more fields..."}

Important

If the log entry does not contain valid structured JSON, the structured field will be absent.

To enable parsing JSON logs for specific logging platforms, see Forwarding logs to third-party systems.

11.2. Configuring JSON log data for Elasticsearch

If your JSON logs follow more than one schema, storing them in a single index might cause type conflicts and cardinality problems. To avoid that, you must configure the ClusterLogForwarder custom resource (CR) to group each schema into a single output definition. This way, each schema is forwarded to a separate index.

Important

If you forward JSON logs to the default Elasticsearch instance managed by OpenShift Logging, it generates new indices based on your configuration. To avoid performance issues associated with having too many indices, consider keeping the number of possible schemas low by standardizing to common schemas.

Structure types

You can use the following structure types in the ClusterLogForwarder CR to construct index names for the Elasticsearch log store:

  • structuredTypeKey (string, optional) is the name of a message field. The value of that field, if present, is used to construct the index name.

    • kubernetes.labels.<key> is the Kubernetes pod label whose value is used to construct the index name.
    • openshift.labels.<key> is the pipeline.label.<key> element in the ClusterLogForwarder CR whose value is used to construct the index name.
    • kubernetes.container_name uses the container name to construct the index name.
  • structuredTypeName: (string, optional) If structuredTypeKey is not set or its key is not present, OpenShift Logging uses the value of structuredTypeName as the structured type. When you use both structuredTypeKey and structuredTypeName together, structuredTypeName provides a fallback index name if the key in structuredTypeKey is missing from the JSON log data.
Note

Although you can set the value of structuredTypeKey to any field shown in the "Log Record Fields" topic, the most useful fields are shown in the preceding list of structure types.

A structuredTypeKey: kubernetes.labels.<key> example

Suppose the following:

  • Your cluster is running application pods that produce JSON logs in two different formats, "apache" and "google".
  • The user labels these application pods with logFormat=apache and logFormat=google.
  • You use the following snippet in your ClusterLogForwarder CR YAML file. 
outputDefaults:
 elasticsearch:
    structuredTypeKey: kubernetes.labels.logFormat 1
    structuredTypeName: nologformat
pipelines:
- inputRefs: <application>
  outputRefs: default
  parse: json 2
1
Uses the value of the key-value pair that is formed by the Kubernetes logFormat label.
2
Enables parsing JSON logs.

In that case, the following structured log record goes to the app-apache-write index: 

{
  "structured":{"name":"fred","home":"bedrock"},
  "kubernetes":{"labels":{"logFormat": "apache", ...}}
}

And the following structured log record goes to the app-google-write index: 

{
  "structured":{"name":"wilma","home":"bedrock"},
  "kubernetes":{"labels":{"logFormat": "google", ...}}
}

A structuredTypeKey: openshift.labels.<key> example

Suppose that you use the following snippet in your ClusterLogForwarder CR YAML file. 

outputDefaults:
 elasticsearch:
    structuredTypeKey: openshift.labels.myLabel 1
    structuredTypeName: nologformat
pipelines:
 - name: application-logs
   inputRefs:
   - application
   - audit
   outputRefs:
   - elasticsearch-secure
   - default
   parse: json
   labels:
     myLabel: myValue 2
1
Uses the value of the key-value pair that is formed by the OpenShift myLabel label.
2
The myLabel element gives its string value, myValue, to the structured log record.

In that case, the following structured log record goes to the app-myValue-write index: 

{
  "structured":{"name":"fred","home":"bedrock"},
  "openshift":{"labels":{"myLabel": "myValue", ...}}
}

Additional considerations

  • The Elasticsearch index for structured records is formed by prepending "app-" to the structured type and appending "-write".
  • Unstructured records are not sent to the structured index. They are indexed as usual in the application, infrastructure, or audit indices.
  • If there is no non-empty structured type, forward an unstructured record with no structured field.

It is important not to overload Elasticsearch with too many indices. Only use distinct structured types for distinct log formats, not for each application or namespace. For example, most Apache applications use the same JSON log format and structured type, such as LogApache.

11.3. Forwarding JSON logs to the Elasticsearch log store

For an Elasticsearch log store, if your JSON log entries follow different schemas, configure the ClusterLogForwarder custom resource (CR) to group each JSON schema into a single output definition. This way, Elasticsearch uses a separate index for each schema.

Important

Because forwarding different schemas to the same index can cause type conflicts and cardinality problems, you must perform this configuration before you forward data to the Elasticsearch store.

To avoid performance issues associated with having too many indices, consider keeping the number of possible schemas low by standardizing to common schemas.

Procedure

  1. Add the following snippet to your ClusterLogForwarder CR YAML file. 

    outputDefaults:
 elasticsearch:
    structuredTypeKey: <log record field>
    structuredTypeName: <name>
pipelines:
- inputRefs:
  - application
  outputRefs: default
  parse: json
  2. Optional: Use structuredTypeKey to specify one of the log record fields, as described in the preceding topic, Configuring JSON log data for Elasticsearch. Otherwise, remove this line.

  3. Optional: Use structuredTypeName to specify a <name>, as described in the preceding topic, Configuring JSON log data for Elasticsearch. Otherwise, remove this line.

    Important

    To parse JSON logs, you must set either structuredTypeKey or structuredTypeName, or both structuredTypeKey and structuredTypeName.

  4. For inputRefs, specify which log types to forward by using that pipeline, such as application, infrastructure, or audit.
  5. Add the parse: json element to pipelines.

  6. Create the CR object: 

    $ oc create -f <file-name>.yaml

    The Red Hat OpenShift Logging Operator redeploys the Fluentd pods. However, if they do not redeploy, delete the Fluentd pods to force them to redeploy. 

    $ oc delete pod --selector logging-infra=collector

Additional resources

Chapter 12. Collecting and storing Kubernetes events

The Red Hat OpenShift Service on AWS Event Router is a pod that watches Kubernetes events and logs them for collection by the logging subsystem. You must manually deploy the Event Router.

The Event Router collects events from all projects and writes them to STDOUT. The collector then forwards those events to the store defined in the ClusterLogForwarder custom resource (CR).

Important

The Event Router adds additional load to Fluentd and can impact the number of other log messages that can be processed.

12.1. Deploying and configuring the Event Router

Use the following steps to deploy the Event Router into your cluster. You should always deploy the Event Router to the openshift-logging project to ensure it collects events from across the cluster.

The following Template object creates the service account, cluster role, and cluster role binding required for the Event Router. The template also configures and deploys the Event Router pod. You can use this template without making changes, or change the deployment object CPU and memory requests.

Prerequisites

  • You need proper permissions to create service accounts and update cluster role bindings. For example, you can run the following template with a user that has the cluster-admin role.
  • The logging subsystem for Red Hat OpenShift must be installed.

Procedure

  1. Create a template for the Event Router: 

    kind: Template
apiVersion: template.openshift.io/v1
metadata:
  name: eventrouter-template
  annotations:
    description: "A pod forwarding kubernetes events to OpenShift Logging stack."
    tags: "events,EFK,logging,cluster-logging"
objects:
  - kind: ServiceAccount 1
    apiVersion: v1
    metadata:
      name: eventrouter
      namespace: ${NAMESPACE}
  - kind: ClusterRole 2
    apiVersion: rbac.authorization.k8s.io/v1
    metadata:
      name: event-reader
    rules:
    - apiGroups: [""]
      resources: ["events"]
      verbs: ["get", "watch", "list"]
  - kind: ClusterRoleBinding  3
    apiVersion: rbac.authorization.k8s.io/v1
    metadata:
      name: event-reader-binding
    subjects:
    - kind: ServiceAccount
      name: eventrouter
      namespace: ${NAMESPACE}
    roleRef:
      kind: ClusterRole
      name: event-reader
  - kind: ConfigMap 4
    apiVersion: v1
    metadata:
      name: eventrouter
      namespace: ${NAMESPACE}
    data:
      config.json: |-
        {
          "sink": "stdout"
        }
  - kind: Deployment 5
    apiVersion: apps/v1
    metadata:
      name: eventrouter
      namespace: ${NAMESPACE}
      labels:
        component: "eventrouter"
        logging-infra: "eventrouter"
        provider: "openshift"
    spec:
      selector:
        matchLabels:
          component: "eventrouter"
          logging-infra: "eventrouter"
          provider: "openshift"
      replicas: 1
      template:
        metadata:
          labels:
            component: "eventrouter"
            logging-infra: "eventrouter"
            provider: "openshift"
          name: eventrouter
        spec:
          serviceAccount: eventrouter
          containers:
            - name: kube-eventrouter
              image: ${IMAGE}
              imagePullPolicy: IfNotPresent
              resources:
                requests:
                  cpu: ${CPU}
                  memory: ${MEMORY}
              volumeMounts:
              - name: config-volume
                mountPath: /etc/eventrouter
          volumes:
            - name: config-volume
              configMap:
                name: eventrouter
parameters:
  - name: IMAGE 6
    displayName: Image
    value: "registry.redhat.io/openshift-logging/eventrouter-rhel8:v0.4"
  - name: CPU  7
    displayName: CPU
    value: "100m"
  - name: MEMORY 8
    displayName: Memory
    value: "128Mi"
  - name: NAMESPACE
    displayName: Namespace
    value: "openshift-logging" 9
    1
    Creates a Service Account in the openshift-logging project for the Event Router.
    2
    Creates a ClusterRole to monitor for events in the cluster.
    3
    Creates a ClusterRoleBinding to bind the ClusterRole to the service account.
    4
    Creates a config map in the openshift-logging project to generate the required config.json file.
    5
    Creates a deployment in the openshift-logging project to generate and configure the Event Router pod.
    6
    Specifies the image, identified by a tag such as v0.4.
    7
    Specifies the minimum amount of CPU to allocate to the Event Router pod. Defaults to 100m.
    8
    Specifies the minimum amount of memory to allocate to the Event Router pod. Defaults to 128Mi.
    9
    Specifies the openshift-logging project to install objects in.

  2. Use the following command to process and apply the template: 

    $ oc process -f <templatefile> | oc apply -n openshift-logging -f -

    For example: 

    $ oc process -f eventrouter.yaml | oc apply -n openshift-logging -f -

    Example output

    serviceaccount/eventrouter created
clusterrole.authorization.openshift.io/event-reader created
clusterrolebinding.authorization.openshift.io/event-reader-binding created
configmap/eventrouter created
deployment.apps/eventrouter created

  3. Validate that the Event Router installed in the openshift-logging project:

    1. View the new Event Router pod: 

      $ oc get pods --selector  component=eventrouter -o name -n openshift-logging

      Example output

      pod/cluster-logging-eventrouter-d649f97c8-qvv8r

    2. View the events collected by the Event Router: 

      $ oc logs <cluster_logging_eventrouter_pod> -n openshift-logging

      For example: 

      $ oc logs cluster-logging-eventrouter-d649f97c8-qvv8r -n openshift-logging

      Example output

      {"verb":"ADDED","event":{"metadata":{"name":"openshift-service-catalog-controller-manager-remover.1632d931e88fcd8f","namespace":"openshift-service-catalog-removed","selfLink":"/api/v1/namespaces/openshift-service-catalog-removed/events/openshift-service-catalog-controller-manager-remover.1632d931e88fcd8f","uid":"787d7b26-3d2f-4017-b0b0-420db4ae62c0","resourceVersion":"21399","creationTimestamp":"2020-09-08T15:40:26Z"},"involvedObject":{"kind":"Job","namespace":"openshift-service-catalog-removed","name":"openshift-service-catalog-controller-manager-remover","uid":"fac9f479-4ad5-4a57-8adc-cb25d3d9cf8f","apiVersion":"batch/v1","resourceVersion":"21280"},"reason":"Completed","message":"Job completed","source":{"component":"job-controller"},"firstTimestamp":"2020-09-08T15:40:26Z","lastTimestamp":"2020-09-08T15:40:26Z","count":1,"type":"Normal"}}

      You can also use Kibana to view events by creating an index pattern using the Elasticsearch infra index.

Chapter 13. Updating OpenShift Logging

13.1. Supported Versions

For version compatibility and support information, see Red Hat OpenShift Container Platform Life Cycle Policy

To upgrade from cluster logging in Red Hat OpenShift Service on AWS version 4.6 and earlier to OpenShift Logging 5.x, you update the Red Hat OpenShift Service on AWS cluster to version 4.7 or 4.8. Then, you update the following operators:

  • From Elasticsearch Operator 4.x to OpenShift Elasticsearch Operator 5.x
  • From Cluster Logging Operator 4.x to Red Hat OpenShift Logging Operator 5.x

To upgrade from a previous version of OpenShift Logging to the current version, you update OpenShift Elasticsearch Operator and Red Hat OpenShift Logging Operator to their current versions.

13.2. Updating Logging to the current version

To update Logging to the current version, you change the subscriptions for the OpenShift Elasticsearch Operator and Red Hat OpenShift Logging Operator.

Important

You must update the OpenShift Elasticsearch Operator before you update the Red Hat OpenShift Logging Operator. You must also update both Operators to the same version.

If you update the Operators in the wrong order, Kibana does not update and the Kibana custom resource (CR) is not created. To work around this problem, you delete the Red Hat OpenShift Logging Operator pod. When the Red Hat OpenShift Logging Operator pod redeploys, it creates the Kibana CR and Kibana becomes available again.

Prerequisites

Procedure

  1. Update the OpenShift Elasticsearch Operator:

    1. In the Red Hat Hybrid Cloud Console, click Operators → Installed Operators.
    2. Select the openshift-Operators-redhat project.
    3. Click the OpenShift Elasticsearch Operator.
    4. Click Subscription → Channel.
    5. In the Change Subscription Update Channel window, select stable-5.x and click Save.
    6. Wait for a few seconds, then click Operators → Installed Operators.
    7. Verify that the OpenShift Elasticsearch Operator version is 5.x.x.
    8. Wait for the Status field to report Succeeded.

  2. Update the Red Hat OpenShift Logging Operator:

    1. In the Red Hat Hybrid Cloud Console, click Operators → Installed Operators.
    2. Select the openshift-logging project.
    3. Click the Red Hat OpenShift Logging Operator.
    4. Click Subscription → Channel.
    5. In the Change Subscription Update Channel window, select stable-5.x and click Save.
    6. Wait for a few seconds, then click Operators → Installed Operators.
    7. Verify that the Red Hat OpenShift Logging Operator version is 5.y.z
    8. Wait for the Status field to report Succeeded.

  3. Check the logging components:

    1. Ensure that all Elasticsearch pods are in the Ready status: 

      $ oc get pod -n openshift-logging --selector component=elasticsearch

      Example output

      NAME                                            READY   STATUS    RESTARTS   AGE
elasticsearch-cdm-1pbrl44l-1-55b7546f4c-mshhk   2/2     Running   0          31m
elasticsearch-cdm-1pbrl44l-2-5c6d87589f-gx5hk   2/2     Running   0          30m
elasticsearch-cdm-1pbrl44l-3-88df5d47-m45jc     2/2     Running   0          29m

    2. Ensure that the Elasticsearch cluster is healthy: 

      $ oc exec -n openshift-logging -c elasticsearch elasticsearch-cdm-1pbrl44l-1-55b7546f4c-mshhk -- health
      {
  "cluster_name" : "elasticsearch",
  "status" : "green",
}

    3. Ensure that the Elasticsearch cron jobs are created: 

      $ oc project openshift-logging
      $ oc get cronjob
      NAME                     SCHEDULE       SUSPEND   ACTIVE   LAST SCHEDULE   AGE
elasticsearch-im-app     */15 * * * *   False     0        <none>          56s
elasticsearch-im-audit   */15 * * * *   False     0        <none>          56s
elasticsearch-im-infra   */15 * * * *   False     0        <none>          56s

    4. Verify that the log store is updated to 5.x and the indices are green: 

      $ oc exec -c elasticsearch <any_es_pod_in_the_cluster> -- indices

    5. Verify that the output includes the app-00000x, infra-00000x, audit-00000x, .security indices.

      Example 13.1. Sample output with indices in a green status

      Tue Jun 30 14:30:54 UTC 2020
health status index                                                                 uuid                   pri rep docs.count docs.deleted store.size pri.store.size
green  open   infra-000008                                                          bnBvUFEXTWi92z3zWAzieQ   3 1       222195            0        289            144
green  open   infra-000004                                                          rtDSzoqsSl6saisSK7Au1Q   3 1       226717            0        297            148
green  open   infra-000012                                                          RSf_kUwDSR2xEuKRZMPqZQ   3 1       227623            0        295            147
green  open   .kibana_7                                                             1SJdCqlZTPWlIAaOUd78yg   1 1            4            0          0              0
green  open   infra-000010                                                          iXwL3bnqTuGEABbUDa6OVw   3 1       248368            0        317            158
green  open   infra-000009                                                          YN9EsULWSNaxWeeNvOs0RA   3 1       258799            0        337            168
green  open   infra-000014                                                          YP0U6R7FQ_GVQVQZ6Yh9Ig   3 1       223788            0        292            146
green  open   infra-000015                                                          JRBbAbEmSMqK5X40df9HbQ   3 1       224371            0        291            145
green  open   .orphaned.2020.06.30                                                  n_xQC2dWQzConkvQqei3YA   3 1            9            0          0              0
green  open   infra-000007                                                          llkkAVSzSOmosWTSAJM_hg   3 1       228584            0        296            148
green  open   infra-000005                                                          d9BoGQdiQASsS3BBFm2iRA   3 1       227987            0        297            148
green  open   infra-000003                                                          1-goREK1QUKlQPAIVkWVaQ   3 1       226719            0        295            147
green  open   .security                                                             zeT65uOuRTKZMjg_bbUc1g   1 1            5            0          0              0
green  open   .kibana-377444158_kubeadmin                                           wvMhDwJkR-mRZQO84K0gUQ   3 1            1            0          0              0
green  open   infra-000006                                                          5H-KBSXGQKiO7hdapDE23g   3 1       226676            0        295            147
green  open   infra-000001                                                          eH53BQ-bSxSWR5xYZB6lVg   3 1       341800            0        443            220
green  open   .kibana-6                                                             RVp7TemSSemGJcsSUmuf3A   1 1            4            0          0              0
green  open   infra-000011                                                          J7XWBauWSTe0jnzX02fU6A   3 1       226100            0        293            146
green  open   app-000001                                                            axSAFfONQDmKwatkjPXdtw   3 1       103186            0        126             57
green  open   infra-000016                                                          m9c1iRLtStWSF1GopaRyCg   3 1        13685            0         19              9
green  open   infra-000002                                                          Hz6WvINtTvKcQzw-ewmbYg   3 1       228994            0        296            148
green  open   infra-000013                                                          KR9mMFUpQl-jraYtanyIGw   3 1       228166            0        298            148
green  open   audit-000001                                                          eERqLdLmQOiQDFES1LBATQ   3 1            0            0          0              0

    6. Verify that the log collector is updated: 

      $ oc get ds collector -o json | grep collector

    7. Verify that the output includes a collectort container: 

      "containerName": "collector"

    8. Verify that the log visualizer is updated to 5.x using the Kibana CRD: 

      $ oc get kibana kibana -o json

    9. Verify that the output includes a Kibana pod with the ready status:

      Example 13.2. Sample output with a ready Kibana pod

      [
{
"clusterCondition": {
"kibana-5fdd766ffd-nb2jj": [
{
"lastTransitionTime": "2020-06-30T14:11:07Z",
"reason": "ContainerCreating",
"status": "True",
"type": ""
},
{
"lastTransitionTime": "2020-06-30T14:11:07Z",
"reason": "ContainerCreating",
"status": "True",
"type": ""
}
]
},
"deployment": "kibana",
"pods": {
"failed": [],
"notReady": []
"ready": []
},
"replicaSets": [
"kibana-5fdd766ffd"
],
"replicas": 1
}
]

Chapter 14. Viewing cluster dashboards

The Logging/Elasticsearch Nodes and Openshift Logging dashboards in the OpenShift Cluster Manager Hybrid Cloud Console contain in-depth details about your Elasticsearch instance and the individual Elasticsearch nodes that you can use to prevent and diagnose problems.

The OpenShift Logging dashboard contains charts that show details about your Elasticsearch instance at a cluster level, including cluster resources, garbage collection, shards in the cluster, and Fluentd statistics.

The Logging/Elasticsearch Nodes dashboard contains charts that show details about your Elasticsearch instance, many at node level, including details on indexing, shards, resources, and so forth.

14.1. Accessing the Elasticsearch and OpenShift Logging dashboards

You can view the Logging/Elasticsearch Nodes and OpenShift Logging dashboards in the OpenShift Cluster Manager Hybrid Cloud Console.

Procedure

To launch the dashboards:

  1. In the Red Hat OpenShift Service on AWS Red Hat Hybrid Cloud Console, click Observe → Dashboards.

  2. On the Dashboards page, select Logging/Elasticsearch Nodes or OpenShift Logging from the Dashboard menu.

    For the Logging/Elasticsearch Nodes dashboard, you can select the Elasticsearch node you want to view and set the data resolution.

    The appropriate dashboard is displayed, showing multiple charts of data.

  3. Optional: Select a different time range to display or refresh rate for the data from the Time Range and Refresh Interval menus.

For information on the dashboard charts, see About the OpenShift Logging dashboard and About the Logging/Elastisearch Nodes dashboard.

14.2. About the OpenShift Logging dashboard

The OpenShift Logging dashboard contains charts that show details about your Elasticsearch instance at a cluster-level that you can use to diagnose and anticipate problems.

Table 14.1. OpenShift Logging charts

MetricDescription

Elastic Cluster Status

The current Elasticsearch status:

  • ONLINE - Indicates that the Elasticsearch instance is online.
  • OFFLINE - Indicates that the Elasticsearch instance is offline.

Elastic Nodes

The total number of Elasticsearch nodes in the Elasticsearch instance.

Elastic Shards

The total number of Elasticsearch shards in the Elasticsearch instance.

Elastic Documents

The total number of Elasticsearch documents in the Elasticsearch instance.

Total Index Size on Disk

The total disk space that is being used for the Elasticsearch indices.

Elastic Pending Tasks

The total number of Elasticsearch changes that have not been completed, such as index creation, index mapping, shard allocation, or shard failure.

Elastic JVM GC time

The amount of time that the JVM spent executing Elasticsearch garbage collection operations in the cluster.

Elastic JVM GC Rate

The total number of times that JVM executed garbage activities per second.

Elastic Query/Fetch Latency Sum

  • Query latency: The average time each Elasticsearch search query takes to execute.
  • Fetch latency: The average time each Elasticsearch search query spends fetching data.

Fetch latency typically takes less time than query latency. If fetch latency is consistently increasing, it might indicate slow disks, data enrichment, or large requests with too many results.

Elastic Query Rate

The total queries executed against the Elasticsearch instance per second for each Elasticsearch node.

CPU

The amount of CPU used by Elasticsearch, Fluentd, and Kibana, shown for each component.

Elastic JVM Heap Used

The amount of JVM memory used. In a healthy cluster, the graph shows regular drops as memory is freed by JVM garbage collection.

Elasticsearch Disk Usage

The total disk space used by the Elasticsearch instance for each Elasticsearch node.

File Descriptors In Use

The total number of file descriptors used by Elasticsearch, Fluentd, and Kibana.

FluentD emit count

The total number of Fluentd messages per second for the Fluentd default output, and the retry count for the default output.

FluentD Buffer Availability

The percent of the Fluentd buffer that is available for chunks. A full buffer might indicate that Fluentd is not able to process the number of logs received.

Elastic rx bytes

The total number of bytes that Elasticsearch has received from FluentD, the Elasticsearch nodes, and other sources.

Elastic Index Failure Rate

The total number of times per second that an Elasticsearch index fails. A high rate might indicate an issue with indexing.

FluentD Output Error Rate

The total number of times per second that FluentD is not able to output logs.

14.3. Charts on the Logging/Elasticsearch nodes dashboard

The Logging/Elasticsearch Nodes dashboard contains charts that show details about your Elasticsearch instance, many at node-level, for further diagnostics.

Elasticsearch status
The Logging/Elasticsearch Nodes dashboard contains the following charts about the status of your Elasticsearch instance.

Table 14.2. Elasticsearch status fields

MetricDescription

Cluster status

The cluster health status during the selected time period, using the Elasticsearch green, yellow, and red statuses:

  • 0 - Indicates that the Elasticsearch instance is in green status, which means that all shards are allocated.
  • 1 - Indicates that the Elasticsearch instance is in yellow status, which means that replica shards for at least one shard are not allocated.
  • 2 - Indicates that the Elasticsearch instance is in red status, which means that at least one primary shard and its replicas are not allocated.

Cluster nodes

The total number of Elasticsearch nodes in the cluster.

Cluster data nodes

The number of Elasticsearch data nodes in the cluster.

Cluster pending tasks

The number of cluster state changes that are not finished and are waiting in a cluster queue, for example, index creation, index deletion, or shard allocation. A growing trend indicates that the cluster is not able to keep up with changes.

Elasticsearch cluster index shard status
Each Elasticsearch index is a logical group of one or more shards, which are basic units of persisted data. There are two types of index shards: primary shards, and replica shards. When a document is indexed into an index, it is stored in one of its primary shards and copied into every replica of that shard. The number of primary shards is specified when the index is created, and the number cannot change during index lifetime. You can change the number of replica shards at any time.

The index shard can be in several states depending on its lifecycle phase or events occurring in the cluster. When the shard is able to perform search and indexing requests, the shard is active. If the shard cannot perform these requests, the shard is non–active. A shard might be non-active if the shard is initializing, reallocating, unassigned, and so forth.

Index shards consist of a number of smaller internal blocks, called index segments, which are physical representations of the data. An index segment is a relatively small, immutable Lucene index that is created when Lucene commits newly-indexed data. Lucene, a search library used by Elasticsearch, merges index segments into larger segments in the background to keep the total number of segments low. If the process of merging segments is slower than the speed at which new segments are created, it could indicate a problem.

When Lucene performs data operations, such as a search operation, Lucene performs the operation against the index segments in the relevant index. For that purpose, each segment contains specific data structures that are loaded in the memory and mapped. Index mapping can have a significant impact on the memory used by segment data structures.

The Logging/Elasticsearch Nodes dashboard contains the following charts about the Elasticsearch index shards.

Table 14.3. Elasticsearch cluster shard status charts

MetricDescription

Cluster active shards

The number of active primary shards and the total number of shards, including replicas, in the cluster. If the number of shards grows higher, the cluster performance can start degrading.

Cluster initializing shards

The number of non-active shards in the cluster. A non-active shard is one that is initializing, being reallocated to a different node, or is unassigned. A cluster typically has non–active shards for short periods. A growing number of non–active shards over longer periods could indicate a problem.

Cluster relocating shards

The number of shards that Elasticsearch is relocating to a new node. Elasticsearch relocates nodes for multiple reasons, such as high memory use on a node or after a new node is added to the cluster.

Cluster unassigned shards

The number of unassigned shards. Elasticsearch shards might be unassigned for reasons such as a new index being added or the failure of a node.

Elasticsearch node metrics
Each Elasticsearch node has a finite amount of resources that can be used to process tasks. When all the resources are being used and Elasticsearch attempts to perform a new task, Elasticsearch put the tasks into a queue until some resources become available.

The Logging/Elasticsearch Nodes dashboard contains the following charts about resource usage for a selected node and the number of tasks waiting in the Elasticsearch queue.

Table 14.4. Elasticsearch node metric charts

MetricDescription

ThreadPool tasks

The number of waiting tasks in individual queues, shown by task type. A long–term accumulation of tasks in any queue could indicate node resource shortages or some other problem.

CPU usage

The amount of CPU being used by the selected Elasticsearch node as a percentage of the total CPU allocated to the host container.

Memory usage

The amount of memory being used by the selected Elasticsearch node.

Disk usage

The total disk space being used for index data and metadata on the selected Elasticsearch node.

Documents indexing rate

The rate that documents are indexed on the selected Elasticsearch node.

Indexing latency

The time taken to index the documents on the selected Elasticsearch node. Indexing latency can be affected by many factors, such as JVM Heap memory and overall load. A growing latency indicates a resource capacity shortage in the instance.

Search rate

The number of search requests run on the selected Elasticsearch node.

Search latency

The time taken to complete search requests on the selected Elasticsearch node. Search latency can be affected by many factors. A growing latency indicates a resource capacity shortage in the instance.

Documents count (with replicas)

The number of Elasticsearch documents stored on the selected Elasticsearch node, including documents stored in both the primary shards and replica shards that are allocated on the node.

Documents deleting rate

The number of Elasticsearch documents being deleted from any of the index shards that are allocated to the selected Elasticsearch node.

Documents merging rate

The number of Elasticsearch documents being merged in any of index shards that are allocated to the selected Elasticsearch node.

Elasticsearch node fielddata
Fielddata is an Elasticsearch data structure that holds lists of terms in an index and is kept in the JVM Heap. Because fielddata building is an expensive operation, Elasticsearch caches the fielddata structures. Elasticsearch can evict a fielddata cache when the underlying index segment is deleted or merged, or if there is not enough JVM HEAP memory for all the fielddata caches.

The Logging/Elasticsearch Nodes dashboard contains the following charts about Elasticsearch fielddata.

Table 14.5. Elasticsearch node fielddata charts

MetricDescription

Fielddata memory size

The amount of JVM Heap used for the fielddata cache on the selected Elasticsearch node.

Fielddata evictions

The number of fielddata structures that were deleted from the selected Elasticsearch node.

Elasticsearch node query cache
If the data stored in the index does not change, search query results are cached in a node-level query cache for reuse by Elasticsearch.

The Logging/Elasticsearch Nodes dashboard contains the following charts about the Elasticsearch node query cache.

Table 14.6. Elasticsearch node query charts

MetricDescription

Query cache size

The total amount of memory used for the query cache for all the shards allocated to the selected Elasticsearch node.

Query cache evictions

The number of query cache evictions on the selected Elasticsearch node.

Query cache hits

The number of query cache hits on the selected Elasticsearch node.

Query cache misses

The number of query cache misses on the selected Elasticsearch node.

Elasticsearch index throttling
When indexing documents, Elasticsearch stores the documents in index segments, which are physical representations of the data. At the same time, Elasticsearch periodically merges smaller segments into a larger segment as a way to optimize resource use. If the indexing is faster then the ability to merge segments, the merge process does not complete quickly enough, which can lead to issues with searches and performance. To prevent this situation, Elasticsearch throttles indexing, typically by reducing the number of threads allocated to indexing down to a single thread.

The Logging/Elasticsearch Nodes dashboard contains the following charts about Elasticsearch index throttling.

Table 14.7. Index throttling charts

MetricDescription

Indexing throttling

The amount of time that Elasticsearch has been throttling the indexing operations on the selected Elasticsearch node.

Merging throttling

The amount of time that Elasticsearch has been throttling the segment merge operations on the selected Elasticsearch node.

Node JVM Heap statistics
The Logging/Elasticsearch Nodes dashboard contains the following charts about JVM Heap operations.

Table 14.8. JVM Heap statistic charts

MetricDescription

Heap used

The amount of the total allocated JVM Heap space that is used on the selected Elasticsearch node.

GC count

The number of garbage collection operations that have been run on the selected Elasticsearch node, by old and young garbage collection.

GC time

The amount of time that the JVM spent running garbage collection operations on the selected Elasticsearch node, by old and young garbage collection.

Chapter 15. Troubleshooting Logging

15.1. Viewing OpenShift Logging status

You can view the status of the Red Hat OpenShift Logging Operator and for a number of logging subsystem components.

15.1.1. Viewing the status of the Red Hat OpenShift Logging Operator

You can view the status of your Red Hat OpenShift Logging Operator.

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.

Procedure

  1. Change to the openshift-logging project. 

    $ oc project openshift-logging

  2. To view the OpenShift Logging status:

    1. Get the OpenShift Logging status: 

      $ oc get clusterlogging instance -o yaml

      Example output

      apiVersion: logging.openshift.io/v1
kind: ClusterLogging

....

status:  1
  collection:
    logs:
      fluentdStatus:
        daemonSet: fluentd  2
        nodes:
          fluentd-2rhqp: ip-10-0-169-13.ec2.internal
          fluentd-6fgjh: ip-10-0-165-244.ec2.internal
          fluentd-6l2ff: ip-10-0-128-218.ec2.internal
          fluentd-54nx5: ip-10-0-139-30.ec2.internal
          fluentd-flpnn: ip-10-0-147-228.ec2.internal
          fluentd-n2frh: ip-10-0-157-45.ec2.internal
        pods:
          failed: []
          notReady: []
          ready:
          - fluentd-2rhqp
          - fluentd-54nx5
          - fluentd-6fgjh
          - fluentd-6l2ff
          - fluentd-flpnn
          - fluentd-n2frh
  logstore: 3
    elasticsearchStatus:
    - ShardAllocationEnabled:  all
      cluster:
        activePrimaryShards:    5
        activeShards:           5
        initializingShards:     0
        numDataNodes:           1
        numNodes:               1
        pendingTasks:           0
        relocatingShards:       0
        status:                 green
        unassignedShards:       0
      clusterName:             elasticsearch
      nodeConditions:
        elasticsearch-cdm-mkkdys93-1:
      nodeCount:  1
      pods:
        client:
          failed:
          notReady:
          ready:
          - elasticsearch-cdm-mkkdys93-1-7f7c6-mjm7c
        data:
          failed:
          notReady:
          ready:
          - elasticsearch-cdm-mkkdys93-1-7f7c6-mjm7c
        master:
          failed:
          notReady:
          ready:
          - elasticsearch-cdm-mkkdys93-1-7f7c6-mjm7c
visualization:  4
    kibanaStatus:
    - deployment: kibana
      pods:
        failed: []
        notReady: []
        ready:
        - kibana-7fb4fd4cc9-f2nls
      replicaSets:
      - kibana-7fb4fd4cc9
      replicas: 1

      1
      In the output, the cluster status fields appear in the status stanza.
      2
      Information on the Fluentd pods.
      3
      Information on the Elasticsearch pods, including Elasticsearch cluster health, green, yellow, or red.
      4
      Information on the Kibana pods.

15.1.1.1. Example condition messages

The following are examples of some condition messages from the Status.Nodes section of the OpenShift Logging instance.

A status message similar to the following indicates a node has exceeded the configured low watermark and no shard will be allocated to this node:

Example output

  nodes:
  - conditions:
    - lastTransitionTime: 2019-03-15T15:57:22Z
      message: Disk storage usage for node is 27.5gb (36.74%). Shards will be not
        be allocated on this node.
      reason: Disk Watermark Low
      status: "True"
      type: NodeStorage
    deploymentName: example-elasticsearch-clientdatamaster-0-1
    upgradeStatus: {}

A status message similar to the following indicates a node has exceeded the configured high watermark and shards will be relocated to other nodes:

Example output

  nodes:
  - conditions:
    - lastTransitionTime: 2019-03-15T16:04:45Z
      message: Disk storage usage for node is 27.5gb (36.74%). Shards will be relocated
        from this node.
      reason: Disk Watermark High
      status: "True"
      type: NodeStorage
    deploymentName: cluster-logging-operator
    upgradeStatus: {}

A status message similar to the following indicates the Elasticsearch node selector in the CR does not match any nodes in the cluster:

Example output

    Elasticsearch Status:
      Shard Allocation Enabled:  shard allocation unknown
      Cluster:
        Active Primary Shards:  0
        Active Shards:          0
        Initializing Shards:    0
        Num Data Nodes:         0
        Num Nodes:              0
        Pending Tasks:          0
        Relocating Shards:      0
        Status:                 cluster health unknown
        Unassigned Shards:      0
      Cluster Name:             elasticsearch
      Node Conditions:
        elasticsearch-cdm-mkkdys93-1:
          Last Transition Time:  2019-06-26T03:37:32Z
          Message:               0/5 nodes are available: 5 node(s) didn't match node selector.
          Reason:                Unschedulable
          Status:                True
          Type:                  Unschedulable
        elasticsearch-cdm-mkkdys93-2:
      Node Count:  2
      Pods:
        Client:
          Failed:
          Not Ready:
            elasticsearch-cdm-mkkdys93-1-75dd69dccd-f7f49
            elasticsearch-cdm-mkkdys93-2-67c64f5f4c-n58vl
          Ready:
        Data:
          Failed:
          Not Ready:
            elasticsearch-cdm-mkkdys93-1-75dd69dccd-f7f49
            elasticsearch-cdm-mkkdys93-2-67c64f5f4c-n58vl
          Ready:
        Master:
          Failed:
          Not Ready:
            elasticsearch-cdm-mkkdys93-1-75dd69dccd-f7f49
            elasticsearch-cdm-mkkdys93-2-67c64f5f4c-n58vl
          Ready:

A status message similar to the following indicates that the requested PVC could not bind to PV:

Example output

      Node Conditions:
        elasticsearch-cdm-mkkdys93-1:
          Last Transition Time:  2019-06-26T03:37:32Z
          Message:               pod has unbound immediate PersistentVolumeClaims (repeated 5 times)
          Reason:                Unschedulable
          Status:                True
          Type:                  Unschedulable

A status message similar to the following indicates that the Fluentd pods cannot be scheduled because the node selector did not match any nodes:

Example output

Status:
  Collection:
    Logs:
      Fluentd Status:
        Daemon Set:  fluentd
        Nodes:
        Pods:
          Failed:
          Not Ready:
          Ready:

15.1.2. Viewing the status of logging subsystem components

You can view the status for a number of logging subsystem components.

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.

Procedure

  1. Change to the openshift-logging project. 

    $ oc project openshift-logging

  2. View the status of the logging subsystem for Red Hat OpenShift environment: 

    $ oc describe deployment cluster-logging-operator

    Example output

    Name:                   cluster-logging-operator

....

Conditions:
  Type           Status  Reason
  ----           ------  ------
  Available      True    MinimumReplicasAvailable
  Progressing    True    NewReplicaSetAvailable

....

Events:
  Type    Reason             Age   From                   Message
  ----    ------             ----  ----                   -------
  Normal  ScalingReplicaSet  62m   deployment-controller  Scaled up replica set cluster-logging-operator-574b8987df to 1----

  3. View the status of the logging subsystem replica set:

    1. Get the name of a replica set:

      Example output

      $ oc get replicaset

      Example output

      NAME                                      DESIRED   CURRENT   READY   AGE
cluster-logging-operator-574b8987df       1         1         1       159m
elasticsearch-cdm-uhr537yu-1-6869694fb    1         1         1       157m
elasticsearch-cdm-uhr537yu-2-857b6d676f   1         1         1       156m
elasticsearch-cdm-uhr537yu-3-5b6fdd8cfd   1         1         1       155m
kibana-5bd5544f87                         1         1         1       157m

    2. Get the status of the replica set: 

      $ oc describe replicaset cluster-logging-operator-574b8987df

      Example output

      Name:           cluster-logging-operator-574b8987df

....

Replicas:       1 current / 1 desired
Pods Status:    1 Running / 0 Waiting / 0 Succeeded / 0 Failed

....

Events:
  Type    Reason            Age   From                   Message
  ----    ------            ----  ----                   -------
  Normal  SuccessfulCreate  66m   replicaset-controller  Created pod: cluster-logging-operator-574b8987df-qjhqv----

15.2. Viewing the status of the Elasticsearch log store

You can view the status of the OpenShift Elasticsearch Operator and for a number of Elasticsearch components.

15.2.1. Viewing the status of the log store

You can view the status of your log store.

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.

Procedure

  1. Change to the openshift-logging project. 

    $ oc project openshift-logging

  2. To view the status:

    1. Get the name of the log store instance: 

      $ oc get Elasticsearch

      Example output

      NAME            AGE
elasticsearch   5h9m

    2. Get the log store status: 

      $ oc get Elasticsearch <Elasticsearch-instance> -o yaml

      For example: 

      $ oc get Elasticsearch elasticsearch -n openshift-logging -o yaml

      The output includes information similar to the following:

      Example output

      status: 1
  cluster: 2
    activePrimaryShards: 30
    activeShards: 60
    initializingShards: 0
    numDataNodes: 3
    numNodes: 3
    pendingTasks: 0
    relocatingShards: 0
    status: green
    unassignedShards: 0
  clusterHealth: ""
  conditions: [] 3
  nodes: 4
  - deploymentName: elasticsearch-cdm-zjf34ved-1
    upgradeStatus: {}
  - deploymentName: elasticsearch-cdm-zjf34ved-2
    upgradeStatus: {}
  - deploymentName: elasticsearch-cdm-zjf34ved-3
    upgradeStatus: {}
  pods: 5
    client:
      failed: []
      notReady: []
      ready:
      - elasticsearch-cdm-zjf34ved-1-6d7fbf844f-sn422
      - elasticsearch-cdm-zjf34ved-2-dfbd988bc-qkzjz
      - elasticsearch-cdm-zjf34ved-3-c8f566f7c-t7zkt
    data:
      failed: []
      notReady: []
      ready:
      - elasticsearch-cdm-zjf34ved-1-6d7fbf844f-sn422
      - elasticsearch-cdm-zjf34ved-2-dfbd988bc-qkzjz
      - elasticsearch-cdm-zjf34ved-3-c8f566f7c-t7zkt
    master:
      failed: []
      notReady: []
      ready:
      - elasticsearch-cdm-zjf34ved-1-6d7fbf844f-sn422
      - elasticsearch-cdm-zjf34ved-2-dfbd988bc-qkzjz
      - elasticsearch-cdm-zjf34ved-3-c8f566f7c-t7zkt
  shardAllocationEnabled: all

      1
      In the output, the cluster status fields appear in the status stanza.
      2
      The status of the log store:
      • The number of active primary shards.
      • The number of active shards.
      • The number of shards that are initializing.
      • The number of log store data nodes.
      • The total number of log store nodes.
      • The number of pending tasks.
      • The log store status: green, red, yellow.
      • The number of unassigned shards.
      3
      Any status conditions, if present. The log store status indicates the reasons from the scheduler if a pod could not be placed. Any events related to the following conditions are shown:
      • Container Waiting for both the log store and proxy containers.
      • Container Terminated for both the log store and proxy containers.
      • Pod unschedulable. Also, a condition is shown for a number of issues; see Example condition messages.
      4
      The log store nodes in the cluster, with upgradeStatus.
      5
      The log store client, data, and master pods in the cluster, listed under 'failed`, notReady, or ready state.

15.2.1.1. Example condition messages

The following are examples of some condition messages from the Status section of the Elasticsearch instance.

The following status message indicates that a node has exceeded the configured low watermark, and no shard will be allocated to this node. 

status:
  nodes:
  - conditions:
    - lastTransitionTime: 2019-03-15T15:57:22Z
      message: Disk storage usage for node is 27.5gb (36.74%). Shards will be not
        be allocated on this node.
      reason: Disk Watermark Low
      status: "True"
      type: NodeStorage
    deploymentName: example-elasticsearch-cdm-0-1
    upgradeStatus: {}

The following status message indicates that a node has exceeded the configured high watermark, and shards will be relocated to other nodes. 

status:
  nodes:
  - conditions:
    - lastTransitionTime: 2019-03-15T16:04:45Z
      message: Disk storage usage for node is 27.5gb (36.74%). Shards will be relocated
        from this node.
      reason: Disk Watermark High
      status: "True"
      type: NodeStorage
    deploymentName: example-elasticsearch-cdm-0-1
    upgradeStatus: {}

The following status message indicates that the log store node selector in the CR does not match any nodes in the cluster: 

status:
    nodes:
    - conditions:
      - lastTransitionTime: 2019-04-10T02:26:24Z
        message: '0/8 nodes are available: 8 node(s) didn''t match node selector.'
        reason: Unschedulable
        status: "True"
        type: Unschedulable

The following status message indicates that the log store CR uses a non-existent persistent volume claim (PVC). 

status:
   nodes:
   - conditions:
     - last Transition Time:  2019-04-10T05:55:51Z
       message:               pod has unbound immediate PersistentVolumeClaims (repeated 5 times)
       reason:                Unschedulable
       status:                True
       type:                  Unschedulable

The following status message indicates that your log store cluster does not have enough nodes to support the redundancy policy. 

status:
  clusterHealth: ""
  conditions:
  - lastTransitionTime: 2019-04-17T20:01:31Z
    message: Wrong RedundancyPolicy selected. Choose different RedundancyPolicy or
      add more nodes with data roles
    reason: Invalid Settings
    status: "True"
    type: InvalidRedundancy

This status message indicates your cluster has too many control plane nodes: 

status:
  clusterHealth: green
  conditions:
    - lastTransitionTime: '2019-04-17T20:12:34Z'
      message: >-
        Invalid master nodes count. Please ensure there are no more than 3 total
        nodes with master roles
      reason: Invalid Settings
      status: 'True'
      type: InvalidMasters

The following status message indicates that Elasticsearch storage does not support the change you tried to make.

For example: 

status:
  clusterHealth: green
  conditions:
    - lastTransitionTime: "2021-05-07T01:05:13Z"
      message: Changing the storage structure for a custom resource is not supported
      reason: StorageStructureChangeIgnored
      status: 'True'
      type: StorageStructureChangeIgnored

The reason and type fields specify the type of unsupported change:

StorageClassNameChangeIgnored
Unsupported change to the storage class name.
StorageSizeChangeIgnored
Unsupported change the storage size.
StorageStructureChangeIgnored

Unsupported change between ephemeral and persistent storage structures.

Important

If you try to configure the ClusterLogging custom resource (CR) to switch from ephemeral to persistent storage, the OpenShift Elasticsearch Operator creates a persistent volume claim (PVC) but does not create a persistent volume (PV). To clear the StorageStructureChangeIgnored status, you must revert the change to the ClusterLogging CR and delete the PVC.

15.2.2. Viewing the status of the log store components

You can view the status for a number of the log store components.

Elasticsearch indices

You can view the status of the Elasticsearch indices.

  1. Get the name of an Elasticsearch pod: 

    $ oc get pods --selector component=elasticsearch -o name

    Example output

    pod/elasticsearch-cdm-1godmszn-1-6f8495-vp4lw
pod/elasticsearch-cdm-1godmszn-2-5769cf-9ms2n
pod/elasticsearch-cdm-1godmszn-3-f66f7d-zqkz7

  2. Get the status of the indices: 

    $ oc exec elasticsearch-cdm-4vjor49p-2-6d4d7db474-q2w7z -- indices

    Example output

    Defaulting container name to elasticsearch.
Use 'oc describe pod/elasticsearch-cdm-4vjor49p-2-6d4d7db474-q2w7z -n openshift-logging' to see all of the containers in this pod.

green  open   infra-000002                                                     S4QANnf1QP6NgCegfnrnbQ   3   1     119926            0        157             78
green  open   audit-000001                                                     8_EQx77iQCSTzFOXtxRqFw   3   1          0            0          0              0
green  open   .security                                                        iDjscH7aSUGhIdq0LheLBQ   1   1          5            0          0              0
green  open   .kibana_-377444158_kubeadmin                                     yBywZ9GfSrKebz5gWBZbjw   3   1          1            0          0              0
green  open   infra-000001                                                     z6Dpe__ORgiopEpW6Yl44A   3   1     871000            0        874            436
green  open   app-000001                                                       hIrazQCeSISewG3c2VIvsQ   3   1       2453            0          3              1
green  open   .kibana_1                                                        JCitcBMSQxKOvIq6iQW6wg   1   1          0            0          0              0
green  open   .kibana_-1595131456_user1                                        gIYFIEGRRe-ka0W3okS-mQ   3   1          1            0          0              0

Log store pods

You can view the status of the pods that host the log store.

  1. Get the name of a pod: 

    $ oc get pods --selector component=elasticsearch -o name

    Example output

    pod/elasticsearch-cdm-1godmszn-1-6f8495-vp4lw
pod/elasticsearch-cdm-1godmszn-2-5769cf-9ms2n
pod/elasticsearch-cdm-1godmszn-3-f66f7d-zqkz7

  2. Get the status of a pod: 

    $ oc describe pod elasticsearch-cdm-1godmszn-1-6f8495-vp4lw

    The output includes the following status information:

    Example output

    ....
Status:             Running

....

Containers:
  elasticsearch:
    Container ID:   cri-o://b7d44e0a9ea486e27f47763f5bb4c39dfd2
    State:          Running
      Started:      Mon, 08 Jun 2020 10:17:56 -0400
    Ready:          True
    Restart Count:  0
    Readiness:  exec [/usr/share/elasticsearch/probe/readiness.sh] delay=10s timeout=30s period=5s #success=1 #failure=3

....

  proxy:
    Container ID:  cri-o://3f77032abaddbb1652c116278652908dc01860320b8a4e741d06894b2f8f9aa1
    State:          Running
      Started:      Mon, 08 Jun 2020 10:18:38 -0400
    Ready:          True
    Restart Count:  0

....

Conditions:
  Type              Status
  Initialized       True
  Ready             True
  ContainersReady   True
  PodScheduled      True

....

Events:          <none>

Log storage pod deployment configuration

You can view the status of the log store deployment configuration.

  1. Get the name of a deployment configuration: 

    $ oc get deployment --selector component=elasticsearch -o name

    Example output

    deployment.extensions/elasticsearch-cdm-1gon-1
deployment.extensions/elasticsearch-cdm-1gon-2
deployment.extensions/elasticsearch-cdm-1gon-3

  2. Get the deployment configuration status: 

    $ oc describe deployment elasticsearch-cdm-1gon-1

    The output includes the following status information:

    Example output

    ....
  Containers:
   elasticsearch:
    Image:      registry.redhat.io/openshift-logging/elasticsearch6-rhel8
    Readiness:  exec [/usr/share/elasticsearch/probe/readiness.sh] delay=10s timeout=30s period=5s #success=1 #failure=3

....

Conditions:
  Type           Status   Reason
  ----           ------   ------
  Progressing    Unknown  DeploymentPaused
  Available      True     MinimumReplicasAvailable

....

Events:          <none>

Log store replica set

You can view the status of the log store replica set.

  1. Get the name of a replica set: 

    $ oc get replicaSet --selector component=elasticsearch -o name

replicaset.extensions/elasticsearch-cdm-1gon-1-6f8495
replicaset.extensions/elasticsearch-cdm-1gon-2-5769cf
replicaset.extensions/elasticsearch-cdm-1gon-3-f66f7d

  2. Get the status of the replica set: 

    $ oc describe replicaSet elasticsearch-cdm-1gon-1-6f8495

    The output includes the following status information:

    Example output

    ....
  Containers:
   elasticsearch:
    Image:      registry.redhat.io/openshift-logging/elasticsearch6-rhel8@sha256:4265742c7cdd85359140e2d7d703e4311b6497eec7676957f455d6908e7b1c25
    Readiness:  exec [/usr/share/elasticsearch/probe/readiness.sh] delay=10s timeout=30s period=5s #success=1 #failure=3

....

Events:          <none>

15.2.3. Elasticsearch cluster status

A dashboard in the Observe section of the OpenShift Cluster Manager Hybrid Cloud Console displays the status of the Elasticsearch cluster.

To get the status of the OpenShift Elasticsearch cluster, visit the dashboard in the Observe section of the OpenShift Cluster Manager Hybrid Cloud Console at <cluster_url>/monitoring/dashboards/grafana-dashboard-cluster-logging.

Elasticsearch status fields

eo_elasticsearch_cr_cluster_management_state

Shows whether the Elasticsearch cluster is in a managed or unmanaged state. For example: 

eo_elasticsearch_cr_cluster_management_state{state="managed"} 1
eo_elasticsearch_cr_cluster_management_state{state="unmanaged"} 0
eo_elasticsearch_cr_restart_total

Shows the number of times the Elasticsearch nodes have restarted for certificate restarts, rolling restarts, or scheduled restarts. For example: 

eo_elasticsearch_cr_restart_total{reason="cert_restart"} 1
eo_elasticsearch_cr_restart_total{reason="rolling_restart"} 1
eo_elasticsearch_cr_restart_total{reason="scheduled_restart"} 3
es_index_namespaces_total

Shows the total number of Elasticsearch index namespaces. For example: 

Total number of Namespaces.
es_index_namespaces_total 5
es_index_document_count

Shows the number of records for each namespace. For example: 

es_index_document_count{namespace="namespace_1"} 25
es_index_document_count{namespace="namespace_2"} 10
es_index_document_count{namespace="namespace_3"} 5

The "Secret Elasticsearch fields are either missing or empty" message

If Elasticsearch is missing the admin-cert, admin-key, logging-es.crt, or logging-es.key files, the dashboard shows a status message similar to the following example: 

message": "Secret \"elasticsearch\" fields are either missing or empty: [admin-cert, admin-key, logging-es.crt, logging-es.key]",
"reason": "Missing Required Secrets",

15.3. Understanding logging subsystem alerts

All of the logging collector alerts are listed on the Alerting UI of the OpenShift Cluster Manager Hybrid Cloud Console.

15.3.1. Viewing logging collector alerts

Alerts are shown in the OpenShift Cluster Manager Hybrid Cloud Console, on the Alerts tab of the Alerting UI. Alerts are in one of the following states:

  • Firing. The alert condition is true for the duration of the timeout. Click the Options menu at the end of the firing alert to view more information or silence the alert.
  • Pending The alert condition is currently true, but the timeout has not been reached.
  • Not Firing. The alert is not currently triggered.

Procedure

To view the logging subsystem and other Red Hat OpenShift Service on AWS alerts:

  1. In the Red Hat OpenShift Service on AWS console, click Observe → Alerting.
  2. Click the Alerts tab. The alerts are listed, based on the filters selected.

Additional resources

15.3.2. About logging collector alerts

The following alerts are generated by the logging collector. You can view these alerts in the OpenShift Cluster Manager Hybrid Cloud Console on the Alerts page of the Alerting UI.

Table 15.1. Fluentd Prometheus alerts

AlertMessageDescriptionSeverity

FluentDHighErrorRate

<value> of records have resulted in an error by fluentd <instance>.

The number of FluentD output errors is high, by default more than 10 in the previous 15 minutes.

Warning

FluentdNodeDown

Prometheus could not scrape fluentd <instance> for more than 10m.

Fluentd is reporting that Prometheus could not scrape a specific Fluentd instance.

Critical

FluentdQueueLengthIncreasing

In the last 12h, fluentd <instance> buffer queue length constantly increased more than 1. Current value is <value>.

Fluentd is reporting that the queue size is increasing.

Critical

FluentDVeryHighErrorRate

<value> of records have resulted in an error by fluentd <instance>.

The number of FluentD output errors is very high, by default more than 25 in the previous 15 minutes.

Critical

15.3.3. About Elasticsearch alerting rules

You can view these alerting rules in Prometheus.

Table 15.2. Alerting rules

AlertDescriptionSeverity

ElasticsearchClusterNotHealthy

The cluster health status has been RED for at least 2 minutes. The cluster does not accept writes, shards may be missing, or the master node hasn’t been elected yet.

Critical

ElasticsearchClusterNotHealthy

The cluster health status has been YELLOW for at least 20 minutes. Some shard replicas are not allocated.

Warning

ElasticsearchDiskSpaceRunningLow

The cluster is expected to be out of disk space within the next 6 hours.

Critical

ElasticsearchHighFileDescriptorUsage

The cluster is predicted to be out of file descriptors within the next hour.

Warning

ElasticsearchJVMHeapUseHigh

The JVM Heap usage on the specified node is high.

Alert

ElasticsearchNodeDiskWatermarkReached

The specified node has hit the low watermark due to low free disk space. Shards can not be allocated to this node anymore. You should consider adding more disk space to the node.

Info

ElasticsearchNodeDiskWatermarkReached

The specified node has hit the high watermark due to low free disk space. Some shards will be re-allocated to different nodes if possible. Make sure more disk space is added to the node or drop old indices allocated to this node.

Warning

ElasticsearchNodeDiskWatermarkReached

The specified node has hit the flood watermark due to low free disk space. Every index that has a shard allocated on this node is enforced a read-only block. The index block must be manually released when the disk use falls below the high watermark.

Critical

ElasticsearchJVMHeapUseHigh

The JVM Heap usage on the specified node is too high.

Alert

ElasticsearchWriteRequestsRejectionJumps

Elasticsearch is experiencing an increase in write rejections on the specified node. This node might not be keeping up with the indexing speed.

Warning

AggregatedLoggingSystemCPUHigh

The CPU used by the system on the specified node is too high.

Alert

ElasticsearchProcessCPUHigh

The CPU used by Elasticsearch on the specified node is too high.

Alert

15.4. Collecting logging data for Red Hat Support

When opening a support case, it is helpful to provide debugging information about your cluster to Red Hat Support.

The must-gather tool enables you to collect diagnostic information for project-level resources, cluster-level resources, and each of the logging subsystem components.

For prompt support, supply diagnostic information for both Red Hat OpenShift Service on AWS and OpenShift Logging.

Note

Do not use the hack/logging-dump.sh script. The script is no longer supported and does not collect data.

15.4.1. About the must-gather tool

The oc adm must-gather CLI command collects the information from your cluster that is most likely needed for debugging issues.

For your logging subsystem, must-gather collects the following information:

  • Project-level resources, including pods, configuration maps, service accounts, roles, role bindings, and events at the project level
  • Cluster-level resources, including nodes, roles, and role bindings at the cluster level
  • OpenShift Logging resources in the openshift-logging and openshift-operators-redhat namespaces, including health status for the log collector, the log store, and the log visualizer

When you run oc adm must-gather, a new pod is created on the cluster. The data is collected on that pod and saved in a new directory that starts with must-gather.local. This directory is created in the current working directory.

15.4.2. Prerequisites

  • The logging subsystem and Elasticsearch must be installed.

15.4.3. Collecting OpenShift Logging data

You can use the oc adm must-gather CLI command to collect information about your logging subsystem.

Procedure

To collect logging subsystem information with must-gather:

  1. Navigate to the directory where you want to store the must-gather information.

  2. Run the oc adm must-gather command against the OpenShift Logging image: 

    $ oc adm must-gather --image=$(oc -n openshift-logging get deployment.apps/cluster-logging-operator -o jsonpath='{.spec.template.spec.containers[?(@.name == "cluster-logging-operator")].image}')

    The must-gather tool creates a new directory that starts with must-gather.local within the current directory. For example: must-gather.local.4157245944708210408.

  3. Create a compressed file from the must-gather directory that was just created. For example, on a computer that uses a Linux operating system, run the following command: 

    $ tar -cvaf must-gather.tar.gz must-gather.local.4157245944708210408
  4. Attach the compressed file to your support case on the Red Hat Customer Portal.

15.5. Troubleshooting for Critical Alerts

15.5.1. Elasticsearch Cluster Health is Red

At least one primary shard and its replicas are not allocated to a node.

Troubleshooting

  1. Check the Elasticsearch cluster health and verify that the cluster status is red. 

    oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- health

  2. List the nodes that have joined the cluster. 

    oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/nodes?v

  3. List the Elasticsearch pods and compare them with the nodes in the command output from the previous step. 

    oc -n openshift-logging get pods -l component=elasticsearch

  4. If some of the Elasticsearch nodes have not joined the cluster, perform the following steps.

    1. Confirm that Elasticsearch has an elected control plane node. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/master?v

    2. Review the pod logs of the elected control plane node for issues. 

      oc logs <elasticsearch_master_pod_name> -c elasticsearch -n openshift-logging

    3. Review the logs of nodes that have not joined the cluster for issues. 

      oc logs <elasticsearch_node_name> -c elasticsearch -n openshift-logging

  5. If all the nodes have joined the cluster, perform the following steps, check if the cluster is in the process of recovering. 

    oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/recovery?active_only=true

    If there is no command output, the recovery process might be delayed or stalled by pending tasks.

  6. Check if there are pending tasks. 

    oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- health |grep  number_of_pending_tasks

  7. If there are pending tasks, monitor their status.

    If their status changes and indicates that the cluster is recovering, continue waiting. The recovery time varies according to the size of the cluster and other factors.

    Otherwise, if the status of the pending tasks does not change, this indicates that the recovery has stalled.

  8. If it seems like the recovery has stalled, check if cluster.routing.allocation.enable is set to none. 

    oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cluster/settings?pretty

  9. If cluster.routing.allocation.enable is set to none, set it to all. 

    oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cluster/settings?pretty -X PUT -d '{"persistent": {"cluster.routing.allocation.enable":"all"}}'

  10. Check which indices are still red. 

    oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/indices?v

  11. If any indices are still red, try to clear them by performing the following steps.

    1. Clear the cache. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name>/_cache/clear?pretty

    2. Increase the max allocation retries. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name>/_settings?pretty -X PUT -d '{"index.allocation.max_retries":10}'

    3. Delete all the scroll items. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_search/scroll/_all -X DELETE

    4. Increase the timeout. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name>/_settings?pretty -X PUT -d '{"index.unassigned.node_left.delayed_timeout":"10m"}'

  12. If the preceding steps do not clear the red indices, delete the indices individually.

    1. Identify the red index name. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cat/indices?v

    2. Delete the red index. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_red_index_name> -X DELETE

  13. If there are no red indices and the cluster status is red, check for a continuous heavy processing load on a data node.

    1. Check if the Elasticsearch JVM Heap usage is high. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_nodes/stats?pretty

      In the command output, review the node_name.jvm.mem.heap_used_percent field to determine the JVM Heap usage.

    2. Check for high CPU utilization.

Additional resources

15.5.2. Elasticsearch Cluster Health is Yellow

Replica shards for at least one primary shard are not allocated to nodes.

Troubleshooting

  1. Increase the node count by adjusting nodeCount in the ClusterLogging CR.

Additional resources

15.5.3. Elasticsearch Node Disk Low Watermark Reached

Elasticsearch does not allocate shards to nodes that reach the low watermark.

Troubleshooting

  1. Identify the node on which Elasticsearch is deployed. 

    oc -n openshift-logging get po -o wide

  2. Check if there are unassigned shards. 

    oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cluster/health?pretty | grep unassigned_shards

  3. If there are unassigned shards, check the disk space on each node. 

    for pod in `oc -n openshift-logging get po -l component=elasticsearch -o jsonpath='{.items[*].metadata.name}'`; do echo $pod; oc -n openshift-logging exec -c elasticsearch $pod -- df -h /elasticsearch/persistent; done

  4. Check the nodes.node_name.fs field to determine the free disk space on that node.

    If the used disk percentage is above 85%, the node has exceeded the low watermark, and shards can no longer be allocated to this node.

  5. Try to increase the disk space on all nodes.
  6. If increasing the disk space is not possible, try adding a new data node to the cluster.

  7. If adding a new data node is problematic, decrease the total cluster redundancy policy.

    1. Check the current redundancyPolicy. 

      oc -n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}'
      Note

      If you are using a ClusterLogging CR, enter: 

      oc -n openshift-logging get cl -o jsonpath='{.items[*].spec.logStore.elasticsearch.redundancyPolicy}'
    2. If the cluster redundancyPolicy is higher than SingleRedundancy, set it to SingleRedundancy and save this change.

  8. If the preceding steps do not fix the issue, delete the old indices.

    1. Check the status of all indices on Elasticsearch. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- indices
    2. Identify an old index that can be deleted.

    3. Delete the index. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name> -X DELETE

Additional resources

15.5.4. Elasticsearch Node Disk High Watermark Reached

Elasticsearch attempts to relocate shards away from a node that has reached the high watermark.

Troubleshooting

  1. Identify the node on which Elasticsearch is deployed. 

    oc -n openshift-logging get po -o wide

  2. Check the disk space on each node. 

    for pod in `oc -n openshift-logging get po -l component=elasticsearch -o jsonpath='{.items[*].metadata.name}'`; do echo $pod; oc -n openshift-logging exec -c elasticsearch $pod -- df -h /elasticsearch/persistent; done

  3. Check if the cluster is rebalancing. 

    oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_cluster/health?pretty | grep relocating_shards

    If the command output shows relocating shards, the High Watermark has been exceeded. The default value of the High Watermark is 90%.

    The shards relocate to a node with low disk usage that has not crossed any watermark threshold limits.

  4. To allocate shards to a particular node, free up some space.
  5. Try to increase the disk space on all nodes.
  6. If increasing the disk space is not possible, try adding a new data node to the cluster.

  7. If adding a new data node is problematic, decrease the total cluster redundancy policy.

    1. Check the current redundancyPolicy. 

      oc -n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}'
      Note

      If you are using a ClusterLogging CR, enter: 

      oc -n openshift-logging get cl -o jsonpath='{.items[*].spec.logStore.elasticsearch.redundancyPolicy}'
    2. If the cluster redundancyPolicy is higher than SingleRedundancy, set it to SingleRedundancy and save this change.

  8. If the preceding steps do not fix the issue, delete the old indices.

    1. Check the status of all indices on Elasticsearch. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- indices
    2. Identify an old index that can be deleted.

    3. Delete the index. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name> -X DELETE

Additional resources

15.5.5. Elasticsearch Node Disk Flood Watermark Reached

Elasticsearch enforces a read-only index block on every index that has both of these conditions:

  • One or more shards are allocated to the node.
  • One or more disks exceed the flood stage.

Troubleshooting

  1. Check the disk space of the Elasticsearch node. 

    for pod in `oc -n openshift-logging get po -l component=elasticsearch -o jsonpath='{.items[*].metadata.name}'`; do echo $pod; oc -n openshift-logging exec -c elasticsearch $pod -- df -h /elasticsearch/persistent; done

    Check the nodes.node_name.fs field to determine the free disk space on that node.

  2. If the used disk percentage is above 95%, it signifies that the node has crossed the flood watermark. Writing is blocked for shards allocated on this particular node.
  3. Try to increase the disk space on all nodes.
  4. If increasing the disk space is not possible, try adding a new data node to the cluster.

  5. If adding a new data node is problematic, decrease the total cluster redundancy policy.

    1. Check the current redundancyPolicy. 

      oc -n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}'
      Note

      If you are using a ClusterLogging CR, enter: 

      oc -n openshift-logging get cl -o jsonpath='{.items[*].spec.logStore.elasticsearch.redundancyPolicy}'
    2. If the cluster redundancyPolicy is higher than SingleRedundancy, set it to SingleRedundancy and save this change.

  6. If the preceding steps do not fix the issue, delete the old indices.

    1. Check the status of all indices on Elasticsearch. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- indices
    2. Identify an old index that can be deleted.

    3. Delete the index. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name> -X DELETE

  7. Continue freeing up and monitoring the disk space until the used disk space drops below 90%. Then, unblock write to this particular node. 

    oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=_all/_settings?pretty -X PUT -d '{"index.blocks.read_only_allow_delete": null}'

Additional resources

15.5.6. Elasticsearch JVM Heap Use is High

The Elasticsearch node JVM Heap memory used is above 75%.

Troubleshooting

Consider increasing the heap size.

15.5.7. Aggregated Logging System CPU is High

System CPU usage on the node is high.

Troubleshooting

Check the CPU of the cluster node. Consider allocating more CPU resources to the node.

15.5.8. Elasticsearch Process CPU is High

Elasticsearch process CPU usage on the node is high.

Troubleshooting

Check the CPU of the cluster node. Consider allocating more CPU resources to the node.

15.5.9. Elasticsearch Disk Space is Running Low

The Elasticsearch Cluster is predicted to be out of disk space within the next 6 hours based on current disk usage.

Troubleshooting

  1. Get the disk space of the Elasticsearch node. 

    for pod in `oc -n openshift-logging get po -l component=elasticsearch -o jsonpath='{.items[*].metadata.name}'`; do echo $pod; oc -n openshift-logging exec -c elasticsearch $pod -- df -h /elasticsearch/persistent; done
  2. In the command output, check the nodes.node_name.fs field to determine the free disk space on that node.
  3. Try to increase the disk space on all nodes.
  4. If increasing the disk space is not possible, try adding a new data node to the cluster.

  5. If adding a new data node is problematic, decrease the total cluster redundancy policy.

    1. Check the current redundancyPolicy. 

      oc -n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}'
      Note

      If you are using a ClusterLogging CR, enter: 

      oc -n openshift-logging get cl -o jsonpath='{.items[*].spec.logStore.elasticsearch.redundancyPolicy}'
    2. If the cluster redundancyPolicy is higher than SingleRedundancy, set it to SingleRedundancy and save this change.

  6. If the preceding steps do not fix the issue, delete the old indices.

    1. Check the status of all indices on Elasticsearch. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- indices
    2. Identify an old index that can be deleted.

    3. Delete the index. 

      oc exec -n openshift-logging -c elasticsearch <elasticsearch_pod_name> -- es_util --query=<elasticsearch_index_name> -X DELETE

Additional resources

15.5.10. Elasticsearch FileDescriptor Usage is high

Based on current usage trends, the predicted number of file descriptors on the node is insufficient.

Troubleshooting

Check and, if needed, configure the value of max_file_descriptors for each node, as described in the Elasticsearch File descriptors topic.

Additional resources

Chapter 16. Uninstalling OpenShift Logging

You can remove the logging subsystem from your Red Hat OpenShift Service on AWS cluster.

16.1. Uninstalling the logging subsystem for Red Hat OpenShift

You can stop log aggregation by deleting the ClusterLogging custom resource (CR). After deleting the CR, there are other logging subsystem components that remain, which you can optionally remove.

Deleting the ClusterLogging CR does not remove the persistent volume claims (PVCs). To preserve or delete the remaining PVCs, persistent volumes (PVs), and associated data, you must take further action.

Prerequisites

  • The Red Hat OpenShift Logging and Elasticsearch Operators must be installed.

Procedure

To remove OpenShift Logging:

  1. Use the OpenShift Cluster Manager Hybrid Cloud Console to remove the ClusterLogging CR:

    1. Switch to the Administration → Custom Resource Definitions page.
    2. On the Custom Resource Definitions page, click ClusterLogging.
    3. On the Custom Resource Definition Details page, click Instances.
    4. Click the Options menu kebab next to the instance and select Delete ClusterLogging.

  2. Optional: Delete the custom resource definitions (CRD):

    1. Switch to the Administration → Custom Resource Definitions page.
    2. Click the Options menu kebab next to ClusterLogForwarder and select Delete Custom Resource Definition.
    3. Click the Options menu kebab next to ClusterLogging and select Delete Custom Resource Definition.
    4. Click the Options menu kebab next to Elasticsearch and select Delete Custom Resource Definition.

  3. Optional: Remove the Red Hat OpenShift Logging Operator and OpenShift Elasticsearch Operator:

    1. Switch to the Operators → Installed Operators page.
    2. Click the Options menu kebab next to the Red Hat OpenShift Logging Operator and select Uninstall Operator.
    3. Click the Options menu kebab next to the OpenShift Elasticsearch Operator and select Uninstall Operator.

  4. Optional: Remove the OpenShift Logging and Elasticsearch projects.

    1. Switch to the Home → Projects page.
    2. Click the Options menu kebab next to the openshift-logging project and select Delete Project.
    3. Confirm the deletion by typing openshift-logging in the dialog box and click Delete.

    4. Click the Options menu kebab next to the openshift-operators-redhat project and select Delete Project.

      Important

      Do not delete the openshift-operators-redhat project if other global operators are installed in this namespace.

    5. Confirm the deletion by typing openshift-operators-redhat in the dialog box and click Delete.
  5. To keep the PVCs for reuse with other pods, keep the labels or PVC names that you need to reclaim the PVCs.

  6. Optional: If you do not want to keep the PVCs, you can delete them.

    Warning

    Releasing or deleting PVCs can delete PVs and cause data loss.

    1. Switch to the Storage → Persistent Volume Claims page.
    2. Click the Options menu kebab next to each PVC and select Delete Persistent Volume Claim.
    3. If you want to recover storage space, you can delete the PVs.

Additional resources

Chapter 17. Log Record Fields

The following fields can be present in log records exported by the logging subsystem. Although log records are typically formatted as JSON objects, the same data model can be applied to other encodings.

To search these fields from Elasticsearch and Kibana, use the full dotted field name when searching. For example, with an Elasticsearch /_search URL, to look for a Kubernetes pod name, use /_search/q=kubernetes.pod_name:name-of-my-pod.

The top level fields may be present in every record.

Chapter 18. message

The original log entry text, UTF-8 encoded. This field may be absent or empty if a non-empty structured field is present. See the description of structured for more.

Data type

text

Example value

HAPPY

Chapter 19. structured

Original log entry as a structured object. This field may be present if the forwarder was configured to parse structured JSON logs. If the original log entry was a valid structured log, this field will contain an equivalent JSON structure. Otherwise this field will be empty or absent, and the message field will contain the original log message. The structured field can have any subfields that are included in the log message, there are no restrictions defined here.

Data type

group

Example value

map[message:starting fluentd worker pid=21631 ppid=21618 worker=0 pid:21631 ppid:21618 worker:0]

Chapter 20. @timestamp

A UTC value that marks when the log payload was created or, if the creation time is not known, when the log payload was first collected. The “@” prefix denotes a field that is reserved for a particular use. By default, most tools look for “@timestamp” with ElasticSearch.

Data type

date

Example value

2015-01-24 14:06:05.071000000 Z

Chapter 21. hostname

The name of the host where this log message originated. In a Kubernetes cluster, this is the same as kubernetes.host.

Data type

keyword

Chapter 22. ipaddr4

The IPv4 address of the source server. Can be an array.

Data type

ip

Chapter 23. ipaddr6

The IPv6 address of the source server, if available. Can be an array.

Data type

ip

Chapter 24. level

The logging level from various sources, including rsyslog(severitytext property), a Python logging module, and others.

The following values come from syslog.h, and are preceded by their numeric equivalents:

  • 0 = emerg, system is unusable.
  • 1 = alert, action must be taken immediately.
  • 2 = crit, critical conditions.
  • 3 = err, error conditions.
  • 4 = warn, warning conditions.
  • 5 = notice, normal but significant condition.
  • 6 = info, informational.
  • 7 = debug, debug-level messages.

The two following values are not part of syslog.h but are widely used:

  • 8 = trace, trace-level messages, which are more verbose than debug messages.
  • 9 = unknown, when the logging system gets a value it doesn’t recognize.

Map the log levels or priorities of other logging systems to their nearest match in the preceding list. For example, from python logging, you can match CRITICAL with crit, ERROR with err, and so on.

Data type

keyword

Example value

info

Chapter 25. pid

The process ID of the logging entity, if available.

Data type

keyword

Chapter 26. service

The name of the service associated with the logging entity, if available. For example, syslog’s APP-NAME and rsyslog’s programname properties are mapped to the service field.

Data type

keyword

Chapter 27. tags

Optional. An operator-defined list of tags placed on each log by the collector or normalizer. The payload can be a string with whitespace-delimited string tokens or a JSON list of string tokens.

Data type

text

Chapter 28. file

The path to the log file from which the collector reads this log entry. Normally, this is a path in the /var/log file system of a cluster node.

Data type

text

Chapter 29. offset

The offset value. Can represent bytes to the start of the log line in the file (zero- or one-based), or log line numbers (zero- or one-based), so long as the values are strictly monotonically increasing in the context of a single log file. The values are allowed to wrap, representing a new version of the log file (rotation).

Data type

long

Chapter 30. kubernetes

The namespace for Kubernetes-specific metadata

Data type

group

30.1. kubernetes.pod_name

The name of the pod

Data type

keyword

30.2. kubernetes.pod_id

The Kubernetes ID of the pod

Data type

keyword

30.3. kubernetes.namespace_name

The name of the namespace in Kubernetes

Data type

keyword

30.4. kubernetes.namespace_id

The ID of the namespace in Kubernetes

Data type

keyword

30.5. kubernetes.host

The Kubernetes node name

Data type

keyword

30.6. kubernetes.container_name

The name of the container in Kubernetes

Data type

keyword

30.7. kubernetes.annotations

Annotations associated with the Kubernetes object

Data type

group

30.8. kubernetes.labels

Labels present on the original Kubernetes Pod

Data type

group

30.9. kubernetes.event

The Kubernetes event obtained from the Kubernetes master API. This event description loosely follows type Event in Event v1 core.

Data type

group

30.9.1. kubernetes.event.verb

The type of event, ADDED, MODIFIED, or DELETED

Data type

keyword

Example value

ADDED

30.9.2. kubernetes.event.metadata

Information related to the location and time of the event creation

Data type

group

30.9.2.1. kubernetes.event.metadata.name

The name of the object that triggered the event creation

Data type

keyword

Example value

java-mainclass-1.14d888a4cfc24890

30.9.2.2. kubernetes.event.metadata.namespace

The name of the namespace where the event originally occurred. Note that it differs from kubernetes.namespace_name, which is the namespace where the eventrouter application is deployed.

Data type

keyword

Example value

default

30.9.2.4. kubernetes.event.metadata.uid

The unique ID of the event

Data type

keyword

Example value

d828ac69-7b58-11e7-9cf5-5254002f560c

30.9.2.5. kubernetes.event.metadata.resourceVersion

A string that identifies the server’s internal version of the event. Clients can use this string to determine when objects have changed.

Data type

integer

Example value

311987

30.9.3. kubernetes.event.involvedObject

The object that the event is about.

Data type

group

30.9.3.1. kubernetes.event.involvedObject.kind

The type of object

Data type

keyword

Example value

ReplicationController

30.9.3.2. kubernetes.event.involvedObject.namespace

The namespace name of the involved object. Note that it may differ from kubernetes.namespace_name, which is the namespace where the eventrouter application is deployed.

Data type

keyword

Example value

default

30.9.3.3. kubernetes.event.involvedObject.name

The name of the object that triggered the event

Data type

keyword

Example value

java-mainclass-1

30.9.3.4. kubernetes.event.involvedObject.uid

The unique ID of the object

Data type

keyword

Example value

e6bff941-76a8-11e7-8193-5254002f560c

30.9.3.5. kubernetes.event.involvedObject.apiVersion

The version of kubernetes master API

Data type

keyword

Example value

v1

30.9.3.6. kubernetes.event.involvedObject.resourceVersion

A string that identifies the server’s internal version of the pod that triggered the event. Clients can use this string to determine when objects have changed.

Data type

keyword

Example value

308882

30.9.4. kubernetes.event.reason

A short machine-understandable string that gives the reason for generating this event

Data type

keyword

Example value

SuccessfulCreate

30.9.5. kubernetes.event.source_component

The component that reported this event

Data type

keyword

Example value

replication-controller

30.9.6. kubernetes.event.firstTimestamp

The time at which the event was first recorded

Data type

date

Example value

2017-08-07 10:11:57.000000000 Z

30.9.7. kubernetes.event.count

The number of times this event has occurred

Data type

integer

Example value

1

30.9.8. kubernetes.event.type

The type of event, Normal or Warning. New types could be added in the future.

Data type

keyword

Example value

Normal

Chapter 31. OpenShift

The namespace for openshift-logging specific metadata

Data type

group

31.1. openshift.labels

Labels added by the Cluster Log Forwarder configuration

Data type

group

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