Logging

OpenShift Container Platform 4.14

Configuring and using logging in OpenShift Container Platform

Red Hat OpenShift Documentation Team

Abstract

Use logging to collect, visualize, forward, and store log data to troubleshoot issues, identify performance bottlenecks, and detect security threats in OpenShift Container Platform.

Chapter 1. Release notes

1.1. Logging 5.9

Note

Logging is provided as an installable component, with a distinct release cycle from the core OpenShift Container Platform. 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.y, where x.y represents the major and minor version of logging you have installed. For example, stable-5.7.

/module included in logging-5-9-release-notes.adoc :_mod-docs-content-type: REFERENCE

1.1.1. Logging 5.9.2

This release includes OpenShift Logging Bug Fix Release 5.9.2

1.1.1.1. Bug Fixes

  • Before this update, changes to the Logging Operator caused an error due to an incorrect configuration in the ClusterLogForwarder CR. As a result, upgrades to logging deleted the daemonset collector. With this update, the Logging Operator re-creates collector daemonsets except when a Not authorized to collect error occurs. (LOG-4910)
  • Before this update, the rotated infrastructure log files were sent to the application index in some scenarios due to an incorrect configuration in the Vector log collector. With this update, the Vector log collector configuration avoids collecting any rotated infrastructure log files. (LOG-5156)
  • Before this update, the Logging Operator did not monitor changes to the grafana-dashboard-cluster-logging config map. With this update, the Logging Operator monitors changes in the ConfigMap objects, ensuring the system stays synchronized and responds effectively to config map modifications. (LOG-5308)
  • Before this update, an issue in the metrics collection code of the Logging Operator caused it to report stale telemetry metrics. With this update, the Logging Operator does not report stale telemetry metrics. (LOG-5426)
  • Before this change, the Fluentd out_http plugin ignored the no_proxy environment variable. With this update, the Fluentd patches the HTTP#start method of ruby to honor the no_proxy environment variable. (LOG-5466)

1.1.1.2. CVEs

1.1.2. Logging 5.9.1

This release includes OpenShift Logging Bug Fix Release 5.9.1

1.1.2.1. Enhancements

  • Before this update, the Loki Operator configured Loki to use path-based style access for the Amazon Simple Storage Service (S3), which has been deprecated. With this update, the Loki Operator defaults to virtual-host style without users needing to change their configuration. (LOG-5401)
  • Before this update, the Loki Operator did not validate the Amazon Simple Storage Service (S3) endpoint used in the storage secret. With this update, the validation process ensures the S3 endpoint is a valid S3 URL, and the LokiStack status updates to indicate any invalid URLs. (LOG-5395)

1.1.2.2. Bug Fixes

  • Before this update, a bug in LogQL parsing left out some line filters from the query. With this update, the parsing now includes all the line filters while keeping the original query unchanged. (LOG-5268)
  • Before this update, a prune filter without a defined pruneFilterSpec would cause a segfault. With this update, there is a validation error if a prune filter is without a defined puneFilterSpec. (LOG-5322)
  • Before this update, a drop filter without a defined dropTestsSpec would cause a segfault. With this update, there is a validation error if a prune filter is without a defined puneFilterSpec. (LOG-5323)
  • Before this update, the Loki Operator did not validate the Amazon Simple Storage Service (S3) endpoint URL format used in the storage secret. With this update, the S3 endpoint URL goes through a validation step that reflects on the status of the LokiStack. (LOG-5397)
  • Before this update, poorly formatted timestamp fields in audit log records led to WARN messages in Red Hat OpenShift Logging Operator logs. With this update, a remap transformation ensures that the timestamp field is properly formatted. (LOG-4672)
  • Before this update, the error message thrown while validating a ClusterLogForwarder resource name and namespace did not correspond to the correct error. With this update, the system checks if a ClusterLogForwarder resource with the same name exists in the same namespace. If not, it corresponds to the correct error. (LOG-5062)
  • Before this update, the validation feature for output config required a TLS URL, even for services such as Amazon CloudWatch or Google Cloud Logging where a URL is not needed by design. With this update, the validation logic for services without URLs are improved, and the error message are more informative. (LOG-5307)
  • Before this update, defining an infrastructure input type did not exclude logging workloads from the collection. With this update, the collection excludes logging services to avoid feedback loops. (LOG-5309)

1.1.2.3. CVEs

No CVEs.

1.1.3. Logging 5.9.0

This release includes OpenShift Logging Bug Fix Release 5.9.0

1.1.3.1. Removal notice

The Logging 5.9 release does not contain an updated version of the OpenShift Elasticsearch Operator. Instances of OpenShift Elasticsearch Operator from prior logging releases, remain supported until the EOL of the logging release. As an alternative to using the OpenShift Elasticsearch Operator to manage the default log storage, you can use the Loki Operator. For more information on the Logging lifecycle dates, see Platform Agnostic Operators.

1.1.3.2. Deprecation notice

  • In Logging 5.9, Fluentd, and Kibana are deprecated and are planned to be removed in Logging 6.0, which is expected to be shipped alongside a future release of OpenShift Container Platform. Red Hat will provide critical and above CVE bug fixes and support for these components during the current release lifecycle, but these components will no longer receive feature enhancements. The Vector-based collector provided by the Red Hat OpenShift Logging Operator and LokiStack provided by the Loki Operator are the preferred Operators for log collection and storage. We encourage all users to adopt the Vector and Loki log stack, as this will be the stack that will be enhanced going forward.
  • In Logging 5.9, the Fields option for the Splunk output type was never implemented and is now deprecated. It will be removed in a future release.

1.1.3.3. Enhancements

1.1.3.3.1. Log Collection
  • This enhancement adds the ability to refine the process of log collection by using a workload’s metadata to drop or prune logs based on their content. Additionally, it allows the collection of infrastructure logs, such as journal or container logs, and audit logs, such as kube api or ovn logs, to only collect individual sources. (LOG-2155)
  • This enhancement introduces a new type of remote log receiver, the syslog receiver. You can configure it to expose a port over a network, allowing external systems to send syslog logs using compatible tools such as rsyslog. (LOG-3527)
  • With this update, the ClusterLogForwarder API now supports log forwarding to Azure Monitor Logs, giving users better monitoring abilities. This feature helps users to maintain optimal system performance and streamline the log analysis processes in Azure Monitor, which speeds up issue resolution and improves operational efficiency. (LOG-4605)
  • This enhancement improves collector resource utilization by deploying collectors as a deployment with two replicas. This occurs when the only input source defined in the ClusterLogForwarder custom resource (CR) is a receiver input instead of using a daemon set on all nodes. Additionally, collectors deployed in this manner do not mount the host file system. To use this enhancement, you need to annotate the ClusterLogForwarder CR with the logging.openshift.io/dev-preview-enable-collector-as-deployment annotation. (LOG-4779)
  • This enhancement introduces the capability for custom tenant configuration across all supported outputs, facilitating the organization of log records in a logical manner. However, it does not permit custom tenant configuration for logging managed storage. (LOG-4843)
  • With this update, the ClusterLogForwarder CR that specifies an application input with one or more infrastructure namespaces like default, openshift*, or kube*, now requires a service account with the collect-infrastructure-logs role. (LOG-4943)
  • This enhancement introduces the capability for tuning some output settings, such as compression, retry duration, and maximum payloads, to match the characteristics of the receiver. Additionally, this feature includes a delivery mode to allow administrators to choose between throughput and log durability. For example, the AtLeastOnce option configures minimal disk buffering of collected logs so that the collector can deliver those logs after a restart. (LOG-5026)
  • This enhancement adds three new Prometheus alerts, warning users about the deprecation of Elasticsearch, Fluentd, and Kibana. (LOG-5055)
1.1.3.3.2. Log Storage
  • This enhancement in LokiStack improves support for OTEL by using the new V13 object storage format and enabling automatic stream sharding by default. This also prepares the collector for future enhancements and configurations. (LOG-4538)
  • This enhancement introduces support for short-lived token workload identity federation with Azure and AWS log stores for STS enabled OpenShift Container Platform 4.14 and later clusters. Local storage requires the addition of a CredentialMode: static annotation under spec.storage.secret in the LokiStack CR. (LOG-4540)
  • With this update, the validation of the Azure storage secret is now extended to give early warning for certain error conditions. (LOG-4571)
  • With this update, Loki now adds upstream and downstream support for GCP workload identity federation mechanism. This allows authenticated and authorized access to the corresponding object storage services. (LOG-4754)

1.1.3.4. Bug Fixes

  • Before this update, the logging must-gather could not collect any logs on a FIPS-enabled cluster. With this update, a new oc client is available in cluster-logging-rhel9-operator, and must-gather works properly on FIPS clusters. (LOG-4403)
  • Before this update, the LokiStack ruler pods could not format the IPv6 pod IP in HTTP URLs used for cross-pod communication. This issue caused querying rules and alerts through the Prometheus-compatible API to fail. With this update, the LokiStack ruler pods encapsulate the IPv6 pod IP in square brackets, resolving the problem. Now, querying rules and alerts through the Prometheus-compatible API works just like in IPv4 environments. (LOG-4709)
  • Before this fix, the YAML content from the logging must-gather was exported in a single line, making it unreadable. With this update, the YAML white spaces are preserved, ensuring that the file is properly formatted. (LOG-4792)
  • Before this update, when the ClusterLogForwarder CR was enabled, the Red Hat OpenShift Logging Operator could run into a nil pointer exception when ClusterLogging.Spec.Collection was nil. With this update, the issue is now resolved in the Red Hat OpenShift Logging Operator. (LOG-5006)
  • Before this update, in specific corner cases, replacing the ClusterLogForwarder CR status field caused the resourceVersion to constantly update due to changing timestamps in Status conditions. This condition led to an infinite reconciliation loop. With this update, all status conditions synchronize, so that timestamps remain unchanged if conditions stay the same. (LOG-5007)
  • Before this update, there was an internal buffering behavior to drop_newest to address high memory consumption by the collector resulting in significant log loss. With this update, the behavior reverts to using the collector defaults. (LOG-5123)
  • Before this update, the Loki Operator ServiceMonitor in the openshift-operators-redhat namespace used static token and CA files for authentication, causing errors in the Prometheus Operator in the User Workload Monitoring spec on the ServiceMonitor configuration. With this update, the Loki Operator ServiceMonitor in openshift-operators-redhat namespace now references a service account token secret by a LocalReference object. This approach allows the User Workload Monitoring spec in the Prometheus Operator to handle the Loki Operator ServiceMonitor successfully, enabling Prometheus to scrape the Loki Operator metrics. (LOG-5165)
  • Before this update, the configuration of the Loki Operator ServiceMonitor could match many Kubernetes services, resulting in the Loki Operator metrics being collected multiple times. With this update, the configuration of ServiceMonitor now only matches the dedicated metrics service. (LOG-5212)

1.1.3.5. Known Issues

None.

1.1.3.6. CVEs

1.2. Logging 5.8

Note

Logging is provided as an installable component, with a distinct release cycle from the core OpenShift Container Platform. 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.y, where x.y represents the major and minor version of logging you have installed. For example, stable-5.7.

1.2.1. Logging 5.8.7

This release includes OpenShift Logging Bug Fix Release 5.8.7 Security Update and OpenShift Logging Bug Fix Release 5.8.7.

1.2.1.1. Bug fixes

  • Before this update, the elasticsearch-im-<type>-* pods failed if no <type> logs (audit, infrastructure, or application) were collected. With this update, the pods no longer fail when <type> logs are not collected. (LOG-4949)
  • Before this update, the validation feature for output config required an SSL/TLS URL, even for services such as Amazon CloudWatch or Google Cloud Logging where a URL is not needed by design. With this update, the validation logic for services without URLs are improved, and the error message is more informative. (LOG-5467)
  • Before this update, an issue in the metrics collection code of the Logging Operator caused it to report stale telemetry metrics. With this update, the Logging Operator does not report stale telemetry metrics. (LOG-5471)
  • Before this update, changes to the Logging Operator caused an error due to an incorrect configuration in the ClusterLogForwarder CR. As a result, upgrades to logging deleted the daemonset collector. With this update, the Logging Operator re-creates collector daemonsets except when a Not authorized to collect error occurs. (LOG-5514)

1.2.1.2. CVEs

1.2.2. Logging 5.8.6

This release includes OpenShift Logging Bug Fix Release 5.8.6 Security Update and OpenShift Logging Bug Fix Release 5.8.6.

1.2.2.1. Enhancements

  • Before this update, the Loki Operator did not validate the Amazon Simple Storage Service (S3) endpoint used in the storage secret. With this update, the validation process ensures the S3 endpoint is a valid S3 URL, and the LokiStack status updates to indicate any invalid URLs. (LOG-5392)
  • Before this update, the Loki Operator configured Loki to use path-based style access for the Amazon Simple Storage Service (S3), which has been deprecated. With this update, the Loki Operator defaults to virtual-host style without users needing to change their configuration. (LOG-5402)

1.2.2.2. Bug fixes

  • Before this update, the Elastisearch Operator ServiceMonitor in the openshift-operators-redhat namespace used static token and certificate authority (CA) files for authentication, causing errors in the Prometheus Operator in the User Workload Monitoring specification on the ServiceMonitor configuration. With this update, the Elastisearch Operator ServiceMonitor in the openshift-operators-redhat namespace now references a service account token secret by a LocalReference object. This approach allows the User Workload Monitoring specifications in the Prometheus Operator to handle the Elastisearch Operator ServiceMonitor successfully. This enables Prometheus to scrape the Elastisearch Operator metrics. (LOG-5164)
  • Before this update, the Loki Operator did not validate the Amazon Simple Storage Service (S3) endpoint URL format used in the storage secret. With this update, the S3 endpoint URL goes through a validation step that reflects on the status of the LokiStack. (LOG-5398)

1.2.2.3. CVEs

1.2.3. Logging 5.8.5

This release includes OpenShift Logging Bug Fix Release 5.8.5.

1.2.3.1. Bug fixes

  • Before this update, the configuration of the Loki Operator’s ServiceMonitor could match many Kubernetes services, resulting in the Loki Operator’s metrics being collected multiple times. With this update, the configuration of ServiceMonitor now only matches the dedicated metrics service. (LOG-5250)
  • Before this update, the Red Hat build pipeline did not use the existing build details in Loki builds and omitted information such as revision, branch, and version. With this update, the Red Hat build pipeline now adds these details to the Loki builds, fixing the issue. (LOG-5201)
  • Before this update, the Loki Operator checked if the pods were running to decide if the LokiStack was ready. With this update, it also checks if the pods are ready, so that the readiness of the LokiStack reflects the state of its components. (LOG-5171)
  • Before this update, running a query for log metrics caused an error in the histogram. With this update, the histogram toggle function and the chart are disabled and hidden because the histogram doesn’t work with log metrics. (LOG-5044)
  • Before this update, the Loki and Elasticsearch bundle had the wrong maxOpenShiftVersion, resulting in IncompatibleOperatorsInstalled alerts. With this update, including 4.16 as the maxOpenShiftVersion property in the bundle fixes the issue. (LOG-5272)
  • Before this update, the build pipeline did not include linker flags for the build date, causing Loki builds to show empty strings for buildDate and goVersion. With this update, adding the missing linker flags in the build pipeline fixes the issue. (LOG-5274)
  • Before this update, a bug in LogQL parsing left out some line filters from the query. With this update, the parsing now includes all the line filters while keeping the original query unchanged. (LOG-5270)
  • Before this update, the Loki Operator ServiceMonitor in the openshift-operators-redhat namespace used static token and CA files for authentication, causing errors in the Prometheus Operator in the User Workload Monitoring spec on the ServiceMonitor configuration. With this update, the Loki Operator ServiceMonitor in openshift-operators-redhat namespace now references a service account token secret by a LocalReference object. This approach allows the User Workload Monitoring spec in the Prometheus Operator to handle the Loki Operator ServiceMonitor successfully, enabling Prometheus to scrape the Loki Operator metrics. (LOG-5240)

1.2.3.2. CVEs

1.2.4. Logging 5.8.4

This release includes OpenShift Logging Bug Fix Release 5.8.4.

1.2.4.1. Bug fixes

  • Before this update, the developer console’s logs did not account for the current namespace, resulting in query rejection for users without cluster-wide log access. With this update, all supported OCP versions ensure correct namespace inclusion. (LOG-4905)
  • Before this update, the Cluster Logging Operator deployed ClusterRoles supporting LokiStack deployments only when the default log output was LokiStack. With this update, the roles are split into two groups: read and write. The write roles deploys based on the setting of the default log storage, just like all the roles used to do before. The read roles deploys based on whether the logging console plugin is active. (LOG-4987)
  • Before this update, multiple ClusterLogForwarders defining the same input receiver name had their service endlessly reconciled because of changing ownerReferences on one service. With this update, each receiver input will have its own service named with the convention of <CLF.Name>-<input.Name>. (LOG-5009)
  • Before this update, the ClusterLogForwarder did not report errors when forwarding logs to cloudwatch without a secret. With this update, the following error message appears when forwarding logs to cloudwatch without a secret: secret must be provided for cloudwatch output. (LOG-5021)
  • Before this update, the log_forwarder_input_info included application, infrastructure, and audit input metric points. With this update, http is also added as a metric point. (LOG-5043)

1.2.4.2. CVEs

1.2.5. Logging 5.8.3

This release includes Logging Bug Fix 5.8.3 and Logging Security Fix 5.8.3

1.2.5.1. Bug fixes

  • Before this update, when configured to read a custom S3 Certificate Authority the Loki Operator would not automatically update the configuration when the name of the ConfigMap or the contents changed. With this update, the Loki Operator is watching for changes to the ConfigMap and automatically updates the generated configuration. (LOG-4969)
  • Before this update, Loki outputs configured without a valid URL caused the collector pods to crash. With this update, outputs are subject to URL validation, resolving the issue. (LOG-4822)
  • Before this update the Cluster Logging Operator would generate collector configuration fields for outputs that did not specify a secret to use the service account bearer token. With this update, an output does not require authentication, resolving the issue. (LOG-4962)
  • Before this update, the tls.insecureSkipVerify field of an output was not set to a value of true without a secret defined. With this update, a secret is no longer required to set this value. (LOG-4963)
  • Before this update, output configurations allowed the combination of an insecure (HTTP) URL with TLS authentication. With this update, outputs configured for TLS authentication require a secure (HTTPS) URL. (LOG-4893)

1.2.5.2. CVEs

1.2.6. Logging 5.8.2

This release includes OpenShift Logging Bug Fix Release 5.8.2.

1.2.6.1. Bug fixes

  • Before this update, the LokiStack ruler pods would not format the IPv6 pod IP in HTTP URLs used for cross pod communication, causing querying rules and alerts through the Prometheus-compatible API to fail. With this update, the LokiStack ruler pods encapsulate the IPv6 pod IP in square brackets, resolving the issue. (LOG-4890)
  • Before this update, the developer console logs did not account for the current namespace, resulting in query rejection for users without cluster-wide log access. With this update, namespace inclusion has been corrected, resolving the issue. (LOG-4947)
  • Before this update, the logging view plugin of the OpenShift Container Platform web console did not allow for custom node placement and tolerations. With this update, defining custom node placements and tolerations has been added to the logging view plugin of the OpenShift Container Platform web console. (LOG-4912)

1.2.6.2. CVEs

1.2.7. Logging 5.8.1

This release includes OpenShift Logging Bug Fix Release 5.8.1 and OpenShift Logging Bug Fix Release 5.8.1 Kibana.

1.2.7.1. Enhancements

1.2.7.1.1. Log Collection
  • With this update, while configuring Vector as a collector, you can add logic to the Red Hat OpenShift Logging Operator to use a token specified in the secret in place of the token associated with the service account. (LOG-4780)
  • With this update, the BoltDB Shipper Loki dashboards are now renamed to Index dashboards. (LOG-4828)

1.2.7.2. Bug fixes

  • Before this update, the ClusterLogForwarder created empty indices after enabling the parsing of JSON logs, even when the rollover conditions were not met. With this update, the ClusterLogForwarder skips the rollover when the write-index is empty. (LOG-4452)
  • Before this update, the Vector set the default log level incorrectly. With this update, the correct log level is set by improving the enhancement of regular expression, or regexp, for log level detection. (LOG-4480)
  • Before this update, during the process of creating index patterns, the default alias was missing from the initial index in each log output. As a result, Kibana users were unable to create index patterns by using OpenShift Elasticsearch Operator. This update adds the missing aliases to OpenShift Elasticsearch Operator, resolving the issue. Kibana users can now create index patterns that include the {app,infra,audit}-000001 indexes. (LOG-4683)
  • Before this update, Fluentd collector pods were in a CrashLoopBackOff state due to binding of the Prometheus server on IPv6 clusters. With this update, the collectors work properly on IPv6 clusters. (LOG-4706)
  • Before this update, the Red Hat OpenShift Logging Operator would undergo numerous reconciliations whenever there was a change in the ClusterLogForwarder. With this update, the Red Hat OpenShift Logging Operator disregards the status changes in the collector daemonsets that triggered the reconciliations. (LOG-4741)
  • Before this update, the Vector log collector pods were stuck in the CrashLoopBackOff state on IBM Power machines. With this update, the Vector log collector pods start successfully on IBM Power architecture machines. (LOG-4768)
  • Before this update, forwarding with a legacy forwarder to an internal LokiStack would produce SSL certificate errors using Fluentd collector pods. With this update, the log collector service account is used by default for authentication, using the associated token and ca.crt. (LOG-4791)
  • Before this update, forwarding with a legacy forwarder to an internal LokiStack would produce SSL certificate errors using Vector collector pods. With this update, the log collector service account is used by default for authentication and also using the associated token and ca.crt. (LOG-4852)
  • Before this fix, IPv6 addresses would not be parsed correctly after evaluating a host or multiple hosts for placeholders. With this update, IPv6 addresses are correctly parsed. (LOG-4811)
  • Before this update, it was necessary to create a ClusterRoleBinding to collect audit permissions for HTTP receiver inputs. With this update, it is not necessary to create the ClusterRoleBinding because the endpoint already depends upon the cluster certificate authority. (LOG-4815)
  • Before this update, the Loki Operator did not mount a custom CA bundle to the ruler pods. As a result, during the process to evaluate alerting or recording rules, object storage access failed. With this update, the Loki Operator mounts the custom CA bundle to all ruler pods. The ruler pods can download logs from object storage to evaluate alerting or recording rules. (LOG-4836)
  • Before this update, while removing the inputs.receiver section in the ClusterLogForwarder, the HTTP input services and its associated secrets were not deleted. With this update, the HTTP input resources are deleted when not needed. (LOG-4612)
  • Before this update, the ClusterLogForwarder indicated validation errors in the status, but the outputs and the pipeline status did not accurately reflect the specific issues. With this update, the pipeline status displays the validation failure reasons correctly in case of misconfigured outputs, inputs, or filters. (LOG-4821)
  • Before this update, changing a LogQL query that used controls such as time range or severity changed the label matcher operator defining it like a regular expression. With this update, regular expression operators remain unchanged when updating the query. (LOG-4841)

1.2.7.3. CVEs

1.2.8. Logging 5.8.0

This release includes OpenShift Logging Bug Fix Release 5.8.0 and OpenShift Logging Bug Fix Release 5.8.0 Kibana.

1.2.8.1. Deprecation notice

In Logging 5.8, Elasticsearch, Fluentd, and Kibana are deprecated and are planned to be removed in Logging 6.0, which is expected to be shipped alongside a future release of OpenShift Container Platform. Red Hat will provide critical and above CVE bug fixes and support for these components during the current release lifecycle, but these components will no longer receive feature enhancements. The Vector-based collector provided by the Red Hat OpenShift Logging Operator and LokiStack provided by the Loki Operator are the preferred Operators for log collection and storage. We encourage all users to adopt the Vector and Loki log stack, as this will be the stack that will be enhanced going forward.

1.2.8.2. Enhancements

1.2.8.2.1. Log Collection
  • With this update, the LogFileMetricExporter is no longer deployed with the collector by default. You must manually create a LogFileMetricExporter custom resource (CR) to generate metrics from the logs produced by running containers. If you do not create the LogFileMetricExporter CR, you may see a No datapoints found message in the OpenShift Container Platform web console dashboard for Produced Logs. (LOG-3819)
  • With this update, you can deploy multiple, isolated, and RBAC-protected ClusterLogForwarder custom resource (CR) instances in any namespace. This allows independent groups to forward desired logs to any destination while isolating their configuration from other collector deployments. (LOG-1343)

    Important

    In order to support multi-cluster log forwarding in additional namespaces other than the openshift-logging namespace, you must update the Red Hat OpenShift Logging Operator to watch all namespaces. This functionality is supported by default in new Red Hat OpenShift Logging Operator version 5.8 installations.

  • With this update, you can use the flow control or rate limiting mechanism to limit the volume of log data that can be collected or forwarded by dropping excess log records. The input limits prevent poorly-performing containers from overloading the Logging and the output limits put a ceiling on the rate of logs shipped to a given data store. (LOG-884)
  • With this update, you can configure the log collector to look for HTTP connections and receive logs as an HTTP server, also known as a webhook. (LOG-4562)
  • With this update, you can configure audit polices to control which Kubernetes and OpenShift API server events are forwarded by the log collector. (LOG-3982)
1.2.8.2.2. Log Storage
  • With this update, LokiStack administrators can have more fine-grained control over who can access which logs by granting access to logs on a namespace basis. (LOG-3841)
  • With this update, the Loki Operator introduces PodDisruptionBudget configuration on LokiStack deployments to ensure normal operations during OpenShift Container Platform cluster restarts by keeping ingestion and the query path available. (LOG-3839)
  • With this update, the reliability of existing LokiStack installations are seamlessly improved by applying a set of default Affinity and Anti-Affinity policies. (LOG-3840)
  • With this update, you can manage zone-aware data replication as an administrator in LokiStack, in order to enhance reliability in the event of a zone failure. (LOG-3266)
  • With this update, a new supported small-scale LokiStack size of 1x.extra-small is introduced for OpenShift Container Platform clusters hosting a few workloads and smaller ingestion volumes (up to 100GB/day). (LOG-4329)
  • With this update, the LokiStack administrator has access to an official Loki dashboard to inspect the storage performance and the health of each component. (LOG-4327)
1.2.8.2.3. Log Console
  • With this update, you can enable the Logging Console Plugin when Elasticsearch is the default Log Store. (LOG-3856)
  • With this update, OpenShift Container Platform application owners can receive notifications for application log-based alerts on the OpenShift Container Platform web console Developer perspective for OpenShift Container Platform version 4.14 and later. (LOG-3548)

1.2.8.3. Known Issues

  • Currently, Splunk log forwarding might not work after upgrading to version 5.8 of the Red Hat OpenShift Logging Operator. This issue is caused by transitioning from OpenSSL version 1.1.1 to version 3.0.7. In the newer OpenSSL version, there is a default behavior change, where connections to TLS 1.2 endpoints are rejected if they do not expose the RFC 5746 extension.

    As a workaround, enable TLS 1.3 support on the TLS terminating load balancer in front of the Splunk HEC (HTTP Event Collector) endpoint. Splunk is a third-party system and this should be configured from the Splunk end.

  • Currently, there is a flaw in handling multiplexed streams in the HTTP/2 protocol, where you can repeatedly make a request for a new multiplex stream and immediately send an RST_STREAM frame to cancel it. This created extra work for the server set up and tore down the streams, resulting in a denial of service due to server resource consumption. There is currently no workaround for this issue. (LOG-4609)
  • Currently, when using FluentD as the collector, the collector pod cannot start on the OpenShift Container Platform IPv6-enabled cluster. The pod logs produce the fluentd pod [error]: unexpected error error_class=SocketError error="getaddrinfo: Name or service not known error. There is currently no workaround for this issue. (LOG-4706)
  • Currently, the log alert is not available on an IPv6-enabled cluster. There is currently no workaround for this issue. (LOG-4709)
  • Currently, must-gather cannot gather any logs on a FIPS-enabled cluster, because the required OpenSSL library is not available in the cluster-logging-rhel9-operator. There is currently no workaround for this issue. (LOG-4403)
  • Currently, when deploying the logging version 5.8 on a FIPS-enabled cluster, the collector pods cannot start and are stuck in CrashLoopBackOff status, while using FluentD as a collector. There is currently no workaround for this issue. (LOG-3933)

1.2.8.4. CVEs

1.3. Logging 5.7

Note

Logging is provided as an installable component, with a distinct release cycle from the core OpenShift Container Platform. 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.y, where x.y represents the major and minor version of logging you have installed. For example, stable-5.7.

1.3.1. Logging 5.7.15

This release includes OpenShift Logging Bug Fix 5.7.15.

1.3.1.1. Bug fixes

  • Before this update, there was a delay in restarting Ingesters when configuring LokiStack, because the Loki Operator sets the write-ahead log replay_memory_ceiling to zero bytes for the 1x.demo size. With this update, the minimum value used for the replay_memory_ceiling has been increased to avoid delays. (LOG-5616)

1.3.1.2. CVEs

1.3.2. Logging 5.7.14

This release includes OpenShift Logging Bug Fix 5.7.14.

1.3.2.1. Bug fixes

  • Before this update, an issue in the metrics collection code of the Logging Operator caused it to report stale telemetry metrics. With this update, the Logging Operator does not report stale telemetry metrics. (LOG-5472)

1.3.2.2. CVEs

1.3.3. Logging 5.7.13

This release includes OpenShift Logging Bug Fix 5.7.13.

1.3.3.1. Enhancements

  • Before this update, the Loki Operator configured Loki to use path-based style access for the Amazon Simple Storage Service (S3), which has been deprecated. With this update, the Loki Operator defaults to virtual-host style without users needing to change their configuration. (LOG-5403)
  • Before this update, the Loki Operator did not validate the Amazon Simple Storage Service (S3) endpoint used in the storage secret. With this update, the validation process ensures the S3 endpoint is a valid S3 URL, and the LokiStack status updates to indicate any invalid URLs. (LOG-5393)

1.3.3.2. Bug fixes

  • Before this update, the Elastisearch Operator ServiceMonitor in the openshift-operators-redhat namespace used static token and certificate authority (CA) files for authentication, causing errors in the Prometheus Operator in the User Workload Monitoring specification on the ServiceMonitor configuration. With this update, the Elastisearch Operator ServiceMonitor in the openshift-operators-redhat namespace now references a service account token secret by a LocalReference object. This approach allows the User Workload Monitoring specifications in the Prometheus Operator to handle the Elastisearch Operator ServiceMonitor successfully. This enables Prometheus to scrape the Elastisearch Operator metrics. (LOG-5243)
  • Before this update, the Loki Operator did not validate the Amazon Simple Storage Service (S3) endpoint URL format used in the storage secret. With this update, the S3 endpoint URL goes through a validation step that reflects on the status of the LokiStack. (LOG-5399)

1.3.3.3. CVEs

1.3.4. Logging 5.7.12

This release includes OpenShift Logging Bug Fix 5.7.12.

1.3.4.1. Bug fixes

  • Before this update, the Loki Operator checked if the pods were running to decide if the LokiStack was ready. With this update, it also checks if the pods are ready, so that the readiness of the LokiStack reflects the state of its components. (LOG-5172)
  • Before this update, the Red Hat build pipeline didn’t use the existing build details in Loki builds and omitted information such as revision, branch, and version. With this update, the Red Hat build pipeline now adds these details to the Loki builds, fixing the issue. (LOG-5202)
  • Before this update, the configuration of the Loki Operator’s ServiceMonitor could match many Kubernetes services, resulting in the Loki Operator’s metrics being collected multiple times. With this update, the configuration of ServiceMonitor now only matches the dedicated metrics service. (LOG-5251)
  • Before this update, the build pipeline did not include linker flags for the build date, causing Loki builds to show empty strings for buildDate and goVersion. With this update, adding the missing linker flags in the build pipeline fixes the issue. (LOG-5275)
  • Before this update, the Loki Operator ServiceMonitor in the openshift-operators-redhat namespace used static token and CA files for authentication, causing errors in the Prometheus Operator in the User Workload Monitoring spec on the ServiceMonitor configuration. With this update, the Loki Operator ServiceMonitor in openshift-operators-redhat namespace now references a service account token secret by a LocalReference object. This approach allows the User Workload Monitoring spec in the Prometheus Operator to handle the Loki Operator ServiceMonitor successfully, enabling Prometheus to scrape the Loki Operator metrics. (LOG-5241)

1.3.4.2. CVEs

1.3.5. Logging 5.7.11

This release includes Logging Bug Fix 5.7.11.

1.3.5.1. Bug fixes

  • Before this update, when configured to read a custom S3 Certificate Authority, the Loki Operator would not automatically update the configuration when the name of the ConfigMap object or the contents changed. With this update, the Loki Operator now watches for changes to the ConfigMap object and automatically updates the generated configuration. (LOG-4968)

1.3.5.2. CVEs

1.3.6. Logging 5.7.10

This release includes OpenShift Logging Bug Fix Release 5.7.10.

1.3.6.1. Bug fix

Before this update, the LokiStack ruler pods would not format the IPv6 pod IP in HTTP URLs used for cross pod communication, causing querying rules and alerts through the Prometheus-compatible API to fail. With this update, the LokiStack ruler pods encapsulate the IPv6 pod IP in square brackets, resolving the issue. (LOG-4891)

1.3.6.2. CVEs

1.3.7. Logging 5.7.9

This release includes OpenShift Logging Bug Fix Release 5.7.9.

1.3.7.1. Bug fixes

  • Before this fix, IPv6 addresses would not be parsed correctly after evaluating a host or multiple hosts for placeholders. With this update, IPv6 addresses are correctly parsed. (LOG-4281)
  • Before this update, the Vector failed to start on IPv4-only nodes. As a result, it failed to create a listener for its metrics endpoint with the following error: Failed to start Prometheus exporter: TCP bind failed: Address family not supported by protocol (os error 97). With this update, the Vector operates normally on IPv4-only nodes. (LOG-4589)
  • Before this update, during the process of creating index patterns, the default alias was missing from the initial index in each log output. As a result, Kibana users were unable to create index patterns by using OpenShift Elasticsearch Operator. This update adds the missing aliases to OpenShift Elasticsearch Operator, resolving the issue. Kibana users can now create index patterns that include the {app,infra,audit}-000001 indexes. (LOG-4806)
  • Before this update, the Loki Operator did not mount a custom CA bundle to the ruler pods. As a result, during the process to evaluate alerting or recording rules, object storage access failed. With this update, the Loki Operator mounts the custom CA bundle to all ruler pods. The ruler pods can download logs from object storage to evaluate alerting or recording rules. (LOG-4837)
  • Before this update, changing a LogQL query using controls such as time range or severity changed the label matcher operator as though it was defined like a regular expression. With this update, regular expression operators remain unchanged when updating the query. (LOG-4842)
  • Before this update, the Vector collector deployments relied upon the default retry and buffering behavior. As a result, the delivery pipeline backed up trying to deliver every message when the availability of an output was unstable. With this update, the Vector collector deployments limit the number of message retries and drop messages after the threshold has been exceeded. (LOG-4536)

1.3.7.2. CVEs

1.3.8. Logging 5.7.8

This release includes OpenShift Logging Bug Fix Release 5.7.8.

1.3.8.1. Bug fixes

  • Before this update, there was a potential conflict when the same name was used for the outputRefs and inputRefs parameters in the ClusterLogForwarder custom resource (CR). As a result, the collector pods entered in a CrashLoopBackOff status. With this update, the output labels contain the OUTPUT_ prefix to ensure a distinction between output labels and pipeline names. (LOG-4383)
  • Before this update, while configuring the JSON log parser, if you did not set the structuredTypeKey or structuredTypeName parameters for the Cluster Logging Operator, no alert would display about an invalid configuration. With this update, the Cluster Logging Operator informs you about the configuration issue. (LOG-4441)
  • Before this update, if the hecToken key was missing or incorrect in the secret specified for a Splunk output, the validation failed because the Vector forwarded logs to Splunk without a token. With this update, if the hecToken key is missing or incorrect, the validation fails with the A non-empty hecToken entry is required error message. (LOG-4580)
  • Before this update, selecting a date from the Custom time range for logs caused an error in the web console. With this update, you can select a date from the time range model in the web console successfully. (LOG-4684)

1.3.8.2. CVEs

1.3.9. Logging 5.7.7

This release includes OpenShift Logging Bug Fix Release 5.7.7.

1.3.9.1. Bug fixes

  • Before this update, FluentD normalized the logs emitted by the EventRouter differently from Vector. With this update, the Vector produces log records in a consistent format. (LOG-4178)
  • Before this update, there was an error in the query used for the FluentD Buffer Availability graph in the metrics dashboard created by the Cluster Logging Operator as it showed the minimum buffer usage. With this update, the graph shows the maximum buffer usage and is now renamed to FluentD Buffer Usage. (LOG-4555)
  • Before this update, deploying a LokiStack on IPv6-only or dual-stack OpenShift Container Platform clusters caused the LokiStack memberlist registration to fail. As a result, the distributor pods went into a crash loop. With this update, an administrator can enable IPv6 by setting the lokistack.spec.hashRing.memberlist.enableIPv6: value to true, which resolves the issue. (LOG-4569)
  • Before this update, the log collector relied on the default configuration settings for reading the container log lines. As a result, the log collector did not read the rotated files efficiently. With this update, there is an increase in the number of bytes read, which allows the log collector to efficiently process rotated files. (LOG-4575)
  • Before this update, the unused metrics in the Event Router caused the container to fail due to excessive memory usage. With this update, there is reduction in the memory usage of the Event Router by removing the unused metrics. (LOG-4686)

1.3.9.2. CVEs

1.3.10. Logging 5.7.6

This release includes OpenShift Logging Bug Fix Release 5.7.6.

1.3.10.1. Bug fixes

  • Before this update, the collector relied on the default configuration settings for reading the container log lines. As a result, the collector did not read the rotated files efficiently. With this update, there is an increase in the number of bytes read, which allows the collector to efficiently process rotated files. (LOG-4501)
  • Before this update, when users pasted a URL with predefined filters, some filters did not reflect. With this update, the UI reflects all the filters in the URL. (LOG-4459)
  • Before this update, forwarding to Loki using custom labels generated an error when switching from Fluentd to Vector. With this update, the Vector configuration sanitizes labels in the same way as Fluentd to ensure the collector starts and correctly processes labels. (LOG-4460)
  • Before this update, the Observability Logs console search field did not accept special characters that it should escape. With this update, it is escaping special characters properly in the query. (LOG-4456)
  • Before this update, the following warning message appeared while sending logs to Splunk: Timestamp was not found. With this update, the change overrides the name of the log field used to retrieve the Timestamp and sends it to Splunk without warning. (LOG-4413)
  • Before this update, the CPU and memory usage of Vector was increasing over time. With this update, the Vector configuration now contains the expire_metrics_secs=60 setting to limit the lifetime of the metrics and cap the associated CPU usage and memory footprint. (LOG-4171)
  • Before this update, the LokiStack gateway cached authorized requests very broadly. As a result, this caused wrong authorization results. With this update, LokiStack gateway caches on a more fine-grained basis which resolves this issue. (LOG-4393)
  • Before this update, the Fluentd runtime image included builder tools which were unnecessary at runtime. With this update, the builder tools are removed, resolving the issue. (LOG-4467)

1.3.10.2. CVEs

1.3.11. Logging 5.7.4

This release includes OpenShift Logging Bug Fix Release 5.7.4.

1.3.11.1. 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)
  • Pipelines with no name field specified in the ClusterLogForwarder custom resource (CR) stopped working after upgrading to OpenShift Logging 5.7. With this update, the error has been resolved. (LOG-4120)

1.3.11.2. CVEs

1.3.12. Logging 5.7.3

This release includes OpenShift Logging Bug Fix Release 5.7.3.

1.3.12.1. Bug fixes

  • Before this update, when viewing logs within the OpenShift Container Platform 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 OpenShift Container Platform 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 OpenShift Container Platform 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 OpenShift Container Platform 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 OpenShift Container Platform 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)

1.3.12.2. CVEs

1.3.13. Logging 5.7.2

This release includes OpenShift Logging Bug Fix Release 5.7.2.

1.3.13.1. 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 OpenShift Container Platform 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 OpenShift Container Platform 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)

1.3.13.2. CVEs

1.3.14. Logging 5.7.1

This release includes: OpenShift Logging Bug Fix Release 5.7.1.

1.3.14.1. 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 OpenShift Container Platform 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 OpenShift Container Platform 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)

1.3.14.2. CVEs

1.3.15. Logging 5.7.0

This release includes OpenShift Logging Bug Fix Release 5.7.0.

1.3.15.1. 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.

1.3.15.2. Known Issues

None.

1.3.15.3. 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)

1.3.15.4. CVEs

Chapter 2. Support

Only the configuration options described in this documentation are supported for logging.

Do not use any other configuration options, 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 be overwritten, because Operators are designed to reconcile any differences.

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 logging instance is not supported and does not receive updates until you return its status to Managed.

Note

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

Logging 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.

Logging 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

2.1. Supported API custom resource definitions

LokiStack development is ongoing. Not all APIs are currently supported.

Table 2.1. Loki API support states

CustomResourceDefinition (CRD)ApiVersionSupport state

LokiStack

lokistack.loki.grafana.com/v1

Supported in 5.5

RulerConfig

rulerconfig.loki.grafana/v1

Supported in 5.7

AlertingRule

alertingrule.loki.grafana/v1

Supported in 5.7

RecordingRule

recordingrule.loki.grafana/v1

Supported in 5.7

2.2. Unsupported configurations

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

  • The Elasticsearch custom resource (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 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.

2.3. 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.

2.4. 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.

You can use the must-gather tool to collect diagnostic information for project-level resources, cluster-level resources, and each of the logging components.

For prompt support, supply diagnostic information for both OpenShift Container Platform and logging.

Note

Do not use the hack/logging-dump.sh script. The script is no longer supported and does not collect data.

2.4.1. 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, 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.

2.4.2. Collecting logging data

You can use the oc adm must-gather CLI command to collect information about logging.

Procedure

To collect logging 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 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.

Chapter 3. Troubleshooting logging

3.1. Viewing Logging status

You can view the status of the Red Hat OpenShift Logging Operator and other logging components.

3.1.1. Viewing the status of the Red Hat OpenShift Logging Operator

You can view the status of the Red Hat OpenShift Logging Operator.

Prerequisites

  • The Red Hat OpenShift Logging Operator and OpenShift Elasticsearch Operator are installed.

Procedure

  1. Change to the openshift-logging project by running the following command:

    $ oc project openshift-logging
  2. Get the ClusterLogging instance status by running the following command:

    $ oc get clusterlogging instance -o yaml

    Example output

    apiVersion: logging.openshift.io/v1
    kind: ClusterLogging
    # ...
    status:  1
      collection:
        logs:
          fluentdStatus:
            daemonSet: fluentd  2
            nodes:
              collector-2rhqp: ip-10-0-169-13.ec2.internal
              collector-6fgjh: ip-10-0-165-244.ec2.internal
              collector-6l2ff: ip-10-0-128-218.ec2.internal
              collector-54nx5: ip-10-0-139-30.ec2.internal
              collector-flpnn: ip-10-0-147-228.ec2.internal
              collector-n2frh: ip-10-0-157-45.ec2.internal
            pods:
              failed: []
              notReady: []
              ready:
              - collector-2rhqp
              - collector-54nx5
              - collector-6fgjh
              - collector-6l2ff
              - collector-flpnn
              - collector-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.

3.1.1.1. Example condition messages

The following are examples of some condition messages from the Status.Nodes section of the ClusterLogging 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:

3.1.2. Viewing the status of logging components

You can view the status for a number of logging components.

Prerequisites

  • The Red Hat OpenShift Logging Operator and OpenShift Elasticsearch Operator are installed.

Procedure

  1. Change to the openshift-logging project.

    $ oc project openshift-logging
  2. View the status of logging 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 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----

3.2. Troubleshooting log forwarding

3.2.1. Redeploying Fluentd pods

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 by running the following command:

    $ oc delete pod --selector logging-infra=collector

3.2.2. Troubleshooting Loki rate limit errors

If the Log Forwarder API forwards a large block of messages that exceeds the rate limit to Loki, Loki generates rate limit (429) errors.

These errors can occur during normal operation. For example, when adding the logging to a cluster that already has some logs, rate limit errors might occur while the logging tries to ingest all of the existing log entries. In this case, if the rate of addition of new logs is less than the total rate limit, the historical data is eventually ingested, and the rate limit errors are resolved without requiring user intervention.

In cases where the rate limit errors continue to occur, you can fix the issue by modifying the LokiStack custom resource (CR).

Important

The LokiStack CR is not available on Grafana-hosted Loki. This topic does not apply to Grafana-hosted Loki servers.

Conditions

  • The Log Forwarder API is configured to forward logs to Loki.
  • Your system sends a block of messages that is larger than 2 MB to Loki. For example:

    "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\"}"]]}]}
  • After you enter oc logs -n openshift-logging -l component=collector, the collector logs in your cluster show a line containing one of the following error messages:

    429 Too Many Requests Ingestion rate limit exceeded

    Example Vector error message

    2023-08-25T16:08:49.301780Z  WARN sink{component_kind="sink" component_id=default_loki_infra component_type=loki component_name=default_loki_infra}: vector::sinks::util::retries: Retrying after error. error=Server responded with an error: 429 Too Many Requests internal_log_rate_limit=true

    Example Fluentd error message

    2023-08-30 14:52:15 +0000 [warn]: [default_loki_infra] failed to flush the buffer. retry_times=2 next_retry_time=2023-08-30 14:52:19 +0000 chunk="604251225bf5378ed1567231a1c03b8b" error_class=Fluent::Plugin::LokiOutput::LogPostError error="429 Too Many Requests Ingestion rate limit exceeded for user infrastructure (limit: 4194304 bytes/sec) while attempting to ingest '4082' lines totaling '7820025' bytes, reduce log volume or contact your Loki administrator to see if the limit can be increased\n"

    The error is also visible on the receiving end. For example, in the LokiStack ingester pod:

    Example Loki ingester error message

    level=warn ts=2023-08-30T14:57:34.155592243Z caller=grpc_logging.go:43 duration=1.434942ms method=/logproto.Pusher/Push err="rpc error: code = Code(429) desc = entry with timestamp 2023-08-30 14:57:32.012778399 +0000 UTC ignored, reason: 'Per stream rate limit exceeded (limit: 3MB/sec) while attempting to ingest for stream

Procedure

  • Update the ingestionBurstSize and ingestionRate fields in the LokiStack CR:

    apiVersion: loki.grafana.com/v1
    kind: LokiStack
    metadata:
      name: logging-loki
      namespace: openshift-logging
    spec:
      limits:
        global:
          ingestion:
            ingestionBurstSize: 16 1
            ingestionRate: 8 2
    # ...
    1
    The ingestionBurstSize field defines the maximum local rate-limited sample size per distributor replica in MB. This value is a hard limit. Set this value to at least the maximum logs size expected in a single push request. Single requests that are larger than the ingestionBurstSize value are not permitted.
    2
    The ingestionRate field is a soft limit on the maximum amount of ingested samples per second in MB. Rate limit errors occur if the rate of logs exceeds the limit, but the collector retries sending the logs. As long as the total average is lower than the limit, the system recovers and errors are resolved without user intervention.

3.3. Troubleshooting logging alerts

You can use the following procedures to troubleshoot logging alerts on your cluster.

3.3.1. Elasticsearch cluster health status is red

At least one primary shard and its replicas are not allocated to a node. Use the following procedure to troubleshoot this alert.

Tip

Some commands in this documentation reference an Elasticsearch pod by using a $ES_POD_NAME shell variable. If you want to copy and paste the commands directly from this documentation, you must set this variable to a value that is valid for your Elasticsearch cluster.

You can list the available Elasticsearch pods by running the following command:

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

Choose one of the pods listed and set the $ES_POD_NAME variable, by running the following command:

$ export ES_POD_NAME=<elasticsearch_pod_name>

You can now use the $ES_POD_NAME variable in commands.

Procedure

  1. Check the Elasticsearch cluster health and verify that the cluster status is red by running the following command:

    $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME -- health
  2. List the nodes that have joined the cluster by running the following command:

    $ oc exec -n openshift-logging -c elasticsearch $ES_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, by running the following command:

    $ 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 master node by running the following command and observing the output:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
        -- es_util --query=_cat/master?v
    2. Review the pod logs of the elected master node for issues by running the following command and observing the output:

      $ 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 by running the following command and observing the output:

      $ oc logs <elasticsearch_node_name> -c elasticsearch -n openshift-logging
  5. If all the nodes have joined the cluster, check if the cluster is in the process of recovering by running the following command and observing the output:

    $ oc exec -n openshift-logging -c elasticsearch $ES_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 by running the following command and observing the output:

    $ oc exec -n openshift-logging -c elasticsearch $ES_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 the cluster.routing.allocation.enable value is set to none, by running the following command and observing the output:

    $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
      -- es_util --query=_cluster/settings?pretty
  9. If the cluster.routing.allocation.enable value is set to none, set it to all, by running the following command:

    $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
      -- es_util --query=_cluster/settings?pretty \
      -X PUT -d '{"persistent": {"cluster.routing.allocation.enable":"all"}}'
  10. Check if any indices are still red by running the following command and observing the output:

    $ oc exec -n openshift-logging -c elasticsearch $ES_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 by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
        -- es_util --query=<elasticsearch_index_name>/_cache/clear?pretty
    2. Increase the max allocation retries by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
        -- es_util --query=<elasticsearch_index_name>/_settings?pretty \
        -X PUT -d '{"index.allocation.max_retries":10}'
    3. Delete all the scroll items by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
        -- es_util --query=_search/scroll/_all -X DELETE
    4. Increase the timeout by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_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 by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
        -- es_util --query=_cat/indices?v
    2. Delete the red index by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_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 by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_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. For more information about CPU utilitzation, see the OpenShift Container Platform "Reviewing monitoring dashboards" documentation.

3.3.2. Elasticsearch cluster health status is yellow

Replica shards for at least one primary shard are not allocated to nodes. Increase the node count by adjusting the nodeCount value in the ClusterLogging custom resource (CR).

3.3.3. Elasticsearch node disk low watermark reached

Elasticsearch does not allocate shards to nodes that reach the low watermark.

Tip

Some commands in this documentation reference an Elasticsearch pod by using a $ES_POD_NAME shell variable. If you want to copy and paste the commands directly from this documentation, you must set this variable to a value that is valid for your Elasticsearch cluster.

You can list the available Elasticsearch pods by running the following command:

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

Choose one of the pods listed and set the $ES_POD_NAME variable, by running the following command:

$ export ES_POD_NAME=<elasticsearch_pod_name>

You can now use the $ES_POD_NAME variable in commands.

Procedure

  1. Identify the node on which Elasticsearch is deployed by running the following command:

    $ oc -n openshift-logging get po -o wide
  2. Check if there are unassigned shards by running the following command:

    $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
      -- es_util --query=_cluster/health?pretty | grep unassigned_shards
  3. If there are unassigned shards, check the disk space on each node, by running the following command:

    $ 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. In the command output, check the Use column to determine the used disk percentage on that node.

    Example output

    elasticsearch-cdm-kcrsda6l-1-586cc95d4f-h8zq8
    Filesystem      Size  Used Avail Use% Mounted on
    /dev/nvme1n1     19G  522M   19G   3% /elasticsearch/persistent
    elasticsearch-cdm-kcrsda6l-2-5b548fc7b-cwwk7
    Filesystem      Size  Used Avail Use% Mounted on
    /dev/nvme2n1     19G  522M   19G   3% /elasticsearch/persistent
    elasticsearch-cdm-kcrsda6l-3-5dfc884d99-59tjw
    Filesystem      Size  Used Avail Use% Mounted on
    /dev/nvme3n1     19G  528M   19G   3% /elasticsearch/persistent

    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. To check the current redundancyPolicy, run the following command:

    $ oc -n openshift-logging get es elasticsearch \
      -o jsonpath='{.spec.redundancyPolicy}'

    If you are using a ClusterLogging resource on your cluster, run the following command:

    $ oc -n openshift-logging get cl \
      -o jsonpath='{.items[*].spec.logStore.elasticsearch.redundancyPolicy}'

    If the cluster redundancyPolicy value is higher than the SingleRedundancy value, set it to the SingleRedundancy value 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 by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME -- indices
    2. Identify an old index that can be deleted.
    3. Delete the index by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
        -- es_util --query=<elasticsearch_index_name> -X DELETE

3.3.4. Elasticsearch node disk high watermark reached

Elasticsearch attempts to relocate shards away from a node that has reached the high watermark to a node with low disk usage that has not crossed any watermark threshold limits.

To allocate shards to a particular node, you must free up some space on that node. If increasing the disk space is not possible, try adding a new data node to the cluster, or decrease the total cluster redundancy policy.

Tip

Some commands in this documentation reference an Elasticsearch pod by using a $ES_POD_NAME shell variable. If you want to copy and paste the commands directly from this documentation, you must set this variable to a value that is valid for your Elasticsearch cluster.

You can list the available Elasticsearch pods by running the following command:

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

Choose one of the pods listed and set the $ES_POD_NAME variable, by running the following command:

$ export ES_POD_NAME=<elasticsearch_pod_name>

You can now use the $ES_POD_NAME variable in commands.

Procedure

  1. Identify the node on which Elasticsearch is deployed by running the following command:

    $ 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 $ES_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%.

  4. Increase the disk space on all nodes. If increasing the disk space is not possible, try adding a new data node to the cluster, or decrease the total cluster redundancy policy.
  5. To check the current redundancyPolicy, run the following command:

    $ oc -n openshift-logging get es elasticsearch \
      -o jsonpath='{.spec.redundancyPolicy}'

    If you are using a ClusterLogging resource on your cluster, run the following command:

    $ oc -n openshift-logging get cl \
      -o jsonpath='{.items[*].spec.logStore.elasticsearch.redundancyPolicy}'

    If the cluster redundancyPolicy value is higher than the SingleRedundancy value, set it to the SingleRedundancy value 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 by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME -- indices
    2. Identify an old index that can be deleted.
    3. Delete the index by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
        -- es_util --query=<elasticsearch_index_name> -X DELETE

3.3.5. 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.

Use the following procedure to troubleshoot this alert.

Tip

Some commands in this documentation reference an Elasticsearch pod by using a $ES_POD_NAME shell variable. If you want to copy and paste the commands directly from this documentation, you must set this variable to a value that is valid for your Elasticsearch cluster.

You can list the available Elasticsearch pods by running the following command:

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

Choose one of the pods listed and set the $ES_POD_NAME variable, by running the following command:

$ export ES_POD_NAME=<elasticsearch_pod_name>

You can now use the $ES_POD_NAME variable in commands.

Procedure

  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 Avail column to determine the free disk space on that node.

    Example output

    elasticsearch-cdm-kcrsda6l-1-586cc95d4f-h8zq8
    Filesystem      Size  Used Avail Use% Mounted on
    /dev/nvme1n1     19G  522M   19G   3% /elasticsearch/persistent
    elasticsearch-cdm-kcrsda6l-2-5b548fc7b-cwwk7
    Filesystem      Size  Used Avail Use% Mounted on
    /dev/nvme2n1     19G  522M   19G   3% /elasticsearch/persistent
    elasticsearch-cdm-kcrsda6l-3-5dfc884d99-59tjw
    Filesystem      Size  Used Avail Use% Mounted on
    /dev/nvme3n1     19G  528M   19G   3% /elasticsearch/persistent

  3. Increase the disk space on all nodes. If increasing the disk space is not possible, try adding a new data node to the cluster, or decrease the total cluster redundancy policy.
  4. To check the current redundancyPolicy, run the following command:

    $ oc -n openshift-logging get es elasticsearch \
      -o jsonpath='{.spec.redundancyPolicy}'

    If you are using a ClusterLogging resource on your cluster, run the following command:

    $ oc -n openshift-logging get cl \
      -o jsonpath='{.items[*].spec.logStore.elasticsearch.redundancyPolicy}'

    If the cluster redundancyPolicy value is higher than the SingleRedundancy value, set it to the SingleRedundancy value and save this change.

  5. If the preceding steps do not fix the issue, delete the old indices.

    1. Check the status of all indices on Elasticsearch by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME -- indices
    2. Identify an old index that can be deleted.
    3. Delete the index by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
        -- es_util --query=<elasticsearch_index_name> -X DELETE
  6. Continue freeing up and monitoring the disk space. After the used disk space drops below 90%, unblock writing to this node by running the following command:

    $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
      -- es_util --query=_all/_settings?pretty \
      -X PUT -d '{"index.blocks.read_only_allow_delete": null}'

3.3.6. Elasticsearch JVM heap usage is high

The Elasticsearch node Java virtual machine (JVM) heap memory used is above 75%. Consider increasing the heap size.

3.3.7. Aggregated logging system CPU is high

System CPU usage on the node is high. Check the CPU of the cluster node. Consider allocating more CPU resources to the node.

3.3.8. Elasticsearch process CPU is high

Elasticsearch process CPU usage on the node is high. Check the CPU of the cluster node. Consider allocating more CPU resources to the node.

3.3.9. Elasticsearch disk space is running low

Elasticsearch is predicted to run out of disk space within the next 6 hours based on current disk usage. Use the following procedure to troubleshoot this alert.

Procedure

  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 Avail column to determine the free disk space on that node.

    Example output

    elasticsearch-cdm-kcrsda6l-1-586cc95d4f-h8zq8
    Filesystem      Size  Used Avail Use% Mounted on
    /dev/nvme1n1     19G  522M   19G   3% /elasticsearch/persistent
    elasticsearch-cdm-kcrsda6l-2-5b548fc7b-cwwk7
    Filesystem      Size  Used Avail Use% Mounted on
    /dev/nvme2n1     19G  522M   19G   3% /elasticsearch/persistent
    elasticsearch-cdm-kcrsda6l-3-5dfc884d99-59tjw
    Filesystem      Size  Used Avail Use% Mounted on
    /dev/nvme3n1     19G  528M   19G   3% /elasticsearch/persistent

  3. Increase the disk space on all nodes. If increasing the disk space is not possible, try adding a new data node to the cluster, or decrease the total cluster redundancy policy.
  4. To check the current redundancyPolicy, run the following command:

    $ oc -n openshift-logging get es elasticsearch -o jsonpath='{.spec.redundancyPolicy}'

    If you are using a ClusterLogging resource on your cluster, run the following command:

    $ oc -n openshift-logging get cl \
      -o jsonpath='{.items[*].spec.logStore.elasticsearch.redundancyPolicy}'

    If the cluster redundancyPolicy value is higher than the SingleRedundancy value, set it to the SingleRedundancy value and save this change.

  5. If the preceding steps do not fix the issue, delete the old indices.

    1. Check the status of all indices on Elasticsearch by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME -- indices
    2. Identify an old index that can be deleted.
    3. Delete the index by running the following command:

      $ oc exec -n openshift-logging -c elasticsearch $ES_POD_NAME \
        -- es_util --query=<elasticsearch_index_name> -X DELETE

3.3.10. Elasticsearch FileDescriptor usage is high

Based on current usage trends, the predicted number of file descriptors on the node is insufficient. Check the value of max_file_descriptors for each node as described in the Elasticsearch File Descriptors documentation.

3.4. 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.

3.4.1. Viewing the status of the Elasticsearch log store

You can view the status of the Elasticsearch log store.

Prerequisites

  • The Red Hat OpenShift Logging Operator and OpenShift Elasticsearch Operator are installed.

Procedure

  1. Change to the openshift-logging project by running the following command:

    $ oc project openshift-logging
  2. To view the status:

    1. Get the name of the Elasticsearch log store instance by running the following command:

      $ oc get Elasticsearch

      Example output

      NAME            AGE
      elasticsearch   5h9m

    2. Get the Elasticsearch log store status by running the following command:

      $ 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 Elasticsearch log store:
      • The number of active primary shards.
      • The number of active shards.
      • The number of shards that are initializing.
      • The number of Elasticsearch log store data nodes.
      • The total number of Elasticsearch log store nodes.
      • The number of pending tasks.
      • The Elasticsearch log store status: green, red, yellow.
      • The number of unassigned shards.
      3
      Any status conditions, if present. The Elasticsearch 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 Elasticsearch log store and proxy containers.
      • Container Terminated for both the Elasticsearch log store and proxy containers.
      • Pod unschedulable. Also, a condition is shown for a number of issues; see Example condition messages.
      4
      The Elasticsearch log store nodes in the cluster, with upgradeStatus.
      5
      The Elasticsearch log store client, data, and master pods in the cluster, listed under failed, notReady, or ready state.

3.4.1.1. 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 Elasticsearch log store node selector in the custom resource (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 Elasticsearch 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 Elasticsearch 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 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.

3.4.2. 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>

3.4.3. Elasticsearch cluster status

A dashboard in the Observe section of the OpenShift Container Platform web console displays the status of the Elasticsearch cluster.

To get the status of the OpenShift Elasticsearch cluster, visit the dashboard in the Observe section of the OpenShift Container Platform web console at <cluster_url>/monitoring/dashboards/grafana-dashboard-cluster-logging.

Elasticsearch status fields

eo_elasticsearch_cr_cluster_management_state

Shows whether the Elasticsearch cluster is in a managed or unmanaged state. For example:

eo_elasticsearch_cr_cluster_management_state{state="managed"} 1
eo_elasticsearch_cr_cluster_management_state{state="unmanaged"} 0
eo_elasticsearch_cr_restart_total

Shows the number of times the Elasticsearch nodes have restarted for certificate restarts, rolling restarts, or scheduled restarts. For example:

eo_elasticsearch_cr_restart_total{reason="cert_restart"} 1
eo_elasticsearch_cr_restart_total{reason="rolling_restart"} 1
eo_elasticsearch_cr_restart_total{reason="scheduled_restart"} 3
es_index_namespaces_total

Shows the total number of Elasticsearch index namespaces. For example:

Total number of Namespaces.
es_index_namespaces_total 5
es_index_document_count

Shows the number of records for each namespace. For example:

es_index_document_count{namespace="namespace_1"} 25
es_index_document_count{namespace="namespace_2"} 10
es_index_document_count{namespace="namespace_3"} 5

The "Secret Elasticsearch fields are either missing or empty" message

If Elasticsearch is missing the admin-cert, admin-key, logging-es.crt, or logging-es.key files, the dashboard shows a status message similar to the following example:

message": "Secret \"elasticsearch\" fields are either missing or empty: [admin-cert, admin-key, logging-es.crt, logging-es.key]",
"reason": "Missing Required Secrets",

Chapter 4. About Logging

As a cluster administrator, you can deploy logging on an OpenShift Container Platform cluster, and use it to collect and aggregate node system audit logs, application container logs, and infrastructure logs. You can forward logs to your chosen log outputs, including on-cluster, Red Hat managed log storage. You can also visualize your log data in the OpenShift Container Platform web console, or the Kibana web console, depending on your deployed log storage solution.

Note

The Kibana web console is now deprecated is planned to be removed in a future logging release.

OpenShift Container Platform cluster administrators can deploy logging by using Operators. For information, see Installing logging.

The Operators are responsible for deploying, upgrading, and maintaining logging. After the Operators are installed, you can create a ClusterLogging custom resource (CR) to schedule logging pods and other resources necessary to support logging. You can also create a ClusterLogForwarder CR to specify which logs are collected, how they are transformed, and where they are forwarded to.

Note

Because the internal OpenShift Container Platform 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.

4.1. Logging architecture

The major components of the logging are:

Collector

The collector is a daemonset that deploys pods to each OpenShift Container Platform node. It collects log data from each node, transforms the data, and forwards it to configured outputs. You can use the Vector collector or the legacy Fluentd collector.

Note

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

Log store

The log store stores log data for analysis and is the default output for the log forwarder. You can use the default LokiStack log store, the legacy Elasticsearch log store, or forward logs to additional external log stores.

Note

The Logging 5.9 release does not contain an updated version of the OpenShift Elasticsearch Operator. If you currently use the OpenShift Elasticsearch Operator released with Logging 5.8, it will continue to work with Logging until the EOL of Logging 5.8. As an alternative to using the OpenShift Elasticsearch Operator to manage the default log storage, you can use the Loki Operator. For more information on the Logging lifecycle dates, see Platform Agnostic Operators.

Visualization

You can use a UI component to view a visual representation of your log data. The UI provides a graphical interface to search, query, and view stored logs. The OpenShift Container Platform web console UI is provided by enabling the OpenShift Container Platform console plugin.

Note

The Kibana web console is now deprecated is planned to be removed in a future logging release.

Logging collects container logs and node logs. These are categorized into types:

Application logs
Container logs generated by user applications running in the cluster, except infrastructure container applications.
Infrastructure logs
Container logs generated by infrastructure namespaces: openshift*, kube*, or default, as well as journald messages from nodes.
Audit logs
Logs generated by auditd, the node audit system, which are stored in the /var/log/audit/audit.log file, and logs from the auditd, kube-apiserver, openshift-apiserver services, as well as the ovn project if enabled.

4.2. About deploying logging

Administrators can deploy the logging by using the OpenShift Container Platform web console or the OpenShift CLI (oc) to install the logging Operators. The Operators are responsible for deploying, upgrading, and maintaining the logging.

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

4.2.1. Logging custom resources

You can configure your logging deployment with custom resource (CR) YAML files implemented by each Operator.

Red Hat OpenShift Logging Operator:

  • ClusterLogging (CL) - After the Operators are installed, you create a ClusterLogging custom resource (CR) to schedule logging pods and other resources necessary to support the logging. The ClusterLogging CR deploys the collector and forwarder, which currently are both implemented by a daemonset running on each node. The Red Hat OpenShift Logging Operator watches the ClusterLogging CR and adjusts the logging deployment accordingly.
  • ClusterLogForwarder (CLF) - Generates collector configuration to forward logs per user configuration.

Loki Operator:

  • LokiStack - Controls the Loki cluster as log store and the web proxy with OpenShift Container Platform authentication integration to enforce multi-tenancy.

OpenShift Elasticsearch Operator:

Note

These CRs are generated and managed by the OpenShift Elasticsearch Operator. Manual changes cannot be made without being overwritten by the Operator.

  • ElasticSearch - Configure and deploy an Elasticsearch instance as the default log store.
  • Kibana - Configure and deploy Kibana instance to search, query and view logs.

4.2.2. About JSON OpenShift Container Platform 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

4.2.3. About collecting and storing Kubernetes events

The OpenShift Container Platform Event Router is a pod that watches Kubernetes events and logs them for collection by OpenShift Container Platform Logging. You must manually deploy the Event Router.

For information, see About collecting and storing Kubernetes events.

4.2.4. About troubleshooting OpenShift Container Platform 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

4.2.5. 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.

4.2.6. About event routing

The Event Router is a pod that watches OpenShift Container Platform events so they can be collected by logging. The Event Router collects events from all projects and writes them to STDOUT. Fluentd collects those events and forwards them into the OpenShift Container Platform 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.

Chapter 5. Installing Logging

You can deploy logging by installing the Red Hat OpenShift Logging Operator. The Red Hat OpenShift Logging Operator creates and manages the components of the logging stack.

Note

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

Important

For new installations, use Vector and LokiStack. Elasticsearch and Fluentd are deprecated and are planned to be removed in a future release.

5.1. Installing the Red Hat OpenShift Logging Operator by using the web console

You can install the Red Hat OpenShift Logging Operator by using the OpenShift Container Platform web console.

Prerequisites

  • You have administrator permissions.
  • You have access to the OpenShift Container Platform web console.

Procedure

  1. In the OpenShift Container Platform web console, click OperatorsOperatorHub.
  2. Type OpenShift Logging in the Filter by keyword box.
  3. Choose Red Hat OpenShift Logging from the list of available Operators, and click Install.
  4. Select stable-5.y as the Update channel.

    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.y, where x.y represents the major and minor version of logging you have installed. For example, stable-5.7.

  5. Select a Version.
  6. Ensure that A specific namespace on the cluster is selected under Installation mode.
  7. Ensure that Operator recommended namespace is openshift-logging under Installed Namespace.
  8. Select an Update approval.

    • 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.
  9. Select Enable or Disable for the Console plugin.
  10. Click Install.

Verification

  1. Verify that the Red Hat OpenShift Logging Operator is installed by switching to the OperatorsInstalled Operators page.
  2. In the Status column, verify that you see green checkmarks with InstallSucceeded and the text Up to date.
Important

An Operator might display a Failed status before the installation finishes. If the Operator install completes with an InstallSucceeded message, refresh the page.

If the Operator does not show as installed, choose one of the following troubleshooting options:

  • Go to the OperatorsInstalled Operators page, and inspect the Status column for any errors or failures.
  • Go to the WorkloadsPods page, and check the logs in any pods in the openshift-logging project that are reporting issues.

5.2. Creating a ClusterLogging object by using the web console

After you have installed the logging Operators, you must create a ClusterLogging custom resource to configure log storage, visualization, and the log collector for your cluster.

Prerequisites

  • You have installed the Red Hat OpenShift Logging Operator.
  • You have access to the OpenShift Container Platform web console Administrator perspective.

Procedure

  1. Navigate to the 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.
  5. In the collection section, select a Collector Implementation.

    Note

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

  6. In the logStore section, select a type.

    Note

    The Logging 5.9 release does not contain an updated version of the OpenShift Elasticsearch Operator. If you currently use the OpenShift Elasticsearch Operator released with Logging 5.8, it will continue to work with Logging until the EOL of Logging 5.8. As an alternative to using the OpenShift Elasticsearch Operator to manage the default log storage, you can use the Loki Operator. For more information on the Logging lifecycle dates, see Platform Agnostic Operators.

  7. Click Create.

5.3. Installing the Red Hat OpenShift Logging Operator by using the CLI

You can use the OpenShift CLI (oc) to install the Red Hat OpenShift Logging Operator.

Prerequisites

  • You have administrator permissions.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Create a Namespace object as a YAML file:

    Example Namespace object

    apiVersion: v1
    kind: Namespace
    metadata:
      name: <name> 1
      annotations:
        openshift.io/node-selector: ""
      labels:
        openshift.io/cluster-monitoring: "true"

    1
    You must specify openshift-logging as the name of the namespace for logging versions 5.7 and earlier versions. For logging 5.8 and later versions, you can use any name.
  2. Apply the Namespace object by running the following command:

    $ oc apply -f <filename>.yaml
  3. Create an OperatorGroup object as a YAML file:

    Example OperatorGroup object

    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 for logging versions 5.7 and older. For logging 5.8 and later versions, you can use any namespace.
  4. Apply the OperatorGroup object by running the following command:

    $ oc apply -f <filename>.yaml
  5. Create a Subscription object to subscribe the namespace to the Red Hat OpenShift Logging Operator:

    Example Subscription object

    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 for logging versions 5.7 and older. For logging 5.8 and later versions, you can use any namespace.
    2
    Specify stable or stable-x.y as the channel.
    3
    Specify redhat-operators. If your OpenShift Container Platform 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).
  6. Apply the subscription by running the following command:

    $ oc apply -f <filename>.yaml

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

Verification

  1. Run the following command:

    $ oc get csv -n <namespace>
  2. Observe the output and confirm that the Red Hat OpenShift Logging Operator exists in the namespace:

    Example output

    NAMESPACE                                               NAME                                         DISPLAY                  VERSION               REPLACES   PHASE
    ...
    openshift-logging                                       clusterlogging.5.8.0-202007012112.p0         OpenShift Logging          5.8.0-202007012112.p0              Succeeded
    ...

5.4. Creating a ClusterLogging object by using the CLI

This default logging configuration supports a wide array of environments. Review the topics on tuning and configuring components for information about modifications you can make.

Prerequisites

  • You have installed the Red Hat OpenShift Logging Operator.
  • You have installed the OpenShift Elasticsearch Operator for your log store.
  • You have installed the OpenShift CLI (oc).

Procedure

  1. Create a ClusterLogging object as a YAML file:

    Example ClusterLogging object

    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:
        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, OpenShift Container Platform 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

    NAME                           READY   UP-TO-DATE   AVAILABLE   AGE
    cluster-logging-operator       1/1     1            1           18h
    elasticsearch-cd-x6kdekli-1    1/1     1            1          6m54s
    elasticsearch-cdm-x6kdekli-1   1/1     1            1           18h
    elasticsearch-cdm-x6kdekli-2   1/1     1            1           6m49s
    elasticsearch-cdm-x6kdekli-3   1/1     1            1           6m44s

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

Verification

You can verify the installation by listing the pods in the openshift-logging project.

  • List the pods by running the following command:

    $ oc get pods -n openshift-logging

    Observe the pods for components of the logging, similar to the following list:

    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

5.5. Postinstallation tasks

After you have installed the Red Hat OpenShift Logging Operator, you can configure your deployment by creating and modifying a ClusterLogging custom resource (CR).

Tip

If you are not using the 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. See Removing unused components if you do not use the Elasticsearch log store.

5.5.1. About the ClusterLogging custom resource

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

Sample ClusterLogging custom resource (CR)

apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  managementState: Managed 3
# ...

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 the state is set to unmanaged, the Operator is in an unsupported state and does not receive updates.

5.5.2. Configuring log storage

You can configure which log storage type your logging uses by modifying the ClusterLogging custom resource (CR).

Prerequisites

  • You have administrator permissions.
  • You have installed the OpenShift CLI (oc).
  • You have installed the Red Hat OpenShift Logging Operator and an internal log store that is either the LokiStack or Elasticsearch.
  • You have created a ClusterLogging CR.
Note

The Logging 5.9 release does not contain an updated version of the OpenShift Elasticsearch Operator. If you currently use the OpenShift Elasticsearch Operator released with Logging 5.8, it will continue to work with Logging until the EOL of Logging 5.8. As an alternative to using the OpenShift Elasticsearch Operator to manage the default log storage, you can use the Loki Operator. For more information on the Logging lifecycle dates, see Platform Agnostic Operators.

Procedure

  1. Modify the ClusterLogging CR logStore spec:

    ClusterLogging CR example

    apiVersion: logging.openshift.io/v1
    kind: ClusterLogging
    metadata:
    # ...
    spec:
    # ...
      logStore:
        type: <log_store_type> 1
        elasticsearch: 2
          nodeCount: <integer>
          resources: {}
          storage: {}
          redundancyPolicy: <redundancy_type> 3
        lokistack: 4
          name: {}
    # ...

    1
    Specify the log store type. This can be either lokistack or elasticsearch.
    2
    Optional configuration options for the Elasticsearch log store.
    3
    Specify the redundancy type. This value can be ZeroRedundancy, SingleRedundancy, MultipleRedundancy, or FullRedundancy.
    4
    Optional configuration options for LokiStack.

    Example ClusterLogging CR to specify LokiStack as the log store

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

  2. Apply the ClusterLogging CR by running the following command:

    $ oc apply -f <filename>.yaml

5.5.3. Configuring the log collector

You can configure which log collector type your logging uses by modifying the ClusterLogging custom resource (CR).

Note

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

Prerequisites

  • You have administrator permissions.
  • You have installed the OpenShift CLI (oc).
  • You have installed the Red Hat OpenShift Logging Operator.
  • You have created a ClusterLogging CR.

Procedure

  1. Modify the ClusterLogging CR collection spec:

    ClusterLogging CR example

    apiVersion: logging.openshift.io/v1
    kind: ClusterLogging
    metadata:
    # ...
    spec:
    # ...
      collection:
        type: <log_collector_type> 1
        resources: {}
        tolerations: {}
    # ...

    1
    The log collector type you want to use for the logging. This can be vector or fluentd.
  2. Apply the ClusterLogging CR by running the following command:

    $ oc apply -f <filename>.yaml

5.5.4. Configuring the log visualizer

You can configure which log visualizer type your logging uses by modifying the ClusterLogging custom resource (CR).

Prerequisites

  • You have administrator permissions.
  • You have installed the OpenShift CLI (oc).
  • You have installed the Red Hat OpenShift Logging Operator.
  • You have created a ClusterLogging CR.
Important

If you want to use the OpenShift Container Platform web console for visualization, you must enable the logging Console Plugin. See the documentation about "Log visualization with the web console".

Procedure

  1. Modify the ClusterLogging CR visualization spec:

    ClusterLogging CR example

    apiVersion: logging.openshift.io/v1
    kind: ClusterLogging
    metadata:
    # ...
    spec:
    # ...
      visualization:
        type: <visualizer_type> 1
        kibana: 2
          resources: {}
          nodeSelector: {}
          proxy: {}
          replicas: {}
          tolerations: {}
        ocpConsole: 3
          logsLimit: {}
          timeout: {}
    # ...

    1
    The type of visualizer you want to use for your logging. This can be either kibana or ocp-console. The Kibana console is only compatible with deployments that use Elasticsearch log storage, while the OpenShift Container Platform console is only compatible with LokiStack deployments.
    2
    Optional configurations for the Kibana console.
    3
    Optional configurations for the OpenShift Container Platform web console.
  2. Apply the ClusterLogging CR by running the following command:

    $ oc apply -f <filename>.yaml

5.5.5. 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 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.

OpenShift Container Platform 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.
multitenant
This mode enforces network isolation. You must join the two logging-related projects to allow traffic between them.
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

Chapter 6. Updating Logging

There are two types of logging updates: minor release updates (5.y.z) and major release updates (5.y).

6.1. Minor release updates

If you installed the logging Operators using the Automatic update approval option, your Operators receive minor version updates automatically. You do not need to complete any manual update steps.

If you installed the logging Operators using the Manual update approval option, you must manually approve minor version updates. For more information, see Manually approving a pending Operator update.

6.2. Major release updates

For major version updates you must complete some manual steps.

For major release version compatibility and support information, see OpenShift Operator Life Cycles.

6.3. Upgrading the Red Hat OpenShift Logging Operator to watch all namespaces

In logging 5.7 and older versions, the Red Hat OpenShift Logging Operator only watches the openshift-logging namespace. If you want the Red Hat OpenShift Logging Operator to watch all namespaces on your cluster, you must redeploy the Operator. You can complete the following procedure to redeploy the Operator without deleting your logging components.

Prerequisites

  • You have installed the OpenShift CLI (oc).
  • You have administrator permissions.

Procedure

  1. Delete the subscription by running the following command:

    $ oc -n openshift-logging delete subscription <subscription>
  2. Delete the Operator group by running the following command:

    $ oc -n openshift-logging delete operatorgroup <operator_group_name>
  3. Delete the cluster service version (CSV) by running the following command:

    $ oc delete clusterserviceversion cluster-logging.<version>
  4. Redeploy the Red Hat OpenShift Logging Operator by following the "Installing Logging" documentation.

Verification

  • Check that the targetNamespaces field in the OperatorGroup resource is not present or is set to an empty string.

    To do this, run the following command and inspect the output:

    $ oc get operatorgroup <operator_group_name> -o yaml

    Example output

    apiVersion: operators.coreos.com/v1
    kind: OperatorGroup
    metadata:
      name: openshift-logging-f52cn
      namespace: openshift-logging
    spec:
      upgradeStrategy: Default
    status:
      namespaces:
      - ""
    # ...

6.4. Updating the Red Hat OpenShift Logging Operator

To update the Red Hat OpenShift Logging Operator to a new major release version, you must modify the update channel for the Operator subscription.

Prerequisites

  • You have installed the Red Hat OpenShift Logging Operator.
  • You have administrator permissions.
  • You have access to the OpenShift Container Platform web console and are viewing the Administrator perspective.

Procedure

  1. Navigate to OperatorsInstalled Operators.
  2. Select the openshift-logging project.
  3. Click the Red Hat OpenShift Logging Operator.
  4. Click Subscription. In the Subscription details section, click the Update channel link. This link text might be stable or stable-5.y, depending on your current update channel.
  5. In the Change Subscription Update Channel window, select the latest major version update channel, stable-5.y, and click Save. Note the cluster-logging.v5.y.z version.

Verification

  1. Wait for a few seconds, then click OperatorsInstalled Operators. Verify that the Red Hat OpenShift Logging Operator version matches the latest cluster-logging.v5.y.z version.
  2. On the OperatorsInstalled Operators page, wait for the Status field to report Succeeded.

6.5. Updating the Loki Operator

To update the Loki Operator to a new major release version, you must modify the update channel for the Operator subscription.

Prerequisites

  • You have installed the Loki Operator.
  • You have administrator permissions.
  • You have access to the OpenShift Container Platform web console and are viewing the Administrator perspective.

Procedure

  1. Navigate to OperatorsInstalled Operators.
  2. Select the openshift-operators-redhat project.
  3. Click the Loki Operator.
  4. Click Subscription. In the Subscription details section, click the Update channel link. This link text might be stable or stable-5.y, depending on your current update channel.
  5. In the Change Subscription Update Channel window, select the latest major version update channel, stable-5.y, and click Save. Note the loki-operator.v5.y.z version.

Verification

  1. Wait for a few seconds, then click OperatorsInstalled Operators. Verify that the Loki Operator version matches the latest loki-operator.v5.y.z version.
  2. On the OperatorsInstalled Operators page, wait for the Status field to report Succeeded.

6.6. Updating the OpenShift Elasticsearch Operator

To update the OpenShift Elasticsearch Operator to the current version, you must modify the subscription.

Note

The Logging 5.9 release does not contain an updated version of the OpenShift Elasticsearch Operator. If you currently use the OpenShift Elasticsearch Operator released with Logging 5.8, it will continue to work with Logging until the EOL of Logging 5.8. As an alternative to using the OpenShift Elasticsearch Operator to manage the default log storage, you can use the Loki Operator. For more information on the Logging lifecycle dates, see Platform Agnostic Operators.

Prerequisites

  • If you are using Elasticsearch as the default log store, and Kibana as the UI, update the OpenShift Elasticsearch Operator before you update the Red Hat OpenShift Logging Operator.

    Important

    If you update the Operators in the wrong order, Kibana does not update and the Kibana custom resource (CR) is not created. To fix this issue, 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.

  • The Logging status is healthy:

    • All pods have a ready status.
    • The Elasticsearch cluster is healthy.
  • Your Elasticsearch and Kibana data is backed up.
  • You have administrator permissions.
  • You have installed the OpenShift CLI (oc) for the verification steps.

Procedure

  1. In the OpenShift Container Platform web console, click OperatorsInstalled Operators.
  2. Select the openshift-operators-redhat project.
  3. Click OpenShift Elasticsearch Operator.
  4. Click SubscriptionChannel.
  5. In the Change Subscription Update Channel window, select stable-5.y and click Save. Note the elasticsearch-operator.v5.y.z version.
  6. Wait for a few seconds, then click OperatorsInstalled Operators. Verify that the OpenShift Elasticsearch Operator version matches the latest elasticsearch-operator.v5.y.z version.
  7. On the OperatorsInstalled Operators page, wait for the Status field to report Succeeded.

Verification

  1. Verify that all Elasticsearch pods have a Ready status by entering the following command and observing the output:

    $ 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. Verify that the Elasticsearch cluster status is green by entering the following command and observing the output:

    $ oc exec -n openshift-logging -c elasticsearch elasticsearch-cdm-1pbrl44l-1-55b7546f4c-mshhk -- health

    Example output

    {
      "cluster_name" : "elasticsearch",
      "status" : "green",
    }

  3. Verify that the Elasticsearch cron jobs are created by entering the following commands and observing the output:

    $ oc project openshift-logging
    $ oc get cronjob

    Example output

    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 the correct version and the indices are green by entering the following command and observing the output:

    $ oc exec -c elasticsearch <any_es_pod_in_the_cluster> -- indices

    Verify that the output includes the app-00000x, infra-00000x, audit-00000x, .security indices:

    Example 6.1. 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
  5. Verify that the log visualizer is updated to the correct version by entering the following command and observing the output:

    $ oc get kibana kibana -o json

    Verify that the output includes a Kibana pod with the ready status:

    Example 6.2. 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 7. Visualizing logs

7.1. About log visualization

You can visualize your log data in the OpenShift Container Platform web console, or the Kibana web console, depending on your deployed log storage solution. The Kibana console can be used with ElasticSearch log stores, and the OpenShift Container Platform web console can be used with the ElasticSearch log store or the LokiStack.

Note

The Kibana web console is now deprecated is planned to be removed in a future logging release.

7.1.1. Configuring the log visualizer

You can configure which log visualizer type your logging uses by modifying the ClusterLogging custom resource (CR).

Prerequisites

  • You have administrator permissions.
  • You have installed the OpenShift CLI (oc).
  • You have installed the Red Hat OpenShift Logging Operator.
  • You have created a ClusterLogging CR.
Important

If you want to use the OpenShift Container Platform web console for visualization, you must enable the logging Console Plugin. See the documentation about "Log visualization with the web console".

Procedure

  1. Modify the ClusterLogging CR visualization spec:

    ClusterLogging CR example

    apiVersion: logging.openshift.io/v1
    kind: ClusterLogging
    metadata:
    # ...
    spec:
    # ...
      visualization:
        type: <visualizer_type> 1
        kibana: 2
          resources: {}
          nodeSelector: {}
          proxy: {}
          replicas: {}
          tolerations: {}
        ocpConsole: 3
          logsLimit: {}
          timeout: {}
    # ...

    1
    The type of visualizer you want to use for your logging. This can be either kibana or ocp-console. The Kibana console is only compatible with deployments that use Elasticsearch log storage, while the OpenShift Container Platform console is only compatible with LokiStack deployments.
    2
    Optional configurations for the Kibana console.
    3
    Optional configurations for the OpenShift Container Platform web console.
  2. Apply the ClusterLogging CR by running the following command:

    $ oc apply -f <filename>.yaml

7.1.2. Viewing logs for a resource

Resource logs are a default feature that provides limited log viewing capability. You can view the logs for various resources, such as builds, deployments, and pods by using the OpenShift CLI (oc) and the web console.

Tip

To enhance your log retrieving and viewing experience, install the logging. The logging aggregates all the logs from your OpenShift Container Platform 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 console or the OpenShift Container Platform web console. Resource logs do not access the logging log store.

7.1.2.1. 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 OpenShift Container Platform console, navigate to WorkloadsPods 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.

7.2. Log visualization with the web console

You can use the OpenShift Container Platform web console to visualize log data by configuring the logging Console Plugin.

For information about configuring the plugin during the logging installation, see Installing the logging using the web console.

If you have already installed the logging and want to configure the plugin, use one of the following procedures.

7.2.1. Enabling the logging Console Plugin after you have installed the Red Hat OpenShift Logging Operator

You can enable the logging Console Plugin as part of the Red Hat OpenShift Logging Operator installation, but you can also enable the plugin if you have already installed the Red Hat OpenShift Logging Operator with the plugin disabled.

Prerequisites

  • You have administrator permissions.
  • You have installed the Red Hat OpenShift Logging Operator and selected Disabled for the Console plugin.
  • You have access to the OpenShift Container Platform web console.

Procedure

  1. In the OpenShift Container Platform web console Administrator perspective, navigate to OperatorsInstalled Operators.
  2. Click Red Hat OpenShift Logging. This takes you to the Operator Details page.
  3. In the Details page, click Disabled for the Console plugin option.
  4. In the Console plugin enablement dialog, select Enable.
  5. Click Save.
  6. Verify that the Console plugin option now shows Enabled.
  7. The web console displays a pop-up window when changes have been applied. The window prompts you to reload the web console. Refresh the browser when you see the pop-up window to apply the changes.

7.2.2. Configuring the logging Console Plugin when you have the Elasticsearch log store and LokiStack installed

In logging version 5.8 and later, if the Elasticsearch log store is your default log store but you have also installed the LokiStack, you can enable the logging Console Plugin by using the following procedure.

Prerequisites

  • You have administrator permissions.
  • You have installed the Red Hat OpenShift Logging Operator, the OpenShift Elasticsearch Operator, and the Loki Operator.
  • You have installed the OpenShift CLI (oc).
  • You have created a ClusterLogging custom resource (CR).

Procedure

  1. Ensure that the logging Console Plugin is enabled by running the following command:

    $ oc get consoles.operator.openshift.io cluster -o yaml |grep logging-view-plugin  \
    || oc patch consoles.operator.openshift.io cluster  --type=merge \
    --patch '{ "spec": { "plugins": ["logging-view-plugin"]}}'
  2. Add the .metadata.annotations.logging.openshift.io/ocp-console-migration-target: lokistack-dev annotation to the ClusterLogging CR, by running the following command:

    $ oc patch clusterlogging instance --type=merge --patch \
    '{ "metadata": { "annotations": { "logging.openshift.io/ocp-console-migration-target": "lokistack-dev" }}}' \
    -n openshift-logging

    Example output

    clusterlogging.logging.openshift.io/instance patched

Verification

  • Verify that the annotation was added successfully, by running the following command and observing the output:

    $ oc get clusterlogging instance \
    -o=jsonpath='{.metadata.annotations.logging\.openshift\.io/ocp-console-migration-target}' \
    -n openshift-logging

    Example output

    "lokistack-dev"

The logging Console Plugin pod is now deployed. You can view logging data by navigating to the OpenShift Container Platform web console and viewing the ObserveLogs page.

7.3. Viewing cluster dashboards

The Logging/Elasticsearch Nodes and Openshift Logging dashboards in the OpenShift Container Platform web 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.

7.3.1. Accessing the Elasticsearch and OpenShift Logging dashboards

You can view the Logging/Elasticsearch Nodes and OpenShift Logging dashboards in the OpenShift Container Platform web console.

Procedure

To launch the dashboards:

  1. In the OpenShift Container Platform web console, click ObserveDashboards.
  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.

7.3.2. 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 7.1. 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 Usage

The percent of the Fluentd buffer that is being used 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.

7.3.3. 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 7.2. 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 7.3. 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 puts 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 7.4. 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 7.5. 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 7.6. 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 7.7. 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 7.8. 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.

7.4. Log visualization with Kibana

If you are using the ElasticSearch log store, you can use the Kibana console to visualize collected log data.

Using Kibana, 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 about using the interface, see the Kibana documentation.

Note

The audit logs are not stored in the internal OpenShift Container Platform 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.

7.4.1. 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 --subresource log -n <project>

    Example output

    yes

    Note

    The audit logs are not stored in the internal OpenShift Container Platform 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 OpenShift Container Platform console, click the Application Launcher app launcher and select Logging.
  2. Create your Kibana index patterns by clicking ManagementIndex PatternsCreate 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.

7.4.2. 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 --subresource log -n <project>

    Example output

    yes

    Note

    The audit logs are not stored in the internal OpenShift Container Platform 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 OpenShift Container Platform console, click the Application Launcher app launcher and select Logging.
  2. Log in using the same credentials you use to log in to the OpenShift Container Platform 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 7.1. 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
      ]
    }

7.4.3. Configuring Kibana

You can configure using the Kibana console by modifying the ClusterLogging custom resource (CR).

7.4.3.1. Configuring CPU and memory limits

The logging 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.

7.4.3.2. 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.

Chapter 8. Configuring your Logging deployment

8.1. Configuring CPU and memory limits for logging components

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

8.1.1. Configuring CPU and memory limits

The logging 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.

8.2. Configuring systemd-journald and Fluentd

Because Fluentd reads from the journal, and the journal default settings are very low, journal entries can be lost because the journal cannot keep up with the logging rate from system services.

We recommend setting RateLimitIntervalSec=30s and RateLimitBurst=10000 (or even higher if necessary) to prevent the journal from losing entries.

8.2.1. Configuring systemd-journald for OpenShift Logging

As you scale up your project, the default logging environment might need some adjustments.

For example, if you are missing logs, you might have to increase the rate limits for journald. You can adjust the number of messages to retain for a specified period of time to ensure that OpenShift Logging does not use excessive resources without dropping logs.

You can also determine if you want the logs compressed, how long to retain logs, how or if the logs are stored, and other settings.

Procedure

  1. Create a Butane config file, 40-worker-custom-journald.bu, that includes an /etc/systemd/journald.conf file with the required settings.

    Note

    See "Creating machine configs with Butane" for information about Butane.

    variant: openshift
    version: 4.14.0
    metadata:
      name: 40-worker-custom-journald
      labels:
        machineconfiguration.openshift.io/role: "worker"
    storage:
      files:
      - path: /etc/systemd/journald.conf
        mode: 0644 1
        overwrite: true
        contents:
          inline: |
            Compress=yes 2
            ForwardToConsole=no 3
            ForwardToSyslog=no
            MaxRetentionSec=1month 4
            RateLimitBurst=10000 5
            RateLimitIntervalSec=30s
            Storage=persistent 6
            SyncIntervalSec=1s 7
            SystemMaxUse=8G 8
            SystemKeepFree=20% 9
            SystemMaxFileSize=10M 10
    1
    Set the permissions for the journald.conf file. It is recommended to set 0644 permissions.
    2
    Specify whether you want logs compressed before they are written to the file system. Specify yes to compress the message or no to not compress. The default is yes.
    3
    Configure whether to forward log messages. Defaults to no for each. Specify:
    • ForwardToConsole to forward logs to the system console.
    • ForwardToKMsg to forward logs to the kernel log buffer.
    • ForwardToSyslog to forward to a syslog daemon.
    • ForwardToWall to forward messages as wall messages to all logged-in users.
    4
    Specify the maximum time to store journal entries. Enter a number to specify seconds. Or include a unit: "year", "month", "week", "day", "h" or "m". Enter 0 to disable. The default is 1month.
    5
    Configure rate limiting. If more logs are received than what is specified in RateLimitBurst during the time interval defined by RateLimitIntervalSec, all further messages within the interval are dropped until the interval is over. It is recommended to set RateLimitIntervalSec=30s and RateLimitBurst=10000, which are the defaults.
    6
    Specify how logs are stored. The default is persistent:
    • volatile to store logs in memory in /run/log/journal/. These logs are lost after rebooting.
    • persistent to store logs to disk in /var/log/journal/. systemd creates the directory if it does not exist.
    • auto to store logs in /var/log/journal/ if the directory exists. If it does not exist, systemd temporarily stores logs in /run/systemd/journal.
    • none to not store logs. systemd drops all logs.
    7
    Specify the timeout before synchronizing journal files to disk for ERR, WARNING, NOTICE, INFO, and DEBUG logs. systemd immediately syncs after receiving a CRIT, ALERT, or EMERG log. The default is 1s.
    8
    Specify the maximum size the journal can use. The default is 8G.
    9
    Specify how much disk space systemd must leave free. The default is 20%.
    10
    Specify the maximum size for individual journal files stored persistently in /var/log/journal. The default is 10M.
    Note

    If you are removing the rate limit, you might see increased CPU utilization on the system logging daemons as it processes any messages that would have previously been throttled.

    For more information on systemd settings, see https://www.freedesktop.org/software/systemd/man/journald.conf.html. The default settings listed on that page might not apply to OpenShift Container Platform.

  2. Use Butane to generate a MachineConfig object file, 40-worker-custom-journald.yaml, containing the configuration to be delivered to the nodes:

    $ butane 40-worker-custom-journald.bu -o 40-worker-custom-journald.yaml
  3. Apply the machine config. For example:

    $ oc apply -f 40-worker-custom-journald.yaml

    The controller detects the new MachineConfig object and generates a new rendered-worker-<hash> version.

  4. Monitor the status of the rollout of the new rendered configuration to each node:

    $ oc describe machineconfigpool/worker

    Example output

    Name:         worker
    Namespace:
    Labels:       machineconfiguration.openshift.io/mco-built-in=
    Annotations:  <none>
    API Version:  machineconfiguration.openshift.io/v1
    Kind:         MachineConfigPool
    
    ...
    
    Conditions:
      Message:
      Reason:                All nodes are updating to rendered-worker-913514517bcea7c93bd446f4830bc64e

Chapter 9. Log collection and forwarding

9.1. About log collection and forwarding

The Red Hat OpenShift Logging Operator deploys a collector based on the ClusterLogForwarder resource specification. There are two collector options supported by this Operator: the legacy Fluentd collector, and the Vector collector.

Note

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

9.1.1. Log collection

The log collector is a daemon set that deploys pods to each OpenShift Container Platform node to collect container and node logs.

By default, the log collector uses the following sources:

  • System and infrastructure logs generated by journald log messages from the operating system, the container runtime, and OpenShift Container Platform.
  • /var/log/containers/*.log for all container logs.

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

The log collector collects the logs from these sources and forwards them internally or externally depending on your logging configuration.

9.1.1.1. Log collector types

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

You can configure which logging collector type your cluster uses by modifying the ClusterLogging custom resource (CR) collection spec:

Example ClusterLogging CR that configures Vector as the collector

apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name: instance
  namespace: openshift-logging
spec:
  collection:
    logs:
      type: vector
      vector: {}
# ...

9.1.1.2. Log collection limitations

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.

9.1.1.3. Log collector features by type

Table 9.1. 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 9.2. Authorization and Authentication

FeatureFluentdVector

Elasticsearch certificates

Elasticsearch username / password

Amazon Cloudwatch keys

Amazon Cloudwatch STS

Kafka certificates

Kafka username / password

Kafka SASL

Loki bearer token

Table 9.3. 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 9.4. Tuning

FeatureFluentdVector

Fluentd readlinelimit

 

Fluentd buffer

 

- chunklimitsize

 

- totallimitsize

 

- overflowaction

 

- flushthreadcount

 

- flushmode

 

- flushinterval

 

- retrywait

 

- retrytype

 

- retrymaxinterval

 

- retrytimeout

 

Table 9.5. Visibility

FeatureFluentdVector

Metrics

Dashboard

Alerts

Table 9.6. Miscellaneous

FeatureFluentdVector

Global proxy support

x86 support

ARM support

IBM Power® support

IBM Z® support

IPv6 support

Log event buffering

 

Disconnected Cluster

9.1.1.4. Collector outputs

The following collector outputs are supported:

Table 9.7. Supported outputs

FeatureFluentdVector

Elasticsearch v6-v8

Fluent forward

 

Syslog RFC3164

✓ (Logging 5.7+)

Syslog RFC5424

✓ (Logging 5.7+)

Kafka

Amazon Cloudwatch

Amazon Cloudwatch STS

Loki

HTTP

✓ (Logging 5.7+)

Google Cloud Logging

Splunk

 

✓ (Logging 5.6+)

9.1.2. Log forwarding

Administrators can create ClusterLogForwarder resources that specify which logs are collected, how they are transformed, and where they are forwarded to.

ClusterLogForwarder resources can be used up to forward container, infrastructure, and audit logs to specific endpoints within or outside of a cluster. Transport Layer Security (TLS) is supported so that log forwarders can be configured to send logs securely.

Administrators can also authorize RBAC permissions that define which service accounts and users can access and forward which types of logs.

9.1.2.1. Log forwarding implementations

There are two log forwarding implementations available: the legacy implementation, and the multi log forwarder feature.

Important

Only the Vector collector is supported for use with the multi log forwarder feature. The Fluentd collector can only be used with legacy implementations.

9.1.2.1.1. Legacy implementation

In legacy implementations, you can only use one log forwarder in your cluster. The ClusterLogForwarder resource in this mode must be named instance, and must be created in the openshift-logging namespace. The ClusterLogForwarder resource also requires a corresponding ClusterLogging resource named instance in the openshift-logging namespace.

9.1.2.1.2. Multi log forwarder feature

The multi log forwarder feature is available in logging 5.8 and later, and provides the following functionality:

  • Administrators can control which users are allowed to define log collection and which logs they are allowed to collect.
  • Users who have the required permissions are able to specify additional log collection configurations.
  • Administrators who are migrating from the deprecated Fluentd collector to the Vector collector can deploy a new log forwarder separately from their existing deployment. The existing and new log forwarders can operate simultaneously while workloads are being migrated.

In multi log forwarder implementations, you are not required to create a corresponding ClusterLogging resource for your ClusterLogForwarder resource. You can create multiple ClusterLogForwarder resources using any name, in any namespace, with the following exceptions:

  • You cannot create a ClusterLogForwarder resource named instance in the openshift-logging namespace, because this is reserved for a log forwarder that supports the legacy workflow using the Fluentd collector.
  • You cannot create a ClusterLogForwarder resource named collector in the openshift-logging namespace, because this is reserved for the collector.

9.1.2.2. Enabling the multi log forwarder feature for a cluster

To use the multi log forwarder feature, you must create a service account and cluster role bindings for that service account. You can then reference the service account in the ClusterLogForwarder resource to control access permissions.

Important

In order to support multi log forwarding in additional namespaces other than the openshift-logging namespace, you must update the Red Hat OpenShift Logging Operator to watch all namespaces. This functionality is supported by default in new Red Hat OpenShift Logging Operator version 5.8 installations.

9.1.2.2.1. Authorizing log collection RBAC permissions

In logging 5.8 and later, the Red Hat OpenShift Logging Operator provides collect-audit-logs, collect-application-logs, and collect-infrastructure-logs cluster roles, which enable the collector to collect audit logs, application logs, and infrastructure logs respectively.

You can authorize RBAC permissions for log collection by binding the required cluster roles to a service account.

Prerequisites

  • The Red Hat OpenShift Logging Operator is installed in the openshift-logging namespace.
  • You have administrator permissions.

Procedure

  1. Create a service account for the collector. If you want to write logs to storage that requires a token for authentication, you must include a token in the service account.
  2. Bind the appropriate cluster roles to the service account:

    Example binding command

    $ oc adm policy add-cluster-role-to-user <cluster_role_name> system:serviceaccount:<namespace_name>:<service_account_name>

9.2. Log output types

Outputs define the destination where logs are sent to from a log forwarder. You can configure multiple types of outputs in the ClusterLogForwarder custom resource (CR) to send logs to servers that support different protocols.

9.2.1. Supported log forwarding outputs

Outputs can be any of the following types:

Table 9.8. Supported log output types

Output typeProtocolTested withLogging versionsSupported collector type

Elasticsearch v6

HTTP 1.1

6.8.1, 6.8.23

5.6+

Fluentd, Vector

Elasticsearch v7

HTTP 1.1

7.12.2, 7.17.7, 7.10.1

5.6+

Fluentd, Vector

Elasticsearch v8

HTTP 1.1

8.4.3, 8.6.1

5.6+

Fluentd [1], Vector

Fluent Forward

Fluentd forward v1

Fluentd 1.14.6, Logstash 7.10.1, Fluentd 1.14.5

5.4+

Fluentd

Google Cloud Logging

REST over HTTPS

Latest

5.7+

Vector

HTTP

HTTP 1.1

Fluentd 1.14.6, Vector 0.21

5.7+

Fluentd, Vector

Kafka

Kafka 0.11

Kafka 2.4.1, 2.7.0, 3.3.1

5.4+

Fluentd, Vector

Loki

REST over HTTP and HTTPS

2.3.0, 2.5.0, 2.7, 2.2.1

5.4+

Fluentd, Vector

Splunk

HEC

8.2.9, 9.0.0

5.7+

Vector

Syslog

RFC3164, RFC5424

Rsyslog 8.37.0-9.el7, rsyslog-8.39.0

5.4+

Fluentd, Vector [2]

Amazon CloudWatch

REST over HTTPS

Latest

5.4+

Fluentd, Vector

  1. Fluentd does not support Elasticsearch 8 in the logging version 5.6.2.
  2. Vector supports Syslog in the logging version 5.7 and higher.

9.2.2. Output type descriptions

default

The on-cluster, Red Hat managed log store. You are not required to configure the default output.

Note

If you configure a default output, you receive an error message, because the default output name is reserved for referencing the on-cluster, Red Hat managed log store.

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.
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.

Important

The fluentdForward output is only supported if you are using the Fluentd collector. It is not supported if you are using the Vector collector. If you are using the Vector collector, you can forward logs to Fluentd by using the http output.

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).

9.3. Enabling JSON log forwarding

You can configure the Log Forwarding API to parse JSON strings into a structured object.

9.3.1. Parsing JSON logs

You can use a ClusterLogForwarder object to parse JSON logs into a structured object and forward them to a supported output.

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"}

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:

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 is absent.

9.3.2. 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 is the name of a message field. The value of that field 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: If the structuredTypeKey field is not set or its key is not present, the structuredTypeName value is used as the structured type. When you use both the structuredTypeKey field and the structuredTypeName field together, the structuredTypeName value provides a fallback index name if the key in the structuredTypeKey field 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.
apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
# ...
  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.

9.3.3. 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. Use structuredTypeKey field to specify one of the log record fields.
  3. Use structuredTypeName field to specify a name.

    Important

    To parse JSON logs, you must set both the structuredTypeKey and structuredTypeName fields.

  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 <filename>.yaml

    The Red Hat OpenShift Logging Operator redeploys the collector pods. However, if they do not redeploy, delete the collector pods to force them to redeploy.

    $ oc delete pod --selector logging-infra=collector

9.3.4. 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 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:
          structuredTypeKey: kubernetes.labels.logFormat 1
          structuredTypeName: nologformat
          enableStructuredContainerLogs: true 2
      pipelines:
      - inputRefs:
        - application
        name: application-logs
        outputRefs:
        - default
        parse: json
    1
    Uses the value of the key-value pair that is formed by the Kubernetes logFormat label.
    2
    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

Additional resources

9.4. Configuring log forwarding

In a logging deployment, container and infrastructure logs are forwarded to the internal log store defined in the ClusterLogging custom resource (CR) by default.

Audit logs are not forwarded to the internal log store by default because this does not provide secure storage. You are responsible for ensuring that the system to which you forward audit logs is compliant with your organizational and governmental regulations, and is properly secured.

If this default configuration meets your needs, you do not need to configure a ClusterLogForwarder CR. If a ClusterLogForwarder CR exists, logs are not forwarded to the internal log store unless a pipeline is defined that contains the default output.

9.4.1. About forwarding logs to third-party systems

To send logs to specific endpoints inside and outside your OpenShift Container Platform 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.

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 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 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: <log_forwarder_name> 1
  namespace: <log_forwarder_namespace> 2
spec:
  serviceAccountName: <service_account_name> 3
  outputs:
   - name: elasticsearch-secure 4
     type: "elasticsearch"
     url: https://elasticsearch.secure.com:9200
     secret:
        name: elasticsearch
   - name: elasticsearch-insecure 5
     type: "elasticsearch"
     url: http://elasticsearch.insecure.com:9200
   - name: kafka-app 6
     type: "kafka"
     url: tls://kafka.secure.com:9093/app-topic
  inputs: 7
   - name: my-app-logs
     application:
        namespaces:
        - my-project
  pipelines:
   - name: audit-logs 8
     inputRefs:
      - audit
     outputRefs:
      - elasticsearch-secure
      - default
     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
In legacy implementations, the CR name must be instance. In multi log forwarder implementations, you can use any name.
2
In legacy implementations, the CR namespace must be openshift-logging. In multi log forwarder implementations, you can use any namespace.
3
The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace.
4
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.
5
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.
6
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.
7
Configuration for an input to filter application logs from the my-project namespace.
8
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.
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. OpenShift Container Platform 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:

  • 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.

9.4.1.1. 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 <namespace> <secret_name> \
  --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.

9.4.2. Creating a log forwarder

To create a log forwarder, you must create a ClusterLogForwarder CR that specifies the log input types that the service account can collect. You can also specify which outputs the logs can be forwarded to. If you are using the multi log forwarder feature, you must also reference the service account in the ClusterLogForwarder CR.

If you are using the multi log forwarder feature on your cluster, you can create ClusterLogForwarder custom resources (CRs) in any namespace, using any name. If you are using a legacy implementation, the ClusterLogForwarder CR must be named instance, and must be created in the openshift-logging namespace.

Important

You need administrator permissions for the namespace where you create the ClusterLogForwarder CR.

ClusterLogForwarder resource example

apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: <log_forwarder_name> 1
  namespace: <log_forwarder_namespace> 2
spec:
  serviceAccountName: <service_account_name> 3
  pipelines:
   - inputRefs:
     - <log_type> 4
     outputRefs:
     - <output_name> 5
  outputs:
  - name: <output_name> 6
    type: <output_type> 7
    url: <log_output_url> 8
# ...

1
In legacy implementations, the CR name must be instance. In multi log forwarder implementations, you can use any name.
2
In legacy implementations, the CR namespace must be openshift-logging. In multi log forwarder implementations, you can use any namespace.
3
The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace.
4
The log types that are collected. The value for this field can be audit for audit logs, application for application logs, infrastructure for infrastructure logs, or a named input that has been defined for your application.
5 7
The type of output that you want to forward logs to. The value of this field can be default, loki, kafka, elasticsearch, fluentdForward, syslog, or cloudwatch.
Note

The default output type is not supported in mutli log forwarder implementations.

6
A name for the output that you want to forward logs to.
8
The URL of the output that you want to forward logs to.

9.4.3. Tuning log payloads and delivery

In logging 5.9 and newer versions, the tuning spec in the ClusterLogForwarder custom resource (CR) provides a means of configuring your deployment to prioritize either throughput or durability of logs.

For example, if you need to reduce the possibility of log loss when the collector restarts, or you require collected log messages to survive a collector restart to support regulatory mandates, you can tune your deployment to prioritize log durability. If you use outputs that have hard limitations on the size of batches they can receive, you may want to tune your deployment to prioritize log throughput.

Important

To use this feature, your logging deployment must be configured to use the Vector collector. The tuning spec in the ClusterLogForwarder CR is not supported when using the Fluentd collector.

The following example shows the ClusterLogForwarder CR options that you can modify to tune log forwarder outputs:

Example ClusterLogForwarder CR tuning options

apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
  tuning:
    delivery: AtLeastOnce 1
    compression: none 2
    maxWrite: <integer> 3
    minRetryDuration: 1s 4
    maxRetryDuration: 1s 5
# ...

1
Specify the delivery mode for log forwarding.
  • AtLeastOnce delivery means that if the log forwarder crashes or is restarted, any logs that were read before the crash but not sent to their destination are re-sent. It is possible that some logs are duplicated after a crash.
  • AtMostOnce delivery means that the log forwarder makes no effort to recover logs lost during a crash. This mode gives better throughput, but may result in greater log loss.
2
Specifying a compression configuration causes data to be compressed before it is sent over the network. Note that not all output types support compression, and if the specified compression type is not supported by the output, this results in an error. The possible values for this configuration are none for no compression, gzip, snappy, zlib, or zstd. lz4 compression is also available if you are using a Kafka output. See the table "Supported compression types for tuning outputs" for more information.
3
Specifies a limit for the maximum payload of a single send operation to the output.
4
Specifies a minimum duration to wait between attempts before retrying delivery after a failure. This value is a string, and can be specified as milliseconds (ms), seconds (s), or minutes (m).
5
Specifies a maximum duration to wait between attempts before retrying delivery after a failure. This value is a string, and can be specified as milliseconds (ms), seconds (s), or minutes (m).

Table 9.9. Supported compression types for tuning outputs

Compression algorithmSplunkAmazon CloudwatchElasticsearch 8LokiStackApache KafkaHTTPSyslogGoogle CloudMicrosoft Azure Monitoring

gzip

X

X

X

X

 

X

   

snappy

 

X

 

X

X

X

   

zlib

 

X

X

  

X

   

zstd

 

X

  

X

X

   

lz4

    

X

    

9.4.4. Enabling multi-line exception detection

Enables multi-line error detection of container logs.

Warning

Enabling this feature could have performance implications and may require additional computing resources or alternate logging solutions.

Log parsers often incorrectly identify separate lines of the same exception as separate exceptions. This leads to extra log entries and an incomplete or inaccurate view of the traced information.

Example java exception

java.lang.NullPointerException: Cannot invoke "String.toString()" because "<param1>" is null
    at testjava.Main.handle(Main.java:47)
    at testjava.Main.printMe(Main.java:19)
    at testjava.Main.main(Main.java:10)

  • 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.

Example ClusterLogForwarder CR

apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: instance
  namespace: openshift-logging
spec:
  pipelines:
    - name: my-app-logs
      inputRefs:
        - application
      outputRefs:
        - default
      detectMultilineErrors: true

9.4.4.1. Details

When log messages appear as a consecutive sequence forming an exception stack trace, they are combined into a single, unified log record. The first log message’s content is replaced with the concatenated content of all the message fields in the sequence.

Table 9.10. Supported languages per collector:

LanguageFluentdVector

Java

JS

Ruby

Python

Golang

PHP

Dart

9.4.4.2. Troubleshooting

When enabled, the collector configuration will include a new section with type: detect_exceptions

Example vector configuration section

[transforms.detect_exceptions_app-logs]
 type = "detect_exceptions"
 inputs = ["application"]
 languages = ["All"]
 group_by = ["kubernetes.namespace_name","kubernetes.pod_name","kubernetes.container_name"]
 expire_after_ms = 2000
 multiline_flush_interval_ms = 1000

Example fluentd config section

<label @MULTILINE_APP_LOGS>
  <match kubernetes.**>
    @type detect_exceptions
    remove_tag_prefix 'kubernetes'
    message message
    force_line_breaks true
    multiline_flush_interval .2
  </match>
</label>

9.4.5. Forwarding logs to Google Cloud Platform (GCP)

You can forward logs to Google Cloud Logging in addition to, or instead of, the internal default OpenShift Container Platform log store.

Note

Using this feature with Fluentd is not supported.

Prerequisites

  • Red Hat OpenShift Logging Operator 5.5.1 and later

Procedure

  1. Create a secret using your Google service account key.

    $ oc -n openshift-logging create secret generic gcp-secret --from-file google-application-credentials.json=<your_service_account_key_file.json>
  2. Create a ClusterLogForwarder Custom Resource YAML using the template below:

    apiVersion: logging.openshift.io/v1
    kind: ClusterLogForwarder
    metadata:
      name: <log_forwarder_name> 1
      namespace: <log_forwarder_namespace> 2
    spec:
      serviceAccountName: <service_account_name> 3
      outputs:
        - name: gcp-1
          type: googleCloudLogging
          secret:
            name: gcp-secret
          googleCloudLogging:
            projectId : "openshift-gce-devel" 4
            logId : "app-gcp" 5
      pipelines:
        - name: test-app
          inputRefs: 6
            - application
          outputRefs:
            - gcp-1
    1
    In legacy implementations, the CR name must be instance. In multi log forwarder implementations, you can use any name.
    2
    In legacy implementations, the CR namespace must be openshift-logging. In multi log forwarder implementations, you can use any namespace.
    3
    The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace.
    4
    Set a projectId, folderId, organizationId, or billingAccountId field and its corresponding value, depending on where you want to store your logs in the GCP resource hierarchy.
    5
    Set the value to add to the logName field of the Log Entry.
    6
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.

9.4.6. Forwarding logs to Splunk

You can forward logs to the Splunk HTTP Event Collector (HEC) in addition to, or instead of, the internal default OpenShift Container Platform log store.

Note

Using this feature with Fluentd is not supported.

Prerequisites

  • Red Hat OpenShift Logging Operator 5.6 or later
  • A ClusterLogging instance with vector specified as the 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: <log_forwarder_name> 1
      namespace: <log_forwarder_namespace> 2
    spec:
      serviceAccountName: <service_account_name> 3
      outputs:
        - name: splunk-receiver 4
          secret:
            name: vector-splunk-secret 5
          type: splunk 6
          url: <http://your.splunk.hec.url:8088> 7
      pipelines: 8
        - inputRefs:
            - application
            - infrastructure
          name: 9
          outputRefs:
            - splunk-receiver 10
    1
    In legacy implementations, the CR name must be instance. In multi log forwarder implementations, you can use any name.
    2
    In legacy implementations, the CR namespace must be openshift-logging. In multi log forwarder implementations, you can use any namespace.
    3
    The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace.
    4
    Specify a name for the output.
    5
    Specify the name of the secret that contains your HEC token.
    6
    Specify the output type as splunk.
    7
    Specify the URL (including port) of your Splunk HEC.
    8
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    9
    Optional: Specify a name for the pipeline.
    10
    Specify the name of the output to use when forwarding logs with this pipeline.

9.4.7. Forwarding logs over HTTP

Forwarding logs over HTTP is supported for both the Fluentd and Vector log collectors. To enable, specify http as the output type in the ClusterLogForwarder custom resource (CR).

Procedure

  • Create or edit the ClusterLogForwarder CR using the template below:

    Example ClusterLogForwarder CR

    apiVersion: logging.openshift.io/v1
    kind: ClusterLogForwarder
    metadata:
      name: <log_forwarder_name> 1
      namespace: <log_forwarder_namespace> 2
    spec:
      serviceAccountName: <service_account_name> 3
      outputs:
        - name: httpout-app
          type: http
          url: 4
          http:
            headers: 5
              h1: v1
              h2: v2
            method: POST
          secret:
            name: 6
          tls:
            insecureSkipVerify: 7
      pipelines:
        - name:
          inputRefs:
            - application
          outputRefs:
            - 8

    1
    In legacy implementations, the CR name must be instance. In multi log forwarder implementations, you can use any name.
    2
    In legacy implementations, the CR namespace must be openshift-logging. In multi log forwarder implementations, you can use any namespace.
    3
    The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace.
    4
    Destination address for logs.
    5
    Additional headers to send with the log record.
    6
    Secret name for destination credentials.
    7
    Values are either true or false.
    8
    This value should be the same as the output name.

9.4.8. Forwarding to Azure Monitor Logs

With logging 5.9 and later, you can forward logs to Azure Monitor Logs in addition to, or instead of, the default log store. This functionality is provided by the Vector Azure Monitor Logs sink.

Prerequisites

  • You are familiar with how to administer and create a ClusterLogging custom resource (CR) instance.
  • You are familiar with how to administer and create a ClusterLogForwarder CR instance.
  • You understand the ClusterLogForwarder CR specifications.
  • You have basic familiarity with Azure services.
  • You have an Azure account configured for Azure Portal or Azure CLI access.
  • You have obtained your Azure Monitor Logs primary or the secondary security key.
  • You have determined which log types to forward.

To enable log forwarding to Azure Monitor Logs via the HTTP Data Collector API:

Create a secret with your shared key:

apiVersion: v1
kind: Secret
metadata:
  name: my-secret
  namespace: openshift-logging
type: Opaque
data:
  shared_key: <your_shared_key> 1
1
Must contain a primary or secondary key for the Log Analytics workspace making the request.

To obtain a shared key, you can use this command in Azure CLI:

Get-AzOperationalInsightsWorkspaceSharedKey -ResourceGroupName "<resource_name>" -Name "<workspace_name>”

Create or edit your ClusterLogForwarder CR using the template matching your log selection.

Forward all logs

apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogForwarder"
metadata:
  name: instance
  namespace: openshift-logging
spec:
  outputs:
  - name: azure-monitor
    type: azureMonitor
    azureMonitor:
      customerId: my-customer-id 1
      logType: my_log_type 2
    secret:
       name: my-secret
  pipelines:
  - name: app-pipeline
    inputRefs:
    - application
    outputRefs:
    - azure-monitor

1
Unique identifier for the Log Analytics workspace. Required field.
2
Azure record type of the data being submitted. May only contain letters, numbers, and underscores (_), and may not exceed 100 characters.

Forward application and infrastructure logs

apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogForwarder"
metadata:
  name: instance
  namespace: openshift-logging
spec:
  outputs:
  - name: azure-monitor-app
    type: azureMonitor
    azureMonitor:
      customerId: my-customer-id
      logType: application_log 1
    secret:
      name: my-secret
  - name: azure-monitor-infra
    type: azureMonitor
    azureMonitor:
      customerId: my-customer-id
      logType: infra_log #
    secret:
      name: my-secret
  pipelines:
    - name: app-pipeline
      inputRefs:
      - application
      outputRefs:
      - azure-monitor-app
    - name: infra-pipeline
      inputRefs:
      - infrastructure
      outputRefs:
      - azure-monitor-infra

1
Azure record type of the data being submitted. May only contain letters, numbers, and underscores (_), and may not exceed 100 characters.

Advanced configuration options

apiVersion: "logging.openshift.io/v1"
kind: "ClusterLogForwarder"
metadata:
  name: instance
  namespace: openshift-logging
spec:
  outputs:
  - name: azure-monitor
    type: azureMonitor
    azureMonitor:
      customerId: my-customer-id
      logType: my_log_type
      azureResourceId: "/subscriptions/111111111" 1
      host: "ods.opinsights.azure.com" 2
    secret:
       name: my-secret
  pipelines:
   - name: app-pipeline
    inputRefs:
    - application
    outputRefs:
    - azure-monitor

1
Resource ID of the Azure resource the data should be associated with. Optional field.
2
Alternative host for dedicated Azure regions. Optional field. Default value is ods.opinsights.azure.com. Default value for Azure Government is ods.opinsights.azure.us.

9.4.9. Forwarding application logs from specific projects

You can forward a copy of the application logs from specific projects to an external log aggregator, in addition to, or instead of, using the internal log store. You must also configure the external log aggregator to receive log data from OpenShift Container Platform.

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:

    Example ClusterLogForwarder CR

    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 8
      pipelines:
       - name: forward-to-fluentd-insecure 9
         inputRefs: 10
         - my-app-logs
         outputRefs: 11
         - fluentd-server-insecure
         labels:
           project: "my-project" 12
       - name: forward-to-fluentd-secure 13
         inputRefs:
         - application 14
         - 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
    The name of the output.
    4
    The output type: elasticsearch, fluentdForward, syslog, or kafka.
    5
    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
    The configuration for an input to filter application logs from the specified projects.
    8
    If no namespace is specified, logs are collected from all namespaces.
    9
    The pipeline configuration directs logs from a named input to a named output. In this example, a pipeline named forward-to-fluentd-insecure forwards logs from an input named my-app-logs to an output named fluentd-server-insecure.
    10
    A list of inputs.
    11
    The name of the output to use.
    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 default log store.
    • Optional: String. One or more labels to add to the logs.
    14
    Note that application logs from all namespaces are collected when using this configuration.
  2. Apply the ClusterLogForwarder CR by running the following command:

    $ oc apply -f <filename>.yaml

9.4.10. 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: <log_forwarder_name> 1
      namespace: <log_forwarder_namespace> 2
    spec:
      pipelines:
        - inputRefs: [ myAppLogData ] 3
          outputRefs: [ default ] 4
      inputs: 5
        - name: myAppLogData
          application:
            selector:
              matchLabels: 6
                environment: production
                app: nginx
            namespaces: 7
            - app1
            - app2
      outputs: 8
        - <output_name>
        ...

    1
    In legacy implementations, the CR name must be instance. In multi log forwarder implementations, you can use any name.
    2
    In legacy implementations, the CR namespace must be openshift-logging. In multi log forwarder implementations, you can use any namespace.
    3
    Specify one or more comma-separated values from inputs[].name.
    4
    Specify one or more comma-separated values from outputs[].
    5
    Define a unique inputs[].name for each application that has a unique set of pod labels.
    6
    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.
    7
    Optional: Specify one or more namespaces.
    8
    Specify one or more outputs to forward your log data to.
  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

9.4.11. Overview of API audit filter

OpenShift API servers generate audit events for each API call, detailing the request, response, and the identity of the requester, leading to large volumes of data. The API Audit filter uses rules to enable the exclusion of non-essential events and the reduction of event size, facilitating a more manageable audit trail. Rules are checked in order, checking stops at the first match. How much data is included in an event is determined by the value of the level field:

  • None: The event is dropped.
  • Metadata: Audit metadata is included, request and response bodies are removed.
  • Request: Audit metadata and the request body are included, the response body is removed.
  • RequestResponse: All data is included: metadata, request body and response body. The response body can be very large. For example, oc get pods -A generates a response body containing the YAML description of every pod in the cluster.

In logging 5.8 and later, the ClusterLogForwarder custom resource (CR) uses the same format as the standard Kubernetes audit policy, while providing the following additional functions:

Wildcards
Names of users, groups, namespaces, and resources can have a leading or trailing * asterisk character. For example, namespace openshift-\* matches openshift-apiserver or openshift-authentication. Resource \*/status matches Pod/status or Deployment/status.
Default Rules

Events that do not match any rule in the policy are filtered as follows:

  • Read-only system events such as get, list, watch are dropped.
  • Service account write events that occur within the same namespace as the service account are dropped.
  • All other events are forwarded, subject to any configured rate limits.

To disable these defaults, either end your rules list with a rule that has only a level field or add an empty rule.

Omit Response Codes
A list of integer status codes to omit. You can drop events based on the HTTP status code in the response by using the OmitResponseCodes field, a list of HTTP status code for which no events are created. The default value is [404, 409, 422, 429]. If the value is an empty list, [], then no status codes are omitted.

The ClusterLogForwarder CR audit policy acts in addition to the OpenShift Container Platform audit policy. The ClusterLogForwarder CR audit filter changes what the log collector forwards, and provides the ability to filter by verb, user, group, namespace, or resource. You can create multiple filters to send different summaries of the same audit stream to different places. For example, you can send a detailed stream to the local cluster log store, and a less detailed stream to a remote site.

Note

The example provided is intended to illustrate the range of rules possible in an audit policy and is not a recommended configuration.

Example audit policy

apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: instance
  namespace: openshift-logging
spec:
  pipelines:
    - name: my-pipeline
      inputRefs: audit 1
      filterRefs: my-policy 2
      outputRefs: default
  filters:
    - name: my-policy
      type: kubeAPIAudit
      kubeAPIAudit:
        # Don't generate audit events for all requests in RequestReceived stage.
        omitStages:
          - "RequestReceived"

        rules:
          # Log pod changes at RequestResponse level
          - level: RequestResponse
            resources:
            - group: ""
              resources: ["pods"]

          # Log "pods/log", "pods/status" at Metadata level
          - level: Metadata
            resources:
            - group: ""
              resources: ["pods/log", "pods/status"]

          # Don't log requests to a configmap called "controller-leader"
          - level: None
            resources:
            - group: ""
              resources: ["configmaps"]
              resourceNames: ["controller-leader"]

          # Don't log watch requests by the "system:kube-proxy" on endpoints or services
          - level: None
            users: ["system:kube-proxy"]
            verbs: ["watch"]
            resources:
            - group: "" # core API group
              resources: ["endpoints", "services"]

          # Don't log authenticated requests to certain non-resource URL paths.
          - level: None
            userGroups: ["system:authenticated"]
            nonResourceURLs:
            - "/api*" # Wildcard matching.
            - "/version"

          # Log the request body of configmap changes in kube-system.
          - level: Request
            resources:
            - group: "" # core API group
              resources: ["configmaps"]
            # This rule only applies to resources in the "kube-system" namespace.
            # The empty string "" can be used to select non-namespaced resources.
            namespaces: ["kube-system"]

          # Log configmap and secret changes in all other namespaces at the Metadata level.
          - level: Metadata
            resources:
            - group: "" # core API group
              resources: ["secrets", "configmaps"]

          # Log all other resources in core and extensions at the Request level.
          - level: Request
            resources:
            - group: "" # core API group
            - group: "extensions" # Version of group should NOT be included.

          # A catch-all rule to log all other requests at the Metadata level.
          - level: Metadata

1
The log types that are collected. The value for this field can be audit for audit logs, application for application logs, infrastructure for infrastructure logs, or a named input that has been defined for your application.
2
The name of your audit policy.

9.4.12. Forwarding logs to an external Loki logging system

You can forward logs to an external Loki logging system in addition to, or instead of, the default log store.

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: <log_forwarder_name> 1
      namespace: <log_forwarder_namespace> 2
    spec:
      serviceAccountName: <service_account_name> 3
      outputs:
      - name: loki-insecure 4
        type: "loki" 5
        url: http://loki.insecure.com:3100 6
        loki:
          tenantKey: kubernetes.namespace_name
          labelKeys:
          - kubernetes.labels.foo
      - name: loki-secure 7
        type: "loki"
        url: https://loki.secure.com:3100
        secret:
          name: loki-secret 8
        loki:
          tenantKey: kubernetes.namespace_name 9
          labelKeys:
          - kubernetes.labels.foo 10
      pipelines:
      - name: application-logs 11
        inputRefs:  12
        - application
        - audit
        outputRefs: 13
        - loki-secure
    1
    In legacy implementations, the CR name must be instance. In multi log forwarder implementations, you can use any name.
    2
    In legacy implementations, the CR namespace must be openshift-logging. In multi log forwarder implementations, you can use any namespace.
    3
    The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace.
    4
    Specify a name for the output.
    5
    Specify the type as "loki".
    6
    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.
    7
    For a secure connection, you can specify an https or http URL that you authenticate by specifying a secret.
    8
    For an https prefix, specify the name of the secret required by the endpoint for TLS communication. The secret must contain a ca-bundle.crt key that points to the certificates it represents. Otherwise, for http and https prefixes, you can specify a secret that contains a username and password. In legacy implementations, the secret must exist in the openshift-logging project. For more information, see the following "Example: Setting a secret that contains a username and password."
    9
    Optional: Specify a metadata 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.
    10
    Optional: Specify a list of metadata 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 metadata keys are replaced with _ to form the label name. For example, the kubernetes.labels.foo metadata 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.
    11
    Optional: Specify a name for the pipeline.
    12
    Specify which