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Logging

Red Hat OpenShift Service on AWS 4

Logging installation, usage, and release notes on Red Hat OpenShift Service on AWS

Red Hat OpenShift Documentation Team

Abstract

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

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 Red Hat OpenShift Service on AWS. The Red Hat OpenShift Container Platform Life Cycle Policy outlines release compatibility.

Note

The stable channel only provides updates to the most recent release of logging. To continue receiving updates for prior releases, you must change your subscription channel to stable-x.y, where x.y represents the major and minor version of logging you have installed. For example, stable-5.7.

1.1.1. Logging 5.9.3

This release includes OpenShift Logging Bug Fix Release 5.9.3

1.1.1.1. 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-5614)
  • Before this update, monitoring the Vector collector output buffer state was not possible. With this update, monitoring and alerting the Vector collector output buffer size is possible that improves observability capabilities and helps keep the system running optimally. (LOG-5586)
1.1.1.2. CVEs

1.1.2. Logging 5.9.2

This release includes OpenShift Logging Bug Fix Release 5.9.2

1.1.2.1. 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)
1.1.2.2. CVEs

1.1.3. Logging 5.9.1

This release includes OpenShift Logging Bug Fix Release 5.9.1

1.1.3.1. 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-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)
1.1.3.2. 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)
1.1.3.3. CVEs

No CVEs.

1.1.4. Logging 5.9.0

This release includes OpenShift Logging Bug Fix Release 5.9.0

1.1.4.1. 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.

1.1.4.2. 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 Red Hat OpenShift Service on AWS. 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.
1.1.4.3. Enhancements
1.1.4.3.1. 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)
1.1.4.3.2. 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 Red Hat OpenShift Service on AWS 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)
1.1.4.4. 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)
1.1.4.5. Known Issues

None.

1.1.4.6. CVEs

1.2. Logging 5.8

Note

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

Note

The stable channel only provides updates to the most recent release of logging. To continue receiving updates for prior releases, you must change your subscription channel to stable-x.y, where x.y represents the major and minor version of logging you have installed. For example, stable-5.7.

1.2.1. Logging 5.8.4

This release includes OpenShift Logging Bug Fix Release 5.8.4.

1.2.1.1. 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)
1.2.1.2. CVEs

1.2.2. Logging 5.8.3

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

1.2.2.1. 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)
1.2.2.2. CVEs

1.2.3. Logging 5.8.2

This release includes OpenShift Logging Bug Fix Release 5.8.2.

1.2.3.1. 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 Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS web console. (LOG-4912)
1.2.3.2. CVEs

1.2.4. 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.

1.2.4.1. Enhancements
1.2.4.1.1. 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)
1.2.4.2. 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)
1.2.4.3. CVEs

1.2.5. 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.

1.2.5.1. 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 Red Hat OpenShift Service on AWS. 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.

1.2.5.2. Enhancements
1.2.5.2.1. 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 Red Hat OpenShift Service on AWS 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)
1.2.5.2.2. 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 Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS 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)
1.2.5.2.3. Log Console
  • With this update, you can enable the Logging Console Plugin when Elasticsearch is the default Log Store. (LOG-3856)
  • With this update, Red Hat OpenShift Service on AWS application owners can receive notifications for application log-based alerts on the Red Hat OpenShift Service on AWS web console Developer perspective for Red Hat OpenShift Service on AWS version 4.14 and later. (LOG-3548)
1.2.5.3. 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 Red Hat OpenShift Service on AWS 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)
1.2.5.4. CVEs

1.3. Logging 5.7

Note

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

Note

The stable channel only provides updates to the most recent release of logging. To continue receiving updates for prior releases, you must change your subscription channel to stable-x.y, where x.y represents the major and minor version of logging you have installed. For example, stable-5.7.

1.3.1. Logging 5.7.8

This release includes OpenShift Logging Bug Fix Release 5.7.8.

1.3.1.1. 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)
1.3.1.2. CVEs

1.3.2. Logging 5.7.7

This release includes OpenShift Logging Bug Fix Release 5.7.7.

1.3.2.1. 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 Red Hat OpenShift Service on AWS 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)
1.3.2.2. CVEs

1.3.3. Logging 5.7.6

This release includes OpenShift Logging Bug Fix Release 5.7.6.

1.3.3.1. 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)
1.3.3.2. CVEs

1.3.4. Logging 5.7.4

This release includes OpenShift Logging Bug Fix Release 5.7.4.

1.3.4.1. 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)
1.3.4.2. CVEs

1.3.5. Logging 5.7.3

This release includes OpenShift Logging Bug Fix Release 5.7.3.

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

1.3.6. Logging 5.7.2

This release includes OpenShift Logging Bug Fix Release 5.7.2.

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

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

    tls.verify_certificate = false
tls.verify_hostname = false

    (LOG-3445)

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

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

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

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

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

1.3.7. Logging 5.7.1

This release includes: OpenShift Logging Bug Fix Release 5.7.1.

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

1.3.8. Logging 5.7.0

This release includes OpenShift Logging Bug Fix Release 5.7.0.

1.3.8.1. 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.

1.3.8.2. Known Issues

None.

1.3.8.3. 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)
1.3.8.4. 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 Red Hat OpenShift Service on AWS releases, and such cases can only be handled gracefully if all configuration possibilities are controlled. If you use configurations other than those described in this documentation, your changes will be overwritten, because Operators are designed to reconcile any differences.

Note

If you must perform configurations not described in the Red Hat OpenShift Service on AWS documentation, you must set your Red Hat OpenShift Logging Operator 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 Red Hat OpenShift Service on AWS. The Red Hat OpenShift Container Platform Life Cycle Policy outlines release compatibility.

Loki is a horizontally scalable, highly available, multi-tenant log aggregation system offered as a GA log store for logging for Red Hat OpenShift that can be visualized with the OpenShift Observability UI. The Loki configuration provided by OpenShift Logging is a short-term log store designed to enable users to perform fast troubleshooting with the collected logs. For that purpose, the logging for Red Hat OpenShift configuration of Loki has short-term storage, and is optimized for very recent queries.

Important

For long-term storage or queries over a long time period, users should look to log stores external to their cluster. Loki sizing is only tested and supported for short term storage, for a maximum of 30 days.

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. Support exception for the Logging UI Plugin

Until the approaching General Availability (GA) release of the Cluster Observability Operator (COO), which is currently in Technology Preview (TP), Red Hat provides support to customers who are using Logging 6.0 or later with the COO for its Logging UI Plugin on Red Hat OpenShift Service on AWS 4.14 or later. This support exception is temporary as the COO includes several independent features, some of which are still TP features, but the Logging UI Plugin is ready for GA.

2.5. 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 Red Hat OpenShift Service on AWS and logging.

Note

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

2.5.1. 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.

2.5.2. 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 Red Hat OpenShift Service on AWS "Reviewing monitoring dashboards" documentation.

Additional resources

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).

Additional resources

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

Additional resources

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 Cluster Manager displays the status of the Elasticsearch cluster.

To get the status of the OpenShift Elasticsearch cluster, visit the dashboard in the Observe section of the OpenShift Cluster Manager 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 Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS 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.

Red Hat OpenShift Service on AWS cluster administrators can deploy logging by using Operators. For information, see xref :../../observability/logging/cluster-logging-deploying.adoc#cluster-logging-deploying[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 Red Hat OpenShift Service on AWS Elasticsearch log store does not provide secure storage for audit logs, audit logs are not stored in the internal Elasticsearch instance by default. If you want to send the audit logs to the default internal Elasticsearch log store, for example to view the audit logs in Kibana, you must use the Log Forwarding API as described in xref :../../observability/logging/log_storage/logging-config-es-store.adoc#cluster-logging-elasticsearch-audit_logging-config-es-store[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 Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS web console UI is provided by enabling the Red Hat OpenShift Service on AWS 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.

Additional resources

  • xref :../../observability/logging/log_visualization/log-visualization-ocp-console.adoc#log-visualization-ocp-console[Log visualization with the web console]

4.2. About deploying logging

Administrators can deploy the logging by using the Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS 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. Logging requirements

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

For information, see xref :../../observability/logging/log_collection_forwarding/log-forwarding.adoc#about-log-collection_log-forwarding[About log collection and forwarding].

4.2.3. About JSON Red Hat OpenShift Service on AWS Logging

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

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

4.2.4. About collecting and storing Kubernetes events

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

For information, see xref :../../observability/logging/log_collection_forwarding/cluster-logging-eventrouter.adoc#cluster-logging-eventrouter[About collecting and storing Kubernetes events].

4.2.5. About troubleshooting Red Hat OpenShift Service on AWS Logging

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

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

4.2.6. 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 xref :../../observability/logging/cluster-logging-exported-fields.adoc#cluster-logging-exported-fields[About exporting fields].

4.2.7. About event routing

The Event Router is a pod that watches Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS Elasticsearch instance. Elasticsearch indexes the events to the infra index.

You must manually deploy the Event Router.

For information, see xref :../../observability/logging/log_collection_forwarding/cluster-logging-eventrouter.adoc#cluster-logging-eventrouter[Collecting and storing Kubernetes events].

Chapter 5. Installing Logging

Red Hat OpenShift Service on AWS Operators use custom resources (CR) to manage applications and their components. High-level configuration and settings are provided by the user within a CR. The Operator translates high-level directives into low-level actions, based on best practices embedded within the Operator’s logic. A custom resource definition (CRD) defines a CR and lists all the configurations available to users of the Operator. Installing an Operator creates the CRDs, which are then used to generate CRs.

Important

You must install the Red Hat OpenShift Logging Operator after the log store Operator.

You deploy logging by installing the Loki Operator or OpenShift Elasticsearch Operator to manage your log store, followed by the Red Hat OpenShift Logging Operator to manage the components of logging. You can use either the Red Hat OpenShift Service on AWS web console or the Red Hat OpenShift Service on AWS CLI to install or configure logging.

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.

Tip

You can alternatively apply all example objects.

5.1. Installing Logging with Elasticsearch using the web console

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

Note

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

Prerequisites

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

    Note

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

Procedure

To install the OpenShift Elasticsearch Operator and Red Hat OpenShift Logging Operator using the Red Hat OpenShift Service on AWS web console:

  1. Install the OpenShift Elasticsearch Operator:

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

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

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

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

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

    6. Select 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.

    7. Select an Approval Strategy.

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

  2. Install the Red Hat OpenShift Logging Operator:

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

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

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

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

    7. Select an Approval Strategy.

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

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

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

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

  3. Create an OpenShift Logging instance:

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

    4. On the ClusterLoggings page, click Create ClusterLogging.

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

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

      Note

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

      apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name: instance 1
  namespace: openshift-logging
spec:
  managementState: Managed 2
  logStore:
    type: elasticsearch 3
    retentionPolicy: 4
      application:
        maxAge: 1d
      infra:
        maxAge: 7d
      audit:
        maxAge: 7d
    elasticsearch:
      nodeCount: 3 5
      storage:
        storageClassName: <storage_class_name> 6
        size: 200G
      resources: 7
          limits:
            memory: 16Gi
          requests:
            memory: 16Gi
      proxy: 8
        resources:
          limits:
            memory: 256Mi
          requests:
            memory: 256Mi
      redundancyPolicy: SingleRedundancy
  visualization:
    type: kibana 9
    kibana:
      replicas: 1
  collection:
    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 master nodes is three. If you specify a nodeCount greater than 3, Red Hat OpenShift Service on AWS creates three Elasticsearch nodes that are Master-eligible nodes, with the master, client, and data roles. The additional Elasticsearch nodes are created as Data-only nodes, using client and data roles. Master nodes perform cluster-wide actions such as creating or deleting an index, shard allocation, and tracking nodes. Data nodes hold the shards and perform data-related operations such as CRUD, search, and aggregations. Data-related operations are I/O-, memory-, and CPU-intensive. It is important to monitor these resources and to add more Data nodes if the current nodes are overloaded.

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

      $ oc get deployment

      Example output

      cluster-logging-operator-66f77ffccb-ppzbg       1/1    Running 0 7m
elasticsearch-cd-tuhduuw-1-f5c885dbf-dlqws      1/1    Running 0 2m4s
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

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

  4. Verify the install:

    1. Switch to the Workloads → Pods page.

    2. Select the openshift-logging project.

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

      Example output

      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

5.2. Installing Logging with Elasticsearch using the CLI

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

Prerequisites

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

    Note

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

Procedure

  1. Create a Namespace object for the OpenShift Elasticsearch Operator:

    Example Namespace object

    apiVersion: v1
kind: Namespace
metadata:
  name: openshift-operators-redhat 1
  annotations:
    openshift.io/node-selector: ""
  labels:
    openshift.io/cluster-monitoring: "true" 2

    1
    You must specify the openshift-operators-redhat namespace. The openshift-operators namespace might contain Community Operators, which are untrusted and could publish a metric with the same name as an Red Hat OpenShift Service on AWS metric, which would cause conflicts.
    2
    A string value that specifies the label as shown to ensure that cluster monitoring scrapes the openshift-operators-redhat namespace.

  2. Apply the Namespace object by running the following command: 

    $ oc apply -f <filename>.yaml

  3. Create a Namespace object for the Red Hat OpenShift Logging Operator:

    Example Namespace object

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

    1
    You must specify openshift-logging as the namespace for logging versions 5.7 and earlier. For logging 5.8 and later, you can use any namespace.

  4. Apply the Namespace object by running the following command: 

    $ oc apply -f <filename>.yaml

  5. Create an OperatorGroup object for the OpenShift Elasticsearch Operator:

    Example OperatorGroup object

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

    1
    You must specify the openshift-operators-redhat namespace.

  6. Apply the OperatorGroup object by running the following command: 

    $ oc apply -f <filename>.yaml

  7. Create a Subscription object to subscribe a namespace to the OpenShift Elasticsearch Operator:

    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.

    Example Subscription object

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

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

  8. Apply the subscription by running the following command: 

    $ oc apply -f <filename>.yaml

  9. Verify the Operator installation by running the following command: 

    $ oc get csv --all-namespaces

    Example output

    NAMESPACE                                          NAME                            DISPLAY                            VERSION          REPLACES                        PHASE
default                                            elasticsearch-operator.v5.8.3   OpenShift Elasticsearch Operator   5.8.3            elasticsearch-operator.v5.8.2   Succeeded
kube-node-lease                                    elasticsearch-operator.v5.8.3   OpenShift Elasticsearch Operator   5.8.3            elasticsearch-operator.v5.8.2   Succeeded
kube-public                                        elasticsearch-operator.v5.8.3   OpenShift Elasticsearch Operator   5.8.3            elasticsearch-operator.v5.8.2   Succeeded
kube-system                                        elasticsearch-operator.v5.8.3   OpenShift Elasticsearch Operator   5.8.3            elasticsearch-operator.v5.8.2   Succeeded
openshift-apiserver-operator                       elasticsearch-operator.v5.8.3   OpenShift Elasticsearch Operator   5.8.3            elasticsearch-operator.v5.8.2   Succeeded
openshift-apiserver                                elasticsearch-operator.v5.8.3   OpenShift Elasticsearch Operator   5.8.3            elasticsearch-operator.v5.8.2   Succeeded
openshift-authentication-operator                  elasticsearch-operator.v5.8.3   OpenShift Elasticsearch Operator   5.8.3            elasticsearch-operator.v5.8.2   Succeeded
openshift-authentication                           elasticsearch-operator.v5.8.3   OpenShift Elasticsearch Operator   5.8.3            elasticsearch-operator.v5.8.2   Succeeded
openshift-cloud-controller-manager-operator        elasticsearch-operator.v5.8.3   OpenShift Elasticsearch Operator   5.8.3            elasticsearch-operator.v5.8.2   Succeeded
openshift-cloud-controller-manager                 elasticsearch-operator.v5.8.3   OpenShift Elasticsearch Operator   5.8.3            elasticsearch-operator.v5.8.2   Succeeded
openshift-cloud-credential-operator                elasticsearch-operator.v5.8.3   OpenShift Elasticsearch Operator   5.8.3            elasticsearch-operator.v5.8.2   Succeeded

  10. Create an OperatorGroup object for the Red Hat OpenShift Logging Operator:

    Example OperatorGroup object

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

    1
    You must specify openshift-logging as the namespace for logging versions 5.7 and earlier. For logging 5.8 and later, you can use any namespace.
    2
    You must specify openshift-logging as the namespace for logging versions 5.7 and earlier. For logging 5.8 and later, you can use any namespace.

  11. Apply the OperatorGroup object by running the following command: 

    $ oc apply -f <filename>.yaml

  12. 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 Red Hat OpenShift Service on AWS cluster is installed on a restricted network, also known as a disconnected cluster, specify the name of the CatalogSource object you created when you configured the Operator Lifecycle Manager (OLM).

  13. Apply the subscription object by running the following command: 

    $ oc apply -f <filename>.yaml

  14. 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.
    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.
    10
    Settings for configuring Fluentd. Using the CR, you can configure Fluentd CPU and memory limits.
    Note

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

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

    $ oc get deployment

    Example output

    cluster-logging-operator-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

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

    $ oc apply -f <filename>.yaml

  16. Verify the installation by running the following command: 

    $ oc get pods -n openshift-logging

    Example output

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

Important

If there is no retention period defined on the s3 bucket or in the LokiStack custom resource (CR), then the logs are not pruned and they stay in the s3 bucket forever, which might fill up the s3 storage.

5.3. Installing Logging and the Loki Operator using the CLI

To install and configure logging on your Red Hat OpenShift Service on AWS cluster, an Operator such as Loki Operator for log storage must be installed first. This can be done from the Red Hat OpenShift Service on AWS CLI.

Prerequisites

  • You have administrator permissions.
  • You installed the OpenShift CLI (oc).
  • You have access to a supported object store. For example: AWS S3, Google Cloud Storage, Azure, Swift, Minio, or OpenShift Data Foundation.
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. Create a Namespace object for Loki Operator:

    Example Namespace object

    apiVersion: v1
kind: Namespace
metadata:
  name: openshift-operators-redhat 1
  annotations:
    openshift.io/node-selector: ""
  labels:
    openshift.io/cluster-monitoring: "true" 2

    1
    You must specify the openshift-operators-redhat namespace. To prevent possible conflicts with metrics, you should configure the Prometheus Cluster Monitoring stack to scrape metrics from the openshift-operators-redhat namespace and not the openshift-operators namespace. The openshift-operators namespace might contain community Operators, which are untrusted and could publish a metric with the same name as an Red Hat OpenShift Service on AWS metric, which would cause conflicts.
    2
    A string value that specifies the label as shown to ensure that cluster monitoring scrapes the openshift-operators-redhat namespace.

  2. Apply the Namespace object by running the following command: 

    $ oc apply -f <filename>.yaml

  3. Create a Subscription object for Loki Operator:

    Example Subscription object

    apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: loki-operator
  namespace: openshift-operators-redhat 1
spec:
  channel: stable 2
  name: loki-operator
  source: redhat-operators 3
  sourceNamespace: openshift-marketplace

    1
    You must specify the openshift-operators-redhat namespace.
    2
    Specify stable, or stable-5.<y> as the channel.
    3
    Specify redhat-operators. If your Red Hat OpenShift Service on AWS cluster is installed on a restricted network, also known as a disconnected cluster, specify the name of the CatalogSource object you created when you configured the Operator Lifecycle Manager (OLM).

  4. Apply the Subscription object by running the following command: 

    $ oc apply -f <filename>.yaml

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

    Example namespace object

    apiVersion: v1
kind: Namespace
metadata:
  name: openshift-logging 1
annotations:
    openshift.io/node-selector: ""
labels:
    openshift.io/cluster-logging: "true"
    openshift.io/cluster-monitoring: "true" 2

    1
    The Red Hat OpenShift Logging Operator is only deployable to the openshift-logging namespace.
    2
    A string value that specifies the label as shown to ensure that cluster monitoring scrapes the openshift-operators-redhat namespace.

  6. Apply the namespace object by running the following command: 

    $ oc apply -f <filename>.yaml

  7. Create an OperatorGroup object

    Example OperatorGroup object

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

    1
    You must specify the openshift-logging namespace.

  8. Apply the OperatorGroup object by running the following command: 

    $ oc apply -f <filename>.yaml

  9. Create a 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.
    2
    Specify stable, or stable-5.<y> as the channel.
    3
    Specify redhat-operators. If your Red Hat OpenShift Service on AWS cluster is installed on a restricted network, also known as a disconnected cluster, specify the name of the CatalogSource object you created when you configured the Operator Lifecycle Manager (OLM).

  10. Apply the Subscription object by running the following command: 

    $ oc apply -f <filename>.yaml

  11. Create a LokiStack CR:

    Example LokiStack CR

    apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki 1
  namespace: openshift-logging 2
spec:
  size: 1x.small 3
  storage:
    schemas:
    - version: v12
      effectiveDate: "2022-06-01"
    secret:
      name: logging-loki-s3 4
      type: s3 5
      credentialMode: 6
  storageClassName: <storage_class_name> 7
  tenants:
    mode: openshift-logging 8

    1
    Use the name logging-loki.
    2
    You must specify the openshift-logging namespace.
    3
    Specify the deployment size. In the logging 5.8 and later versions, the supported size options for production instances of Loki are 1x.extra-small, 1x.small, or 1x.medium.
    4
    Specify the name of your log store secret.
    5
    Specify the corresponding storage type.
    6
    Optional field, logging 5.9 and later. Supported user configured values are as follows: static is the default authentication mode available for all supported object storage types using credentials stored in a Secret. token for short-lived tokens retrieved from a credential source. In this mode the static configuration does not contain credentials needed for the object storage. Instead, they are generated during runtime using a service, which allows for shorter-lived credentials and much more granular control. This authentication mode is not supported for all object storage types. token-cco is the default value when Loki is running on managed STS mode and using CCO on STS/WIF clusters.
    7
    Specify the name of a storage class for temporary storage. For best performance, specify a storage class that allocates block storage. Available storage classes for your cluster can be listed by using the oc get storageclasses command.
    8
    LokiStack defaults to running in multi-tenant mode, which cannot be modified. One tenant is provided for each log type: audit, infrastructure, and application logs. This enables access control for individual users and user groups to different log streams.

  12. Apply the LokiStack CR object by running the following command: 

    $ oc apply -f <filename>.yaml

  13. Create a ClusterLogging CR object:

    Example ClusterLogging CR object

    apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  collection:
    type: vector
  logStore:
    lokistack:
      name: logging-loki
    retentionPolicy:
      application:
        maxAge: 7d
      audit:
        maxAge: 7d
      infra:
        maxAge: 7d
    type: lokistack
  visualization:
    type: ocp-console
    ocpConsole:
      logsLimit: 15
  managementState: Managed

    1
    Name must be instance.
    2
    Namespace must be openshift-logging.

  14. Apply the ClusterLogging CR object by running the following command: 

    $ oc apply -f <filename>.yaml

  15. Verify the installation by running the following command: 

    $ oc get pods -n openshift-logging

    Example output

    $ oc get pods -n openshift-logging
NAME                                               READY   STATUS    RESTARTS   AGE
cluster-logging-operator-fb7f7cf69-8jsbq           1/1     Running   0          98m
collector-222js                                    2/2     Running   0          18m
collector-g9ddv                                    2/2     Running   0          18m
collector-hfqq8                                    2/2     Running   0          18m
collector-sphwg                                    2/2     Running   0          18m
collector-vv7zn                                    2/2     Running   0          18m
collector-wk5zz                                    2/2     Running   0          18m
logging-view-plugin-6f76fbb78f-n2n4n               1/1     Running   0          18m
lokistack-sample-compactor-0                       1/1     Running   0          42m
lokistack-sample-distributor-7d7688bcb9-dvcj8      1/1     Running   0          42m
lokistack-sample-gateway-5f6c75f879-bl7k9          2/2     Running   0          42m
lokistack-sample-gateway-5f6c75f879-xhq98          2/2     Running   0          42m
lokistack-sample-index-gateway-0                   1/1     Running   0          42m
lokistack-sample-ingester-0                        1/1     Running   0          42m
lokistack-sample-querier-6b7b56bccc-2v9q4          1/1     Running   0          42m
lokistack-sample-query-frontend-84fb57c578-gq2f7   1/1     Running   0          42m

5.4. Installing Logging and the Loki Operator using the web console

To install and configure logging on your Red Hat OpenShift Service on AWS cluster, an Operator such as Loki Operator for log storage must be installed first. This can be done from the OperatorHub within the web console.

Prerequisites

  • You have access to a supported object store (AWS S3, Google Cloud Storage, Azure, Swift, Minio, OpenShift Data Foundation).
  • You have administrator permissions.
  • You have access to the Red Hat OpenShift Service on AWS web console.

Procedure

  1. In the Red Hat OpenShift Service on AWS web console Administrator perspective, go to Operators → OperatorHub.

  2. Type Loki Operator in the Filter by keyword field. Click Loki Operator in the list of available Operators, and then click Install.

    Important

    The Community Loki Operator is not supported by Red Hat.

  3. Select stable or stable-x.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.

    The Loki Operator must be deployed to the global operator group namespace openshift-operators-redhat, so the Installation mode and Installed Namespace are already selected. If this namespace does not already exist, it is created for you.

  4. Select Enable Operator-recommended cluster monitoring on this namespace.

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

  5. For Update approval select Automatic, then click Install.

    If the approval strategy in the subscription is set to Automatic, the update process initiates as soon as a new Operator version is available in the selected channel. If the approval strategy is set to Manual, you must manually approve pending updates.

  6. Install the Red Hat OpenShift Logging Operator:

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

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

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

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

    7. Select an Approval Strategy.

      • The Automatic strategy allows Operator Lifecycle Manager (OLM) to automatically update the Operator when a new version is available.
      • The Manual strategy requires a user with appropriate credentials to approve the Operator update.
    8. Click Install.
  7. Go to the Operators → Installed Operators page. Click the All instances tab.
  8. From the Create new drop-down list, select LokiStack.

  9. Select YAML view, and then use the following template to create a LokiStack CR:

    Example LokiStack CR

    apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki 1
  namespace: openshift-logging 2
spec:
  size: 1x.small 3
  storage:
    schemas:
    - version: v12
      effectiveDate: "2022-06-01"
    secret:
      name: logging-loki-s3 4
      type: s3 5
      credentialMode: 6
  storageClassName: <storage_class_name> 7
  tenants:
    mode: openshift-logging 8

    1
    Use the name logging-loki.
    2
    You must specify the openshift-logging namespace.
    3
    Specify the deployment size. In the logging 5.8 and later versions, the supported size options for production instances of Loki are 1x.extra-small, 1x.small, or 1x.medium.
    4
    Specify the name of your log store secret.
    5
    Specify the corresponding storage type.
    6
    Optional field, logging 5.9 and later. Supported user configured values are as follows: static is the default authentication mode available for all supported object storage types using credentials stored in a Secret. token for short-lived tokens retrieved from a credential source. In this mode the static configuration does not contain credentials needed for the object storage. Instead, they are generated during runtime using a service, which allows for shorter-lived credentials and much more granular control. This authentication mode is not supported for all object storage types. token-cco is the default value when Loki is running on managed STS mode and using CCO on STS/WIF clusters.
    7
    Specify the name of a storage class for temporary storage. For best performance, specify a storage class that allocates block storage. Available storage classes for your cluster can be listed by using the oc get storageclasses command.
    8
    LokiStack defaults to running in multi-tenant mode, which cannot be modified. One tenant is provided for each log type: audit, infrastructure, and application logs. This enables access control for individual users and user groups to different log streams.
    Important

    It is not possible to change the number 1x for the deployment size.

  10. Click Create.

  11. Create an OpenShift Logging instance:

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

    4. On the ClusterLoggings page, click Create ClusterLogging.

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

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

      apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  collection:
    type: vector
  logStore:
    lokistack:
      name: logging-loki
    retentionPolicy:
      application:
        maxAge: 7d
      audit:
        maxAge: 7d
      infra:
        maxAge: 7d
    type: lokistack
  visualization:
    type: ocp-console
    ocpConsole:
      logsLimit: 15

  managementState: Managed
      1
      Name must be instance.
      2
      Namespace must be openshift-logging.

Verification

  1. Go to Operators → Installed Operators.
  2. Make sure the openshift-logging project is selected.
  3. In the Status column, verify that you see green checkmarks with InstallSucceeded and the text Up to date.
Note

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

Additional resources

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 Red Hat OpenShift Service on AWS web console and are viewing the Administrator perspective.

Procedure

  1. Navigate to Operators → Installed 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 Operators → Installed Operators. Verify that the Red Hat OpenShift Logging Operator version matches the latest cluster-logging.v5.y.z version.
  2. On the Operators → Installed 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 Red Hat OpenShift Service on AWS web console and are viewing the Administrator perspective.

Procedure

  1. Navigate to Operators → Installed 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 Operators → Installed Operators. Verify that the Loki Operator version matches the latest loki-operator.v5.y.z version.
  2. On the Operators → Installed 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 Red Hat Hybrid Cloud Console, click Operators → Installed Operators.
  2. Select the openshift-operators-redhat project.
  3. Click OpenShift Elasticsearch Operator.
  4. Click Subscription → Channel.
  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 Operators → Installed Operators. Verify that the OpenShift Elasticsearch Operator version matches the latest elasticsearch-operator.v5.y.z version.
  7. On the Operators → Installed 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 Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS console is only compatible with LokiStack deployments.
    2
    Optional configurations for the Kibana console.
    3
    Optional configurations for the Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS cluster, such as node system audit logs, application container logs, and infrastructure logs, into a dedicated log store. You can then query, discover, and visualize your log data through the Kibana console or the Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS console, navigate to Workloads → Pods or navigate to the pod through the resource you want to investigate.

    Note

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

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

Procedure (CLI)

  • View the log for a specific pod: 

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

    where:

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

    For example: 

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

    The contents of log files are printed out.

  • View the log for a specific resource: 

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

    For example: 

    $ oc logs deployment/ruby

    The contents of log files are printed out.

7.2. Log visualization with the web console

You can use the Red Hat OpenShift Service on AWS web console to visualize log data by configuring the logging Console Plugin. Options for configuration are available during installation of logging on the web console.

If you have already installed 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 Red Hat OpenShift Service on AWS web console.

Procedure

  1. In the Red Hat OpenShift Service on AWS web console Administrator perspective, navigate to Operators → Installed 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 Red Hat OpenShift Service on AWS web console and viewing the Observe → Logs page.

7.3. Viewing cluster dashboards

The Logging/Elasticsearch Nodes and Openshift Logging dashboards in the OpenShift Cluster Manager 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 Cluster Manager.

Procedure

To launch the dashboards:

  1. In the Red Hat OpenShift Service on AWS Red Hat Hybrid Cloud Console, click Observe → Dashboards.

  2. On the Dashboards page, select Logging/Elasticsearch Nodes or OpenShift Logging from the Dashboard menu.

    For the Logging/Elasticsearch Nodes dashboard, you can select the Elasticsearch node you want to view and set the data resolution.

    The appropriate dashboard is displayed, showing multiple charts of data.

  3. Optional: Select a different time range to display or refresh rate for the data from the Time Range and Refresh Interval menus.

For information on the dashboard charts, see About the OpenShift Logging dashboard and About the Logging/Elastisearch Nodes dashboard.

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 Red Hat OpenShift Service on AWS Elasticsearch instance by default. To view the audit logs in Kibana, you must use the Log Forwarding API to configure a pipeline that uses the default output for audit logs.

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 Red Hat OpenShift Service on AWS Elasticsearch instance by default. To view the audit logs in Kibana, you must use the Log Forwarding API to configure a pipeline that uses the default output for audit logs.

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

Procedure

To define index patterns and create visualizations in Kibana:

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

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

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

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 Red Hat OpenShift Service on AWS Elasticsearch instance by default. To view the audit logs in Kibana, you must use the Log Forwarding API to configure a pipeline that uses the default output for audit logs.

Procedure

To view logs in Kibana:

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

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

    The Kibana interface launches.

  3. In Kibana, click Discover.

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

    The log data displays as time-stamped documents.

  5. Expand one of the time-stamped documents.

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

    Example 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.

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 Red Hat OpenShift Service on AWS 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 Red Hat OpenShift Service on AWS.
  • /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>

Additional resources

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).
cloudlogging
Google Cloud Logging, a monitoring and log storage service hosted by Google Cloud Platform (GCP).

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

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. Red Hat OpenShift Service on AWS rotates the logs and deletes them. You cannot adjust the buffer size or add a persistent volume claim (PVC) to the Fluentd daemon set or pods.

Supported Authorization Keys

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

Transport Layer Security (TLS)

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

  • 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 Splunk

You can forward logs to the Splunk HTTP Event Collector (HEC) in addition to, or instead of, the internal default Red Hat OpenShift Service on AWS log store.

Note

Using this feature with Fluentd is not supported.

Prerequisites

  • Red Hat OpenShift Logging Operator 5.6 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.

9.4.6. 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.

9.4.7. 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.

9.4.8. 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 Red Hat OpenShift Service on AWS.

To configure forwarding application logs from a project, you must create a ClusterLogForwarder custom resource (CR) with at least one input from a project, optional outputs for other log aggregators, and pipelines that use those inputs and outputs.

Prerequisites

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

Procedure

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

    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

9.4.9. 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

9.4.10. 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.
Note

You can use this feature only if the Vector collector is set up in your logging deployment.

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 Red Hat OpenShift Service on AWS 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.

Additional resources

9.4.11. 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 log types to forward by using the pipeline: application, infrastructure, or audit.
    13
    Specify the name of the output to use when forwarding logs with this pipeline.
    Note

    Because Loki requires log streams to be correctly ordered by timestamp, labelKeys always includes the kubernetes_host label set, even if you do not specify it. This inclusion ensures that each stream originates from a single host, which prevents timestamps from becoming disordered due to clock differences on different hosts.

  2. Apply the ClusterLogForwarder CR object by running the following command: 

    $ oc apply -f <filename>.yaml

Additional resources

9.4.12. Forwarding logs to an external Elasticsearch instance

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

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

To forward logs to both an external and the internal Elasticsearch instance, create outputs and pipelines to the external instance and a pipeline that uses the default output to forward logs to the internal instance.

Note

If you only want to forward logs to an internal Elasticsearch instance, you do not need to create a ClusterLogForwarder CR.

Prerequisites

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

Procedure

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

    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: elasticsearch-example 4
     type: elasticsearch 5
     elasticsearch:
       version: 8 6
     url: http://elasticsearch.example.com:9200 7
     secret:
       name: es-secret 8
  pipelines:
   - name: application-logs 9
     inputRefs: 10
     - application
     - audit
     outputRefs:
     - elasticsearch-example 11
     - default 12
     labels:
       myLabel: "myValue" 13
# ...

    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 elasticsearch type.
    6
    Specify the Elasticsearch version. This can be 6, 7, or 8.
    7
    Specify the URL and port of the external Elasticsearch instance as a valid absolute URL. You can use the http (insecure) or https (secure HTTP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP Address.
    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 certificate 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 name for the pipeline.
    10
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    11
    Specify the name of the output to use when forwarding logs with this pipeline.
    12
    Optional: Specify the default output to send the logs to the internal Elasticsearch instance.
    13
    Optional: String. One or more labels to add to the logs.

  2. Apply the ClusterLogForwarder CR: 

    $ oc apply -f <filename>.yaml

Example: Setting a secret that contains a username and password

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

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

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

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

  2. Create the secret: 

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

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

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

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

  4. Apply the CR object: 

    $ oc apply -f <filename>.yaml

9.4.13. Forwarding logs using the Fluentd forward protocol

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

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

Prerequisites

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

Procedure

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

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  outputs:
   - name: fluentd-server-secure 3
     type: fluentdForward 4
     url: 'tls://fluentdserver.security.example.com:24224' 5
     secret: 6
        name: fluentd-secret
   - name: fluentd-server-insecure
     type: fluentdForward
     url: 'tcp://fluentdserver.home.example.com:24224'
  pipelines:
   - name: forward-to-fluentd-secure 7
     inputRefs:  8
     - application
     - audit
     outputRefs:
     - fluentd-server-secure 9
     - default 10
     labels:
       clusterId: "C1234" 11
   - name: forward-to-fluentd-insecure 12
     inputRefs:
     - infrastructure
     outputRefs:
     - fluentd-server-insecure
     labels:
       clusterId: "C1234"
    1
    The name of the ClusterLogForwarder CR must be instance.
    2
    The namespace for the ClusterLogForwarder CR must be openshift-logging.
    3
    Specify a name for the output.
    4
    Specify the fluentdForward type.
    5
    Specify the URL and port of the external Fluentd instance as a valid absolute URL. You can use the tcp (insecure) or tls (secure TCP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP address.
    6
    If you are using a tls prefix, you must specify the name of the secret required by the endpoint for TLS communication. The secret must exist in the openshift-logging project and must contain a ca-bundle.crt key that points to the certificate it represents.
    7
    Optional: Specify a name for the pipeline.
    8
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    9
    Specify the name of the output to use when forwarding logs with this pipeline.
    10
    Optional: Specify the default output to forward logs to the internal Elasticsearch instance.
    11
    Optional: String. One or more labels to add to the logs.
    12
    Optional: Configure multiple outputs to forward logs to other external log aggregators of any supported type:
    • A name to describe the pipeline.
    • The inputRefs is the log type to forward by using the pipeline: application, infrastructure, or audit.
    • The outputRefs is the name of the output to use.
    • Optional: String. One or more labels to add to the logs.

  2. Create the CR object: 

    $ oc create -f <file-name>.yaml
9.4.13.1. Enabling nanosecond precision for Logstash to ingest data from fluentd

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

Procedure

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

Example Logstash configuration file

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

9.4.14. Forwarding logs using the syslog protocol

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

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

Prerequisites

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

Procedure

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

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: <log_forwarder_name> 1
  namespace: <log_forwarder_namespace> 2
spec:
  serviceAccountName: <service_account_name> 3
  outputs:
   - name: rsyslog-east 4
     type: syslog 5
     syslog: 6
       facility: local0
       rfc: RFC3164
       payloadKey: message
       severity: informational
     url: 'tls://rsyslogserver.east.example.com:514' 7
     secret: 8
        name: syslog-secret
   - name: rsyslog-west
     type: syslog
     syslog:
      appName: myapp
      facility: user
      msgID: mymsg
      procID: myproc
      rfc: RFC5424
      severity: debug
     url: 'tcp://rsyslogserver.west.example.com:514'
  pipelines:
   - name: syslog-east 9
     inputRefs: 10
     - audit
     - application
     outputRefs: 11
     - rsyslog-east
     - default 12
     labels:
       secure: "true" 13
       syslog: "east"
   - name: syslog-west 14
     inputRefs:
     - infrastructure
     outputRefs:
     - rsyslog-west
     - default
     labels:
       syslog: "west"
    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 syslog type.
    6
    Optional: Specify the syslog parameters, listed below.
    7
    Specify the URL and port of the external syslog instance. You can use the udp (insecure), tcp (insecure) or tls (secure TCP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP address.
    8
    If using a tls prefix, you must 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 certificate it represents. In legacy implementations, the secret must exist in the openshift-logging project.
    9
    Optional: Specify a name for the pipeline.
    10
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    11
    Specify the name of the output to use when forwarding logs with this pipeline.
    12
    Optional: Specify the default output to forward logs to the internal Elasticsearch instance.
    13
    Optional: String. One or more labels to add to the logs. Quote values like "true" so they are recognized as string values, not as a boolean.
    14
    Optional: Configure multiple outputs to forward logs to other external log aggregators of any supported type:
    • A name to describe the pipeline.
    • The inputRefs is the log type to forward by using the pipeline: application, infrastructure, or audit.
    • The outputRefs is the name of the output to use.
    • Optional: String. One or more labels to add to the logs.

  2. Create the CR object: 

    $ oc create -f <filename>.yaml
9.4.14.1. Adding log source information to message output

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

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

This configuration is compatible with both RFC3164 and RFC5424.

Example syslog message output without AddLogSource

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

Example syslog message output with AddLogSource

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

9.4.14.2. Syslog parameters

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

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

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

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

    Note

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

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

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

    • 0 or Emergency for messages indicating the system is unusable
    • 1 or Alert for messages indicating action must be taken immediately
    • 2 or Critical for messages indicating critical conditions
    • 3 or Error for messages indicating error conditions
    • 4 or Warning for messages indicating warning conditions
    • 5 or Notice for messages indicating normal but significant conditions
    • 6 or Informational for messages indicating informational messages
    • 7 or Debug for messages indicating debug-level messages, the default
  • tag: Tag specifies a record field to use as a tag on the syslog message.
  • trimPrefix: Remove the specified prefix from the tag.
9.4.14.3. Additional RFC5424 syslog parameters

The following parameters apply to RFC5424:

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

9.4.15. Forwarding logs to a Kafka broker

You can forward logs to an external Kafka broker in addition to, or instead of, the default log store.

To configure log forwarding to an external Kafka instance, you must create a ClusterLogForwarder custom resource (CR) with an output to that instance, and a pipeline that uses the output. You can include a specific Kafka topic in the output or use the default. The Kafka output can use a TCP (insecure) or TLS (secure TCP) connection.

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: app-logs 4
     type: kafka 5
     url: tls://kafka.example.devlab.com:9093/app-topic 6
     secret:
       name: kafka-secret 7
   - name: infra-logs
     type: kafka
     url: tcp://kafka.devlab2.example.com:9093/infra-topic 8
   - name: audit-logs
     type: kafka
     url: tls://kafka.qelab.example.com:9093/audit-topic
     secret:
        name: kafka-secret-qe
  pipelines:
   - name: app-topic 9
     inputRefs: 10
     - application
     outputRefs: 11
     - app-logs
     labels:
       logType: "application" 12
   - name: infra-topic 13
     inputRefs:
     - infrastructure
     outputRefs:
     - infra-logs
     labels:
       logType: "infra"
   - name: audit-topic
     inputRefs:
     - audit
     outputRefs:
     - audit-logs
     labels:
       logType: "audit"
    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 kafka type.
    6
    Specify the URL and port of the Kafka broker as a valid absolute URL, optionally with a specific topic. You can use the tcp (insecure) or tls (secure TCP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP address.
    7
    If you are using a tls prefix, you must 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 certificate it represents. In legacy implementations, the secret must exist in the openshift-logging project.
    8
    Optional: To send an insecure output, use a tcp prefix in front of the URL. Also omit the secret key and its name from this output.
    9
    Optional: Specify a name for the pipeline.
    10
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    11
    Specify the name of the output to use when forwarding logs with this pipeline.
    12
    Optional: String. One or more labels to add to the logs.
    13
    Optional: Configure multiple outputs to forward logs to other external log aggregators of any supported type:
    • A name to describe the pipeline.
    • The inputRefs is the log type to forward by using the pipeline: application, infrastructure, or audit.
    • The outputRefs is the name of the output to use.
    • Optional: String. One or more labels to add to the logs.

  2. Optional: To forward a single output to multiple Kafka brokers, specify an array of Kafka brokers as shown in the following example: 

    # ...
spec:
  outputs:
  - name: app-logs
    type: kafka
    secret:
      name: kafka-secret-dev
    kafka:  1
      brokers: 2
        - tls://kafka-broker1.example.com:9093/
        - tls://kafka-broker2.example.com:9093/
      topic: app-topic 3
# ...
    1
    Specify a kafka key that has a brokers and topic key.
    2
    Use the brokers key to specify an array of one or more brokers.
    3
    Use the topic key to specify the target topic that receives the logs.

  3. Apply the ClusterLogForwarder CR by running the following command: 

    $ oc apply -f <filename>.yaml

9.4.16. Forwarding logs to Amazon CloudWatch

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

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

Procedure

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

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

  2. Create the secret. For example: 

    $ oc apply -f cw-secret.yaml

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

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: <log_forwarder_name> 1
  namespace: <log_forwarder_namespace> 2
spec:
  serviceAccountName: <service_account_name> 3
  outputs:
   - name: cw 4
     type: cloudwatch 5
     cloudwatch:
       groupBy: logType 6
       groupPrefix: <group prefix> 7
       region: us-east-2 8
     secret:
        name: cw-secret 9
  pipelines:
    - name: infra-logs 10
      inputRefs: 11
        - infrastructure
        - audit
        - application
      outputRefs:
        - cw 12
    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 cloudwatch type.
    6
    Optional: Specify how to group the logs:
    • logType creates log groups for each log type.
    • namespaceName creates a log group for each application name space. It also creates separate log groups for infrastructure and audit logs.
    • namespaceUUID creates a new log groups for each application namespace UUID. It also creates separate log groups for infrastructure and audit logs.
    7
    Optional: Specify a string to replace the default infrastructureName prefix in the names of the log groups.
    8
    Specify the AWS region.
    9
    Specify the name of the secret that contains your AWS credentials.
    10
    Optional: Specify a name for the pipeline.
    11
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    12
    Specify the name of the output to use when forwarding logs with this pipeline.

  4. Create the CR object: 

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

Example: Using ClusterLogForwarder with Amazon CloudWatch

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

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

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

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

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

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

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

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

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

Each region in CloudWatch contains three levels of objects:

  • log group

    • log stream

      • log event

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

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

Each of the log groups contains log streams: 

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

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

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

Example: Customizing the prefix in log group names

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

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

The value of groupPrefix replaces the default infrastructureName prefix: 

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

Example: Naming log groups after application namespace names

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

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

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

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

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

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

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

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

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

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

Example: Naming log groups after application namespace UUIDs

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

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

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

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

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

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

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

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

9.4.17. Creating a secret for AWS CloudWatch with an existing AWS role

If you have an existing role for AWS, you can create a secret for AWS with STS using the oc create secret --from-literal command.

Procedure

  • In the CLI, enter the following to generate a secret for AWS: 

    $ oc create secret generic cw-sts-secret -n openshift-logging --from-literal=role_arn=arn:aws:iam::123456789012:role/my-role_with-permissions

    Example Secret

    apiVersion: v1
kind: Secret
metadata:
  namespace: openshift-logging
  name: my-secret-name
stringData:
  role_arn: arn:aws:iam::123456789012:role/my-role_with-permissions

9.4.18. Forwarding logs to Amazon CloudWatch from STS enabled clusters

For clusters with AWS Security Token Service (STS) enabled, you must create the AWS IAM roles and policies that enable log forwarding, and a ClusterLogForwarder custom resource (CR) with an output for CloudWatch.

Prerequisites

  • Logging for Red Hat OpenShift: 5.5 and later

Procedure

  1. Prepare the AWS account:

    1. Create an IAM policy JSON file with the following content: 

      {
"Version": "2012-10-17",
"Statement": [
    {
    "Effect": "Allow",
    "Action": [
      "logs:CreateLogGroup",
      "logs:CreateLogStream",
      "logs:DescribeLogGroups",
      "logs:DescribeLogStreams",
      "logs:PutLogEvents",
      "logs:PutRetentionPolicy"
    ],
    "Resource": "arn:aws:logs:*:*:*"
    }
  ]
}

    2. Create an IAM trust JSON file with the following content: 

      {
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Federated": "arn:aws:iam::<your_aws_account_id>:oidc-provider/<openshift_oidc_provider>" 1
      },
      "Action": "sts:AssumeRoleWithWebIdentity",
      "Condition": {
        "StringEquals": {
          "<openshift_oidc_provider>:sub": "system:serviceaccount:openshift-logging:logcollector" 2
        }
      }
    }
  ]
}
      1
      Specify your AWS account ID and the OpenShift OIDC provider endpoint. Obtain the endpoint by running the following command: 
      $ rosa describe cluster \
  -c $(oc get clusterversion -o jsonpath='{.items[].spec.clusterID}{"\n"}') \
  -o yaml | awk '/oidc_endpoint_url/ {print $2}' | cut -d '/' -f 3,4
      2
      Specify the OpenShift OIDC endpoint again.

    3. Create the IAM role: 

      $ aws iam create-role
  --role-name “<your_rosa_cluster_name>-RosaCloudWatch” \
  --assume-role-policy-document file://<your_trust_file_name>.json \
  --query Role.Arn \
  --output text

      Save the output. You will use it in the next steps.

    4. Create the IAM policy: 

      $ aws iam create-policy \
--policy-name "RosaCloudWatch" \
--policy-document file:///<your_policy_file_name>.json \
--query Policy.Arn \
--output text

      Save the output. You will use it in the next steps.

    5. Attach the IAM policy to the IAM role: 

      $ aws iam attach-role-policy \
 --role-name “<your_rosa_cluster_name>-RosaCloudWatch” \
 --policy-arn <policy_ARN> 1
      1
      Replace policy_ARN with the output you saved while creating the policy.

  2. Create a Secret YAML file for the Red Hat OpenShift Logging Operator: 

    apiVersion: v1
kind: Secret
metadata:
  name: cloudwatch-credentials
  namespace: openshift-logging
stringData:
  credentials: |-
    [default]
    sts_regional_endpoints = regional
    role_arn: <role_ARN>  1
    web_identity_token_file = /var/run/secrets/openshift/serviceaccount/token
    1
    Replace role_ARN with the output you saved while creating the role.

  3. Create the secret: 

    $ oc apply -f cloudwatch-credentials.yaml

  4. Create or edit a ClusterLogForwarder custom resource: 

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: <log_forwarder_name> 1
  namespace: <log_forwarder_namespace> 2
spec:
  serviceAccountName: <service_account_name> 3
  outputs:
   - name: cw 4
     type: cloudwatch 5
     cloudwatch:
       groupBy: logType 6
       groupPrefix: <group prefix> 7
       region: us-east-2 8
     secret:
        name: <your_secret_name> 9
  pipelines:
    - name: to-cloudwatch 10
      inputRefs: 11
        - infrastructure
        - audit
        - application
      outputRefs:
        - cw 12
    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 cloudwatch type.
    6
    Optional: Specify how to group the logs:
    • logType creates log groups for each log type
    • namespaceName creates a log group for each application name space. Infrastructure and audit logs are unaffected, remaining grouped by logType.
    • namespaceUUID creates a new log groups for each application namespace UUID. It also creates separate log groups for infrastructure and audit logs.
    7
    Optional: Specify a string to replace the default infrastructureName prefix in the names of the log groups.
    8
    Specify the AWS region.
    9
    Specify the name of the secret you created previously.
    10
    Optional: Specify a name for the pipeline.
    11
    Specify which log types to forward by using the pipeline: application, infrastructure, or audit.
    12
    Specify the name of the output to use when forwarding logs with this pipeline.

Additional resources

9.5. Configuring the logging collector

Logging for Red Hat OpenShift collects operations and application logs from your cluster and enriches the data with Kubernetes pod and project metadata. All supported modifications to the log collector can be performed though the spec.collection stanza in the ClusterLogging custom resource (CR).

9.5.1. 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

9.5.2. Creating a LogFileMetricExporter resource

In logging version 5.8 and newer versions, 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 Red Hat OpenShift Service on AWS web console dashboard for Produced Logs.

Prerequisites

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

Procedure

  1. Create a LogFileMetricExporter CR as a YAML file:

    Example LogFileMetricExporter CR

    apiVersion: logging.openshift.io/v1alpha1
kind: LogFileMetricExporter
metadata:
  name: instance
  namespace: openshift-logging
spec:
  nodeSelector: {} 1
  resources: 2
    limits:
      cpu: 500m
      memory: 256Mi
    requests:
      cpu: 200m
      memory: 128Mi
  tolerations: [] 3
# ...

    1
    Optional: The nodeSelector stanza defines which nodes the pods are scheduled on.
    2
    The resources stanza defines resource requirements for the LogFileMetricExporter CR.
    3
    Optional: The tolerations stanza defines the tolerations that the pods accept.

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

    $ oc apply -f <filename>.yaml

Verification

A logfilesmetricexporter pod runs concurrently with a collector pod on each node.

  • Verify that the logfilesmetricexporter pods are running in the namespace where you have created the LogFileMetricExporter CR, by running the following command and observing the output: 

    $ oc get pods -l app.kubernetes.io/component=logfilesmetricexporter -n openshift-logging

    Example output

    NAME                           READY   STATUS    RESTARTS   AGE
logfilesmetricexporter-9qbjj   1/1     Running   0          2m46s
logfilesmetricexporter-cbc4v   1/1     Running   0          2m46s

9.5.3. Configure log collector CPU and memory limits

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

Procedure

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

    $ oc -n openshift-logging edit ClusterLogging instance
    apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name: instance
  namespace: openshift-logging
spec:
  collection:
    type: fluentd
    resources:
      limits: 1
        memory: 736Mi
      requests:
        cpu: 100m
        memory: 736Mi
# ...
    1
    Specify the CPU and memory limits and requests as needed. The values shown are the default values.

9.5.4. Configuring input receivers

The Red Hat OpenShift Logging Operator deploys a service for each configured input receiver so that clients can write to the collector. This service exposes the port specified for the input receiver. The service name is generated based on the following:

  • For multi log forwarder ClusterLogForwarder CR deployments, the service name is in the format <ClusterLogForwarder_CR_name>-<input_name>. For example, example-http-receiver.
  • For legacy ClusterLogForwarder CR deployments, meaning those named instance and located in the openshift-logging namespace, the service name is in the format collector-<input_name>. For example, collector-http-receiver.
9.5.4.1. Configuring the collector to receive audit logs as an HTTP server

You can configure your log collector to listen for HTTP connections and receive audit logs as an HTTP server by specifying http as a receiver input in the ClusterLogForwarder custom resource (CR). This enables you to use a common log store for audit logs that are collected from both inside and outside of your Red Hat OpenShift Service on AWS cluster.

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 ClusterLogForwarder CR.

Procedure

  1. Modify the ClusterLogForwarder CR to add configuration for the http receiver input:

    Example ClusterLogForwarder CR if you are using a multi log forwarder deployment

    apiVersion: logging.openshift.io/v1beta1
kind: ClusterLogForwarder
metadata:
# ...
spec:
  serviceAccountName: <service_account_name>
  inputs:
    - name: http-receiver 1
      receiver:
        type: http 2
        http:
          format: kubeAPIAudit 3
          port: 8443 4
  pipelines: 5
    - name: http-pipeline
      inputRefs:
        - http-receiver
# ...

    1
    Specify a name for your input receiver.
    2
    Specify the input receiver type as http.
    3
    Currently, only the kube-apiserver webhook format is supported for http input receivers.
    4
    Optional: Specify the port that the input receiver listens on. This must be a value between 1024 and 65535. The default value is 8443 if this is not specified.
    5
    Configure a pipeline for your input receiver.

    Example ClusterLogForwarder CR if you are using a legacy deployment

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: instance
  namespace: openshift-logging
spec:
  inputs:
    - name: http-receiver 1
      receiver:
        type: http 2
        http:
          format: kubeAPIAudit 3
          port: 8443 4
  pipelines: 5
  - inputRefs:
    - http-receiver
    name: http-pipeline
# ...

    1
    Specify a name for your input receiver.
    2
    Specify the input receiver type as http.
    3
    Currently, only the kube-apiserver webhook format is supported for http input receivers.
    4
    Optional: Specify the port that the input receiver listens on. This must be a value between 1024 and 65535. The default value is 8443 if this is not specified.
    5
    Configure a pipeline for your input receiver.

  2. Apply the changes to the ClusterLogForwarder CR by running the following command: 

    $ oc apply -f <filename>.yaml

Additional resources

9.5.5. Advanced configuration for the Fluentd log forwarder

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.

Logging includes multiple Fluentd parameters that you can use for tuning the performance of the Fluentd log forwarder. With these parameters, you can change the following Fluentd behaviors:

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

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

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

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

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

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

Note

These parameters are:

  • Not relevant to most users. The default settings should give good general performance.
  • Only for advanced users with detailed knowledge of Fluentd configuration and performance.
  • Only for performance tuning. They have no effect on functional aspects of logging.
Table 9.11. Advanced Fluentd Configuration Parameters
ParameterDescriptionDefault

chunkLimitSize

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

8m

totalLimitSize

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

Approximately 15% of the node disk distributed across all outputs.

flushInterval

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

1s

flushMode

The method to perform flushes:

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

interval

flushThreadCount

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

2

overflowAction

The chunking behavior when the queue is full:

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

block

retryMaxInterval

The maximum time in seconds for the exponential_backoff retry method.

300s

retryType

The retry method when flushing fails:

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

exponential_backoff

retryTimeOut

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

60m

retryWait

The time in seconds before the next chunk flush.

1s

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

Procedure

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

    $ oc edit ClusterLogging instance

  2. Add or modify any of the following parameters: 

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

  3. Verify that the Fluentd pods are redeployed: 

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

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

    $ oc extract configmap/collector-config --confirm

    Example fluentd.conf

    <buffer>
  @type file
  path '/var/lib/fluentd/default'
  flush_mode interval
  flush_interval 5s
  flush_thread_count 3
  retry_type periodic
  retry_wait 1s
  retry_max_interval 300s
  retry_timeout 60m
  queued_chunks_limit_size "#{ENV['BUFFER_QUEUE_LIMIT'] || '32'}"
  total_limit_size "#{ENV['TOTAL_LIMIT_SIZE_PER_BUFFER'] || '8589934592'}"
  chunk_limit_size 8m
  overflow_action throw_exception
  disable_chunk_backup true
</buffer>

9.6. Collecting and storing Kubernetes events

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

The Event Router collects events from all projects and writes them to STDOUT. The collector then forwards those events to the store defined in the ClusterLogForwarder custom resource (CR).

Important

The Event Router adds additional load to Fluentd and can impact the number of other log messages that can be processed.

9.6.1. Deploying and configuring the Event Router

Use the following steps to deploy the Event Router into your cluster. You should always deploy the Event Router to the openshift-logging project to ensure it collects events from across the cluster.

Note

The Event Router image is not a part of the Red Hat OpenShift Logging Operator and must be downloaded separately.

The following Template object creates the service account, cluster role, and cluster role binding required for the Event Router. The template also configures and deploys the Event Router pod. You can either use this template without making changes or edit the template to change the deployment object CPU and memory requests.

Prerequisites

  • You need proper permissions to create service accounts and update cluster role bindings. For example, you can run the following template with a user that has the cluster-admin role.
  • The Red Hat OpenShift Logging Operator must be installed.

Procedure

  1. Create a template for the Event Router: 

    apiVersion: template.openshift.io/v1
kind: Template
metadata:
  name: eventrouter-template
  annotations:
    description: "A pod forwarding kubernetes events to OpenShift Logging stack."
    tags: "events,EFK,logging,cluster-logging"
objects:
  - kind: ServiceAccount 1
    apiVersion: v1
    metadata:
      name: eventrouter
      namespace: ${NAMESPACE}
  - kind: ClusterRole 2
    apiVersion: rbac.authorization.k8s.io/v1
    metadata:
      name: event-reader
    rules:
    - apiGroups: [""]
      resources: ["events"]
      verbs: ["get", "watch", "list"]
  - kind: ClusterRoleBinding 3
    apiVersion: rbac.authorization.k8s.io/v1
    metadata:
      name: event-reader-binding
    subjects:
    - kind: ServiceAccount
      name: eventrouter
      namespace: ${NAMESPACE}
    roleRef:
      kind: ClusterRole
      name: event-reader
  - kind: ConfigMap 4
    apiVersion: v1
    metadata:
      name: eventrouter
      namespace: ${NAMESPACE}
    data:
      config.json: |-
        {
          "sink": "stdout"
        }
  - kind: Deployment 5
    apiVersion: apps/v1
    metadata:
      name: eventrouter
      namespace: ${NAMESPACE}
      labels:
        component: "eventrouter"
        logging-infra: "eventrouter"
        provider: "openshift"
    spec:
      selector:
        matchLabels:
          component: "eventrouter"
          logging-infra: "eventrouter"
          provider: "openshift"
      replicas: 1
      template:
        metadata:
          labels:
            component: "eventrouter"
            logging-infra: "eventrouter"
            provider: "openshift"
          name: eventrouter
        spec:
          serviceAccount: eventrouter
          containers:
            - name: kube-eventrouter
              image: ${IMAGE}
              imagePullPolicy: IfNotPresent
              resources:
                requests:
                  cpu: ${CPU}
                  memory: ${MEMORY}
              volumeMounts:
              - name: config-volume
                mountPath: /etc/eventrouter
              securityContext:
                allowPrivilegeEscalation: false
                capabilities:
                  drop: ["ALL"]
          securityContext:
            runAsNonRoot: true
            seccompProfile:
              type: RuntimeDefault
          volumes:
          - name: config-volume
            configMap:
              name: eventrouter
parameters:
  - name: IMAGE 6
    displayName: Image
    value: "registry.redhat.io/openshift-logging/eventrouter-rhel9:v0.4"
  - name: CPU 7
    displayName: CPU
    value: "100m"
  - name: MEMORY 8
    displayName: Memory
    value: "128Mi"
  - name: NAMESPACE
    displayName: Namespace
    value: "openshift-logging" 9
    1
    Creates a Service Account in the openshift-logging project for the Event Router.
    2
    Creates a ClusterRole to monitor for events in the cluster.
    3
    Creates a ClusterRoleBinding to bind the ClusterRole to the service account.
    4
    Creates a config map in the openshift-logging project to generate the required config.json file.
    5
    Creates a deployment in the openshift-logging project to generate and configure the Event Router pod.
    6
    Specifies the image, identified by a tag such as v0.4.
    7
    Specifies the minimum amount of CPU to allocate to the Event Router pod. Defaults to 100m.
    8
    Specifies the minimum amount of memory to allocate to the Event Router pod. Defaults to 128Mi.
    9
    Specifies the openshift-logging project to install objects in.

  2. Use the following command to process and apply the template: 

    $ oc process -f <templatefile> | oc apply -n openshift-logging -f -

    For example: 

    $ oc process -f eventrouter.yaml | oc apply -n openshift-logging -f -

    Example output

    serviceaccount/eventrouter created
clusterrole.rbac.authorization.k8s.io/event-reader created
clusterrolebinding.rbac.authorization.k8s.io/event-reader-binding created
configmap/eventrouter created
deployment.apps/eventrouter created

  3. Validate that the Event Router installed in the openshift-logging project:

    1. View the new Event Router pod: 

      $ oc get pods --selector  component=eventrouter -o name -n openshift-logging

      Example output

      pod/cluster-logging-eventrouter-d649f97c8-qvv8r

    2. View the events collected by the Event Router: 

      $ oc logs <cluster_logging_eventrouter_pod> -n openshift-logging

      For example: 

      $ oc logs cluster-logging-eventrouter-d649f97c8-qvv8r -n openshift-logging

      Example output

      {"verb":"ADDED","event":{"metadata":{"name":"openshift-service-catalog-controller-manager-remover.1632d931e88fcd8f","namespace":"openshift-service-catalog-removed","selfLink":"/api/v1/namespaces/openshift-service-catalog-removed/events/openshift-service-catalog-controller-manager-remover.1632d931e88fcd8f","uid":"787d7b26-3d2f-4017-b0b0-420db4ae62c0","resourceVersion":"21399","creationTimestamp":"2020-09-08T15:40:26Z"},"involvedObject":{"kind":"Job","namespace":"openshift-service-catalog-removed","name":"openshift-service-catalog-controller-manager-remover","uid":"fac9f479-4ad5-4a57-8adc-cb25d3d9cf8f","apiVersion":"batch/v1","resourceVersion":"21280"},"reason":"Completed","message":"Job completed","source":{"component":"job-controller"},"firstTimestamp":"2020-09-08T15:40:26Z","lastTimestamp":"2020-09-08T15:40:26Z","count":1,"type":"Normal"}}

      You can also use Kibana to view events by creating an index pattern using the Elasticsearch infra index.

Chapter 10. Log storage

10.1. About log storage

You can use an internal Loki or Elasticsearch log store on your cluster for storing logs, or you can use a ClusterLogForwarder custom resource (CR) to forward logs to an external store.

10.1.1. Log storage types

Loki is a horizontally scalable, highly available, multi-tenant log aggregation system offered as a GA log store for logging for Red Hat OpenShift that can be visualized with the OpenShift Observability UI. The Loki configuration provided by OpenShift Logging is a short-term log store designed to enable users to perform fast troubleshooting with the collected logs. For that purpose, the logging for Red Hat OpenShift configuration of Loki has short-term storage, and is optimized for very recent queries. For long-term storage or queries over a long time period, users should look to log stores external to their cluster.

Elasticsearch indexes incoming log records completely during ingestion. Loki indexes only a few fixed labels during ingestion and defers more complex parsing until after the logs have been stored. This means Loki can collect logs more quickly.

10.1.1.1. About the Elasticsearch log store

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

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

Note

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

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

Note

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

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

10.1.2. Querying log stores

You can query Loki by using the LogQL log query language.

10.1.3. Additional resources

10.2. Installing log storage

You can use the OpenShift CLI (oc) or the Red Hat OpenShift Service on AWS web console to deploy a log store on your Red Hat OpenShift Service on AWS cluster.

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.

10.2.1. Deploying a Loki log store

You can use the Loki Operator to deploy an internal Loki log store on your Red Hat OpenShift Service on AWS cluster. After install the Loki Operator, you must configure Loki object storage by creating a secret, and create a LokiStack custom resource (CR).

10.2.1.1. Loki deployment sizing

Sizing for Loki follows the format of 1x.<size> where the value 1x is number of instances and <size> specifies performance capabilities.

Important

It is not possible to change the number 1x for the deployment size.

Table 10.1. Loki sizing
 1x.demo1x.extra-small1x.small1x.medium

Data transfer

Demo use only

100GB/day

500GB/day

2TB/day

Queries per second (QPS)

Demo use only

1-25 QPS at 200ms

25-50 QPS at 200ms

25-75 QPS at 200ms

Replication factor

None

2

2

2

Total CPU requests

None

14 vCPUs

34 vCPUs

54 vCPUs

Total CPU requests if using the ruler

None

16 vCPUs

42 vCPUs

70 vCPUs

Total memory requests

None

31Gi

67Gi

139Gi

Total memory requests if using the ruler

None

35Gi

83Gi

171Gi

Total disk requests

40Gi

430Gi

430Gi

590Gi

Total disk requests if using the ruler

80Gi

750Gi

750Gi

910Gi

10.2.1.2. Installing Logging and the Loki Operator using the web console

To install and configure logging on your Red Hat OpenShift Service on AWS cluster, an Operator such as Loki Operator for log storage must be installed first. This can be done from the OperatorHub within the web console.

Prerequisites

  • You have access to a supported object store (AWS S3, Google Cloud Storage, Azure, Swift, Minio, OpenShift Data Foundation).
  • You have administrator permissions.
  • You have access to the Red Hat OpenShift Service on AWS web console.

Procedure

  1. In the Red Hat OpenShift Service on AWS web console Administrator perspective, go to Operators → OperatorHub.

  2. Type Loki Operator in the Filter by keyword field. Click Loki Operator in the list of available Operators, and then click Install.

    Important

    The Community Loki Operator is not supported by Red Hat.

  3. Select stable or stable-x.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.

    The Loki Operator must be deployed to the global operator group namespace openshift-operators-redhat, so the Installation mode and Installed Namespace are already selected. If this namespace does not already exist, it is created for you.

  4. Select Enable Operator-recommended cluster monitoring on this namespace.

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

  5. For Update approval select Automatic, then click Install.

    If the approval strategy in the subscription is set to Automatic, the update process initiates as soon as a new Operator version is available in the selected channel. If the approval strategy is set to Manual, you must manually approve pending updates.

  6. Install the Red Hat OpenShift Logging Operator:

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

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

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

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

    7. Select an Approval Strategy.

      • The Automatic strategy allows Operator Lifecycle Manager (OLM) to automatically update the Operator when a new version is available.
      • The Manual strategy requires a user with appropriate credentials to approve the Operator update.
    8. Click Install.
  7. Go to the Operators → Installed Operators page. Click the All instances tab.
  8. From the Create new drop-down list, select LokiStack.

  9. Select YAML view, and then use the following template to create a LokiStack CR:

    Example LokiStack CR

    apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki 1
  namespace: openshift-logging 2
spec:
  size: 1x.small 3
  storage:
    schemas:
    - version: v12
      effectiveDate: "2022-06-01"
    secret:
      name: logging-loki-s3 4
      type: s3 5
      credentialMode: 6
  storageClassName: <storage_class_name> 7
  tenants:
    mode: openshift-logging 8

    1
    Use the name logging-loki.
    2
    You must specify the openshift-logging namespace.
    3
    Specify the deployment size. In the logging 5.8 and later versions, the supported size options for production instances of Loki are 1x.extra-small, 1x.small, or 1x.medium.
    4
    Specify the name of your log store secret.
    5
    Specify the corresponding storage type.
    6
    Optional field, logging 5.9 and later. Supported user configured values are as follows: static is the default authentication mode available for all supported object storage types using credentials stored in a Secret. token for short-lived tokens retrieved from a credential source. In this mode the static configuration does not contain credentials needed for the object storage. Instead, they are generated during runtime using a service, which allows for shorter-lived credentials and much more granular control. This authentication mode is not supported for all object storage types. token-cco is the default value when Loki is running on managed STS mode and using CCO on STS/WIF clusters.
    7
    Specify the name of a storage class for temporary storage. For best performance, specify a storage class that allocates block storage. Available storage classes for your cluster can be listed by using the oc get storageclasses command.
    8
    LokiStack defaults to running in multi-tenant mode, which cannot be modified. One tenant is provided for each log type: audit, infrastructure, and application logs. This enables access control for individual users and user groups to different log streams.
    Important

    It is not possible to change the number 1x for the deployment size.

  10. Click Create.

  11. Create an OpenShift Logging instance:

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

    4. On the ClusterLoggings page, click Create ClusterLogging.

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

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

      apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  collection:
    type: vector
  logStore:
    lokistack:
      name: logging-loki
    retentionPolicy:
      application:
        maxAge: 7d
      audit:
        maxAge: 7d
      infra:
        maxAge: 7d
    type: lokistack
  visualization:
    type: ocp-console
    ocpConsole:
      logsLimit: 15

  managementState: Managed
      1
      Name must be instance.
      2
      Namespace must be openshift-logging.

Verification

  1. Go to Operators → Installed Operators.
  2. Make sure the openshift-logging project is selected.
  3. In the Status column, verify that you see green checkmarks with InstallSucceeded and the text Up to date.
Note

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

10.2.1.3. Creating a secret for Loki object storage by using the web console

To configure Loki object storage, you must create a secret. You can create a secret by using the Red Hat OpenShift Service on AWS web console.

Prerequisites

  • You have administrator permissions.
  • You have access to the Red Hat OpenShift Service on AWS web console.
  • You installed the Loki Operator.

Procedure

  1. Go to Workloads → Secrets in the Administrator perspective of the Red Hat OpenShift Service on AWS web console.
  2. From the Create drop-down list, select From YAML.

  3. Create a secret that uses the access_key_id and access_key_secret fields to specify your credentials and the bucketnames, endpoint, and region fields to define the object storage location. AWS is used in the following example:

    Example Secret object

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

Additional resources

10.2.1.4. Workload identity federation

Workload identity federation enables authentication to cloud-based log stores using short-lived tokens.

Prerequisites

  • Red Hat OpenShift Service on AWS 4.14 and later
  • Logging 5.9 and later

Procedure

  • If you use the Red Hat OpenShift Service on AWS web console to install the Loki Operator, clusters that use short-lived tokens are automatically detected. You are prompted to create roles and supply the data required for the Loki Operator to create a CredentialsRequest object, which populates a secret.
  • If you use the OpenShift CLI (oc) to install the Loki Operator, you must manually create a subscription object using the appropriate template for your storage provider, as shown in the following examples. This authentication strategy is only supported for the storage providers indicated.

Azure sample subscription

apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: loki-operator
  namespace: openshift-operators-redhat
spec:
  channel: "stable-5.9"
  installPlanApproval: Manual
  name: loki-operator
  source: redhat-operators
  sourceNamespace: openshift-marketplace
  config:
    env:
      - name: CLIENTID
        value: <your_client_id>
      - name: TENANTID
        value: <your_tenant_id>
      - name: SUBSCRIPTIONID
        value: <your_subscription_id>
      - name: REGION
        value: <your_region>

AWS sample subscription

apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: loki-operator
  namespace: openshift-operators-redhat
spec:
  channel: "stable-5.9"
  installPlanApproval: Manual
  name: loki-operator
  source: redhat-operators
  sourceNamespace: openshift-marketplace
  config:
    env:
    - name: ROLEARN
      value: <role_ARN>

10.2.1.5. Creating a LokiStack custom resource by using the web console

You can create a LokiStack custom resource (CR) by using the Red Hat OpenShift Service on AWS web console.

Prerequisites

  • You have administrator permissions.
  • You have access to the Red Hat OpenShift Service on AWS web console.
  • You installed the Loki Operator.

Procedure

  1. Go to the Operators → Installed Operators page. Click the All instances tab.
  2. From the Create new drop-down list, select LokiStack.

  3. Select YAML view, and then use the following template to create a LokiStack CR: 

    apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki 1
  namespace: openshift-logging
spec:
  size: 1x.small 2
  storage:
    schemas:
      - effectiveDate: '2023-10-15'
        version: v13
    secret:
      name: logging-loki-s3 3
      type: s3 4
      credentialMode: 5
  storageClassName: <storage_class_name> 6
  tenants:
    mode: openshift-logging
    1
    Use the name logging-loki.
    2
    Specify the deployment size. In the logging 5.8 and later versions, the supported size options for production instances of Loki are 1x.extra-small, 1x.small, or 1x.medium.
    3
    Specify the secret used for your log storage.
    4
    Specify the corresponding storage type.
    5
    Optional field, logging 5.9 and later. Supported user configured values are as follows: static is the default authentication mode available for all supported object storage types using credentials stored in a Secret. token for short-lived tokens retrieved from a credential source. In this mode the static configuration does not contain credentials needed for the object storage. Instead, they are generated during runtime using a service, which allows for shorter-lived credentials and much more granular control. This authentication mode is not supported for all object storage types. token-cco is the default value when Loki is running on managed STS mode and using CCO on STS/WIF clusters.
    6
    Enter the name of a storage class for temporary storage. For best performance, specify a storage class that allocates block storage. Available storage classes for your cluster can be listed by using the oc get storageclasses command.
10.2.1.6. Installing Logging and the Loki Operator using the CLI

To install and configure logging on your Red Hat OpenShift Service on AWS cluster, an Operator such as Loki Operator for log storage must be installed first. This can be done from the Red Hat OpenShift Service on AWS CLI.

Prerequisites

  • You have administrator permissions.
  • You installed the OpenShift CLI (oc).
  • You have access to a supported object store. For example: AWS S3, Google Cloud Storage, Azure, Swift, Minio, or OpenShift Data Foundation.
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. Create a Namespace object for Loki Operator:

    Example Namespace object

    apiVersion: v1
kind: Namespace
metadata:
  name: openshift-operators-redhat 1
  annotations:
    openshift.io/node-selector: ""
  labels:
    openshift.io/cluster-monitoring: "true" 2

    1
    You must specify the openshift-operators-redhat namespace. To prevent possible conflicts with metrics, you should configure the Prometheus Cluster Monitoring stack to scrape metrics from the openshift-operators-redhat namespace and not the openshift-operators namespace. The openshift-operators namespace might contain community Operators, which are untrusted and could publish a metric with the same name as an Red Hat OpenShift Service on AWS metric, which would cause conflicts.
    2
    A string value that specifies the label as shown to ensure that cluster monitoring scrapes the openshift-operators-redhat namespace.

  2. Apply the Namespace object by running the following command: 

    $ oc apply -f <filename>.yaml

  3. Create a Subscription object for Loki Operator:

    Example Subscription object

    apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: loki-operator
  namespace: openshift-operators-redhat 1
spec:
  channel: stable 2
  name: loki-operator
  source: redhat-operators 3
  sourceNamespace: openshift-marketplace

    1
    You must specify the openshift-operators-redhat namespace.
    2
    Specify stable, or stable-5.<y> as the channel.
    3
    Specify redhat-operators. If your Red Hat OpenShift Service on AWS cluster is installed on a restricted network, also known as a disconnected cluster, specify the name of the CatalogSource object you created when you configured the Operator Lifecycle Manager (OLM).

  4. Apply the Subscription object by running the following command: 

    $ oc apply -f <filename>.yaml

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

    Example namespace object

    apiVersion: v1
kind: Namespace
metadata:
  name: openshift-logging 1
annotations:
    openshift.io/node-selector: ""
labels:
    openshift.io/cluster-logging: "true"
    openshift.io/cluster-monitoring: "true" 2

    1
    The Red Hat OpenShift Logging Operator is only deployable to the openshift-logging namespace.
    2
    A string value that specifies the label as shown to ensure that cluster monitoring scrapes the openshift-operators-redhat namespace.

  6. Apply the namespace object by running the following command: 

    $ oc apply -f <filename>.yaml

  7. Create an OperatorGroup object

    Example OperatorGroup object

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

    1
    You must specify the openshift-logging namespace.

  8. Apply the OperatorGroup object by running the following command: 

    $ oc apply -f <filename>.yaml

  9. Create a 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.
    2
    Specify stable, or stable-5.<y> as the channel.
    3
    Specify redhat-operators. If your Red Hat OpenShift Service on AWS cluster is installed on a restricted network, also known as a disconnected cluster, specify the name of the CatalogSource object you created when you configured the Operator Lifecycle Manager (OLM).

  10. Apply the Subscription object by running the following command: 

    $ oc apply -f <filename>.yaml

  11. Create a LokiStack CR:

    Example LokiStack CR

    apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki 1
  namespace: openshift-logging 2
spec:
  size: 1x.small 3
  storage:
    schemas:
    - version: v12
      effectiveDate: "2022-06-01"
    secret:
      name: logging-loki-s3 4
      type: s3 5
      credentialMode: 6
  storageClassName: <storage_class_name> 7
  tenants:
    mode: openshift-logging 8

    1
    Use the name logging-loki.
    2
    You must specify the openshift-logging namespace.
    3
    Specify the deployment size. In the logging 5.8 and later versions, the supported size options for production instances of Loki are 1x.extra-small, 1x.small, or 1x.medium.
    4
    Specify the name of your log store secret.
    5
    Specify the corresponding storage type.
    6
    Optional field, logging 5.9 and later. Supported user configured values are as follows: static is the default authentication mode available for all supported object storage types using credentials stored in a Secret. token for short-lived tokens retrieved from a credential source. In this mode the static configuration does not contain credentials needed for the object storage. Instead, they are generated during runtime using a service, which allows for shorter-lived credentials and much more granular control. This authentication mode is not supported for all object storage types. token-cco is the default value when Loki is running on managed STS mode and using CCO on STS/WIF clusters.
    7
    Specify the name of a storage class for temporary storage. For best performance, specify a storage class that allocates block storage. Available storage classes for your cluster can be listed by using the oc get storageclasses command.
    8
    LokiStack defaults to running in multi-tenant mode, which cannot be modified. One tenant is provided for each log type: audit, infrastructure, and application logs. This enables access control for individual users and user groups to different log streams.

  12. Apply the LokiStack CR object by running the following command: 

    $ oc apply -f <filename>.yaml

  13. Create a ClusterLogging CR object:

    Example ClusterLogging CR object

    apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name: instance 1
  namespace: openshift-logging 2
spec:
  collection:
    type: vector
  logStore:
    lokistack:
      name: logging-loki
    retentionPolicy:
      application:
        maxAge: 7d
      audit:
        maxAge: 7d
      infra:
        maxAge: 7d
    type: lokistack
  visualization:
    type: ocp-console
    ocpConsole:
      logsLimit: 15
  managementState: Managed

    1
    Name must be instance.
    2
    Namespace must be openshift-logging.

  14. Apply the ClusterLogging CR object by running the following command: 

    $ oc apply -f <filename>.yaml

  15. Verify the installation by running the following command: 

    $ oc get pods -n openshift-logging

    Example output

    $ oc get pods -n openshift-logging
NAME                                               READY   STATUS    RESTARTS   AGE
cluster-logging-operator-fb7f7cf69-8jsbq           1/1     Running   0          98m
collector-222js                                    2/2     Running   0          18m
collector-g9ddv                                    2/2     Running   0          18m
collector-hfqq8                                    2/2     Running   0          18m
collector-sphwg                                    2/2     Running   0          18m
collector-vv7zn                                    2/2     Running   0          18m
collector-wk5zz                                    2/2     Running   0          18m
logging-view-plugin-6f76fbb78f-n2n4n               1/1     Running   0          18m
lokistack-sample-compactor-0                       1/1     Running   0          42m
lokistack-sample-distributor-7d7688bcb9-dvcj8      1/1     Running   0          42m
lokistack-sample-gateway-5f6c75f879-bl7k9          2/2     Running   0          42m
lokistack-sample-gateway-5f6c75f879-xhq98          2/2     Running   0          42m
lokistack-sample-index-gateway-0                   1/1     Running   0          42m
lokistack-sample-ingester-0                        1/1     Running   0          42m
lokistack-sample-querier-6b7b56bccc-2v9q4          1/1     Running   0          42m
lokistack-sample-query-frontend-84fb57c578-gq2f7   1/1     Running   0          42m

10.2.1.7. Creating a secret for Loki object storage by using the CLI

To configure Loki object storage, you must create a secret. You can do this by using the OpenShift CLI (oc).

Prerequisites

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

Procedure

  • Create a secret in the directory that contains your certificate and key files by running the following command: 

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

Use generic or opaque secrets for best results.

Verification

  • Verify that a secret was created by running the following command: 

    $ oc get secrets

Additional resources

10.2.1.8. Creating a LokiStack custom resource by using the CLI

You can create a LokiStack custom resource (CR) by using the OpenShift CLI (oc).

Prerequisites

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

Procedure

  1. Create a LokiStack CR:

Example LokiStack CR

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki 1
  namespace: openshift-logging
spec:
  size: 1x.small 2
  storage:
    schemas:
      - effectiveDate: '2023-10-15'
        version: v13
    secret:
      name: logging-loki-s3 3
      type: s3 4
      credentialMode: 5
  storageClassName: <storage_class_name> 6
  tenants:
    mode: openshift-logging

1
Use the name logging-loki.
2
Specify the deployment size. In the logging 5.8 and later versions, the supported size options for production instances of Loki are 1x.extra-small, 1x.small, or 1x.medium.
3
Specify the secret used for your log storage.
4
Specify the corresponding storage type.
5
Optional field, logging 5.9 and later. Supported user configured values are as follows: static is the default authentication mode available for all supported object storage types using credentials stored in a Secret. token for short-lived tokens retrieved from a credential source. In this mode the static configuration does not contain credentials needed for the object storage. Instead, they are generated during runtime using a service, which allows for shorter-lived credentials and much more granular control. This authentication mode is not supported for all object storage types. token-cco is the default value when Loki is running on managed STS mode and using CCO on STS/WIF clusters.
6
Enter the name of a storage class for temporary storage. For best performance, specify a storage class that allocates block storage. Available storage classes for your cluster can be listed by using the oc get storageclasses command.
  1. Apply the LokiStack CR by running the following command:

Verification

  • Verify the installation by listing the pods in the openshift-logging project by running the following command and observing the output: 

    $ oc get pods -n openshift-logging

    Confirm that you see several pods for components of the logging, similar to the following list:

    Example output

    NAME                                           READY   STATUS    RESTARTS   AGE
cluster-logging-operator-78fddc697-mnl82       1/1     Running   0          14m
collector-6cglq                                2/2     Running   0          45s
collector-8r664                                2/2     Running   0          45s
collector-8z7px                                2/2     Running   0          45s
collector-pdxl9                                2/2     Running   0          45s
collector-tc9dx                                2/2     Running   0          45s
collector-xkd76                                2/2     Running   0          45s
logging-loki-compactor-0                       1/1     Running   0          8m2s
logging-loki-distributor-b85b7d9fd-25j9g       1/1     Running   0          8m2s
logging-loki-distributor-b85b7d9fd-xwjs6       1/1     Running   0          8m2s
logging-loki-gateway-7bb86fd855-hjhl4          2/2     Running   0          8m2s
logging-loki-gateway-7bb86fd855-qjtlb          2/2     Running   0          8m2s
logging-loki-index-gateway-0                   1/1     Running   0          8m2s
logging-loki-index-gateway-1                   1/1     Running   0          7m29s
logging-loki-ingester-0                        1/1     Running   0          8m2s
logging-loki-ingester-1                        1/1     Running   0          6m46s
logging-loki-querier-f5cf9cb87-9fdjd           1/1     Running   0          8m2s
logging-loki-querier-f5cf9cb87-fp9v5           1/1     Running   0          8m2s
logging-loki-query-frontend-58c579fcb7-lfvbc   1/1     Running   0          8m2s
logging-loki-query-frontend-58c579fcb7-tjf9k   1/1     Running   0          8m2s
logging-view-plugin-79448d8df6-ckgmx           1/1     Running   0          46s

10.2.2. Loki object storage

The Loki Operator supports AWS S3, as well as other S3 compatible object stores such as Minio and OpenShift Data Foundation. Azure, GCS, and Swift are also supported.

The recommended nomenclature for Loki storage is logging-loki-<your_storage_provider>.

The following table shows the type values within the LokiStack custom resource (CR) for each storage provider. For more information, see the section on your storage provider.

Table 10.2. Secret type quick reference
Storage providerSecret type value

AWS

s3

Azure

azure

Google Cloud

gcs

Minio

s3

OpenShift Data Foundation

s3

Swift

swift

10.2.2.1. AWS storage

Prerequisites

Procedure

  • Create an object storage secret with the name logging-loki-aws by running the following command: 

    $ oc create secret generic logging-loki-aws \
  --from-literal=bucketnames="<bucket_name>" \
  --from-literal=endpoint="<aws_bucket_endpoint>" \
  --from-literal=access_key_id="<aws_access_key_id>" \
  --from-literal=access_key_secret="<aws_access_key_secret>" \
  --from-literal=region="<aws_region_of_your_bucket>"
10.2.2.1.1. AWS storage for STS enabled clusters

If your cluster has STS enabled, the Cloud Credential Operator (CCO) supports short-term authentication using AWS tokens.

You can create the Loki object storage secret manually by running the following command: 

$ oc -n openshift-logging create secret generic "logging-loki-aws" \
--from-literal=bucketnames="<s3_bucket_name>" \
--from-literal=region="<bucket_region>" \
--from-literal=audience="<oidc_audience>" 1
1
Optional annotation, default value is openshift.
10.2.2.2. Azure storage

Prerequisites

  • You installed the Loki Operator.
  • You installed the OpenShift CLI (oc).
  • You created a bucket on Azure.

Procedure

  • Create an object storage secret with the name logging-loki-azure by running the following command: 

    $ oc create secret generic logging-loki-azure \
  --from-literal=container="<azure_container_name>" \
  --from-literal=environment="<azure_environment>" \ 1
  --from-literal=account_name="<azure_account_name>" \
  --from-literal=account_key="<azure_account_key>"
    1
    Supported environment values are AzureGlobal, AzureChinaCloud, AzureGermanCloud, or AzureUSGovernment.
10.2.2.2.1. Azure storage for Microsoft Entra Workload ID enabled clusters

If your cluster has Microsoft Entra Workload ID enabled, the Cloud Credential Operator (CCO) supports short-term authentication using Workload ID.

You can create the Loki object storage secret manually by running the following command: 

$ oc -n openshift-logging create secret generic logging-loki-azure \
--from-literal=environment="<azure_environment>" \
--from-literal=account_name="<storage_account_name>" \
--from-literal=container="<container_name>"
10.2.2.3. Google Cloud Platform storage

Prerequisites

  • You installed the Loki Operator.
  • You installed the OpenShift CLI (oc).
  • You created a project on Google Cloud Platform (GCP).
  • You created a bucket in the same project.
  • You created a service account in the same project for GCP authentication.

Procedure

  1. Copy the service account credentials received from GCP into a file called key.json.

  2. Create an object storage secret with the name logging-loki-gcs by running the following command: 

    $ oc create secret generic logging-loki-gcs \
  --from-literal=bucketname="<bucket_name>" \
  --from-file=key.json="<path/to/key.json>"
10.2.2.4. Minio storage

Prerequisites

  • You installed the Loki Operator.
  • You installed the OpenShift CLI (oc).
  • You have Minio deployed on your cluster.
  • You created a bucket on Minio.

Procedure

  • Create an object storage secret with the name logging-loki-minio by running the following command: 

    $ oc create secret generic logging-loki-minio \
  --from-literal=bucketnames="<bucket_name>" \
  --from-literal=endpoint="<minio_bucket_endpoint>" \
  --from-literal=access_key_id="<minio_access_key_id>" \
  --from-literal=access_key_secret="<minio_access_key_secret>"
10.2.2.5. OpenShift Data Foundation storage

Prerequisites

Procedure

  1. Create an ObjectBucketClaim custom resource in the openshift-logging namespace: 

    apiVersion: objectbucket.io/v1alpha1
kind: ObjectBucketClaim
metadata:
  name: loki-bucket-odf
  namespace: openshift-logging
spec:
  generateBucketName: loki-bucket-odf
  storageClassName: openshift-storage.noobaa.io

  2. Get bucket properties from the associated ConfigMap object by running the following command: 

    BUCKET_HOST=$(oc get -n openshift-logging configmap loki-bucket-odf -o jsonpath='{.data.BUCKET_HOST}')
BUCKET_NAME=$(oc get -n openshift-logging configmap loki-bucket-odf -o jsonpath='{.data.BUCKET_NAME}')
BUCKET_PORT=$(oc get -n openshift-logging configmap loki-bucket-odf -o jsonpath='{.data.BUCKET_PORT}')

  3. Get bucket access key from the associated secret by running the following command: 

    ACCESS_KEY_ID=$(oc get -n openshift-logging secret loki-bucket-odf -o jsonpath='{.data.AWS_ACCESS_KEY_ID}' | base64 -d)
SECRET_ACCESS_KEY=$(oc get -n openshift-logging secret loki-bucket-odf -o jsonpath='{.data.AWS_SECRET_ACCESS_KEY}' | base64 -d)

  4. Create an object storage secret with the name logging-loki-odf by running the following command: 

    $ oc create -n openshift-logging secret generic logging-loki-odf \
--from-literal=access_key_id="<access_key_id>" \
--from-literal=access_key_secret="<secret_access_key>" \
--from-literal=bucketnames="<bucket_name>" \
--from-literal=endpoint="https://<bucket_host>:<bucket_port>"
10.2.2.6. Swift storage

Prerequisites

  • You installed the Loki Operator.
  • You installed the OpenShift CLI (oc).
  • You created a bucket on Swift.

Procedure

  • Create an object storage secret with the name logging-loki-swift by running the following command: 

    $ oc create secret generic logging-loki-swift \
  --from-literal=auth_url="<swift_auth_url>" \
  --from-literal=username="<swift_usernameclaim>" \
  --from-literal=user_domain_name="<swift_user_domain_name>" \
  --from-literal=user_domain_id="<swift_user_domain_id>" \
  --from-literal=user_id="<swift_user_id>" \
  --from-literal=password="<swift_password>" \
  --from-literal=domain_id="<swift_domain_id>" \
  --from-literal=domain_name="<swift_domain_name>" \
  --from-literal=container_name="<swift_container_name>"

  • You can optionally provide project-specific data, region, or both by running the following command: 

    $ oc create secret generic logging-loki-swift \
  --from-literal=auth_url="<swift_auth_url>" \
  --from-literal=username="<swift_usernameclaim>" \
  --from-literal=user_domain_name="<swift_user_domain_name>" \
  --from-literal=user_domain_id="<swift_user_domain_id>" \
  --from-literal=user_id="<swift_user_id>" \
  --from-literal=password="<swift_password>" \
  --from-literal=domain_id="<swift_domain_id>" \
  --from-literal=domain_name="<swift_domain_name>" \
  --from-literal=container_name="<swift_container_name>" \
  --from-literal=project_id="<swift_project_id>" \
  --from-literal=project_name="<swift_project_name>" \
  --from-literal=project_domain_id="<swift_project_domain_id>" \
  --from-literal=project_domain_name="<swift_project_domain_name>" \
  --from-literal=region="<swift_region>"

10.2.3. Deploying an Elasticsearch log store

You can use the OpenShift Elasticsearch Operator to deploy an internal Elasticsearch log store on your Red Hat OpenShift Service on AWS cluster.

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.

10.2.3.1. Storage considerations for Elasticsearch

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

Note

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

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

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

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

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

Note

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

10.2.3.2. Installing the OpenShift Elasticsearch Operator by using the web console

The OpenShift Elasticsearch Operator creates and manages the Elasticsearch cluster used by OpenShift Logging.

Prerequisites

  • Elasticsearch is a memory-intensive application. Each Elasticsearch node needs at least 16GB of memory for both memory requests and limits, unless you specify otherwise in the ClusterLogging custom resource.

    The initial set of Red Hat OpenShift Service on AWS nodes might not be large enough to support the Elasticsearch cluster. You must add additional nodes to the Red Hat OpenShift Service on AWS cluster to run with the recommended or higher memory, up to a maximum of 64GB for each Elasticsearch node.

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

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

    Note

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

Procedure

  1. In the Red Hat OpenShift Service on AWS web console, click Operators → OperatorHub.
  2. Click OpenShift Elasticsearch Operator from the list of available Operators, and click Install.
  3. Ensure that the All namespaces on the cluster is selected under Installation mode.

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

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

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

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

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

  7. Select an Update approval strategy:

    • The Automatic strategy allows Operator Lifecycle Manager (OLM) to automatically update the Operator when a new version is available.
    • The Manual strategy requires a user with appropriate credentials to approve the Operator update.
  8. Click Install.

Verification

  1. Verify that the OpenShift Elasticsearch Operator installed by switching to the Operators → Installed Operators page.
  2. Ensure that OpenShift Elasticsearch Operator is listed in all projects with a Status of Succeeded.
10.2.3.3. Installing the OpenShift Elasticsearch Operator by using the CLI

You can use the OpenShift CLI (oc) to install the OpenShift Elasticsearch Operator.

Prerequisites

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

    Note

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

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

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

Procedure

  1. Create a Namespace object as a YAML file: 

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

  2. Apply the Namespace object by running the following command: 

    $ oc apply -f <filename>.yaml

  3. Create an OperatorGroup object as a YAML file: 

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

  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 OpenShift Elasticsearch Operator:

    Example Subscription

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

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

    Specifying stable installs the current version of the latest stable release. Using stable with installPlanApproval: "Automatic" automatically upgrades your Operators to the latest stable major and minor release.

    Specifying stable-x.y installs the current minor version of a specific major release. Using stable-x.y with installPlanApproval: "Automatic" automatically upgrades your Operators to the latest stable minor release within the major release.

  6. Apply the subscription by running the following command: 

    $ oc apply -f <filename>.yaml

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

Verification

  1. Run the following command: 

    $ oc get csv -n --all-namespaces

  2. Observe the output and confirm that pods for the OpenShift Elasticsearch Operator exist in each namespace

    Example output

    NAMESPACE                                          NAME                            DISPLAY                            VERSION          REPLACES                        PHASE
default                                            elasticsearch-operator.v5.8.1   OpenShift Elasticsearch Operator   5.8.1            elasticsearch-operator.v5.8.0   Succeeded
kube-node-lease                                    elasticsearch-operator.v5.8.1   OpenShift Elasticsearch Operator   5.8.1            elasticsearch-operator.v5.8.0   Succeeded
kube-public                                        elasticsearch-operator.v5.8.1   OpenShift Elasticsearch Operator   5.8.1            elasticsearch-operator.v5.8.0   Succeeded
kube-system                                        elasticsearch-operator.v5.8.1   OpenShift Elasticsearch Operator   5.8.1            elasticsearch-operator.v5.8.0   Succeeded
non-destructive-test                               elasticsearch-operator.v5.8.1   OpenShift Elasticsearch Operator   5.8.1            elasticsearch-operator.v5.8.0   Succeeded
openshift-apiserver-operator                       elasticsearch-operator.v5.8.1   OpenShift Elasticsearch Operator   5.8.1            elasticsearch-operator.v5.8.0   Succeeded
openshift-apiserver                                elasticsearch-operator.v5.8.1   OpenShift Elasticsearch Operator   5.8.1            elasticsearch-operator.v5.8.0   Succeeded
...

10.2.4. 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

10.3. Configuring the LokiStack log store

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

10.3.1. Creating a new group for the cluster-admin user role

Important

Querying application logs for multiple namespaces as a cluster-admin user, where the sum total of characters of all of the namespaces in the cluster is greater than 5120, results in the error Parse error: input size too long (XXXX > 5120). For better control over access to logs in LokiStack, make the cluster-admin user a member of the cluster-admin group. If the cluster-admin group does not exist, create it and add the desired users to it.

Use the following procedure to create a new group for users with cluster-admin permissions.

Procedure

  1. Enter the following command to create a new group: 

    $ oc adm groups new cluster-admin

  2. Enter the following command to add the desired user to the cluster-admin group: 

    $ oc adm groups add-users cluster-admin <username>

  3. Enter the following command to add cluster-admin user role to the group: 

    $ oc adm policy add-cluster-role-to-group cluster-admin cluster-admin

10.3.2. LokiStack behavior during cluster restarts

In logging version 5.8 and newer versions, when an Red Hat OpenShift Service on AWS cluster is restarted, LokiStack ingestion and the query path continue to operate within the available CPU and memory resources available for the node. This means that there is no downtime for the LokiStack during Red Hat OpenShift Service on AWS cluster updates. This behavior is achieved by using PodDisruptionBudget resources. The Loki Operator provisions PodDisruptionBudget resources for Loki, which determine the minimum number of pods that must be available per component to ensure normal operations under certain conditions.

Additional resources

10.3.3. Configuring Loki to tolerate node failure

In the logging 5.8 and later versions, the Loki Operator supports setting pod anti-affinity rules to request that pods of the same component are scheduled on different available nodes in the cluster.

Affinity is a property of pods that controls the nodes on which they prefer to be scheduled. Anti-affinity is a property of pods that prevents a pod from being scheduled on a node.

In Red Hat OpenShift Service on AWS, pod affinity and pod anti-affinity allow you to constrain which nodes your pod is eligible to be scheduled on based on the key-value labels on other pods.

The Operator sets default, preferred podAntiAffinity rules for all Loki components, which includes the compactor, distributor, gateway, indexGateway, ingester, querier, queryFrontend, and ruler components.

You can override the preferred podAntiAffinity settings for Loki components by configuring required settings in the requiredDuringSchedulingIgnoredDuringExecution field:

Example user settings for the ingester component

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
# ...
  template:
    ingester:
      podAntiAffinity:
      # ...
        requiredDuringSchedulingIgnoredDuringExecution: 1
        - labelSelector:
            matchLabels: 2
              app.kubernetes.io/component: ingester
          topologyKey: kubernetes.io/hostname
# ...

1
The stanza to define a required rule.
2
The key-value pair (label) that must be matched to apply the rule.

Additional resources

10.3.4. Zone aware data replication

In the logging 5.8 and later versions, the Loki Operator offers support for zone-aware data replication through pod topology spread constraints. Enabling this feature enhances reliability and safeguards against log loss in the event of a single zone failure. When configuring the deployment size as 1x.extra.small, 1x.small, or 1x.medium, the replication.factor field is automatically set to 2.

To ensure proper replication, you need to have at least as many availability zones as the replication factor specifies. While it is possible to have more availability zones than the replication factor, having fewer zones can lead to write failures. Each zone should host an equal number of instances for optimal operation.

Example LokiStack CR with zone replication enabled

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
 name: logging-loki
 namespace: openshift-logging
spec:
 replicationFactor: 2 1
 replication:
   factor: 2 2
   zones:
   -  maxSkew: 1 3
      topologyKey: topology.kubernetes.io/zone 4

1
Deprecated field, values entered are overwritten by replication.factor.
2
This value is automatically set when deployment size is selected at setup.
3
The maximum difference in number of pods between any two topology domains. The default is 1, and you cannot specify a value of 0.
4
Defines zones in the form of a topology key that corresponds to a node label.
10.3.4.1. Recovering Loki pods from failed zones

In Red Hat OpenShift Service on AWS a zone failure happens when specific availability zone resources become inaccessible. Availability zones are isolated areas within a cloud provider’s data center, aimed at enhancing redundancy and fault tolerance. If your Red Hat OpenShift Service on AWS cluster is not configured to handle this, a zone failure can lead to service or data loss.

Loki pods are part of a StatefulSet, and they come with Persistent Volume Claims (PVCs) provisioned by a StorageClass object. Each Loki pod and its PVCs reside in the same zone. When a zone failure occurs in a cluster, the StatefulSet controller automatically attempts to recover the affected pods in the failed zone.

Warning

The following procedure will delete the PVCs in the failed zone, and all data contained therein. To avoid complete data loss the replication factor field of the LokiStack CR should always be set to a value greater than 1 to ensure that Loki is replicating.

Prerequisites

  • Logging version 5.8 or later.
  • Verify your LokiStack CR has a replication factor greater than 1.
  • Zone failure detected by the control plane, and nodes in the failed zone are marked by cloud provider integration.

The StatefulSet controller automatically attempts to reschedule pods in a failed zone. Because the associated PVCs are also in the failed zone, automatic rescheduling to a different zone does not work. You must manually delete the PVCs in the failed zone to allow successful re-creation of the stateful Loki Pod and its provisioned PVC in the new zone.

Procedure

  1. List the pods in Pending status by running the following command: 

    oc get pods --field-selector status.phase==Pending -n openshift-logging

    Example oc get pods output

    NAME                           READY   STATUS    RESTARTS   AGE 1
logging-loki-index-gateway-1   0/1     Pending   0          17m
logging-loki-ingester-1        0/1     Pending   0          16m
logging-loki-ruler-1           0/1     Pending   0          16m

    1
    These pods are in Pending status because their corresponding PVCs are in the failed zone.

  2. List the PVCs in Pending status by running the following command: 

    oc get pvc -o=json -n openshift-logging | jq '.items[] | select(.status.phase == "Pending") | .metadata.name' -r

    Example oc get pvc output

    storage-logging-loki-index-gateway-1
storage-logging-loki-ingester-1
wal-logging-loki-ingester-1
storage-logging-loki-ruler-1
wal-logging-loki-ruler-1

  3. Delete the PVC(s) for a pod by running the following command: 

    oc delete pvc __<pvc_name>__  -n openshift-logging

  4. Then delete the pod(s) by running the following command: 

    oc delete pod __<pod_name>__  -n openshift-logging

Once these objects have been successfully deleted, they should automatically be rescheduled in an available zone.

10.3.4.1.1. Troubleshooting PVC in a terminating state

The PVCs might hang in the terminating state without being deleted, if PVC metadata finalizers are set to kubernetes.io/pv-protection. Removing the finalizers should allow the PVCs to delete successfully.

  1. Remove the finalizer for each PVC by running the command below, then retry deletion. 

    oc patch pvc __<pvc_name>__ -p '{"metadata":{"finalizers":null}}' -n openshift-logging

Additional resources

10.3.5. Fine grained access for Loki logs

In logging 5.8 and later, the Red Hat OpenShift Logging Operator does not grant all users access to logs by default. As an administrator, you must configure your users' access unless the Operator was upgraded and prior configurations are in place. Depending on your configuration and need, you can configure fine grain access to logs using the following:

  • Cluster wide policies
  • Namespace scoped policies
  • Creation of custom admin groups

As an administrator, you need to create the role bindings and cluster role bindings appropriate for your deployment. The Red Hat OpenShift Logging Operator provides the following cluster roles:

  • cluster-logging-application-view grants permission to read application logs.
  • cluster-logging-infrastructure-view grants permission to read infrastructure logs.
  • cluster-logging-audit-view grants permission to read audit logs.

If you have upgraded from a prior version, an additional cluster role logging-application-logs-reader and associated cluster role binding logging-all-authenticated-application-logs-reader provide backward compatibility, allowing any authenticated user read access in their namespaces.

Note

Users with access by namespace must provide a namespace when querying application logs.

10.3.5.1. Cluster wide access

Cluster role binding resources reference cluster roles, and set permissions cluster wide.

Example ClusterRoleBinding

kind: ClusterRoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: logging-all-application-logs-reader
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: cluster-logging-application-view 1
subjects: 2
- kind: Group
  name: system:authenticated
  apiGroup: rbac.authorization.k8s.io

1
Additional ClusterRoles are cluster-logging-infrastructure-view, and cluster-logging-audit-view.
2
Specifies the users or groups this object applies to.
10.3.5.2. Namespaced access

RoleBinding resources can be used with ClusterRole objects to define the namespace a user or group has access to logs for.

Example RoleBinding

kind: RoleBinding
apiVersion: rbac.authorization.k8s.io/v1
metadata:
  name: allow-read-logs
  namespace: log-test-0 1
roleRef:
  apiGroup: rbac.authorization.k8s.io
  kind: ClusterRole
  name: cluster-logging-application-view
subjects:
- kind: User
  apiGroup: rbac.authorization.k8s.io
  name: testuser-0

1
Specifies the namespace this RoleBinding applies to.
10.3.5.3. Custom admin group access

If you have a large deployment with several users who require broader permissions, you can create a custom group using the adminGroup field. Users who are members of any group specified in the adminGroups field of the LokiStack CR are considered administrators.

Administrator users have access to all application logs in all namespaces, if they also get assigned the cluster-logging-application-view role.

Example LokiStack CR

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
  tenants:
    mode: openshift-logging 1
    openshift:
      adminGroups: 2
      - cluster-admin
      - custom-admin-group 3

1
Custom admin groups are only available in this mode.
2
Entering an empty list [] value for this field disables admin groups.
3
Overrides the default groups (system:cluster-admins, cluster-admin, dedicated-admin)

10.3.6. Enabling stream-based retention with Loki

Additional resources

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

Important

If there is no retention period defined on the s3 bucket or in the LokiStack custom resource (CR), then the logs are not pruned and they stay in the s3 bucket forever, which might fill up the s3 storage.

Note

Although logging version 5.9 and higher supports schema v12, v13 is recommended.

  1. To enable stream-based retention, create a LokiStack CR:

    Example global stream-based retention for AWS

    apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
  limits:
   global: 1
      retention: 2
        days: 20
        streams:
        - days: 4
          priority: 1
          selector: '{kubernetes_namespace_name=~"test.+"}' 3
        - days: 1
          priority: 1
          selector: '{log_type="infrastructure"}'
  managementState: Managed
  replicationFactor: 1
  size: 1x.small
  storage:
    schemas:
    - effectiveDate: "2020-10-11"
      version: v11
    secret:
      name: logging-loki-s3
      type: aws
  storageClassName: gp3-csi
  tenants:
    mode: openshift-logging

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

Example per-tenant stream-based retention for AWS

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
  limits:
    global:
      retention:
        days: 20
    tenants: 1
      application:
        retention:
          days: 1
          streams:
            - days: 4
              selector: '{kubernetes_namespace_name=~"test.+"}' 2
      infrastructure:
        retention:
          days: 5
          streams:
            - days: 1
              selector: '{kubernetes_namespace_name=~"openshift-cluster.+"}'
  managementState: Managed
  replicationFactor: 1
  size: 1x.small
  storage:
    schemas:
    - effectiveDate: "2020-10-11"
      version: v11
    secret:
      name: logging-loki-s3
      type: aws
  storageClassName: gp3-csi
  tenants:
    mode: openshift-logging

1
Sets retention policy by tenant. Valid tenant types are application, audit, and infrastructure.
2
Contains the LogQL query used to define the log stream.

2 Apply the LokiStack CR: 

$ oc apply -f <filename>.yaml

10.3.7. 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.

10.3.8. Configuring Loki to tolerate memberlist creation failure

In an OpenShift cluster, administrators generally use a non-private IP network range. As a result, the LokiStack memberlist configuration fails because, by default, it only uses private IP networks.

As an administrator, you can select the pod network for the memberlist configuration. You can modify the LokiStack CR to use the podIP in the hashRing spec. To configure the LokiStack CR, use the following command: 

$ oc patch LokiStack logging-loki -n openshift-logging  --type=merge -p '{"spec": {"hashRing":{"memberlist":{"instanceAddrType":"podIP","type": "memberlist"}}}}'

Example LokiStack to include podIP

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
# ...
  hashRing:
    type: memberlist
    memberlist:
      instanceAddrType: podIP
# ...

10.3.9. Additional resources

10.4. Configuring the Elasticsearch log store

You can use Elasticsearch 6 to store and organize log data.

You can make modifications to your log store, including:

  • Storage for your Elasticsearch cluster
  • Shard replication across data nodes in the cluster, from full replication to no replication
  • External access to Elasticsearch data

10.4.1. 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

10.4.2. Forwarding audit logs to the log store

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.

Procedure

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

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

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

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

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

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

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

Additional resources

10.4.3. Configuring log retention time

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

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

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

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

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

Prerequisites

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

Procedure

To configure the log retention time:

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

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

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

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

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

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

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

    $ oc get cronjob

    Example output

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

10.4.4. Configuring CPU and memory requests for the log store

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

Note

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

Each Elasticsearch node can operate with a lower memory setting though this is not recommended for production deployments. For production use, you should have no less than the default 16Gi allocated to each pod. Preferably you should allocate as much as possible, up to 64Gi per pod.

Prerequisites

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

Procedure

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

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

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

For example: 

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

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

10.4.5. Configuring replication policy for the log store

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

Prerequisites

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

Procedure

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

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

....

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

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

10.4.6. Scaling down Elasticsearch pods

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

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

Note

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

10.4.7. Configuring persistent storage for the log store

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

Warning

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

Prerequisites

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

Procedure

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

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

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

Note

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

10.4.8. Configuring the log store for emptyDir storage

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

Note

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

Prerequisites

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

Procedure

  1. Edit the ClusterLogging CR to specify emptyDir: 

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

10.4.9. Performing an Elasticsearch rolling cluster restart

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

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

Prerequisites

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

Procedure

To perform a rolling cluster restart:

  1. Change to the openshift-logging project: 

    $ oc project openshift-logging

  2. Get the names of the Elasticsearch pods: 

    $ oc get pods -l component=elasticsearch

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

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

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

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

    For example: 

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

    Example output

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

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

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

    For example: 

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

    Example output

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

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

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

      $ oc rollout resume deployment/<deployment-name>

      For example: 

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

      Example output

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

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

      $ oc get pods -l component=elasticsearch-

      Example output

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

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

      $ oc rollout pause deployment/<deployment-name>

      For example: 

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

      Example output

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

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

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

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

      For example: 

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

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

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

    For example: 

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

    Example output

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

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

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

10.4.10. Exposing the log store service as a route

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

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

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

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

Example output

172.30.183.229

$ oc get service elasticsearch -n openshift-logging

Example output

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

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

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

Example output

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

Prerequisites

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

Procedure

To expose the log store externally:

  1. Change to the openshift-logging project: 

    $ oc project openshift-logging

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

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

    Example output

    admin-ca

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

    1. Create a YAML file with the following: 

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

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

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

    3. Create the route: 

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

      Example output

      route.route.openshift.io/elasticsearch created

  4. Check that the Elasticsearch service is exposed:

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

      $ token=$(oc whoami -t)

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

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

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

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

      The response appears similar to the following:

      Example output

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

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

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

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

Prerequisites

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

    outputRefs:
- default
Warning

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

Procedure

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

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

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

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

  4. Verify that the collector pods are redeployed: 

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

Chapter 11. Logging alerts

11.1. Default logging alerts

Logging alerts are installed as part of the Red Hat OpenShift Logging Operator installation. Alerts depend on metrics exported by the log collection and log storage backends. These metrics are enabled if you selected the option to Enable Operator recommended cluster monitoring on this namespace when installing the Red Hat OpenShift Logging Operator.

Default logging alerts are sent to the Red Hat OpenShift Service on AWS monitoring stack Alertmanager in the openshift-monitoring namespace, unless you have disabled the local Alertmanager instance.

11.1.1. Accessing the Alerting UI in the Administrator and Developer perspectives

The Alerting UI is accessible through the Administrator perspective and the Developer perspective of the Red Hat OpenShift Service on AWS web console.

  • In the Administrator perspective, go to Observe → Alerting. The three main pages in the Alerting UI in this perspective are the Alerts, Silences, and Alerting rules pages.
  • In the Developer perspective, go to Observe → <project_name> → Alerts. In this perspective, alerts, silences, and alerting rules are all managed from the Alerts page. The results shown in the Alerts page are specific to the selected project.
Note

In the Developer perspective, you can select from core Red Hat OpenShift Service on AWS and user-defined projects that you have access to in the Project: <project_name> list. However, alerts, silences, and alerting rules relating to core Red Hat OpenShift Service on AWS projects are not displayed if you are not logged in as a cluster administrator.

11.1.2. Logging collector alerts

In logging 5.8 and later versions, the following alerts are generated by the Red Hat OpenShift Logging Operator. You can view these alerts in the Red Hat OpenShift Service on AWS web console.

Alert NameMessageDescriptionSeverity

CollectorNodeDown

Prometheus could not scrape namespace/pod collector component for more than 10m.

Collector cannot be scraped.

Critical

CollectorHighErrorRate

value% of records have resulted in an error by namespace/pod collector component.

namespace/pod collector component errors are high.

Critical

CollectorVeryHighErrorRate

value% of records have resulted in an error by namespace/pod collector component.

namespace/pod collector component errors are very high.

Critical

11.1.3. Vector collector alerts

In logging 5.7 and later versions, the following alerts are generated by the Vector collector. You can view these alerts in the Red Hat OpenShift Service on AWS web console.

Table 11.1. Vector collector alerts
AlertMessageDescriptionSeverity

CollectorHighErrorRate

<value> of records have resulted in an error by vector <instance>.

The number of vector output errors is high, by default more than 10 in the previous 15 minutes.

Warning

CollectorNodeDown

Prometheus could not scrape vector <instance> for more than 10m.

Vector is reporting that Prometheus could not scrape a specific Vector instance.

Critical

CollectorVeryHighErrorRate

<value> of records have resulted in an error by vector <instance>.

The number of Vector component errors are very high, by default more than 25 in the previous 15 minutes.

Critical

FluentdQueueLengthIncreasing

In the last 1h, fluentd <instance> buffer queue length constantly increased more than 1. Current value is <value>.

Fluentd is reporting that the queue size is increasing.

Warning

11.1.4. Fluentd collector alerts

The following alerts are generated by the legacy Fluentd log collector. You can view these alerts in the Red Hat OpenShift Service on AWS web console.

Table 11.2. Fluentd collector alerts
AlertMessageDescriptionSeverity

FluentDHighErrorRate

<value> of records have resulted in an error by fluentd <instance>.

The number of FluentD output errors is high, by default more than 10 in the previous 15 minutes.

Warning

FluentdNodeDown

Prometheus could not scrape fluentd <instance> for more than 10m.

Fluentd is reporting that Prometheus could not scrape a specific Fluentd instance.

Critical

FluentdQueueLengthIncreasing

In the last 1h, fluentd <instance> buffer queue length constantly increased more than 1. Current value is <value>.

Fluentd is reporting that the queue size is increasing.

Warning

FluentDVeryHighErrorRate

<value> of records have resulted in an error by fluentd <instance>.

The number of FluentD output errors is very high, by default more than 25 in the previous 15 minutes.

Critical

11.1.5. Elasticsearch alerting rules

You can view these alerting rules in the Red Hat OpenShift Service on AWS web console.

Table 11.3. Alerting rules
AlertDescriptionSeverity

ElasticsearchClusterNotHealthy

The cluster health status has been RED for at least 2 minutes. The cluster does not accept writes, shards may be missing, or the master node has not been elected yet.

Critical

ElasticsearchClusterNotHealthy

The cluster health status has been YELLOW for at least 20 minutes. Some shard replicas are not allocated.

Warning

ElasticsearchDiskSpaceRunningLow

The cluster is expected to be out of disk space within the next 6 hours.

Critical

ElasticsearchHighFileDescriptorUsage

The cluster is predicted to be out of file descriptors within the next hour.

Warning

ElasticsearchJVMHeapUseHigh

The JVM Heap usage on the specified node is high.

Alert

ElasticsearchNodeDiskWatermarkReached

The specified node has hit the low watermark due to low free disk space. Shards can not be allocated to this node anymore. You should consider adding more disk space to the node.

Info

ElasticsearchNodeDiskWatermarkReached

The specified node has hit the high watermark due to low free disk space. Some shards will be re-allocated to different nodes if possible. Make sure more disk space is added to the node or drop old indices allocated to this node.

Warning

ElasticsearchNodeDiskWatermarkReached

The specified node has hit the flood watermark due to low free disk space. Every index that has a shard allocated on this node is enforced a read-only block. The index block must be manually released when the disk use falls below the high watermark.

Critical

ElasticsearchJVMHeapUseHigh

The JVM Heap usage on the specified node is too high.

Alert

ElasticsearchWriteRequestsRejectionJumps

Elasticsearch is experiencing an increase in write rejections on the specified node. This node might not be keeping up with the indexing speed.

Warning

AggregatedLoggingSystemCPUHigh

The CPU used by the system on the specified node is too high.

Alert

ElasticsearchProcessCPUHigh

The CPU used by Elasticsearch on the specified node is too high.

Alert

11.1.6. Additional resources

11.2. Custom logging alerts

In logging 5.7 and later versions, users can configure the LokiStack deployment to produce customized alerts and recorded metrics. If you want to use customized alerting and recording rules, you must enable the LokiStack ruler component.

LokiStack log-based alerts and recorded metrics are triggered by providing LogQL expressions to the ruler component. The Loki Operator manages a ruler that is optimized for the selected LokiStack size, which can be 1x.extra-small, 1x.small, or 1x.medium.

To provide these expressions, you must create an AlertingRule custom resource (CR) containing Prometheus-compatible alerting rules, or a RecordingRule CR containing Prometheus-compatible recording rules.

Administrators can configure log-based alerts or recorded metrics for application, audit, or infrastructure tenants. Users without administrator permissions can configure log-based alerts or recorded metrics for application tenants of the applications that they have access to.

Application, audit, and infrastructure alerts are sent by default to the Red Hat OpenShift Service on AWS monitoring stack Alertmanager in the openshift-monitoring namespace, unless you have disabled the local Alertmanager instance. If the Alertmanager that is used to monitor user-defined projects in the openshift-user-workload-monitoring namespace is enabled, application alerts are sent to the Alertmanager in this namespace by default.

11.2.1. Configuring the ruler

When the LokiStack ruler component is enabled, users can define a group of LogQL expressions that trigger logging alerts or recorded metrics.

Administrators can enable the ruler by modifying the LokiStack custom resource (CR).

Prerequisites

  • You have installed the Red Hat OpenShift Logging Operator and the Loki Operator.
  • You have created a LokiStack CR.
  • You have administrator permissions.

Procedure

  • Enable the ruler by ensuring that the LokiStack CR contains the following spec configuration: 

    apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: <name>
  namespace: <namespace>
spec:
# ...
  rules:
    enabled: true 1
    selector:
      matchLabels:
        openshift.io/<label_name>: "true" 2
    namespaceSelector:
      matchLabels:
        openshift.io/<label_name>: "true" 3
    1
    Enable Loki alerting and recording rules in your cluster.
    2
    Add a custom label that can be added to namespaces where you want to enable the use of logging alerts and metrics.
    3
    Add a custom label that can be added to namespaces where you want to enable the use of logging alerts and metrics.

11.2.2. Authorizing LokiStack rules RBAC permissions

Administrators can allow users to create and manage their own alerting and recording rules by binding cluster roles to usernames. Cluster roles are defined as ClusterRole objects that contain necessary role-based access control (RBAC) permissions for users.

In logging 5.8 and later, the following cluster roles for alerting and recording rules are available for LokiStack:

Rule nameDescription

alertingrules.loki.grafana.com-v1-admin

Users with this role have administrative-level access to manage alerting rules. This cluster role grants permissions to create, read, update, delete, list, and watch AlertingRule resources within the loki.grafana.com/v1 API group.

alertingrules.loki.grafana.com-v1-crdview

Users with this role can view the definitions of Custom Resource Definitions (CRDs) related to AlertingRule resources within the loki.grafana.com/v1 API group, but do not have permissions for modifying or managing these resources.

alertingrules.loki.grafana.com-v1-edit

Users with this role have permission to create, update, and delete AlertingRule resources.

alertingrules.loki.grafana.com-v1-view

Users with this role can read AlertingRule resources within the loki.grafana.com/v1 API group. They can inspect configurations, labels, and annotations for existing alerting rules but cannot make any modifications to them.

recordingrules.loki.grafana.com-v1-admin

Users with this role have administrative-level access to manage recording rules. This cluster role grants permissions to create, read, update, delete, list, and watch RecordingRule resources within the loki.grafana.com/v1 API group.

recordingrules.loki.grafana.com-v1-crdview

Users with this role can view the definitions of Custom Resource Definitions (CRDs) related to RecordingRule resources within the loki.grafana.com/v1 API group, but do not have permissions for modifying or managing these resources.

recordingrules.loki.grafana.com-v1-edit

Users with this role have permission to create, update, and delete RecordingRule resources.

recordingrules.loki.grafana.com-v1-view

Users with this role can read RecordingRule resources within the loki.grafana.com/v1 API group. They can inspect configurations, labels, and annotations for existing alerting rules but cannot make any modifications to them.

11.2.2.1. Examples

To apply cluster roles for a user, you must bind an existing cluster role to a specific username.

Cluster roles can be cluster or namespace scoped, depending on which type of role binding you use. When a RoleBinding object is used, as when using the oc adm policy add-role-to-user command, the cluster role only applies to the specified namespace. When a ClusterRoleBinding object is used, as when using the oc adm policy add-cluster-role-to-user command, the cluster role applies to all namespaces in the cluster.

The following example command gives the specified user create, read, update and delete (CRUD) permissions for alerting rules in a specific namespace in the cluster:

Example cluster role binding command for alerting rule CRUD permissions in a specific namespace

$ oc adm policy add-role-to-user alertingrules.loki.grafana.com-v1-admin -n <namespace> <username>

The following command gives the specified user administrator permissions for alerting rules in all namespaces:

Example cluster role binding command for administrator permissions

$ oc adm policy add-cluster-role-to-user alertingrules.loki.grafana.com-v1-admin <username>

11.2.3. Creating a log-based alerting rule with Loki

The AlertingRule CR contains a set of specifications and webhook validation definitions to declare groups of alerting rules for a single LokiStack instance. In addition, the webhook validation definition provides support for rule validation conditions:

  • If an AlertingRule CR includes an invalid interval period, it is an invalid alerting rule
  • If an AlertingRule CR includes an invalid for period, it is an invalid alerting rule.
  • If an AlertingRule CR includes an invalid LogQL expr, it is an invalid alerting rule.
  • If an AlertingRule CR includes two groups with the same name, it is an invalid alerting rule.
  • If none of above applies, an alerting rule is considered valid.
Tenant typeValid namespaces for AlertingRule CRs

application

 

audit

openshift-logging

infrastructure

openshift-/*, kube-/\*, default

Prerequisites

  • Red Hat OpenShift Logging Operator 5.7 and later
  • Red Hat OpenShift Service on AWS 4.13 and later

Procedure

  1. Create an AlertingRule custom resource (CR):

    Example infrastructure AlertingRule CR

      apiVersion: loki.grafana.com/v1
  kind: AlertingRule
  metadata:
    name: loki-operator-alerts
    namespace: openshift-operators-redhat 1
    labels: 2
      openshift.io/<label_name>: "true"
  spec:
    tenantID: "infrastructure" 3
    groups:
      - name: LokiOperatorHighReconciliationError
        rules:
          - alert: HighPercentageError
            expr: | 4
              sum(rate({kubernetes_namespace_name="openshift-operators-redhat", kubernetes_pod_name=~"loki-operator-controller-manager.*"} |= "error" [1m])) by (job)
                /
              sum(rate({kubernetes_namespace_name="openshift-operators-redhat", kubernetes_pod_name=~"loki-operator-controller-manager.*"}[1m])) by (job)
                > 0.01
            for: 10s
            labels:
              severity: critical 5
            annotations:
              summary: High Loki Operator Reconciliation Errors 6
              description: High Loki Operator Reconciliation Errors 7

    1
    The namespace where this AlertingRule CR is created must have a label matching the LokiStack spec.rules.namespaceSelector definition.
    2
    The labels block must match the LokiStack spec.rules.selector definition.
    3
    AlertingRule CRs for infrastructure tenants are only supported in the openshift-*, kube-\*, or default namespaces.
    4
    The value for kubernetes_namespace_name: must match the value for metadata.namespace.
    5
    The value of this mandatory field must be critical, warning, or info.
    6
    This field is mandatory.
    7
    This field is mandatory.

    Example application AlertingRule CR

      apiVersion: loki.grafana.com/v1
  kind: AlertingRule
  metadata:
    name: app-user-workload
    namespace: app-ns 1
    labels: 2
      openshift.io/<label_name>: "true"
  spec:
    tenantID: "application"
    groups:
      - name: AppUserWorkloadHighError
        rules:
          - alert:
            expr: | 3
            sum(rate({kubernetes_namespace_name="app-ns", kubernetes_pod_name=~"podName.*"} |= "error" [1m])) by (job)
            for: 10s
            labels:
              severity: critical 4
            annotations:
              summary:  5
              description:  6

    1
    The namespace where this AlertingRule CR is created must have a label matching the LokiStack spec.rules.namespaceSelector definition.
    2
    The labels block must match the LokiStack spec.rules.selector definition.
    3
    Value for kubernetes_namespace_name: must match the value for metadata.namespace.
    4
    The value of this mandatory field must be critical, warning, or info.
    5
    The value of this mandatory field is a summary of the rule.
    6
    The value of this mandatory field is a detailed description of the rule.

  2. Apply the AlertingRule CR: 

    $ oc apply -f <filename>.yaml

11.2.4. Additional resources

Chapter 12. Performance and reliability tuning

12.1. Flow control mechanisms

If logs are produced faster than they can be collected, it can be difficult to predict or control the volume of logs being sent to an output. Not being able to predict or control the volume of logs being sent to an output can result in logs being lost. If there is a system outage and log buffers are accumulated without user control, this can also cause long recovery times and high latency when the connection is restored.

As an administrator, you can limit logging rates by configuring flow control mechanisms for your logging.

12.1.1. Benefits of flow control mechanisms

  • The cost and volume of logging can be predicted more accurately in advance.
  • Noisy containers cannot produce unbounded log traffic that drowns out other containers.
  • Ignoring low-value logs reduces the load on the logging infrastructure.
  • High-value logs can be preferred over low-value logs by assigning higher rate limits.

12.1.2. Configuring rate limits

Rate limits are configured per collector, which means that the maximum rate of log collection is the number of collector instances multiplied by the rate limit.

Because logs are collected from each node’s file system, a collector is deployed on each cluster node. For example, in a 3-node cluster, with a maximum rate limit of 10 records per second per collector, the maximum rate of log collection is 30 records per second.

Because the exact byte size of a record as written to an output can vary due to transformations, different encodings, or other factors, rate limits are set in number of records instead of bytes.

You can configure rate limits in the ClusterLogForwarder custom resource (CR) in two ways:

Output rate limit
Limit the rate of outbound logs to selected outputs, for example, to match the network or storage capacity of an output. The output rate limit controls the aggregated per-output rate.
Input rate limit
Limit the per-container rate of log collection for selected containers.

12.1.3. Configuring log forwarder output rate limits

You can limit the rate of outbound logs to a specified output by configuring the ClusterLogForwarder custom resource (CR).

Prerequisites

  • You have installed the Red Hat OpenShift Logging Operator.
  • You have administrator permissions.

Procedure

  1. Add a maxRecordsPerSecond limit value to the ClusterLogForwarder CR for a specified output.

    The following example shows how to configure a per collector output rate limit for a Kafka broker output named kafka-example:

    Example ClusterLogForwarder CR

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
# ...
  outputs:
    - name: kafka-example 1
      type: kafka 2
      limit:
        maxRecordsPerSecond: 1000000 3
# ...

    1
    The output name.
    2
    The type of output.
    3
    The log output rate limit. This value sets the maximum Quantity of logs that can be sent to the Kafka broker per second. This value is not set by default. The default behavior is best effort, and records are dropped if the log forwarder cannot keep up. If this value is 0, no logs are forwarded.

  2. Apply the ClusterLogForwarder CR:

    Example command

    $ oc apply -f <filename>.yaml

Additional resources

12.1.4. Configuring log forwarder input rate limits

You can limit the rate of incoming logs that are collected by configuring the ClusterLogForwarder custom resource (CR). You can set input limits on a per-container or per-namespace basis.

Prerequisites

  • You have installed the Red Hat OpenShift Logging Operator.
  • You have administrator permissions.

Procedure

  1. Add a maxRecordsPerSecond limit value to the ClusterLogForwarder CR for a specified input.

    The following examples show how to configure input rate limits for different scenarios:

    Example ClusterLogForwarder CR that sets a per-container limit for containers with certain labels

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
# ...
  inputs:
    - name: <input_name> 1
      application:
        selector:
          matchLabels: { example: label } 2
        containerLimit:
          maxRecordsPerSecond: 0 3
# ...

    1
    The input name.
    2
    A list of labels. If these labels match labels that are applied to a pod, the per-container limit specified in the maxRecordsPerSecond field is applied to those containers.
    3
    Configures the rate limit. Setting the maxRecordsPerSecond field to 0 means that no logs are collected for the container. Setting the maxRecordsPerSecond field to some other value means that a maximum of that number of records per second are collected for the container.

    Example ClusterLogForwarder CR that sets a per-container limit for containers in selected namespaces

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
# ...
  inputs:
    - name: <input_name> 1
      application:
        namespaces: [ example-ns-1, example-ns-2 ] 2
        containerLimit:
          maxRecordsPerSecond: 10 3
    - name: <input_name>
      application:
        namespaces: [ test ]
        containerLimit:
          maxRecordsPerSecond: 1000
# ...

    1
    The input name.
    2
    A list of namespaces. The per-container limit specified in the maxRecordsPerSecond field is applied to all containers in the namespaces listed.
    3
    Configures the rate limit. Setting the maxRecordsPerSecond field to 10 means that a maximum of 10 records per second are collected for each container in the namespaces listed.

  2. Apply the ClusterLogForwarder CR:

    Example command

    $ oc apply -f <filename>.yaml

12.2. Filtering logs by content

Collecting all logs from a cluster might produce a large amount of data, which can be expensive to transport and store.

You can reduce the volume of your log data by filtering out low priority data that does not need to be stored. Logging provides content filters that you can use to reduce the volume of log data.

Note

Content filters are distinct from input selectors. input selectors select or ignore entire log streams based on source metadata. Content filters edit log streams to remove and modify records based on the record content.

Log data volume can be reduced by using one of the following methods:

12.2.1. Configuring content filters to drop unwanted log records

When the drop filter is configured, the log collector evaluates log streams according to the filters before forwarding. The collector drops unwanted log records that match the specified configuration.

Prerequisites

  • You have installed the Red Hat OpenShift Logging Operator.
  • You have administrator permissions.
  • You have created a ClusterLogForwarder custom resource (CR).

Procedure

  1. Add a configuration for a filter to the filters spec in the ClusterLogForwarder CR.

    The following example shows how to configure the ClusterLogForwarder CR to drop log records based on regular expressions:

    Example ClusterLogForwarder CR

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
  filters:
  - name: <filter_name>
    type: drop 1
    drop: 2
    - test: 3
      - field: .kubernetes.labels."foo-bar/baz" 4
        matches: .+ 5
      - field: .kubernetes.pod_name
        notMatches: "my-pod" 6
  pipelines:
  - name: <pipeline_name> 7
    filterRefs: ["<filter_name>"]
# ...

    1
    Specifies the type of filter. The drop filter drops log records that match the filter configuration.
    2
    Specifies configuration options for applying the drop filter.
    3
    Specifies the configuration for tests that are used to evaluate whether a log record is dropped.
    • If all the conditions specified for a test are true, the test passes and the log record is dropped.
    • When multiple tests are specified for the drop filter configuration, if any of the tests pass, the record is dropped.
    • If there is an error evaluating a condition, for example, the field is missing from the log record being evaluated, that condition evaluates to false.
    4
    Specifies a dot-delimited field path, which is a path to a field in the log record. The path can contain alpha-numeric characters and underscores (a-zA-Z0-9_), for example, .kubernetes.namespace_name. If segments contain characters outside of this range, the segment must be in quotes, for example, .kubernetes.labels."foo.bar-bar/baz". You can include multiple field paths in a single test configuration, but they must all evaluate to true for the test to pass and the drop filter to be applied.
    5
    Specifies a regular expression. If log records match this regular expression, they are dropped. You can set either the matches or notMatches condition for a single field path, but not both.
    6
    Specifies a regular expression. If log records do not match this regular expression, they are dropped. You can set either the matches or notMatches condition for a single field path, but not both.
    7
    Specifies the pipeline that the drop filter is applied to.

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

    $ oc apply -f <filename>.yaml

Additional examples

The following additional example shows how you can configure the drop filter to only keep higher priority log records: 

apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
  filters:
  - name: important
    type: drop
    drop:
      test:
      - field: .message
        notMatches: "(?i)critical|error"
      - field: .level
        matches: "info|warning"
# ...

In addition to including multiple field paths in a single test configuration, you can also include additional tests that are treated as OR checks. In the following example, records are dropped if either test configuration evaluates to true. However, for the second test configuration, both field specs must be true for it to be evaluated to true: 

apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
  filters:
  - name: important
    type: drop
    drop:
      test:
      - field: .kubernetes.namespace_name
        matches: "^open"
      test:
      - field: .log_type
        matches: "application"
      - field: .kubernetes.pod_name
        notMatches: "my-pod"
# ...

12.2.2. Configuring content filters to prune log records

When the prune filter is configured, the log collector evaluates log streams according to the filters before forwarding. The collector prunes log records by removing low value fields such as pod annotations.

Prerequisites

  • You have installed the Red Hat OpenShift Logging Operator.
  • You have administrator permissions.
  • You have created a ClusterLogForwarder custom resource (CR).

Procedure

  1. Add a configuration for a filter to the prune spec in the ClusterLogForwarder CR.

    The following example shows how to configure the ClusterLogForwarder CR to prune log records based on field paths:

    Important

    If both are specified, records are pruned based on the notIn array first, which takes precedence over the in array. After records have been pruned by using the notIn array, they are then pruned by using the in array.

    Example ClusterLogForwarder CR

    apiVersion: logging.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
  filters:
  - name: <filter_name>
    type: prune 1
    prune: 2
      in: [.kubernetes.annotations, .kubernetes.namespace_id] 3
      notIn: [.kubernetes,.log_type,.message,."@timestamp"] 4
  pipelines:
  - name: <pipeline_name> 5
    filterRefs: ["<filter_name>"]
# ...

    1
    Specify the type of filter. The prune filter prunes log records by configured fields.
    2
    Specify configuration options for applying the prune filter. The in and notIn fields are specified as arrays of dot-delimited field paths, which are paths to fields in log records. These paths can contain alpha-numeric characters and underscores (a-zA-Z0-9_), for example, .kubernetes.namespace_name. If segments contain characters outside of this range, the segment must be in quotes, for example, .kubernetes.labels."foo.bar-bar/baz".
    3
    Optional: Any fields that are specified in this array are removed from the log record.
    4
    Optional: Any fields that are not specified in this array are removed from the log record.
    5
    Specify the pipeline that the prune filter is applied to.

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

    $ oc apply -f <filename>.yaml

12.2.3. Additional resources

12.3. Filtering logs by metadata

You can filter logs in the ClusterLogForwarder CR to select or ignore an entire log stream based on the metadata by using the input selector. As an administrator or developer, you can include or exclude the log collection to reduce the memory and CPU load on the collector.

Important

You can use this feature only if the Vector collector is set up in your logging deployment.

Note

input spec filtering is different from content filtering. input selectors select or ignore entire log streams based on the source metadata. Content filters edit the log streams to remove and modify the records based on the record content.

12.3.1. Filtering application logs at input by including or excluding the namespace or container name

You can include or exclude the application logs based on the namespace and container name by using the input selector.

Prerequisites

  • You have installed the Red Hat OpenShift Logging Operator.
  • You have administrator permissions.
  • You have created a ClusterLogForwarder custom resource (CR).

Procedure

  1. Add a configuration to include or exclude the namespace and container names in the ClusterLogForwarder CR.

    The following example shows how to configure the ClusterLogForwarder CR to include or exclude namespaces and container names:

    Example ClusterLogForwarder CR

    apiVersion: "logging.openshift.io/v1"
kind: ClusterLogForwarder
# ...
spec:
  inputs:
    - name: mylogs
      application:
        includes:
          - namespace: "my-project" 1
            container: "my-container" 2
        excludes:
          - container: "other-container*" 3
            namespace: "other-namespace" 4
# ...

    1
    Specifies that the logs are only collected from these namespaces.
    2
    Specifies that the logs are only collected from these containers.
    3
    Specifies the pattern of namespaces to ignore when collecting the logs.
    4
    Specifies the set of containers to ignore when collecting the logs.

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

    $ oc apply -f <filename>.yaml

The excludes option takes precedence over includes.

12.3.2. Filtering application logs at input by including either the label expressions or matching label key and values

You can include the application logs based on the label expressions or a matching label key and its values by using the input selector.

Prerequisites

  • You have installed the Red Hat OpenShift Logging Operator.
  • You have administrator permissions.
  • You have created a ClusterLogForwarder custom resource (CR).

Procedure

  1. Add a configuration for a filter to the input spec in the ClusterLogForwarder CR.

    The following example shows how to configure the ClusterLogForwarder CR to include logs based on label expressions or matched label key/values:

    Example ClusterLogForwarder CR

    apiVersion: "logging.openshift.io/v1"
kind: ClusterLogForwarder
# ...
spec:
  inputs:
    - name: mylogs
      application:
        selector:
          matchExpressions:
          - key: env 1
            operator: In 2
            values: [“prod”, “qa”] 3
          - key: zone
            operator: NotIn
            values: [“east”, “west”]
          matchLabels: 4
            app: one
            name: app1
# ...

    1
    Specifies the label key to match.
    2
    Specifies the operator. Valid values include: In, NotIn, Exists, and DoesNotExist.
    3
    Specifies an array of string values. If the operator value is either Exists or DoesNotExist, the value array must be empty.
    4
    Specifies an exact key or value mapping.

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

    $ oc apply -f <filename>.yaml

12.3.3. Filtering the audit and infrastructure log inputs by source

You can define the list of audit and infrastructure sources to collect the logs by using the input selector.

Prerequisites

  • You have installed the Red Hat OpenShift Logging Operator.
  • You have administrator permissions.
  • You have created a ClusterLogForwarder custom resource (CR).

Procedure

  1. Add a configuration to define the audit and infrastructure sources in the ClusterLogForwarder CR.

    The following example shows how to configure the ClusterLogForwarder CR to define aduit and infrastructure sources:

    Example ClusterLogForwarder CR

    apiVersion: "logging.openshift.io/v1"
kind: ClusterLogForwarder
# ...
spec:
  inputs:
    - name: mylogs1
      infrastructure:
        sources: 1
          - node
    - name: mylogs2
      audit:
        sources: 2
          - kubeAPI
          - openshiftAPI
          - ovn
# ...

    1
    Specifies the list of infrastructure sources to collect. The valid sources include:
    • node: Journal log from the node
    • container: Logs from the workloads deployed in the namespaces
    2
    Specifies the list of audit sources to collect. The valid sources include:
    • kubeAPI: Logs from the Kubernetes API servers
    • openshiftAPI: Logs from the OpenShift API servers
    • auditd: Logs from a node auditd service
    • ovn: Logs from an open virtual network service

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

    $ oc apply -f <filename>.yaml

Chapter 13. Scheduling resources

13.1. Using node selectors to move logging resources

A node selector specifies a map of key/value pairs that are defined using custom labels on nodes and selectors specified in pods.

For the pod to be eligible to run on a node, the pod must have the same key/value node selector as the label on the node.

13.1.1. About node selectors

You can use node selectors on pods and labels on nodes to control where the pod is scheduled. With node selectors, Red Hat OpenShift Service on AWS schedules the pods on nodes that contain matching labels.

You can use a node selector to place specific pods on specific nodes, cluster-wide node selectors to place new pods on specific nodes anywhere in the cluster, and project node selectors to place new pods in a project on specific nodes.

For example, as a cluster administrator, you can create an infrastructure where application developers can deploy pods only onto the nodes closest to their geographical location by including a node selector in every pod they create. In this example, the cluster consists of five data centers spread across two regions. In the U.S., label the nodes as us-east, us-central, or us-west. In the Asia-Pacific region (APAC), label the nodes as apac-east or apac-west. The developers can add a node selector to the pods they create to ensure the pods get scheduled on those nodes.

A pod is not scheduled if the Pod object contains a node selector, but no node has a matching label.

Important

If you are using node selectors and node affinity in the same pod configuration, the following rules control pod placement onto nodes:

  • If you configure both nodeSelector and nodeAffinity, both conditions must be satisfied for the pod to be scheduled onto a candidate node.
  • If you specify multiple nodeSelectorTerms associated with nodeAffinity types, then the pod can be scheduled onto a node if one of the nodeSelectorTerms is satisfied.
  • If you specify multiple matchExpressions associated with nodeSelectorTerms, then the pod can be scheduled onto a node only if all matchExpressions are satisfied.
Node selectors on specific pods and nodes

You can control which node a specific pod is scheduled on by using node selectors and labels.

To use node selectors and labels, first label the node to avoid pods being descheduled, then add the node selector to the pod.

Note

You cannot add a node selector directly to an existing scheduled pod. You must label the object that controls the pod, such as deployment config.

For example, the following Node object has the region: east label:

Sample Node object with a label

kind: Node
apiVersion: v1
metadata:
  name: ip-10-0-131-14.ec2.internal
  selfLink: /api/v1/nodes/ip-10-0-131-14.ec2.internal
  uid: 7bc2580a-8b8e-11e9-8e01-021ab4174c74
  resourceVersion: '478704'
  creationTimestamp: '2019-06-10T14:46:08Z'
  labels:
    kubernetes.io/os: linux
    topology.kubernetes.io/zone: us-east-1a
    node.openshift.io/os_version: '4.5'
    node-role.kubernetes.io/worker: ''
    topology.kubernetes.io/region: us-east-1
    node.openshift.io/os_id: rhcos
    node.kubernetes.io/instance-type: m4.large
    kubernetes.io/hostname: ip-10-0-131-14
    kubernetes.io/arch: amd64
    region: east 1
    type: user-node
#...

1
Labels to match the pod node selector.

A pod has the type: user-node,region: east node selector:

Sample Pod object with node selectors

apiVersion: v1
kind: Pod
metadata:
  name: s1
#...
spec:
  nodeSelector: 1
    region: east
    type: user-node
#...

1
Node selectors to match the node label. The node must have a label for each node selector.

When you create the pod using the example pod spec, it can be scheduled on the example node.

Default cluster-wide node selectors

With default cluster-wide node selectors, when you create a pod in that cluster, Red Hat OpenShift Service on AWS adds the default node selectors to the pod and schedules the pod on nodes with matching labels.

For example, the following Scheduler object has the default cluster-wide region=east and type=user-node node selectors:

Example Scheduler Operator Custom Resource

apiVersion: config.openshift.io/v1
kind: Scheduler
metadata:
  name: cluster
#...
spec:
  defaultNodeSelector: type=user-node,region=east
#...

A node in that cluster has the type=user-node,region=east labels:

Example Node object

apiVersion: v1
kind: Node
metadata:
  name: ci-ln-qg1il3k-f76d1-hlmhl-worker-b-df2s4
#...
  labels:
    region: east
    type: user-node
#...

Example Pod object with a node selector

apiVersion: v1
kind: Pod
metadata:
  name: s1
#...
spec:
  nodeSelector:
    region: east
#...

When you create the pod using the example pod spec in the example cluster, the pod is created with the cluster-wide node selector and is scheduled on the labeled node:

Example pod list with the pod on the labeled node

NAME     READY   STATUS    RESTARTS   AGE   IP           NODE                                       NOMINATED NODE   READINESS GATES
pod-s1   1/1     Running   0          20s   10.131.2.6   ci-ln-qg1il3k-f76d1-hlmhl-worker-b-df2s4   <none>           <none>

Note

If the project where you create the pod has a project node selector, that selector takes preference over a cluster-wide node selector. Your pod is not created or scheduled if the pod does not have the project node selector.

Project node selectors

With project node selectors, when you create a pod in this project, Red Hat OpenShift Service on AWS adds the node selectors to the pod and schedules the pods on a node with matching labels. If there is a cluster-wide default node selector, a project node selector takes preference.

For example, the following project has the region=east node selector:

Example Namespace object

apiVersion: v1
kind: Namespace
metadata:
  name: east-region
  annotations:
    openshift.io/node-selector: "region=east"
#...

The following node has the type=user-node,region=east labels:

Example Node object

apiVersion: v1
kind: Node
metadata:
  name: ci-ln-qg1il3k-f76d1-hlmhl-worker-b-df2s4
#...
  labels:
    region: east
    type: user-node
#...

When you create the pod using the example pod spec in this example project, the pod is created with the project node selectors and is scheduled on the labeled node:

Example Pod object

apiVersion: v1
kind: Pod
metadata:
  namespace: east-region
#...
spec:
  nodeSelector:
    region: east
    type: user-node
#...

Example pod list with the pod on the labeled node

NAME     READY   STATUS    RESTARTS   AGE   IP           NODE                                       NOMINATED NODE   READINESS GATES
pod-s1   1/1     Running   0          20s   10.131.2.6   ci-ln-qg1il3k-f76d1-hlmhl-worker-b-df2s4   <none>           <none>

A pod in the project is not created or scheduled if the pod contains different node selectors. For example, if you deploy the following pod into the example project, it is not created:

Example Pod object with an invalid node selector

apiVersion: v1
kind: Pod
metadata:
  name: west-region
#...
spec:
  nodeSelector:
    region: west
#...

13.1.2. Loki pod placement

You can control which nodes the Loki pods run on, and prevent other workloads from using those nodes, by using tolerations or node selectors on the pods.

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

Example LokiStack with node selectors

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
# ...
  template:
    compactor: 1
      nodeSelector:
        node-role.kubernetes.io/infra: "" 2
    distributor:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
    gateway:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
    indexGateway:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
    ingester:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
    querier:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
    queryFrontend:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
    ruler:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
# ...

1
Specifies the component pod type that applies to the node selector.
2
Specifies the pods that are moved to nodes containing the defined label.

In the previous example configuration, all Loki pods are moved to nodes containing the node-role.kubernetes.io/infra: "" label.

Example LokiStack CR with node selectors and tolerations

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
# ...
  template:
    compactor:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    distributor:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    indexGateway:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    ingester:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    querier:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    queryFrontend:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    ruler:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    gateway:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
# ...

To configure the nodeSelector and tolerations fields of the LokiStack (CR), you can use the oc explain command to view the description and fields for a particular resource: 

$ oc explain lokistack.spec.template

Example output

KIND:     LokiStack
VERSION:  loki.grafana.com/v1

RESOURCE: template <Object>

DESCRIPTION:
     Template defines the resource/limits/tolerations/nodeselectors per
     component

FIELDS:
   compactor	<Object>
     Compactor defines the compaction component spec.

   distributor	<Object>
     Distributor defines the distributor component spec.
...

For more detailed information, you can add a specific field: 

$ oc explain lokistack.spec.template.compactor

Example output

KIND:     LokiStack
VERSION:  loki.grafana.com/v1

RESOURCE: compactor <Object>

DESCRIPTION:
     Compactor defines the compaction component spec.

FIELDS:
   nodeSelector	<map[string]string>
     NodeSelector defines the labels required by a node to schedule the
     component onto it.
...

13.1.3. Configuring resources and scheduling for logging collectors

Administrators can modify the resources or scheduling of the collector by creating a ClusterLogging custom resource (CR) that is in the same namespace and has the same name as the ClusterLogForwarder CR that it supports.

The applicable stanzas for the ClusterLogging CR when using multiple log forwarders in a deployment are managementState and collection. All other stanzas are ignored.

Prerequisites

  • You have administrator permissions.
  • You have installed the Red Hat OpenShift Logging Operator version 5.8 or newer.
  • You have created a ClusterLogForwarder CR.

Procedure

  1. Create a ClusterLogging CR that supports your existing ClusterLogForwarder CR:

    Example ClusterLogging CR YAML

    apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name:  <name> 1
  namespace: <namespace> 2
spec:
  managementState: "Managed"
  collection:
    type: "vector"
    tolerations:
    - key: "logging"
      operator: "Exists"
      effect: "NoExecute"
      tolerationSeconds: 6000
    resources:
      limits:
        memory: 1Gi
      requests:
        cpu: 100m
        memory: 1Gi
    nodeSelector:
      collector: needed
# ...

    1
    The name must be the same name as the ClusterLogForwarder CR.
    2
    The namespace must be the same namespace as the ClusterLogForwarder CR.

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

    $ oc apply -f <filename>.yaml

13.1.4. Viewing logging collector pods

You can view the logging collector pods and the corresponding nodes that they are running on.

Procedure

  • Run the following command in a project to view the logging collector pods and their details: 

    $ oc get pods --selector component=collector -o wide -n <project_name>

    Example output

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

13.1.5. Additional resources

13.2. Using taints and tolerations to control logging pod placement

Taints and tolerations allow the node to control which pods should (or should not) be scheduled on them.

13.2.1. Understanding taints and tolerations

A taint allows a node to refuse a pod to be scheduled unless that pod has a matching toleration.

You apply taints to a node through the Node specification (NodeSpec) and apply tolerations to a pod through the Pod specification (PodSpec). When you apply a taint a node, the scheduler cannot place a pod on that node unless the pod can tolerate the taint.

Example taint in a node specification

apiVersion: v1
kind: Node
metadata:
  name: my-node
#...
spec:
  taints:
  - effect: NoExecute
    key: key1
    value: value1
#...

Example toleration in a Pod spec

apiVersion: v1
kind: Pod
metadata:
  name: my-pod
#...
spec:
  tolerations:
  - key: "key1"
    operator: "Equal"
    value: "value1"
    effect: "NoExecute"
    tolerationSeconds: 3600
#...

Taints and tolerations consist of a key, value, and effect.

Table 13.1. Taint and toleration components
ParameterDescription

key

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

value

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

effect

The effect is one of the following:

NoSchedule [1]

  • New pods that do not match the taint are not scheduled onto that node.
  • Existing pods on the node remain.

PreferNoSchedule

  • New pods that do not match the taint might be scheduled onto that node, but the scheduler tries not to.
  • Existing pods on the node remain.

NoExecute

  • New pods that do not match the taint cannot be scheduled onto that node.
  • Existing pods on the node that do not have a matching toleration are removed.

operator

Equal

The key/value/effect parameters must match. This is the default.

Exists

The key/effect parameters must match. You must leave a blank value parameter, which matches any.

  1. If you add a NoSchedule taint to a control plane node, the node must have the node-role.kubernetes.io/master=:NoSchedule taint, which is added by default.

    For example: 

    apiVersion: v1
kind: Node
metadata:
  annotations:
    machine.openshift.io/machine: openshift-machine-api/ci-ln-62s7gtb-f76d1-v8jxv-master-0
    machineconfiguration.openshift.io/currentConfig: rendered-master-cdc1ab7da414629332cc4c3926e6e59c
  name: my-node
#...
spec:
  taints:
  - effect: NoSchedule
    key: node-role.kubernetes.io/master
#...

A toleration matches a taint:

  • If the operator parameter is set to Equal:

    • the key parameters are the same;
    • the value parameters are the same;
    • the effect parameters are the same.

  • If the operator parameter is set to Exists:

    • the key parameters are the same;
    • the effect parameters are the same.

The following taints are built into Red Hat OpenShift Service on AWS:

  • node.kubernetes.io/not-ready: The node is not ready. This corresponds to the node condition Ready=False.
  • node.kubernetes.io/unreachable: The node is unreachable from the node controller. This corresponds to the node condition Ready=Unknown.
  • node.kubernetes.io/memory-pressure: The node has memory pressure issues. This corresponds to the node condition MemoryPressure=True.
  • node.kubernetes.io/disk-pressure: The node has disk pressure issues. This corresponds to the node condition DiskPressure=True.
  • node.kubernetes.io/network-unavailable: The node network is unavailable.
  • node.kubernetes.io/unschedulable: The node is unschedulable.
  • node.cloudprovider.kubernetes.io/uninitialized: When the node controller is started with an external cloud provider, this taint is set on a node to mark it as unusable. After a controller from the cloud-controller-manager initializes this node, the kubelet removes this taint.

  • node.kubernetes.io/pid-pressure: The node has pid pressure. This corresponds to the node condition PIDPressure=True.

    Important

    Red Hat OpenShift Service on AWS does not set a default pid.available evictionHard.

13.2.2. Loki pod placement

You can control which nodes the Loki pods run on, and prevent other workloads from using those nodes, by using tolerations or node selectors on the pods.

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

Example LokiStack with node selectors

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
# ...
  template:
    compactor: 1
      nodeSelector:
        node-role.kubernetes.io/infra: "" 2
    distributor:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
    gateway:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
    indexGateway:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
    ingester:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
    querier:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
    queryFrontend:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
    ruler:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
# ...

1
Specifies the component pod type that applies to the node selector.
2
Specifies the pods that are moved to nodes containing the defined label.

In the previous example configuration, all Loki pods are moved to nodes containing the node-role.kubernetes.io/infra: "" label.

Example LokiStack CR with node selectors and tolerations

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
# ...
  template:
    compactor:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    distributor:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    indexGateway:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    ingester:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    querier:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    queryFrontend:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    ruler:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
    gateway:
      nodeSelector:
        node-role.kubernetes.io/infra: ""
      tolerations:
      - effect: NoSchedule
        key: node-role.kubernetes.io/infra
        value: reserved
      - effect: NoExecute
        key: node-role.kubernetes.io/infra
        value: reserved
# ...

To configure the nodeSelector and tolerations fields of the LokiStack (CR), you can use the oc explain command to view the description and fields for a particular resource: 

$ oc explain lokistack.spec.template

Example output

KIND:     LokiStack
VERSION:  loki.grafana.com/v1

RESOURCE: template <Object>

DESCRIPTION:
     Template defines the resource/limits/tolerations/nodeselectors per
     component

FIELDS:
   compactor	<Object>
     Compactor defines the compaction component spec.

   distributor	<Object>
     Distributor defines the distributor component spec.
...

For more detailed information, you can add a specific field: 

$ oc explain lokistack.spec.template.compactor

Example output

KIND:     LokiStack
VERSION:  loki.grafana.com/v1

RESOURCE: compactor <Object>

DESCRIPTION:
     Compactor defines the compaction component spec.

FIELDS:
   nodeSelector	<map[string]string>
     NodeSelector defines the labels required by a node to schedule the
     component onto it.
...

13.2.3. Using tolerations to control log collector pod placement

By default, log collector pods have the following tolerations configuration: 

apiVersion: v1
kind: Pod
metadata:
  name: collector-example
  namespace: openshift-logging
spec:
# ...
  collection:
    type: vector
    tolerations:
    - effect: NoSchedule
      key: node-role.kubernetes.io/master
      operator: Exists
    - effect: NoSchedule
      key: node.kubernetes.io/disk-pressure
      operator: Exists
    - effect: NoExecute
      key: node.kubernetes.io/not-ready
      operator: Exists
    - effect: NoExecute
      key: node.kubernetes.io/unreachable
      operator: Exists
    - effect: NoSchedule
      key: node.kubernetes.io/memory-pressure
      operator: Exists
    - effect: NoSchedule
      key: node.kubernetes.io/pid-pressure
      operator: Exists
    - effect: NoSchedule
      key: node.kubernetes.io/unschedulable
      operator: Exists
# ...

Prerequisites

  • You have installed the Red Hat OpenShift Logging Operator and OpenShift CLI (oc).

Procedure

  1. Add a taint to a node where you want logging collector pods to schedule logging collector pods by running the following command: 

    $ oc adm taint nodes <node_name> <key>=<value>:<effect>

    Example command

    $ oc adm taint nodes node1 collector=node:NoExecute

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

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

    apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
# ...
spec:
# ...
  collection:
    type: vector
    tolerations:
    - key: collector 1
      operator: Exists 2
      effect: NoExecute 3
      tolerationSeconds: 6000 4
    resources:
      limits:
        memory: 2Gi
      requests:
        cpu: 100m
        memory: 1Gi
# ...
    1
    Specify the key that you added to the node.
    2
    Specify the Exists operator to require the key/value/effect parameters to match.
    3
    Specify the NoExecute effect.
    4
    Optionally, specify the tolerationSeconds parameter to set how long a pod can remain bound to a node before being evicted.

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

13.2.4. Configuring resources and scheduling for logging collectors

Administrators can modify the resources or scheduling of the collector by creating a ClusterLogging custom resource (CR) that is in the same namespace and has the same name as the ClusterLogForwarder CR that it supports.

The applicable stanzas for the ClusterLogging CR when using multiple log forwarders in a deployment are managementState and collection. All other stanzas are ignored.

Prerequisites

  • You have administrator permissions.
  • You have installed the Red Hat OpenShift Logging Operator version 5.8 or newer.
  • You have created a ClusterLogForwarder CR.

Procedure

  1. Create a ClusterLogging CR that supports your existing ClusterLogForwarder CR:

    Example ClusterLogging CR YAML

    apiVersion: logging.openshift.io/v1
kind: ClusterLogging
metadata:
  name:  <name> 1
  namespace: <namespace> 2
spec:
  managementState: "Managed"
  collection:
    type: "vector"
    tolerations:
    - key: "logging"
      operator: "Exists"
      effect: "NoExecute"
      tolerationSeconds: 6000
    resources:
      limits:
        memory: 1Gi
      requests:
        cpu: 100m
        memory: 1Gi
    nodeSelector:
      collector: needed
# ...

    1
    The name must be the same name as the ClusterLogForwarder CR.
    2
    The namespace must be the same namespace as the ClusterLogForwarder CR.

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

    $ oc apply -f <filename>.yaml

13.2.5. Viewing logging collector pods

You can view the logging collector pods and the corresponding nodes that they are running on.

Procedure

  • Run the following command in a project to view the logging collector pods and their details: 

    $ oc get pods --selector component=collector -o wide -n <project_name>

    Example output

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

13.2.6. Additional resources

Chapter 14. Uninstalling Logging

You can remove logging from your Red Hat OpenShift Service on AWS cluster by removing installed Operators and related custom resources (CRs).

14.1. Uninstalling the logging

You can stop aggregating logs by deleting the Red Hat OpenShift Logging Operator and the ClusterLogging custom resource (CR).

Prerequisites

  • You have administrator permissions.
  • You have access to the Administrator perspective of the Red Hat OpenShift Service on AWS web console.

Procedure

  1. Go to the Administration → Custom Resource Definitions page, and click ClusterLogging.
  2. On the Custom Resource Definition Details page, click Instances.
  3. Click the options menu kebab next to the instance, and click Delete ClusterLogging.
  4. Go to the Administration → Custom Resource Definitions page.

  5. Click the options menu kebab next to ClusterLogging, and select Delete Custom Resource Definition.

    Warning

    Deleting the ClusterLogging CR does not remove the persistent volume claims (PVCs). To delete the remaining PVCs, persistent volumes (PVs), and associated data, you must take further action. Releasing or deleting PVCs can delete PVs and cause data loss.

  6. If you have created a ClusterLogForwarder CR, click the options menu kebab next to ClusterLogForwarder, and then click Delete Custom Resource Definition.
  7. Go to the Operators → Installed Operators page.
  8. Click the options menu kebab next to the Red Hat OpenShift Logging Operator, and then click Uninstall Operator.

  9. Optional: Delete the openshift-logging project.

    Warning

    Deleting the openshift-logging project deletes everything in that namespace, including any persistent volume claims (PVCs). If you want to preserve logging data, do not delete the openshift-logging project.

    1. Go to the Home → Projects page.
    2. Click the options menu kebab next to the openshift-logging project, and then click Delete Project.
    3. Confirm the deletion by typing openshift-logging in the dialog box, and then click Delete.

14.2. Deleting logging PVCs

To keep persistent volume claims (PVCs) for reuse with other pods, keep the labels or PVC names that you need to reclaim the PVCs. If you do not want to keep the PVCs, you can delete them. If you want to recover storage space, you can also delete the persistent volumes (PVs).

Prerequisites

  • You have administrator permissions.
  • You have access to the Administrator perspective of the Red Hat OpenShift Service on AWS web console.

Procedure

  1. Go to the Storage → Persistent Volume Claims page.
  2. Click the options menu kebab next to each PVC, and select Delete Persistent Volume Claim.

14.3. Uninstalling Loki

Prerequisites

  • You have administrator permissions.
  • You have access to the Administrator perspective of the Red Hat OpenShift Service on AWS web console.
  • If you have not already removed the Red Hat OpenShift Logging Operator and related resources, you have removed references to LokiStack from the ClusterLogging custom resource.

Procedure

  1. Go to the Administration → Custom Resource Definitions page, and click LokiStack.
  2. On the Custom Resource Definition Details page, click Instances.
  3. Click the options menu kebab next to the instance, and then click Delete LokiStack.
  4. Go to the Administration → Custom Resource Definitions page.
  5. Click the options menu kebab next to LokiStack, and select Delete Custom Resource Definition.
  6. Delete the object storage secret.
  7. Go to the Operators → Installed Operators page.
  8. Click the options menu kebab next to the Loki Operator, and then click Uninstall Operator.

  9. Optional: Delete the openshift-operators-redhat project.

    Important

    Do not delete the openshift-operators-redhat project if other global Operators are installed in this namespace.

    1. Go to the Home → Projects page.
    2. Click the options menu kebab next to the openshift-operators-redhat project, and then click Delete Project.
    3. Confirm the deletion by typing openshift-operators-redhat in the dialog box, and then click Delete.

14.4. Uninstalling Elasticsearch

Prerequisites

  • You have administrator permissions.
  • You have access to the Administrator perspective of the Red Hat OpenShift Service on AWS web console.
  • If you have not already removed the Red Hat OpenShift Logging Operator and related resources, you must remove references to Elasticsearch from the ClusterLogging custom resource.

Procedure

  1. Go to the Administration → Custom Resource Definitions page, and click Elasticsearch.
  2. On the Custom Resource Definition Details page, click Instances.
  3. Click the options menu kebab next to the instance, and then click Delete Elasticsearch.
  4. Go to the Administration → Custom Resource Definitions page.
  5. Click the options menu kebab next to Elasticsearch, and select Delete Custom Resource Definition.
  6. Delete the object storage secret.
  7. Go to the Operators → Installed Operators page.
  8. Click the options menu kebab next to the OpenShift Elasticsearch Operator, and then click Uninstall Operator.

  9. Optional: Delete the openshift-operators-redhat project.

    Important

    Do not delete the openshift-operators-redhat project if other global Operators are installed in this namespace.

    1. Go to the Home → Projects page.
    2. Click the options menu kebab next to the openshift-operators-redhat project, and then click Delete Project.
    3. Confirm the deletion by typing openshift-operators-redhat in the dialog box, and then click Delete.

14.5. Deleting Operators from a cluster using the CLI

Cluster administrators can delete installed Operators from a selected namespace by using the CLI.

Prerequisites

  • You have access to an Red Hat OpenShift Service on AWS cluster using an account with dedicated-admin permissions.
  • The OpenShift CLI (oc) is installed on your workstation.

Procedure

  1. Ensure the latest version of the subscribed operator (for example, serverless-operator) is identified in the currentCSV field. 

    $ oc get subscription.operators.coreos.com serverless-operator -n openshift-serverless -o yaml | grep currentCSV

    Example output

      currentCSV: serverless-operator.v1.28.0

  2. Delete the subscription (for example, serverless-operator): 

    $ oc delete subscription.operators.coreos.com serverless-operator -n openshift-serverless

    Example output

    subscription.operators.coreos.com "serverless-operator" deleted

  3. Delete the CSV for the Operator in the target namespace using the currentCSV value from the previous step: 

    $ oc delete clusterserviceversion serverless-operator.v1.28.0 -n openshift-serverless

    Example output

    clusterserviceversion.operators.coreos.com "serverless-operator.v1.28.0" deleted

Additional resources

Chapter 15. Log Record Fields

The following fields can be present in log records exported by the logging. Although log records are typically formatted as JSON objects, the same data model can be applied to other encodings.

To search these fields from Elasticsearch and Kibana, use the full dotted field name when searching. For example, with an Elasticsearch /_search URL, to look for a Kubernetes pod name, use /_search/q=kubernetes.pod_name:name-of-my-pod.

The top level fields may be present in every record.

message

The original log entry text, UTF-8 encoded. This field may be absent or empty if a non-empty structured field is present. See the description of structured for more.

Data type

text

Example value

HAPPY

structured

Original log entry as a structured object. This field may be present if the forwarder was configured to parse structured JSON logs. If the original log entry was a valid structured log, this field will contain an equivalent JSON structure. Otherwise this field will be empty or absent, and the message field will contain the original log message. The structured field can have any subfields that are included in the log message, there are no restrictions defined here.

Data type

group

Example value

map[message:starting fluentd worker pid=21631 ppid=21618 worker=0 pid:21631 ppid:21618 worker:0]

@timestamp

A UTC value that marks when the log payload was created or, if the creation time is not known, when the log payload was first collected. The “@” prefix denotes a field that is reserved for a particular use. By default, most tools look for “@timestamp” with ElasticSearch.

Data type

date

Example value

2015-01-24 14:06:05.071000000 Z

hostname

The name of the host where this log message originated. In a Kubernetes cluster, this is the same as kubernetes.host.

Data type

keyword

ipaddr4

The IPv4 address of the source server. Can be an array.

Data type

ip

ipaddr6

The IPv6 address of the source server, if available. Can be an array.

Data type

ip

level

The logging level from various sources, including rsyslog(severitytext property), a Python logging module, and others.

The following values come from syslog.h, and are preceded by their numeric equivalents:

  • 0 = emerg, system is unusable.
  • 1 = alert, action must be taken immediately.
  • 2 = crit, critical conditions.
  • 3 = err, error conditions.
  • 4 = warn, warning conditions.
  • 5 = notice, normal but significant condition.
  • 6 = info, informational.
  • 7 = debug, debug-level messages.

The two following values are not part of syslog.h but are widely used:

  • 8 = trace, trace-level messages, which are more verbose than debug messages.
  • 9 = unknown, when the logging system gets a value it does not recognize.

Map the log levels or priorities of other logging systems to their nearest match in the preceding list. For example, from python logging, you can match CRITICAL with crit, ERROR with err, and so on.

Data type

keyword

Example value

info

pid

The process ID of the logging entity, if available.

Data type

keyword

service

The name of the service associated with the logging entity, if available. For example, syslog’s APP-NAME and rsyslog’s programname properties are mapped to the service field.

Data type

keyword

Chapter 16. tags

Optional. An operator-defined list of tags placed on each log by the collector or normalizer. The payload can be a string with whitespace-delimited string tokens or a JSON list of string tokens.

Data type

text

file

The path to the log file from which the collector reads this log entry. Normally, this is a path in the /var/log file system of a cluster node.

Data type

text

offset

The offset value. Can represent bytes to the start of the log line in the file (zero- or one-based), or log line numbers (zero- or one-based), so long as the values are strictly monotonically increasing in the context of a single log file. The values are allowed to wrap, representing a new version of the log file (rotation).

Data type

long

Chapter 17. kubernetes

The namespace for Kubernetes-specific metadata

Data type

group

17.1. kubernetes.pod_name

The name of the pod

Data type

keyword

17.2. kubernetes.pod_id

The Kubernetes ID of the pod

Data type

keyword

17.3. kubernetes.namespace_name

The name of the namespace in Kubernetes

Data type

keyword

17.4. kubernetes.namespace_id

The ID of the namespace in Kubernetes

Data type

keyword

17.5. kubernetes.host

The Kubernetes node name

Data type

keyword

17.6. kubernetes.container_name

The name of the container in Kubernetes

Data type

keyword

17.7. kubernetes.annotations

Annotations associated with the Kubernetes object

Data type

group

17.8. kubernetes.labels

Labels present on the original Kubernetes Pod

Data type

group

17.9. kubernetes.event

The Kubernetes event obtained from the Kubernetes master API. This event description loosely follows type Event in Event v1 core.

Data type

group

17.9.1. kubernetes.event.verb

The type of event, ADDED, MODIFIED, or DELETED

Data type

keyword

Example value

ADDED

17.9.2. kubernetes.event.metadata

Information related to the location and time of the event creation

Data type

group

17.9.2.1. kubernetes.event.metadata.name

The name of the object that triggered the event creation

Data type

keyword

Example value

java-mainclass-1.14d888a4cfc24890

17.9.2.2. kubernetes.event.metadata.namespace

The name of the namespace where the event originally occurred. Note that it differs from kubernetes.namespace_name, which is the namespace where the eventrouter application is deployed.

Data type

keyword

Example value

default

17.9.2.4. kubernetes.event.metadata.uid

The unique ID of the event

Data type

keyword

Example value

d828ac69-7b58-11e7-9cf5-5254002f560c

17.9.2.5. kubernetes.event.metadata.resourceVersion

A string that identifies the server’s internal version of the event. Clients can use this string to determine when objects have changed.

Data type

integer

Example value

311987

17.9.3. kubernetes.event.involvedObject

The object that the event is about.

Data type

group

17.9.3.1. kubernetes.event.involvedObject.kind

The type of object

Data type

keyword

Example value

ReplicationController

17.9.3.2. kubernetes.event.involvedObject.namespace

The namespace name of the involved object. Note that it may differ from kubernetes.namespace_name, which is the namespace where the eventrouter application is deployed.

Data type

keyword

Example value

default

17.9.3.3. kubernetes.event.involvedObject.name

The name of the object that triggered the event

Data type

keyword

Example value

java-mainclass-1

17.9.3.4. kubernetes.event.involvedObject.uid

The unique ID of the object

Data type

keyword

Example value

e6bff941-76a8-11e7-8193-5254002f560c

17.9.3.5. kubernetes.event.involvedObject.apiVersion

The version of kubernetes master API

Data type

keyword

Example value

v1

17.9.3.6. kubernetes.event.involvedObject.resourceVersion

A string that identifies the server’s internal version of the pod that triggered the event. Clients can use this string to determine when objects have changed.

Data type

keyword

Example value

308882

17.9.4. kubernetes.event.reason

A short machine-understandable string that gives the reason for generating this event

Data type

keyword

Example value

SuccessfulCreate

17.9.5. kubernetes.event.source_component

The component that reported this event

Data type

keyword

Example value

replication-controller

17.9.6. kubernetes.event.firstTimestamp

The time at which the event was first recorded

Data type

date

Example value

2017-08-07 10:11:57.000000000 Z

17.9.7. kubernetes.event.count

The number of times this event has occurred

Data type

integer

Example value

1

17.9.8. kubernetes.event.type

The type of event, Normal or Warning. New types could be added in the future.

Data type

keyword

Example value

Normal

Chapter 18. OpenShift

The namespace for openshift-logging specific metadata

Data type

group

18.1. openshift.labels

Labels added by the Cluster Log Forwarder configuration

Data type

group

Chapter 19. API reference

19.1. 5.6 Logging API reference

19.1.1. Logging 5.6 API reference

19.1.1.1. ClusterLogForwarder

ClusterLogForwarder is an API to configure forwarding logs.

You configure forwarding by specifying a list of pipelines, which forward from a set of named inputs to a set of named outputs.

There are built-in input names for common log categories, and you can define custom inputs to do additional filtering.

There is a built-in output name for the default openshift log store, but you can define your own outputs with a URL and other connection information to forward logs to other stores or processors, inside or outside the cluster.

For more details see the documentation on the API fields.

PropertyTypeDescription

spec

object

Specification of the desired behavior of ClusterLogForwarder

status

object

Status of the ClusterLogForwarder

19.1.1.1.1. .spec
19.1.1.1.1.1. Description

ClusterLogForwarderSpec defines how logs should be forwarded to remote targets.

19.1.1.1.1.1.1. Type
  • object
PropertyTypeDescription

inputs

array

(optional) Inputs are named filters for log messages to be forwarded.

outputDefaults

object

(optional) DEPRECATED OutputDefaults specify forwarder config explicitly for the default store.

outputs

array

(optional) Outputs are named destinations for log messages.

pipelines

array

Pipelines forward the messages selected by a set of inputs to a set of outputs.

19.1.1.1.2. .spec.inputs[]
19.1.1.1.2.1. Description

InputSpec defines a selector of log messages.

19.1.1.1.2.1.1. Type
  • array
PropertyTypeDescription

application

object

(optional) Application, if present, enables named set of application logs that

name

string

Name used to refer to the input of a pipeline.

19.1.1.1.3. .spec.inputs[].application
19.1.1.1.3.1. Description

Application log selector. All conditions in the selector must be satisfied (logical AND) to select logs.

19.1.1.1.3.1.1. Type
  • object
PropertyTypeDescription

namespaces

array

(optional) Namespaces from which to collect application logs.

selector

object

(optional) Selector for logs from pods with matching labels.

19.1.1.1.4. .spec.inputs[].application.namespaces[]
19.1.1.1.4.1. Description
19.1.1.1.4.1.1. Type
  • array
19.1.1.1.5. .spec.inputs[].application.selector
19.1.1.1.5.1. Description

A label selector is a label query over a set of resources.

19.1.1.1.5.1.1. Type
  • object
PropertyTypeDescription

matchLabels

object

(optional) matchLabels is a map of {key,value} pairs. A single {key,value} in the matchLabels

19.1.1.1.6. .spec.inputs[].application.selector.matchLabels
19.1.1.1.6.1. Description
19.1.1.1.6.1.1. Type
  • object
19.1.1.1.7. .spec.outputDefaults
19.1.1.1.7.1. Description
19.1.1.1.7.1.1. Type
  • object
PropertyTypeDescription

elasticsearch

object

(optional) Elasticsearch OutputSpec default values

19.1.1.1.8. .spec.outputDefaults.elasticsearch
19.1.1.1.8.1. Description

ElasticsearchStructuredSpec is spec related to structured log changes to determine the elasticsearch index

19.1.1.1.8.1.1. Type
  • object
PropertyTypeDescription

enableStructuredContainerLogs

bool

(optional) EnableStructuredContainerLogs enables multi-container structured logs to allow

structuredTypeKey

string

(optional) StructuredTypeKey specifies the metadata key to be used as name of elasticsearch index

structuredTypeName

string

(optional) StructuredTypeName specifies the name of elasticsearch schema

19.1.1.1.9. .spec.outputs[]
19.1.1.1.9.1. Description

Output defines a destination for log messages.

19.1.1.1.9.1.1. Type
  • array
PropertyTypeDescription

syslog

object

(optional)

fluentdForward

object

(optional)

elasticsearch

object

(optional)

kafka

object

(optional)

cloudwatch

object

(optional)

loki

object

(optional)

googleCloudLogging

object

(optional)

splunk

object

(optional)

name

string

Name used to refer to the output from a pipeline.

secret

object

(optional) Secret for authentication.

tls

object

TLS contains settings for controlling options on TLS client connections.

type

string

Type of output plugin.

url

string

(optional) URL to send log records to.

19.1.1.1.10. .spec.outputs[].secret
19.1.1.1.10.1. Description

OutputSecretSpec is a secret reference containing name only, no namespace.

19.1.1.1.10.1.1. Type
  • object
PropertyTypeDescription

name

string

Name of a secret in the namespace configured for log forwarder secrets.

19.1.1.1.11. .spec.outputs[].tls
19.1.1.1.11.1. Description

OutputTLSSpec contains options for TLS connections that are agnostic to the output type.

19.1.1.1.11.1.1. Type
  • object
PropertyTypeDescription

insecureSkipVerify

bool

If InsecureSkipVerify is true, then the TLS client will be configured to ignore errors with certificates.

19.1.1.1.12. .spec.pipelines[]
19.1.1.1.12.1. Description

PipelinesSpec link a set of inputs to a set of outputs.

19.1.1.1.12.1.1. Type
  • array
PropertyTypeDescription

detectMultilineErrors

bool

(optional) DetectMultilineErrors enables multiline error detection of container logs

inputRefs

array

InputRefs lists the names (input.name) of inputs to this pipeline.

labels

object

(optional) Labels applied to log records passing through this pipeline.

name

string

(optional) Name is optional, but must be unique in the pipelines list if provided.

outputRefs

array

OutputRefs lists the names (output.name) of outputs from this pipeline.

parse

string

(optional) Parse enables parsing of log entries into structured logs

19.1.1.1.13. .spec.pipelines[].inputRefs[]
19.1.1.1.13.1. Description
19.1.1.1.13.1.1. Type
  • array
19.1.1.1.14. .spec.pipelines[].labels
19.1.1.1.14.1. Description
19.1.1.1.14.1.1. Type
  • object
19.1.1.1.15. .spec.pipelines[].outputRefs[]
19.1.1.1.15.1. Description
19.1.1.1.15.1.1. Type
  • array
19.1.1.1.16. .status
19.1.1.1.16.1. Description

ClusterLogForwarderStatus defines the observed state of ClusterLogForwarder

19.1.1.1.16.1.1. Type
  • object
PropertyTypeDescription

conditions

object

Conditions of the log forwarder.

inputs

Conditions

Inputs maps input name to condition of the input.

outputs

Conditions

Outputs maps output name to condition of the output.

pipelines

Conditions

Pipelines maps pipeline name to condition of the pipeline.

19.1.1.1.17. .status.conditions
19.1.1.1.17.1. Description
19.1.1.1.17.1.1. Type
  • object
19.1.1.1.18. .status.inputs
19.1.1.1.18.1. Description
19.1.1.1.18.1.1. Type
  • Conditions
19.1.1.1.19. .status.outputs
19.1.1.1.19.1. Description
19.1.1.1.19.1.1. Type
  • Conditions
19.1.1.1.20. .status.pipelines
19.1.1.1.20.1. Description
19.1.1.1.20.1.1. Type
  • Conditions== ClusterLogging A Red Hat OpenShift Logging instance. ClusterLogging is the Schema for the clusterloggings API
PropertyTypeDescription

spec

object

Specification of the desired behavior of ClusterLogging

status

object

Status defines the observed state of ClusterLogging

19.1.1.1.21. .spec
19.1.1.1.21.1. Description

ClusterLoggingSpec defines the desired state of ClusterLogging

19.1.1.1.21.1.1. Type
  • object
PropertyTypeDescription

collection

object

Specification of the Collection component for the cluster

curation

object

(DEPRECATED) (optional) Deprecated. Specification of the Curation component for the cluster

forwarder

object

(DEPRECATED) (optional) Deprecated. Specification for Forwarder component for the cluster

logStore

object

(optional) Specification of the Log Storage component for the cluster

managementState

string

(optional) Indicator if the resource is 'Managed' or 'Unmanaged' by the operator

visualization

object

(optional) Specification of the Visualization component for the cluster

19.1.1.1.22. .spec.collection
19.1.1.1.22.1. Description

This is the struct that will contain information pertinent to Log and event collection

19.1.1.1.22.1.1. Type
  • object
PropertyTypeDescription

resources

object

(optional) The resource requirements for the collector

nodeSelector

object

(optional) Define which Nodes the Pods are scheduled on.

tolerations

array

(optional) Define the tolerations the Pods will accept

fluentd

object

(optional) Fluentd represents the configuration for forwarders of type fluentd.

logs

object

(DEPRECATED) (optional) Deprecated. Specification of Log Collection for the cluster

type

string

(optional) The type of Log Collection to configure

19.1.1.1.23. .spec.collection.fluentd
19.1.1.1.23.1. Description

FluentdForwarderSpec represents the configuration for forwarders of type fluentd.

19.1.1.1.23.1.1. Type
  • object
PropertyTypeDescription

buffer

object

 

inFile

object

 
19.1.1.1.24. .spec.collection.fluentd.buffer
19.1.1.1.24.1. Description

FluentdBufferSpec represents a subset of fluentd buffer parameters to tune the buffer configuration for all fluentd outputs. It supports a subset of parameters to configure buffer and queue sizing, flush operations and retry flushing.

For general parameters refer to: https://docs.fluentd.org/configuration/buffer-section#buffering-parameters

For flush parameters refer to: https://docs.fluentd.org/configuration/buffer-section#flushing-parameters

For retry parameters refer to: https://docs.fluentd.org/configuration/buffer-section#retries-parameters

19.1.1.1.24.1.1. Type
  • object
PropertyTypeDescription

chunkLimitSize

string

(optional) ChunkLimitSize represents the maximum size of each chunk. Events will be

flushInterval

string

(optional) FlushInterval represents the time duration to wait between two consecutive flush

flushMode

string

(optional) FlushMode represents the mode of the flushing thread to write chunks. The mode

flushThreadCount

int

(optional) FlushThreadCount reprents the number of threads used by the fluentd buffer

overflowAction

string

(optional) OverflowAction represents the action for the fluentd buffer plugin to

retryMaxInterval

string

(optional) RetryMaxInterval represents the maximum time interval for exponential backoff

retryTimeout

string

(optional) RetryTimeout represents the maximum time interval to attempt retries before giving up

retryType

string

(optional) RetryType represents the type of retrying flush operations. Flush operations can

retryWait

string

(optional) RetryWait represents the time duration between two consecutive retries to flush

totalLimitSize

string

(optional) TotalLimitSize represents the threshold of node space allowed per fluentd

19.1.1.1.25. .spec.collection.fluentd.inFile
19.1.1.1.25.1. Description

FluentdInFileSpec represents a subset of fluentd in-tail plugin parameters to tune the configuration for all fluentd in-tail inputs.

For general parameters refer to: https://docs.fluentd.org/input/tail#parameters

19.1.1.1.25.1.1. Type
  • object
PropertyTypeDescription

readLinesLimit

int

(optional) ReadLinesLimit represents the number of lines to read with each I/O operation

19.1.1.1.26. .spec.collection.logs
19.1.1.1.26.1. Description
19.1.1.1.26.1.1. Type
  • object
PropertyTypeDescription

fluentd

object

Specification of the Fluentd Log Collection component

type

string

The type of Log Collection to configure

19.1.1.1.27. .spec.collection.logs.fluentd
19.1.1.1.27.1. Description

CollectorSpec is spec to define scheduling and resources for a collector

19.1.1.1.27.1.1. Type
  • object
PropertyTypeDescription

nodeSelector

object

(optional) Define which Nodes the Pods are scheduled on.

resources

object

(optional) The resource requirements for the collector

tolerations

array

(optional) Define the tolerations the Pods will accept

19.1.1.1.28. .spec.collection.logs.fluentd.nodeSelector
19.1.1.1.28.1. Description
19.1.1.1.28.1.1. Type
  • object
19.1.1.1.29. .spec.collection.logs.fluentd.resources
19.1.1.1.29.1. Description
19.1.1.1.29.1.1. Type
  • object
PropertyTypeDescription

limits

object

(optional) Limits describes the maximum amount of compute resources allowed.

requests

object

(optional) Requests describes the minimum amount of compute resources required.

19.1.1.1.30. .spec.collection.logs.fluentd.resources.limits
19.1.1.1.30.1. Description
19.1.1.1.30.1.1. Type
  • object
19.1.1.1.31. .spec.collection.logs.fluentd.resources.requests
19.1.1.1.31.1. Description
19.1.1.1.31.1.1. Type
  • object
19.1.1.1.32. .spec.collection.logs.fluentd.tolerations[]
19.1.1.1.32.1. Description
19.1.1.1.32.1.1. Type
  • array
PropertyTypeDescription

effect

string

(optional) Effect indicates the taint effect to match. Empty means match all taint effects.

key

string

(optional) Key is the taint key that the toleration applies to. Empty means match all taint keys.

operator

string

(optional) Operator represents a key's relationship to the value.

tolerationSeconds

int

(optional) TolerationSeconds represents the period of time the toleration (which must be

value

string

(optional) Value is the taint value the toleration matches to.

19.1.1.1.33. .spec.collection.logs.fluentd.tolerations[].tolerationSeconds
19.1.1.1.33.1. Description
19.1.1.1.33.1.1. Type
  • int
19.1.1.1.34. .spec.curation
19.1.1.1.34.1. Description

This is the struct that will contain information pertinent to Log curation (Curator)

19.1.1.1.34.1.1. Type
  • object
PropertyTypeDescription

curator

object

The specification of curation to configure

type

string

The kind of curation to configure

19.1.1.1.35. .spec.curation.curator
19.1.1.1.35.1. Description
19.1.1.1.35.1.1. Type
  • object
PropertyTypeDescription

nodeSelector

object

Define which Nodes the Pods are scheduled on.

resources

object

(optional) The resource requirements for Curator

schedule

string

The cron schedule that the Curator job is run. Defaults to "30 3 * * *"

tolerations

array

 
19.1.1.1.36. .spec.curation.curator.nodeSelector
19.1.1.1.36.1. Description
19.1.1.1.36.1.1. Type
  • object
19.1.1.1.37. .spec.curation.curator.resources
19.1.1.1.37.1. Description
19.1.1.1.37.1.1. Type
  • object
PropertyTypeDescription

limits

object

(optional) Limits describes the maximum amount of compute resources allowed.

requests

object

(optional) Requests describes the minimum amount of compute resources required.

19.1.1.1.38. .spec.curation.curator.resources.limits
19.1.1.1.38.1. Description
19.1.1.1.38.1.1. Type
  • object
19.1.1.1.39. .spec.curation.curator.resources.requests
19.1.1.1.39.1. Description
19.1.1.1.39.1.1. Type
  • object
19.1.1.1.40. .spec.curation.curator.tolerations[]
19.1.1.1.40.1. Description
19.1.1.1.40.1.1. Type
  • array
PropertyTypeDescription

effect

string

(optional) Effect indicates the taint effect to match. Empty means match all taint effects.

key

string

(optional) Key is the taint key that the toleration applies to. Empty means match all taint keys.

operator

string

(optional) Operator represents a key's relationship to the value.

tolerationSeconds

int

(optional) TolerationSeconds represents the period of time the toleration (which must be

value

string

(optional) Value is the taint value the toleration matches to.

19.1.1.1.41. .spec.curation.curator.tolerations[].tolerationSeconds
19.1.1.1.41.1. Description
19.1.1.1.41.1.1. Type
  • int
19.1.1.1.42. .spec.forwarder
19.1.1.1.42.1. Description

ForwarderSpec contains global tuning parameters for specific forwarder implementations. This field is not required for general use, it allows performance tuning by users familiar with the underlying forwarder technology. Currently supported: fluentd.

19.1.1.1.42.1.1. Type
  • object
PropertyTypeDescription

fluentd

object

 
19.1.1.1.43. .spec.forwarder.fluentd
19.1.1.1.43.1. Description

FluentdForwarderSpec represents the configuration for forwarders of type fluentd.

19.1.1.1.43.1.1. Type
  • object
PropertyTypeDescription

buffer

object

 

inFile

object

 
19.1.1.1.44. .spec.forwarder.fluentd.buffer
19.1.1.1.44.1. Description

FluentdBufferSpec represents a subset of fluentd buffer parameters to tune the buffer configuration for all fluentd outputs. It supports a subset of parameters to configure buffer and queue sizing, flush operations and retry flushing.

For general parameters refer to: https://docs.fluentd.org/configuration/buffer-section#buffering-parameters

For flush parameters refer to: https://docs.fluentd.org/configuration/buffer-section#flushing-parameters

For retry parameters refer to: https://docs.fluentd.org/configuration/buffer-section#retries-parameters

19.1.1.1.44.1.1. Type
  • object
PropertyTypeDescription

chunkLimitSize

string

(optional) ChunkLimitSize represents the maximum size of each chunk. Events will be

flushInterval

string

(optional) FlushInterval represents the time duration to wait between two consecutive flush

flushMode

string

(optional) FlushMode represents the mode of the flushing thread to write chunks. The mode

flushThreadCount

int

(optional) FlushThreadCount reprents the number of threads used by the fluentd buffer

overflowAction

string

(optional) OverflowAction represents the action for the fluentd buffer plugin to

retryMaxInterval

string

(optional) RetryMaxInterval represents the maximum time interval for exponential backoff

retryTimeout

string

(optional) RetryTimeout represents the maximum time interval to attempt retries before giving up

retryType

string

(optional) RetryType represents the type of retrying flush operations. Flush operations can

retryWait

string

(optional) RetryWait represents the time duration between two consecutive retries to flush

totalLimitSize

string

(optional) TotalLimitSize represents the threshold of node space allowed per fluentd

19.1.1.1.45. .spec.forwarder.fluentd.inFile
19.1.1.1.45.1. Description

FluentdInFileSpec represents a subset of fluentd in-tail plugin parameters to tune the configuration for all fluentd in-tail inputs.

For general parameters refer to: https://docs.fluentd.org/input/tail#parameters

19.1.1.1.45.1.1. Type
  • object
PropertyTypeDescription

readLinesLimit

int

(optional) ReadLinesLimit represents the number of lines to read with each I/O operation

19.1.1.1.46. .spec.logStore
19.1.1.1.46.1. Description

The LogStoreSpec contains information about how logs are stored.

19.1.1.1.46.1.1. Type
  • object
PropertyTypeDescription

elasticsearch

object

Specification of the Elasticsearch Log Store component

lokistack

object

LokiStack contains information about which LokiStack to use for log storage if Type is set to LogStoreTypeLokiStack.

retentionPolicy

object

(optional) Retention policy defines the maximum age for an index after which it should be deleted

type

string

The Type of Log Storage to configure. The operator currently supports either using ElasticSearch

19.1.1.1.47. .spec.logStore.elasticsearch
19.1.1.1.47.1. Description
19.1.1.1.47.1.1. Type
  • object
PropertyTypeDescription

nodeCount

int

Number of nodes to deploy for Elasticsearch

nodeSelector

object

Define which Nodes the Pods are scheduled on.

proxy

object

Specification of the Elasticsearch Proxy component

redundancyPolicy

string

(optional)

resources

object

(optional) The resource requirements for Elasticsearch

storage

object

(optional) The storage specification for Elasticsearch data nodes

tolerations

array

 
19.1.1.1.48. .spec.logStore.elasticsearch.nodeSelector
19.1.1.1.48.1. Description
19.1.1.1.48.1.1. Type
  • object
19.1.1.1.49. .spec.logStore.elasticsearch.proxy
19.1.1.1.49.1. Description
19.1.1.1.49.1.1. Type
  • object
PropertyTypeDescription

resources

object

 
19.1.1.1.50. .spec.logStore.elasticsearch.proxy.resources
19.1.1.1.50.1. Description
19.1.1.1.50.1.1. Type
  • object
PropertyTypeDescription

limits

object

(optional) Limits describes the maximum amount of compute resources allowed.

requests

object

(optional) Requests describes the minimum amount of compute resources required.

19.1.1.1.51. .spec.logStore.elasticsearch.proxy.resources.limits
19.1.1.1.51.1. Description
19.1.1.1.51.1.1. Type
  • object
19.1.1.1.52. .spec.logStore.elasticsearch.proxy.resources.requests
19.1.1.1.52.1. Description
19.1.1.1.52.1.1. Type
  • object
19.1.1.1.53. .spec.logStore.elasticsearch.resources
19.1.1.1.53.1. Description
19.1.1.1.53.1.1. Type
  • object
PropertyTypeDescription

limits

object

(optional) Limits describes the maximum amount of compute resources allowed.

requests

object

(optional) Requests describes the minimum amount of compute resources required.

19.1.1.1.54. .spec.logStore.elasticsearch.resources.limits
19.1.1.1.54.1. Description
19.1.1.1.54.1.1. Type
  • object
19.1.1.1.55. .spec.logStore.elasticsearch.resources.requests
19.1.1.1.55.1. Description
19.1.1.1.55.1.1. Type
  • object
19.1.1.1.56. .spec.logStore.elasticsearch.storage
19.1.1.1.56.1. Description
19.1.1.1.56.1.1. Type
  • object
PropertyTypeDescription

size

object

The max storage capacity for the node to provision.

storageClassName

string

(optional) The name of the storage class to use with creating the node's PVC.

19.1.1.1.57. .spec.logStore.elasticsearch.storage.size
19.1.1.1.57.1. Description
19.1.1.1.57.1.1. Type
  • object
PropertyTypeDescription

Format

string

Change Format at will. See the comment for Canonicalize for

d

object

d is the quantity in inf.Dec form if d.Dec != nil

i

int

i is the quantity in int64 scaled form, if d.Dec == nil

s

string

s is the generated value of this quantity to avoid recalculation

19.1.1.1.58. .spec.logStore.elasticsearch.storage.size.d
19.1.1.1.58.1. Description
19.1.1.1.58.1.1. Type
  • object
PropertyTypeDescription

Dec

object

 
19.1.1.1.59. .spec.logStore.elasticsearch.storage.size.d.Dec
19.1.1.1.59.1. Description
19.1.1.1.59.1.1. Type
  • object
PropertyTypeDescription

scale

int

 

unscaled

object

 
19.1.1.1.60. .spec.logStore.elasticsearch.storage.size.d.Dec.unscaled
19.1.1.1.60.1. Description
19.1.1.1.60.1.1. Type
  • object
PropertyTypeDescription

abs

Word

sign

neg

bool

 
19.1.1.1.61. .spec.logStore.elasticsearch.storage.size.d.Dec.unscaled.abs
19.1.1.1.61.1. Description
19.1.1.1.61.1.1. Type
  • Word
19.1.1.1.62. .spec.logStore.elasticsearch.storage.size.i
19.1.1.1.62.1. Description
19.1.1.1.62.1.1. Type
  • int
PropertyTypeDescription

scale

int

 

value

int

 
19.1.1.1.63. .spec.logStore.elasticsearch.tolerations[]
19.1.1.1.63.1. Description
19.1.1.1.63.1.1. Type
  • array
PropertyTypeDescription

effect

string

(optional) Effect indicates the taint effect to match. Empty means match all taint effects.

key

string

(optional) Key is the taint key that the toleration applies to. Empty means match all taint keys.

operator

string

(optional) Operator represents a key's relationship to the value.

tolerationSeconds

int

(optional) TolerationSeconds represents the period of time the toleration (which must be

value

string

(optional) Value is the taint value the toleration matches to.

19.1.1.1.64. .spec.logStore.elasticsearch.tolerations[].tolerationSeconds
19.1.1.1.64.1. Description
19.1.1.1.64.1.1. Type
  • int
19.1.1.1.65. .spec.logStore.lokistack
19.1.1.1.65.1. Description

LokiStackStoreSpec is used to set up cluster-logging to use a LokiStack as logging storage. It points to an existing LokiStack in the same namespace.

19.1.1.1.65.1.1. Type
  • object
PropertyTypeDescription

name

string

Name of the LokiStack resource.

19.1.1.1.66. .spec.logStore.retentionPolicy
19.1.1.1.66.1. Description
19.1.1.1.66.1.1. Type
  • object
PropertyTypeDescription

application

object

 

audit

object

 

infra

object

 
19.1.1.1.67. .spec.logStore.retentionPolicy.application
19.1.1.1.67.1. Description
19.1.1.1.67.1.1. Type
  • object
PropertyTypeDescription

diskThresholdPercent

int

(optional) The threshold percentage of ES disk usage that when reached, old indices should be deleted (e.g. 75)

maxAge

string

(optional)

namespaceSpec

array

(optional) The per namespace specification to delete documents older than a given minimum age

pruneNamespacesInterval

string

(optional) How often to run a new prune-namespaces job

19.1.1.1.68. .spec.logStore.retentionPolicy.application.namespaceSpec[]
19.1.1.1.68.1. Description
19.1.1.1.68.1.1. Type
  • array
PropertyTypeDescription

minAge

string

(optional) Delete the records matching the namespaces which are older than this MinAge (e.g. 1d)

namespace

string

Target Namespace to delete logs older than MinAge (defaults to 7d)

19.1.1.1.69. .spec.logStore.retentionPolicy.audit
19.1.1.1.69.1. Description
19.1.1.1.69.1.1. Type
  • object
PropertyTypeDescription

diskThresholdPercent

int

(optional) The threshold percentage of ES disk usage that when reached, old indices should be deleted (e.g. 75)

maxAge

string

(optional)

namespaceSpec

array

(optional) The per namespace specification to delete documents older than a given minimum age

pruneNamespacesInterval

string

(optional) How often to run a new prune-namespaces job

19.1.1.1.70. .spec.logStore.retentionPolicy.audit.namespaceSpec[]
19.1.1.1.70.1. Description
19.1.1.1.70.1.1. Type
  • array
PropertyTypeDescription

minAge

string

(optional) Delete the records matching the namespaces which are older than this MinAge (e.g. 1d)

namespace

string

Target Namespace to delete logs older than MinAge (defaults to 7d)

19.1.1.1.71. .spec.logStore.retentionPolicy.infra
19.1.1.1.71.1. Description
19.1.1.1.71.1.1. Type
  • object
PropertyTypeDescription

diskThresholdPercent

int

(optional) The threshold percentage of ES disk usage that when reached, old indices should be deleted (e.g. 75)

maxAge

string

(optional)

namespaceSpec

array

(optional) The per namespace specification to delete documents older than a given minimum age

pruneNamespacesInterval

string

(optional) How often to run a new prune-namespaces job

19.1.1.1.72. .spec.logStore.retentionPolicy.infra.namespaceSpec[]
19.1.1.1.72.1. Description
19.1.1.1.72.1.1. Type
  • array
PropertyTypeDescription

minAge

string

(optional) Delete the records matching the namespaces which are older than this MinAge (e.g. 1d)

namespace

string

Target Namespace to delete logs older than MinAge (defaults to 7d)

19.1.1.1.73. .spec.visualization
19.1.1.1.73.1. Description

This is the struct that will contain information pertinent to Log visualization (Kibana)

19.1.1.1.73.1.1. Type
  • object
PropertyTypeDescription

kibana

object

Specification of the Kibana Visualization component

type

string

The type of Visualization to configure

19.1.1.1.74. .spec.visualization.kibana
19.1.1.1.74.1. Description
19.1.1.1.74.1.1. Type
  • object
PropertyTypeDescription

nodeSelector

object

Define which Nodes the Pods are scheduled on.

proxy

object

Specification of the Kibana Proxy component

replicas

int

Number of instances to deploy for a Kibana deployment

resources

object

(optional) The resource requirements for Kibana

tolerations

array

 
19.1.1.1.75. .spec.visualization.kibana.nodeSelector
19.1.1.1.75.1. Description
19.1.1.1.75.1.1. Type
  • object
19.1.1.1.76. .spec.visualization.kibana.proxy
19.1.1.1.76.1. Description
19.1.1.1.76.1.1. Type
  • object
PropertyTypeDescription

resources

object

 
19.1.1.1.77. .spec.visualization.kibana.proxy.resources
19.1.1.1.77.1. Description
19.1.1.1.77.1.1. Type
  • object
PropertyTypeDescription

limits

object

(optional) Limits describes the maximum amount of compute resources allowed.

requests

object

(optional) Requests describes the minimum amount of compute resources required.

19.1.1.1.78. .spec.visualization.kibana.proxy.resources.limits
19.1.1.1.78.1. Description
19.1.1.1.78.1.1. Type
  • object
19.1.1.1.79. .spec.visualization.kibana.proxy.resources.requests
19.1.1.1.79.1. Description
19.1.1.1.79.1.1. Type
  • object
19.1.1.1.80. .spec.visualization.kibana.replicas
19.1.1.1.80.1. Description
19.1.1.1.80.1.1. Type
  • int
19.1.1.1.81. .spec.visualization.kibana.resources
19.1.1.1.81.1. Description
19.1.1.1.81.1.1. Type
  • object
PropertyTypeDescription

limits

object

(optional) Limits describes the maximum amount of compute resources allowed.

requests

object

(optional) Requests describes the minimum amount of compute resources required.

19.1.1.1.82. .spec.visualization.kibana.resources.limits
19.1.1.1.82.1. Description
19.1.1.1.82.1.1. Type
  • object
19.1.1.1.83. .spec.visualization.kibana.resources.requests
19.1.1.1.83.1. Description
19.1.1.1.83.1.1. Type
  • object
19.1.1.1.84. .spec.visualization.kibana.tolerations[]
19.1.1.1.84.1. Description
19.1.1.1.84.1.1. Type
  • array
PropertyTypeDescription

effect

string

(optional) Effect indicates the taint effect to match. Empty means match all taint effects.

key

string

(optional) Key is the taint key that the toleration applies to. Empty means match all taint keys.

operator

string

(optional) Operator represents a key's relationship to the value.

tolerationSeconds

int

(optional) TolerationSeconds represents the period of time the toleration (which must be

value

string

(optional) Value is the taint value the toleration matches to.

19.1.1.1.85. .spec.visualization.kibana.tolerations[].tolerationSeconds
19.1.1.1.85.1. Description
19.1.1.1.85.1.1. Type
  • int
19.1.1.1.86. .status
19.1.1.1.86.1. Description

ClusterLoggingStatus defines the observed state of ClusterLogging

19.1.1.1.86.1.1. Type
  • object
PropertyTypeDescription

collection

object

(optional)

conditions

object

(optional)

curation

object

(optional)

logStore

object

(optional)

visualization

object

(optional)

19.1.1.1.87. .status.collection
19.1.1.1.87.1. Description
19.1.1.1.87.1.1. Type
  • object
PropertyTypeDescription

logs

object

(optional)

19.1.1.1.88. .status.collection.logs
19.1.1.1.88.1. Description
19.1.1.1.88.1.1. Type
  • object
PropertyTypeDescription

fluentdStatus

object

(optional)

19.1.1.1.89. .status.collection.logs.fluentdStatus
19.1.1.1.89.1. Description
19.1.1.1.89.1.1. Type
  • object
PropertyTypeDescription

clusterCondition

object

(optional)

daemonSet

string

(optional)

nodes

object

(optional)

pods

string

(optional)

19.1.1.1.90. .status.collection.logs.fluentdStatus.clusterCondition
19.1.1.1.90.1. Description

operator-sdk generate crds does not allow map-of-slice, must use a named type.

19.1.1.1.90.1.1. Type
  • object
19.1.1.1.91. .status.collection.logs.fluentdStatus.nodes
19.1.1.1.91.1. Description
19.1.1.1.91.1.1. Type
  • object
19.1.1.1.92. .status.conditions
19.1.1.1.92.1. Description
19.1.1.1.92.1.1. Type
  • object
19.1.1.1.93. .status.curation
19.1.1.1.93.1. Description
19.1.1.1.93.1.1. Type
  • object
PropertyTypeDescription

curatorStatus

array

(optional)

19.1.1.1.94. .status.curation.curatorStatus[]
19.1.1.1.94.1. Description
19.1.1.1.94.1.1. Type
  • array
PropertyTypeDescription

clusterCondition

object

(optional)

cronJobs

string

(optional)

schedules

string

(optional)

suspended

bool

(optional)

19.1.1.1.95. .status.curation.curatorStatus[].clusterCondition
19.1.1.1.95.1. Description

operator-sdk generate crds does not allow map-of-slice, must use a named type.

19.1.1.1.95.1.1. Type
  • object
19.1.1.1.96. .status.logStore
19.1.1.1.96.1. Description
19.1.1.1.96.1.1. Type
  • object
PropertyTypeDescription

elasticsearchStatus

array

(optional)

19.1.1.1.97. .status.logStore.elasticsearchStatus[]
19.1.1.1.97.1. Description
19.1.1.1.97.1.1. Type
  • array
PropertyTypeDescription

cluster

object

(optional)

clusterConditions

object

(optional)

clusterHealth

string

(optional)

clusterName

string

(optional)

deployments

array

(optional)

nodeConditions

object

(optional)

nodeCount

int

(optional)

pods

object

(optional)

replicaSets

array

(optional)

shardAllocationEnabled

string

(optional)

statefulSets

array

(optional)

19.1.1.1.98. .status.logStore.elasticsearchStatus[].cluster
19.1.1.1.98.1. Description
19.1.1.1.98.1.1. Type
  • object
PropertyTypeDescription

activePrimaryShards

int

The number of Active Primary Shards for the Elasticsearch Cluster

activeShards

int

The number of Active Shards for the Elasticsearch Cluster

initializingShards

int

The number of Initializing Shards for the Elasticsearch Cluster

numDataNodes

int

The number of Data Nodes for the Elasticsearch Cluster

numNodes

int

The number of Nodes for the Elasticsearch Cluster

pendingTasks

int

 

relocatingShards

int

The number of Relocating Shards for the Elasticsearch Cluster

status

string

The current Status of the Elasticsearch Cluster

unassignedShards

int

The number of Unassigned Shards for the Elasticsearch Cluster

19.1.1.1.99. .status.logStore.elasticsearchStatus[].clusterConditions
19.1.1.1.99.1. Description
19.1.1.1.99.1.1. Type
  • object
19.1.1.1.100. .status.logStore.elasticsearchStatus[].deployments[]
19.1.1.1.100.1. Description
19.1.1.1.100.1.1. Type
  • array
19.1.1.1.101. .status.logStore.elasticsearchStatus[].nodeConditions
19.1.1.1.101.1. Description
19.1.1.1.101.1.1. Type
  • object
19.1.1.1.102. .status.logStore.elasticsearchStatus[].pods
19.1.1.1.102.1. Description
19.1.1.1.102.1.1. Type
  • object
19.1.1.1.103. .status.logStore.elasticsearchStatus[].replicaSets[]
19.1.1.1.103.1. Description
19.1.1.1.103.1.1. Type
  • array
19.1.1.1.104. .status.logStore.elasticsearchStatus[].statefulSets[]
19.1.1.1.104.1. Description
19.1.1.1.104.1.1. Type
  • array
19.1.1.1.105. .status.visualization
19.1.1.1.105.1. Description
19.1.1.1.105.1.1. Type
  • object
PropertyTypeDescription

kibanaStatus

array

(optional)

19.1.1.1.106. .status.visualization.kibanaStatus[]
19.1.1.1.106.1. Description
19.1.1.1.106.1.1. Type
  • array
PropertyTypeDescription

clusterCondition

object

(optional)

deployment

string

(optional)

pods

string

(optional) The status for each of the Kibana pods for the Visualization component

replicaSets

array

(optional)

replicas

int

(optional)

19.1.1.1.107. .status.visualization.kibanaStatus[].clusterCondition
19.1.1.1.107.1. Description
19.1.1.1.107.1.1. Type
  • object
19.1.1.1.108. .status.visualization.kibanaStatus[].replicaSets[]
19.1.1.1.108.1. Description
19.1.1.1.108.1.1. Type
  • array

Chapter 20. Glossary

This glossary defines common terms that are used in the logging documentation.

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

Legal Notice

Copyright © 2024 Red Hat, Inc.

OpenShift documentation is licensed under the Apache License 2.0 (https://www.apache.org/licenses/LICENSE-2.0).

Modified versions must remove all Red Hat trademarks.

Portions adapted from https://github.com/kubernetes-incubator/service-catalog/ with modifications by Red Hat.

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