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Chapter 2. Logging 6.1

2.1. Logging 6.1

2.1.1. Logging 6.1.0 Release Notes

This release includes {OCP-short}logging Release 6.1.0.

2.1.1.1. New Features and Enhancements

2.1.1.1.1. Log Collection

This enhancement adds the source iostream to the attributes sent from collected container logs. The value is set to either stdout or stderr based on how the collector received it. (LOG-5292)
With this update, the default memory limit for the collector increases from 1024 Mi to 2048 Mi. Users should adjust resource limits based on their cluster’s specific needs and specifications. (LOG-6072)
With this update, users can now set the syslog output delivery mode of the ClusterLogForwarder CR to either AtLeastOnce or AtMostOnce. (LOG-6355)

2.1.1.1.2. Log Storage

With this update, the new 1x.pico LokiStack size supports clusters with fewer workloads and lower log volumes (up to 50GB/day). (LOG-5939)

2.1.1.2. Technology Preview

Important

The OpenTelemetry Protocol (OTLP) output log forwarder is a Technology Preview feature only. Technology Preview features are not supported with Red Hat production service level agreements (SLAs) and might not be functionally complete. Red Hat does not recommend using them in production. These features provide early access to upcoming product features, enabling customers to test functionality and provide feedback during the development process.

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

With this update, OpenTelemetry logs can now be forwarded using the OTel (OpenTelemetry) data model to a Red Hat Managed LokiStack instance. To enable this feature, add the observability.openshift.io/tech-preview-otlp-output: "enabled" annotation to your ClusterLogForwarder configuration. For additional configuration information, see OTLP Forwarding.
With this update, a dataModel field has been added to the lokiStack output specification. Set the dataModel to Otel to configure log forwarding using the OpenTelemetry data format. The default is set to Viaq. For information about data mapping see OTLP Specification.

2.1.1.3. Bug Fixes

None.

2.1.1.4. CVEs

2.2. Logging 6.1

context: logging-6x-6.1

The ClusterLogForwarder custom resource (CR) is the central configuration point for log collection and forwarding.

2.2.1. Inputs and outputs

Inputs specify the sources of logs to be forwarded. Logging provides built-in input types: application, receiver, infrastructure, and audit, which select logs from different parts of your cluster. You can also define custom inputs based on namespaces or pod labels to fine-tune log selection.

Outputs define the destinations where logs are sent. Each output type has its own set of configuration options, allowing you to customize the behavior and authentication settings.

2.2.2. Receiver input type

The receiver input type enables the Logging system to accept logs from external sources. It supports two formats for receiving logs: http and syslog.

The ReceiverSpec defines the configuration for a receiver input.

2.2.3. Pipelines and filters

Pipelines determine the flow of logs from inputs to outputs. A pipeline consists of one or more input refs, output refs, and optional filter refs. Filters can be used to transform or drop log messages within a pipeline. The order of filters matters, as they are applied sequentially, and earlier filters can prevent log messages from reaching later stages.

2.2.4. Operator behavior

The Cluster Logging Operator manages the deployment and configuration of the collector based on the managementState field of the ClusterLogForwarder resource:

When set to Managed (default), the operator actively manages the logging resources to match the configuration defined in the spec.
When set to Unmanaged, the operator does not take any action, allowing you to manually manage the logging components.

2.2.5. Validation

Logging includes extensive validation rules and default values to ensure a smooth and error-free configuration experience. The ClusterLogForwarder resource enforces validation checks on required fields, dependencies between fields, and the format of input values. Default values are provided for certain fields, reducing the need for explicit configuration in common scenarios.

2.2.6. Quick start

OpenShift Logging supports two data models:

ViaQ (General Availability)
OpenTelemetry (Technology Preview)

You can select either of these data models based on your requirement by configuring the lokiStack.dataModel field in the ClusterLogForwarder. ViaQ is the default data model when forwarding logs to LokiStack.

Note

In future releases of OpenShift Logging, the default data model will change from ViaQ to OpenTelemetry.

2.2.6.1. Quick start with ViaQ

To use the default ViaQ data model, follow these steps:

Prerequisites

Cluster administrator permissions

Procedure

Install the Red Hat OpenShift Logging Operator, Loki Operator, and Cluster Observability Operator (COO) from OperatorHub.

Create a LokiStack custom resource (CR) in the openshift-logging namespace:

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
  managementState: Managed
  size: 1x.extra-small
  storage:
    schemas:
    - effectiveDate: '2024-10-01'
      version: v13
    secret:
      name: logging-loki-s3
      type: s3
  storageClassName: gp3-csi
  tenants:
    mode: openshift-logging

Note

Ensure that the logging-loki-s3 secret is created beforehand. The contents of this secret vary depending on the object storage in use. For more information, see Secrets and TLS Configuration.

Create a service account for the collector:

$ oc create sa collector -n openshift-logging

Allow the collector’s service account to write data to the LokiStack CR:
```
$ oc adm policy add-cluster-role-to-user logging-collector-logs-writer -z collector
```
Note
The ClusterRole resource is created automatically during the Cluster Logging Operator installation and does not need to be created manually.
Allow the collector’s service account to collect logs:
```
$ oc project openshift-logging
```
```
$ oc adm policy add-cluster-role-to-user collect-application-logs -z collector
```
```
$ oc adm policy add-cluster-role-to-user collect-audit-logs -z collector
```
```
$ oc adm policy add-cluster-role-to-user collect-infrastructure-logs -z collector
```
Note
The example binds the collector to all three roles (application, infrastructure, and audit), but by default, only application and infrastructure logs are collected. To collect audit logs, update your ClusterLogForwarder configuration to include them. Assign roles based on the specific log types required for your environment.

Create a UIPlugin CR to enable the Log section in the Observe tab:

apiVersion: observability.openshift.io/v1alpha1
kind: UIPlugin
metadata:
  name: logging
spec:
  type: Logging
  logging:
    lokiStack:
      name: logging-loki

Create a ClusterLogForwarder CR to configure log forwarding:

apiVersion: observability.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: collector
  namespace: openshift-logging
spec:
  serviceAccount:
    name: collector
  outputs:
  - name: default-lokistack
    type: lokiStack
    lokiStack:
      authentication:
        token:
          from: serviceAccount
      target:
        name: logging-loki
        namespace: openshift-logging
    tls:
      ca:
        key: service-ca.crt
        configMapName: openshift-service-ca.crt
  pipelines:
  - name: default-logstore
    inputRefs:
    - application
    - infrastructure
    outputRefs:
    - default-lokistack

Note

The dataModel field is optional and left unset (dataModel: "") by default. This allows the Cluster Logging Operator (CLO) to automatically select a data model. Currently, the CLO defaults to the ViaQ model when the field is unset, but this will change in future releases. Specifying dataModel: ViaQ ensures the configuration remains compatible if the default changes.

Verification

Verify that logs are visible in the Log section of the Observe tab in the OpenShift web console.

2.2.6.2. Quick start with OpenTelemetry

Important

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

To configure OTLP ingestion and enable the OpenTelemetry data model, follow these steps:

Prerequisites

Cluster administrator permissions

Procedure

Install the Red Hat OpenShift Logging Operator, Loki Operator, and Cluster Observability Operator (COO) from OperatorHub.

Create a LokiStack custom resource (CR) in the openshift-logging namespace:

apiVersion: loki.grafana.com/v1
kind: LokiStack
metadata:
  name: logging-loki
  namespace: openshift-logging
spec:
  managementState: Managed
  size: 1x.extra-small
  storage:
    schemas:
    - effectiveDate: '2024-10-01'
      version: v13
    secret:
      name: logging-loki-s3
      type: s3
  storageClassName: gp3-csi
  tenants:
    mode: openshift-logging

Note

Ensure that the logging-loki-s3 secret is created beforehand. The contents of this secret vary depending on the object storage in use. For more information, see "Secrets and TLS Configuration".

Create a service account for the collector:

$ oc create sa collector -n openshift-logging

Allow the collector’s service account to write data to the LokiStack CR:
```
$ oc adm policy add-cluster-role-to-user logging-collector-logs-writer -z collector
```
Note
The ClusterRole resource is created automatically during the Cluster Logging Operator installation and does not need to be created manually.
Allow the collector’s service account to collect logs:
```
$ oc project openshift-logging
```
```
$ oc adm policy add-cluster-role-to-user collect-application-logs -z collector
```
```
$ oc adm policy add-cluster-role-to-user collect-audit-logs -z collector
```
```
$ oc adm policy add-cluster-role-to-user collect-infrastructure-logs -z collector
```
Note
The example binds the collector to all three roles (application, infrastructure, and audit). By default, only application and infrastructure logs are collected. To collect audit logs, update your ClusterLogForwarder configuration to include them. Assign roles based on the specific log types required for your environment.

Create a UIPlugin CR to enable the Log section in the Observe tab:

apiVersion: observability.openshift.io/v1alpha1
kind: UIPlugin
metadata:
  name: logging
spec:
  type: Logging
  logging:
    lokiStack:
      name: logging-loki

Create a ClusterLogForwarder CR to configure log forwarding:

apiVersion: observability.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: collector
  namespace: openshift-logging
  annotations:
    observability.openshift.io/tech-preview-otlp-output: "enabled" 1
spec:
  serviceAccount:
    name: collector
  outputs:
  - name: loki-otlp
    type: lokiStack 2
    lokiStack:
      target:
        name: logging-loki
        namespace: openshift-logging
      dataModel: Otel 3
      authentication:
        token:
          from: serviceAccount
    tls:
      ca:
        key: service-ca.crt
        configMapName: openshift-service-ca.crt
  pipelines:
  - name: my-pipeline
    inputRefs:
    - application
    - infrastructure
    outputRefs:
    - loki-otlp

1: Use the annotation to enable the Otel data model, which is a Technology Preview feature.
2: Define the output type as lokiStack.
3: Specifies the OpenTelemetry data model.

Note

You cannot use lokiStack.labelKeys when dataModel is Otel. To achieve similar functionality when dataModel is Otel, refer to "Configuring LokiStack for OTLP data ingestion".

Verification

Verify that OTLP is functioning correctly by going to Observe OpenShift Logging LokiStack Writes in the OpenShift web console, and checking Distributor - Structured Metadata.

2.3. Configuring log forwarding

The ClusterLogForwarder (CLF) allows users to configure forwarding of logs to various destinations. It provides a flexible way to select log messages from different sources, send them through a pipeline that can transform or filter them, and forward them to one or more outputs.

Key Functions of the ClusterLogForwarder

Selects log messages using inputs
Forwards logs to external destinations using outputs
Filters, transforms, and drops log messages using filters
Defines log forwarding pipelines connecting inputs, filters and outputs

2.3.1. Setting up log collection

This release of Cluster Logging requires administrators to explicitly grant log collection permissions to the service account associated with ClusterLogForwarder. This was not required in previous releases for the legacy logging scenario consisting of a ClusterLogging and, optionally, a ClusterLogForwarder.logging.openshift.io resource.

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.

Setup log collection by binding the required cluster roles to your service account.

2.3.1.1. Legacy service accounts

To use the existing legacy service account logcollector, create the following ClusterRoleBinding:

$ oc adm policy add-cluster-role-to-user collect-application-logs system:serviceaccount:openshift-logging:logcollector
$ oc adm policy add-cluster-role-to-user collect-infrastructure-logs system:serviceaccount:openshift-logging:logcollector

Additionally, create the following ClusterRoleBinding if collecting audit logs:

$ oc adm policy add-cluster-role-to-user collect-audit-logs system:serviceaccount:openshift-logging:logcollector

2.3.1.2. Creating service accounts

Prerequisites

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

Procedure

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.

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>

2.3.1.2.1. Cluster Role Binding for your Service Account

The role_binding.yaml file binds the ClusterLogging operator’s ClusterRole to a specific ServiceAccount, allowing it to manage Kubernetes resources cluster-wide.

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata:
  name: manager-rolebinding
roleRef:                                           1
  apiGroup: rbac.authorization.k8s.io              2
  kind: ClusterRole                                3
  name: cluster-logging-operator                   4
subjects:                                          5
  - kind: ServiceAccount                           6
    name: cluster-logging-operator                 7
    namespace: openshift-logging                   8

1: roleRef: References the ClusterRole to which the binding applies.
2: apiGroup: Indicates the RBAC API group, specifying that the ClusterRole is part of Kubernetes' RBAC system.
3: kind: Specifies that the referenced role is a ClusterRole, which applies cluster-wide.
4: name: The name of the ClusterRole being bound to the ServiceAccount, here cluster-logging-operator.
5: subjects: Defines the entities (users or service accounts) that are being granted the permissions from the ClusterRole.
6: kind: Specifies that the subject is a ServiceAccount.
7: Name: The name of the ServiceAccount being granted the permissions.
8: namespace: Indicates the namespace where the ServiceAccount is located.

2.3.1.2.2. Writing application logs

The write-application-logs-clusterrole.yaml file defines a ClusterRole that grants permissions to write application logs to the Loki logging application.

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: cluster-logging-write-application-logs
rules:                                              1
  - apiGroups:                                      2
      - loki.grafana.com                            3
    resources:                                      4
      - application                                 5
    resourceNames:                                  6
      - logs                                        7
    verbs:                                          8
      - create                                      9
Annotations
<1> rules: Specifies the permissions granted by this ClusterRole.
<2> apiGroups: Refers to the API group loki.grafana.com, which relates to the Loki logging system.
<3> loki.grafana.com: The API group for managing Loki-related resources.
<4> resources: The resource type that the ClusterRole grants permission to interact with.
<5> application: Refers to the application resources within the Loki logging system.
<6> resourceNames: Specifies the names of resources that this role can manage.
<7> logs: Refers to the log resources that can be created.
<8> verbs: The actions allowed on the resources.
<9> create: Grants permission to create new logs in the Loki system.

2.3.1.2.3. Writing audit logs

The write-audit-logs-clusterrole.yaml file defines a ClusterRole that grants permissions to create audit logs in the Loki logging system.

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: cluster-logging-write-audit-logs
rules:                                              1
  - apiGroups:                                      2
      - loki.grafana.com                            3
    resources:                                      4
      - audit                                       5
    resourceNames:                                  6
      - logs                                        7
    verbs:                                          8
      - create                                      9

1 1: rules: Defines the permissions granted by this ClusterRole.
2 2: apiGroups: Specifies the API group loki.grafana.com.
3 3: loki.grafana.com: The API group responsible for Loki logging resources.
4 4: resources: Refers to the resource type this role manages, in this case, audit.
5 5: audit: Specifies that the role manages audit logs within Loki.
6 6: resourceNames: Defines the specific resources that the role can access.
7 7: logs: Refers to the logs that can be managed under this role.
8 8: verbs: The actions allowed on the resources.
9 9: create: Grants permission to create new audit logs.

2.3.1.2.4. Writing infrastructure logs

The write-infrastructure-logs-clusterrole.yaml file defines a ClusterRole that grants permission to create infrastructure logs in the Loki logging system.

Sample YAML

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: cluster-logging-write-infrastructure-logs
rules:                                              1
  - apiGroups:                                      2
      - loki.grafana.com                            3
    resources:                                      4
      - infrastructure                              5
    resourceNames:                                  6
      - logs                                        7
    verbs:                                          8
      - create                                      9

1: rules: Specifies the permissions this ClusterRole grants.
2: apiGroups: Specifies the API group for Loki-related resources.
3: loki.grafana.com: The API group managing the Loki logging system.
4: resources: Defines the resource type that this role can interact with.
5: infrastructure: Refers to infrastructure-related resources that this role manages.
6: resourceNames: Specifies the names of resources this role can manage.
7: logs: Refers to the log resources related to infrastructure.
8: verbs: The actions permitted by this role.
9: create: Grants permission to create infrastructure logs in the Loki system.

2.3.1.2.5. ClusterLogForwarder editor role

The clusterlogforwarder-editor-role.yaml file defines a ClusterRole that allows users to manage ClusterLogForwarders in OpenShift.

apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata:
  name: clusterlogforwarder-editor-role
rules:                                              1
  - apiGroups:                                      2
      - observability.openshift.io                  3
    resources:                                      4
      - clusterlogforwarders                        5
    verbs:                                          6
      - create                                      7
      - delete                                      8
      - get                                         9
      - list                                        10
      - patch                                       11
      - update                                      12
      - watch                                       13

1: rules: Specifies the permissions this ClusterRole grants.
2: apiGroups: Refers to the OpenShift-specific API group
3: obervability.openshift.io: The API group for managing observability resources, like logging.
4: resources: Specifies the resources this role can manage.
5: clusterlogforwarders: Refers to the log forwarding resources in OpenShift.
6: verbs: Specifies the actions allowed on the ClusterLogForwarders.
7: create: Grants permission to create new ClusterLogForwarders.
8: delete: Grants permission to delete existing ClusterLogForwarders.
9: get: Grants permission to retrieve information about specific ClusterLogForwarders.
10: list: Allows listing all ClusterLogForwarders.
11: patch: Grants permission to partially modify ClusterLogForwarders.
12: update: Grants permission to update existing ClusterLogForwarders.
13: watch: Grants permission to monitor changes to ClusterLogForwarders.

2.3.2. Modifying log level in collector

To modify the log level in the collector, you can set the observability.openshift.io/log-level annotation to trace, debug, info, warn, error, and off.

Example log level annotation

apiVersion: observability.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: collector
  annotations:
    observability.openshift.io/log-level: debug
# ...

2.3.3. Managing the Operator

The ClusterLogForwarder resource has a managementState field that controls whether the operator actively manages its resources or leaves them Unmanaged:

Managed: (default) The operator will drive the logging resources to match the desired state in the CLF spec.
Unmanaged: The operator will not take any action related to the logging components.

This allows administrators to temporarily pause log forwarding by setting managementState to Unmanaged.

2.3.4. Structure of the ClusterLogForwarder

The CLF has a spec section that contains the following key components:

Inputs: Select log messages to be forwarded. Built-in input types application, infrastructure and audit forward logs from different parts of the cluster. You can also define custom inputs.
Outputs: Define destinations to forward logs to. Each output has a unique name and type-specific configuration.
Pipelines: Define the path logs take from inputs, through filters, to outputs. Pipelines have a unique name and consist of a list of input, output and filter names.
Filters: Transform or drop log messages in the pipeline. Users can define filters that match certain log fields and drop or modify the messages. Filters are applied in the order specified in the pipeline.

2.3.4.1. Inputs

Inputs are configured in an array under spec.inputs. There are three built-in input types:

application: Selects logs from all application containers, excluding those in infrastructure namespaces such as default, openshift, or any namespace with the kube- or openshift- prefix.
infrastructure: Selects logs from infrastructure components running in default and openshift namespaces and node logs.
audit: Selects logs from the OpenShift API server audit logs, Kubernetes API server audit logs, ovn audit logs, and node audit logs from auditd.

Users can define custom inputs of type application that select logs from specific namespaces or using pod labels.

2.3.4.2. Outputs

Outputs are configured in an array under spec.outputs. Each output must have a unique name and a type. Supported types are:

azureMonitor: Forwards logs to Azure Monitor.
cloudwatch: Forwards logs to AWS CloudWatch.
elasticsearch: Forwards logs to an external Elasticsearch instance.
googleCloudLogging: Forwards logs to Google Cloud Logging.
http: Forwards logs to a generic HTTP endpoint.
kafka: Forwards logs to a Kafka broker.
loki: Forwards logs to a Loki logging backend.
lokistack: Forwards logs to the logging supported combination of Loki and web proxy with OpenShift Container Platform authentication integration. LokiStack’s proxy uses OpenShift Container Platform authentication to enforce multi-tenancy
otlp: Forwards logs using the OpenTelemetry Protocol.
splunk: Forwards logs to Splunk.
syslog: Forwards logs to an external syslog server.

Each output type has its own configuration fields.

2.3.5. Configuring OTLP output

Cluster administrators can use the OpenTelemetry Protocol (OTLP) output to collect and forward logs to OTLP receivers. The OTLP output uses the specification defined by the OpenTelemetry Observability framework to send data over HTTP with JSON encoding.

Important

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

Procedure

Create or edit a ClusterLogForwarder custom resource (CR) to enable forwarding using OTLP by adding the following annotation:

Example ClusterLogForwarder CR

apiVersion: observability.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  annotations:
    observability.openshift.io/tech-preview-otlp-output: "enabled" 1
  name: clf-otlp
spec:
  serviceAccount:
    name: <service_account_name>
  outputs:
  - name: otlp
    type: otlp
    otlp:
      tuning:
        compression: gzip
        deliveryMode: AtLeastOnce
        maxRetryDuration: 20
        maxWrite: 10M
        minRetryDuration: 5
      url: <otlp_url> 2
  pipelines:
  - inputRefs:
    - application
    - infrastructure
    - audit
    name: otlp-logs
    outputRefs:
    - otlp

1: Use this annotation to enable the OpenTelemetry Protocol (OTLP) output, which is a Technology Preview feature.
2: This URL must be absolute and is a placeholder for the OTLP endpoint where logs are sent.

Note

The OTLP output uses the OpenTelemetry data model, which is different from the ViaQ data model that is used by other output types. It adheres to the OTLP using OpenTelemetry Semantic Conventions defined by the OpenTelemetry Observability framework.

2.3.5.1. Pipelines

Pipelines are configured in an array under spec.pipelines. Each pipeline must have a unique name and consists of:

inputRefs: Names of inputs whose logs should be forwarded to this pipeline.
outputRefs: Names of outputs to send logs to.
filterRefs: (optional) Names of filters to apply.

The order of filterRefs matters, as they are applied sequentially. Earlier filters can drop messages that will not be processed by later filters.

2.3.5.2. Filters

Filters are configured in an array under spec.filters. They can match incoming log messages based on the value of structured fields and modify or drop them.

Administrators can configure the following types of filters:

2.3.5.3. 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 under the .spec.filters.

Example ClusterLogForwarder CR

apiVersion: "observability.openshift.io/v1"
kind: ClusterLogForwarder
metadata:
  name: <log_forwarder_name>
  namespace: <log_forwarder_namespace>
spec:
  serviceAccount:
    name: <service_account_name>
  filters:
  - name: <name>
    type: detectMultilineException
  pipelines:
    - inputRefs:
        - <input-name>
      name: <pipeline-name>
      filterRefs:
        - <filter-name>
      outputRefs:
        - <output-name>

2.3.5.3.1. 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.

The collector supports the following languages:

Java
JS
Ruby
Python
Golang
PHP
Dart

2.3.5.4. 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.

Procedure

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: observability.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
  serviceAccount:
    name: <service_account_name>
  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.
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: observability.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
  serviceAccount:
    name: <service_account_name>
  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: observability.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
  serviceAccount:
    name: <service_account_name>
  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"
# ...

2.3.5.5. 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, and checking stops at the first match. The amount of data that 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.

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, the 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, and 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, which lists HTTP status codes for which no events are created. The default value is [404, 409, 422, 429]. If the value is an empty list, [], then no status codes are omitted.

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

Note

You must have a cluster role collect-audit-logs to collect the audit logs. The following 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: observability.openshift.io/v1
kind: ClusterLogForwarder
metadata:
  name: <log_forwarder_name>
  namespace: <log_forwarder_namespace>
spec:
  serviceAccount:
    name: <service_account_name>
  pipelines:
    - name: my-pipeline
      inputRefs: audit 1
      filterRefs: my-policy 2
  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.

2.3.5.6. Filtering application logs at input by including the label expressions or a 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.

Procedure

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: observability.openshift.io/v1
kind: ClusterLogForwarder
# ...
spec:
  serviceAccount:
    name: <service_account_name>
  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
      type: application
# ...

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.

Apply the ClusterLogForwarder CR by running the following command:
```
$ oc apply -f <filename>.yaml
```

2.3.5.7. 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.

Procedure

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: observability.openshift.io/v1
kind: ClusterLogForwarder
metadata:
# ...
spec:
  serviceAccount:
    name: <service_account_name>
  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.
Note
The filters exempts the log_type, .log_source, and .message fields.
Apply the ClusterLogForwarder CR by running the following command:
```
$ oc apply -f <filename>.yaml
```

2.3.6. 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.

Procedure

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 audit and infrastructure sources:
Example ClusterLogForwarder CR
```
apiVersion: observability.openshift.io/v1
kind: ClusterLogForwarder
# ...
spec:
  serviceAccount:
    name: <service_account_name>
  inputs:
    - name: mylogs1
      type: infrastructure
      infrastructure:
        sources: 1
          - node
    - name: mylogs2
      type: audit
      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
Apply the ClusterLogForwarder CR by running the following command:
```
$ oc apply -f <filename>.yaml
```

2.3.7. 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.

Procedure

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: observability.openshift.io/v1
kind: ClusterLogForwarder
# ...
spec:
  serviceAccount:
    name: <service_account_name>
  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.
Note
The excludes field takes precedence over the includes field.
Apply the ClusterLogForwarder CR by running the following command:
```
$ oc apply -f <filename>.yaml
```

2.4. Storing logs with LokiStack

You can configure a LokiStack CR to store application, audit, and infrastructure-related logs.

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.

2.4.1. 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.

The 1x.pico configuration defines a single Loki deployment with minimal resource and limit requirements, offering high availability (HA) support for all Loki components. This configuration is suited for deployments that do not require a single replication factor or auto-compaction.

Disk requests are similar across size configurations, allowing customers to test different sizes to determine the best fit for their deployment needs.

Important

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

Table 2.1. Loki sizing
	1x.demo	1x.pico [6.1+ only]	1x.extra-small	1x.small	1x.medium
Data transfer	Demo use only	50GB/day	100GB/day	500GB/day	2TB/day
Queries per second (QPS)	Demo use only	1-25 QPS at 200ms	1-25 QPS at 200ms	25-50 QPS at 200ms	25-75 QPS at 200ms
Replication factor	None	2	2	2	2
Total CPU requests	None	7 vCPUs	14 vCPUs	34 vCPUs	54 vCPUs
Total CPU requests if using the ruler	None	8 vCPUs	16 vCPUs	42 vCPUs	70 vCPUs
Total memory requests	None	17Gi	31Gi	67Gi	139Gi
Total memory requests if using the ruler	None	18Gi	35Gi	83Gi	171Gi
Total disk requests	40Gi	590Gi	430Gi	430Gi	590Gi
Total disk requests if using the ruler	80Gi	910Gi	750Gi	750Gi	910Gi

2.4.2. Prerequisites

You have installed the Loki Operator by using the CLI or web console.
You have a serviceAccount in the same namespace in which you create the ClusterLogForwarder.
The serviceAccount is assigned collect-audit-logs, collect-application-logs, and collect-infrastructure-logs cluster roles.

2.4.3. Core Setup and Configuration

Role-based access controls, basic monitoring, and pod placement to deploy Loki.

2.4.4. 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.

The following cluster roles for alerting and recording rules are available for LokiStack:

Rule name	Description
`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.

2.4.4.1. 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>

2.4.5. 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 the above applies, an alerting rule is considered valid.

Table 2.2. AlertingRule definitions
Tenant type	Valid namespaces for `AlertingRule` CRs
application	`<your_application_namespace>`
audit	`openshift-logging`
infrastructure	`openshift-/`, `kube-/\`, `default`

Procedure

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.

Apply the AlertingRule CR:
```
$ oc apply -f <filename>.yaml
```

2.4.6. Configuring Loki to tolerate memberlist creation failure

In an OpenShift Container Platform 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 custom resource (CR) to use the podIP address 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
# ...

2.4.7. Enabling stream-based retention with Loki

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

Schema v13 is recommended.

Procedure

Create a LokiStack CR:

Enable stream-based retention globally as shown in the following example:

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: v13
    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:

Enable stream-based retention per-tenant basis as shown in the following example:

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: v13
    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.

Apply the LokiStack CR:
```
$ oc apply -f <filename>.yaml
```

2.4.8. 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.

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.
...

2.4.8.1. Enhanced Reliability and Performance

Configurations to ensure Loki’s reliability and efficiency in production.

2.4.8.2. Enabling authentication to cloud-based log stores using short-lived tokens

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

Procedure

Use one of the following options to enable authentication:

If you use the OpenShift Container Platform 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.

Example Azure sample subscription

apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: loki-operator
  namespace: openshift-operators-redhat
spec:
  channel: "stable-6.0"
  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>

Example AWS sample subscription

apiVersion: operators.coreos.com/v1alpha1
kind: Subscription
metadata:
  name: loki-operator
  namespace: openshift-operators-redhat
spec:
  channel: "stable-6.0"
  installPlanApproval: Manual
  name: loki-operator
  source: redhat-operators
  sourceNamespace: openshift-marketplace
  config:
    env:
    - name: ROLEARN
      value: <role_ARN>

2.4.8.3. Configuring Loki to tolerate node failure

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 OpenShift Container Platform, 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.

2.4.8.4. LokiStack behavior during cluster restarts

When an OpenShift Container Platform 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 OpenShift Container Platform 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.

2.4.8.5. Advanced Deployment and Scalability

Specialized configurations for high availability, scalability, and error handling.

2.4.8.6. Zone aware data replication

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.

2.4.8.7. Recovering Loki pods from failed zones

In OpenShift Container Platform 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 OpenShift Container Platform 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

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

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.

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

Delete the PVC(s) for a pod by running the following command:
```
$ oc delete pvc <pvc_name>  -n openshift-logging
```
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.

2.4.8.7.1. 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.

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

2.4.8.8. 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.

2.5. OTLP data ingestion in Loki

Logging 6.1 enables an API endpoint using the OpenTelemetry Protocol (OTLP). As OTLP is a standardized format not specifically designed for Loki, it requires additional configuration on Loki’s side to map OpenTelemetry’s data format to Loki’s data model. OTLP lacks concepts such as stream labels or structured metadata. Instead, OTLP provides metadata about log entries as attributes, grouped into three categories:

Resource
Scope
Log

This allows metadata to be set for multiple entries simultaneously or individually as needed.

2.5.1. Configuring LokiStack for OTLP data ingestion

Important

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

To configure a LokiStack custom resource (CR) for OTLP ingestion, follow these steps:

Prerequisites

Ensure that your Loki setup supports structured metadata, introduced in schema version 13 to enable OTLP log ingestion.

Procedure

Set the schema version:
- When creating a new LokiStack CR, set version: v13 in the storage schema configuration.
  Note
  For existing configurations, add a new schema entry with version: v13 and an effectiveDate in the future. For more information on updating schema versions, see Upgrading Schemas (Grafana documentation).
Configure the storage schema as follows:
Example configure storage schema
```
# ...
spec:
  storage:
    schemas:
    - version: v13
      effectiveDate: 2024-10-25
```
Once the effectiveDate has passed, the v13 schema takes effect, enabling your LokiStack to store structured metadata.

2.5.2. Attribute mapping

When the Loki Operator is set to openshift-logging mode, it automatically applies a default set of attribute mappings. These mappings align specific OTLP attributes with Loki’s stream labels and structured metadata.

For typical setups, these default mappings should be sufficient. However, you might need to customize attribute mapping in the following cases:

Using a custom Collector: If your setup includes a custom collector that generates additional attributes, consider customizing the mapping to ensure these attributes are retained in Loki.
Adjusting attribute detail levels: If the default attribute set is more detailed than necessary, you can reduce it to essential attributes only. This can avoid excessive data storage and streamline the logging process.

Important

Attributes that are not mapped to either stream labels or structured metadata are not stored in Loki.

2.5.2.1. Custom attribute mapping for OpenShift

When using the Loki Operator in openshift-logging mode, attribute mapping follow OpenShift defaults, but custom mappings can be configured to adjust these. Custom mappings allow further configurations to meet specific needs.

In openshift-logging mode, custom attribute mappings can be configured globally for all tenants or for individual tenants as needed. When custom mappings are defined, they are appended to the OpenShift defaults. If default recommended labels are not required, they can be disabled in the tenant configuration.

Note

A major difference between the Loki Operator and Loki itself lies in inheritance handling. Loki only copies default_resource_attributes_as_index_labels to tenants by default, while the Loki Operator applies the entire global configuration to each tenant in openshift-logging mode.

Within LokiStack, attribute mapping configuration is managed through the limits setting:

# ...
spec:
  limits:
    global:
      otlp: {} 1
    tenants:
      application:
        otlp: {} 2

1: Global OTLP attribute configuration.
2: OTLP attribute configuration for the application tenant within openshift-logging mode.

Note

Both global and per-tenant OTLP configurations can map attributes to stream labels or structured metadata. At least one stream label is required to save a log entry to Loki storage, so ensure this configuration meets that requirement.

Stream labels derive only from resource-level attributes, which the LokiStack resource structure reflects:

spec:
  limits:
    global:
      otlp:
        streamLabels:
          resourceAttributes:
          - name: "k8s.namespace.name"
          - name: "k8s.pod.name"
          - name: "k8s.container.name"

Structured metadata, in contrast, can be generated from resource, scope or log-level attributes:

# ...
spec:
  limits:
    global:
      otlp:
        streamLabels:
          # ...
        structuredMetadata:
          resourceAttributes:
          - name: "process.command_line"
          - name: "k8s\\.pod\\.labels\\..+"
            regex: true
          scopeAttributes:
          - name: "service.name"
          logAttributes:
          - name: "http.route"

Tip

Use regular expressions by setting regex: true for attributes names when mapping similar attributes in Loki.

Important

Avoid using regular expressions for stream labels, as this can increase data volume.

2.5.2.2. Customizing OpenShift defaults

In openshift-logging mode, certain attributes are required and cannot be removed from the configuration due to their role in OpenShift functions. Other attributes, labeled recommended, might be disabled if performance is impacted.

When using the openshift-logging mode without custom attributes, you can achieve immediate compatibility with OpenShift tools. If additional attributes are needed as stream labels or structured metadata, use custom configuration. Custom configurations can merge with default configurations.

2.5.2.3. Removing recommended attributes

To reduce default attributes in openshift-logging mode, disable recommended attributes:

# ...
spec:
  tenants:
    mode: openshift-logging
    openshift:
      otlp:
        disableRecommendedAttributes: true 1

1: Set disableRecommendedAttributes: true to remove recommended attributes, which limits default attributes to the required attributes.

Note

This option is beneficial if the default attributes causes performance or storage issues. This setting might negatively impact query performance, as it removes default stream labels. You should pair this option with a custom attribute configuration to retain attributes essential for queries.

2.5.3. Additional resources

2.6. OpenTelemetry data model

This document outlines the protocol and semantic conventions for Red Hat OpenShift Logging’s OpenTelemetry support with Logging 6.1.

Important

For more information about the support scope of Red Hat Technology Preview features, see Technology Preview Features Support Scope.

2.6.1. Forwarding and ingestion protocol

Red Hat OpenShift Logging collects and forwards logs to OpenTelemetry endpoints using OTLP Specification. OTLP encodes, transports, and delivers telemetry data. You can also deploy Loki storage, which provides an OTLP endpont to ingest log streams. This document defines the semantic conventions for the logs collected from various OpenShift cluster sources.

2.6.2. Semantic conventions

The log collector in this solution gathers the following log streams:

Container logs
Cluster node journal logs
Cluster node auditd logs
Kubernetes and OpenShift API server logs
OpenShift Virtual Network (OVN) logs

You can forward these streams according to the semantic conventions defined by OpenTelemetry semantic attributes. The semantic conventions in OpenTelemetry define a resource as an immutable representation of the entity producing telemetry, identified by attributes. For example, a process running in a container includes attributes such as container_name, cluster_id, pod_name, namespace, and possibly deployment or app_name. These attributes are grouped under the resource object, which helps reduce repetition and optimizes log transmission as telemetry data.

In addition to resource attributes, logs might also contain scope attributes specific to instrumentation libraries and log attributes specific to each log entry. These attributes provide greater detail about each log entry and enhance filtering capabilities when querying logs in storage.

The following sections define the attributes that are generally forwarded.

2.6.2.1. Log entry structure

All log streams include the following log data fields:

The Applicable Sources column indicates which log sources each field applies to:

all: This field is present in all logs.
container: This field is present in Kubernetes container logs, both application and infrastructure.
audit: This field is present in Kubernetes, OpenShift API, and OVN logs.
auditd: This field is present in node auditd logs.
journal: This field is present in node journal logs.

Name	Applicable Sources	Comment
`body`	all
`observedTimeUnixNano`	all
`timeUnixNano`	all
`severityText`	container, journal
`attributes`	all	(Optional) Present when forwarding stream specific attributes

2.6.2.2. Attributes

Log entries include a set of resource, scope, and log attributes based on their source, as described in the following table.

The Location column specifies the type of attribute:

resource: Indicates a resource attribute
scope: Indicates a scope attribute
log: Indicates a log attribute

The Storage column indicates whether the attribute is stored in a LokiStack using the default openshift-logging mode and specifies where the attribute is stored:

stream label:
- Enables efficient filtering and querying based on specific labels.
- Can be labeled as required if the Loki Operator enforces this attribute in the configuration.
structured metadata:
- Allows for detailed filtering and storage of key-value pairs.
- Enables users to use direct labels for streamlined queries without requiring JSON parsing.

With OTLP, users can filter queries directly by labels rather than using JSON parsing, improving the speed and efficiency of queries.

Name	Location	Applicable Sources	Storage (LokiStack)	Comment
`log_source`	resource	all	required stream label	(DEPRECATED) Compatibility attribute, contains same information as `openshift.log.source`
`log_type`	resource	all	required stream label	(DEPRECATED) Compatibility attribute, contains same information as `openshift.log.type`
`kubernetes.container_name`	resource	container	stream label	(DEPRECATED) Compatibility attribute, contains same information as `k8s.container.name`
`kubernetes.host`	resource	all	stream label	(DEPRECATED) Compatibility attribute, contains same information as `k8s.node.name`
`kubernetes.namespace_name`	resource	container	required stream label	(DEPRECATED) Compatibility attribute, contains same information as `k8s.namespace.name`
`kubernetes.pod_name`	resource	container	stream label	(DEPRECATED) Compatibility attribute, contains same information as `k8s.pod.name`
`openshift.cluster_id`	resource	all		(DEPRECATED) Compatibility attribute, contains same information as `openshift.cluster.uid`
`level`	log	container, journal		(DEPRECATED) Compatibility attribute, contains same information as `severityText`
`openshift.cluster.uid`	resource	all	required stream label
`openshift.log.source`	resource	all	required stream label
`openshift.log.type`	resource	all	required stream label
`openshift.labels.*`	resource	all	structured metadata
`k8s.node.name`	resource	all	stream label
`k8s.namespace.name`	resource	container	required stream label
`k8s.container.name`	resource	container	stream label
`k8s.pod.labels.*`	resource	container	structured metadata
`k8s.pod.name`	resource	container	stream label
`k8s.pod.uid`	resource	container	structured metadata
`k8s.cronjob.name`	resource	container	stream label	Conditionally forwarded based on creator of pod
`k8s.daemonset.name`	resource	container	stream label	Conditionally forwarded based on creator of pod
`k8s.deployment.name`	resource	container	stream label	Conditionally forwarded based on creator of pod
`k8s.job.name`	resource	container	stream label	Conditionally forwarded based on creator of pod
`k8s.replicaset.name`	resource	container	structured metadata	Conditionally forwarded based on creator of pod
`k8s.statefulset.name`	resource	container	stream label	Conditionally forwarded based on creator of pod
`log.iostream`	log	container	structured metadata
`k8s.audit.event.level`	log	audit	structured metadata
`k8s.audit.event.stage`	log	audit	structured metadata
`k8s.audit.event.user_agent`	log	audit	structured metadata
`k8s.audit.event.request.uri`	log	audit	structured metadata
`k8s.audit.event.response.code`	log	audit	structured metadata
`k8s.audit.event.annotation.*`	log	audit	structured metadata
`k8s.audit.event.object_ref.resource`	log	audit	structured metadata
`k8s.audit.event.object_ref.name`	log	audit	structured metadata
`k8s.audit.event.object_ref.namespace`	log	audit	structured metadata
`k8s.audit.event.object_ref.api_group`	log	audit	structured metadata
`k8s.audit.event.object_ref.api_version`	log	audit	structured metadata
`k8s.user.username`	log	audit	structured metadata
`k8s.user.groups`	log	audit	structured metadata
`process.executable.name`	resource	journal	structured metadata
`process.executable.path`	resource	journal	structured metadata
`process.command_line`	resource	journal	structured metadata
`process.pid`	resource	journal	structured metadata
`service.name`	resource	journal	stream label
`systemd.t.*`	log	journal	structured metadata
`systemd.u.*`	log	journal	structured metadata

Note

Attributes marked as Compatibility attribute support minimal backward compatibility with the ViaQ data model. These attributes are deprecated and function as a compatibility layer to ensure continued UI functionality. These attributes will remain supported until the Logging UI fully supports the OpenTelemetry counterparts in future releases.

Loki changes the attribute names when persisting them to storage. The names will be lowercased, and all characters in the set: (.,/,-) will be replaced by underscores (_). For example, k8s.namespace.name will become k8s_namespace_name.

2.6.3. Additional resources

2.7. Visualization for logging

Visualization for logging is provided by deploying the Logging UI Plugin of the Cluster Observability Operator, which requires Operator installation.

Important

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 OpenShift Container Platform 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.

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Chapter 2. Logging 6.1

2.1. Logging 6.1

2.1.1. Logging 6.1.0 Release Notes

2.1.1.1. New Features and Enhancements

2.1.1.1.1. Log Collection

2.1.1.1.2. Log Storage

2.1.1.2. Technology Preview

2.1.1.3. Bug Fixes

2.1.1.4. CVEs

2.2. Logging 6.1

2.2.1. Inputs and outputs

2.2.2. Receiver input type

2.2.3. Pipelines and filters

2.2.4. Operator behavior

2.2.5. Validation

2.2.6. Quick start

2.2.6.1. Quick start with ViaQ

2.2.6.2. Quick start with OpenTelemetry

2.3. Configuring log forwarding

2.3.1. Setting up log collection

2.3.1.1. Legacy service accounts

2.3.1.2. Creating service accounts

2.3.1.2.1. Cluster Role Binding for your Service Account

2.3.1.2.2. Writing application logs

2.3.1.2.3. Writing audit logs

2.3.1.2.4. Writing infrastructure logs

2.3.1.2.5. ClusterLogForwarder editor role

2.3.2. Modifying log level in collector

2.3.3. Managing the Operator

2.3.4. Structure of the ClusterLogForwarder

2.3.4.1. Inputs

2.3.4.2. Outputs

2.3.5. Configuring OTLP output

2.3.5.1. Pipelines

2.3.5.2. Filters

2.3.5.3. Enabling multi-line exception detection

2.3.5.3.1. Details

2.3.5.4. Configuring content filters to drop unwanted log records

2.3.5.5. Overview of API audit filter

2.3.5.6. Filtering application logs at input by including the label expressions or a matching label key and values

2.3.5.7. Configuring content filters to prune log records

2.3.6. Filtering the audit and infrastructure log inputs by source

2.3.7. Filtering application logs at input by including or excluding the namespace or container name

2.4. Storing logs with LokiStack

2.4.1. Loki deployment sizing

2.4.2. Prerequisites

2.4.3. Core Setup and Configuration

2.4.4. Authorizing LokiStack rules RBAC permissions

2.4.4.1. Examples

2.4.5. Creating a log-based alerting rule with Loki

2.4.6. Configuring Loki to tolerate memberlist creation failure

2.4.7. Enabling stream-based retention with Loki

2.4.8. Loki pod placement

2.4.8.1. Enhanced Reliability and Performance

2.4.8.2. Enabling authentication to cloud-based log stores using short-lived tokens

2.4.8.3. Configuring Loki to tolerate node failure

2.4.8.4. LokiStack behavior during cluster restarts

2.4.8.5. Advanced Deployment and Scalability

2.4.8.6. Zone aware data replication

2.4.8.7. Recovering Loki pods from failed zones

2.4.8.7.1. Troubleshooting PVC in a terminating state

2.4.8.8. Troubleshooting Loki rate limit errors

2.5. OTLP data ingestion in Loki

2.5.1. Configuring LokiStack for OTLP data ingestion

2.5.2. Attribute mapping

2.5.2.1. Custom attribute mapping for OpenShift

2.5.2.2. Customizing OpenShift defaults

2.5.2.3. Removing recommended attributes

2.5.3. Additional resources

2.6. OpenTelemetry data model

2.6.1. Forwarding and ingestion protocol

2.6.2. Semantic conventions

2.6.2.1. Log entry structure

2.6.2.2. Attributes

2.6.3. Additional resources

2.7. Visualization for logging

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