Chapter 9. Log collection and forwarding
Chapter 9. Log collection and forwarding 9.1. About log collection and forwarding The Red Hat OpenShift Logging Operator deploys a collector based on the ClusterLogForwarder resource specification. There are two collector options supported by this Operator: the legacy Fluentd collector, and the Vector collector. Note Fluentd is deprecated and is planned to be removed in a future release. Red Hat provides bug fixes and support for this feature during the current release lifecycle, but this feature no longer receives enhancements. As an alternative to Fluentd, you can use Vector instead. 9.1.1. Log collection The log collector is a daemon set that deploys pods to each OpenShift Dedicated node to collect container and node logs. By default, the log collector uses the following sources: System and infrastructure logs generated by journald log messages from the operating system, the container runtime, and OpenShift Dedicated. /var/log/containers/*.log for all container logs. If you configure the log collector to collect audit logs, it collects them from /var/log/audit/audit.log . The log collector collects the logs from these sources and forwards them internally or externally depending on your logging configuration. 9.1.1.1. Log collector types Vector is a log collector offered as an alternative to Fluentd for the logging. You can configure which logging collector type your cluster uses by modifying the ClusterLogging custom resource (CR) collection spec: Example ClusterLogging CR that configures Vector as the collector apiVersion: logging.openshift.io/v1 kind: ClusterLogging metadata: name: instance namespace: openshift-logging spec: collection: logs: type: vector vector: {} # ... 9.1.1.2. Log collection limitations The container runtimes provide minimal information to identify the source of log messages: project, pod name, and container ID. This information is not sufficient to uniquely identify the source of the logs. If a pod with a given name and project is deleted before the log collector begins processing its logs, information from the API server, such as labels and annotations, might not be available. There might not be a way to distinguish the log messages from a similarly named pod and project or trace the logs to their source. This limitation means that log collection and normalization are considered best effort . Important The available container runtimes provide minimal information to identify the source of log messages and do not guarantee unique individual log messages or that these messages can be traced to their source. 9.1.1.3. Log collector features by type Table 9.1. Log Sources Feature Fluentd Vector App container logs [✓] [✓] App-specific routing [✓] [✓] App-specific routing by namespace [✓] [✓] Infra container logs [✓] [✓] Infra journal logs [✓] [✓] Kube API audit logs [✓] [✓] OpenShift API audit logs [✓] [✓] Open Virtual Network (OVN) audit logs [✓] [✓] Table 9.2. Authorization and Authentication Feature Fluentd Vector Elasticsearch certificates [✓] [✓] Elasticsearch username / password [✓] [✓] Amazon Cloudwatch keys [✓] [✓] Amazon Cloudwatch STS [✓] [✓] Kafka certificates [✓] [✓] Kafka username / password [✓] [✓] Kafka SASL [✓] [✓] Loki bearer token [✓] [✓] Table 9.3. Normalizations and Transformations Feature Fluentd Vector Viaq data model - app [✓] [✓] Viaq data model - infra [✓] [✓] Viaq data model - infra(journal) [✓] [✓] Viaq data model - Linux audit [✓] [✓] Viaq data model - kube-apiserver audit [✓] [✓] Viaq data model - OpenShift API audit [✓] [✓] Viaq data model - OVN [✓] [✓] Loglevel Normalization [✓] [✓] JSON parsing [✓] [✓] Structured Index [✓] [✓] Multiline error detection [✓] [✓] Multicontainer / split indices [✓] [✓] Flatten labels [✓] [✓] CLF static labels [✓] [✓] Table 9.4. Tuning Feature Fluentd Vector Fluentd readlinelimit [✓] Fluentd buffer [✓] - chunklimitsize [✓] - totallimitsize [✓] - overflowaction [✓] - flushthreadcount [✓] - flushmode [✓] - flushinterval [✓] - retrywait [✓] - retrytype [✓] - retrymaxinterval [✓] - retrytimeout [✓] Table 9.5. Visibility Feature Fluentd Vector Metrics [✓] [✓] Dashboard [✓] [✓] Alerts [✓] [✓] Table 9.6. Miscellaneous Feature Fluentd Vector Global proxy support [✓] [✓] x86 support [✓] [✓] ARM support [✓] [✓] IBM Power(R) support [✓] [✓] IBM Z(R) support [✓] [✓] IPv6 support [✓] [✓] Log event buffering [✓] Disconnected Cluster [✓] [✓] 9.1.1.4. Collector outputs The following collector outputs are supported: Table 9.7. Supported outputs Feature Fluentd Vector Elasticsearch v6-v8 [✓] [✓] Fluent forward [✓] Syslog RFC3164 [✓] [✓] (Logging 5.7+) Syslog RFC5424 [✓] [✓] (Logging 5.7+) Kafka [✓] [✓] Amazon Cloudwatch [✓] [✓] Amazon Cloudwatch STS [✓] [✓] Loki [✓] [✓] HTTP [✓] [✓] (Logging 5.7+) Google Cloud Logging [✓] [✓] Splunk [✓] (Logging 5.6+) 9.1.2. Log forwarding Administrators can create ClusterLogForwarder resources that specify which logs are collected, how they are transformed, and where they are forwarded to. ClusterLogForwarder resources can be used up to forward container, infrastructure, and audit logs to specific endpoints within or outside of a cluster. Transport Layer Security (TLS) is supported so that log forwarders can be configured to send logs securely. Administrators can also authorize RBAC permissions that define which service accounts and users can access and forward which types of logs. 9.1.2.1. Log forwarding implementations There are two log forwarding implementations available: the legacy implementation, and the multi log forwarder feature. Important Only the Vector collector is supported for use with the multi log forwarder feature. The Fluentd collector can only be used with legacy implementations. 9.1.2.1.1. Legacy implementation In legacy implementations, you can only use one log forwarder in your cluster. The ClusterLogForwarder resource in this mode must be named instance , and must be created in the openshift-logging namespace. The ClusterLogForwarder resource also requires a corresponding ClusterLogging resource named instance in the openshift-logging namespace. 9.1.2.1.2. Multi log forwarder feature The multi log forwarder feature is available in logging 5.8 and later, and provides the following functionality: Administrators can control which users are allowed to define log collection and which logs they are allowed to collect. Users who have the required permissions are able to specify additional log collection configurations. Administrators who are migrating from the deprecated Fluentd collector to the Vector collector can deploy a new log forwarder separately from their existing deployment. The existing and new log forwarders can operate simultaneously while workloads are being migrated. In multi log forwarder implementations, you are not required to create a corresponding ClusterLogging resource for your ClusterLogForwarder resource. You can create multiple ClusterLogForwarder resources using any name, in any namespace, with the following exceptions: You cannot create a ClusterLogForwarder resource named instance in the openshift-logging namespace, because this is reserved for a log forwarder that supports the legacy workflow using the Fluentd collector. You cannot create a ClusterLogForwarder resource named collector in the openshift-logging namespace, because this is reserved for the collector. 9.1.2.2. Enabling the multi log forwarder feature for a cluster To use the multi log forwarder feature, you must create a service account and cluster role bindings for that service account. You can then reference the service account in the ClusterLogForwarder resource to control access permissions. Important In order to support multi log forwarding in additional namespaces other than the openshift-logging namespace, you must update the Red Hat OpenShift Logging Operator to watch all namespaces . This functionality is supported by default in new Red Hat OpenShift Logging Operator version 5.8 installations. 9.1.2.2.1. Authorizing log collection RBAC permissions In logging 5.8 and later, the Red Hat OpenShift Logging Operator provides collect-audit-logs , collect-application-logs , and collect-infrastructure-logs cluster roles, which enable the collector to collect audit logs, application logs, and infrastructure logs respectively. You can authorize RBAC permissions for log collection by binding the required cluster roles to a service account. Prerequisites The Red Hat OpenShift Logging Operator is installed in the openshift-logging namespace. You have administrator permissions. Procedure 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 USD oc adm policy add-cluster-role-to-user <cluster_role_name> system:serviceaccount:<namespace_name>:<service_account_name> Additional resources Using RBAC Authorization Kubernetes documentation 9.2. Log output types Outputs define the destination where logs are sent to from a log forwarder. You can configure multiple types of outputs in the ClusterLogForwarder custom resource (CR) to send logs to servers that support different protocols. 9.2.1. Supported log forwarding outputs Outputs can be any of the following types: Table 9.8. Supported log output types Output type Protocol Tested with Logging versions Supported collector type Elasticsearch v6 HTTP 1.1 6.8.1, 6.8.23 5.6+ Fluentd, Vector Elasticsearch v7 HTTP 1.1 7.12.2, 7.17.7, 7.10.1 5.6+ Fluentd, Vector Elasticsearch v8 HTTP 1.1 8.4.3, 8.6.1 5.6+ Fluentd [1] , Vector Fluent Forward Fluentd forward v1 Fluentd 1.14.6, Logstash 7.10.1, Fluentd 1.14.5 5.4+ Fluentd Google Cloud Logging REST over HTTPS Latest 5.7+ Vector HTTP HTTP 1.1 Fluentd 1.14.6, Vector 0.21 5.7+ Fluentd, Vector Kafka Kafka 0.11 Kafka 2.4.1, 2.7.0, 3.3.1 5.4+ Fluentd, Vector Loki REST over HTTP and HTTPS 2.3.0, 2.5.0, 2.7, 2.2.1 5.4+ Fluentd, Vector Splunk HEC 8.2.9, 9.0.0 5.7+ Vector Syslog RFC3164, RFC5424 Rsyslog 8.37.0-9.el7, rsyslog-8.39.0 5.4+ Fluentd, Vector [2] Amazon CloudWatch REST over HTTPS Latest 5.4+ Fluentd, Vector Fluentd does not support Elasticsearch 8 in the logging version 5.6.2. Vector supports Syslog in the logging version 5.7 and higher. 9.2.2. Output type descriptions default The on-cluster, Red Hat managed log store. You are not required to configure the default output. Note If you configure a default output, you receive an error message, because the default output name is reserved for referencing the on-cluster, Red Hat managed log store. loki Loki, a horizontally scalable, highly available, multi-tenant log aggregation system. kafka A Kafka broker. The kafka output can use a TCP or TLS connection. elasticsearch An external Elasticsearch instance. The elasticsearch output can use a TLS connection. fluentdForward An external log aggregation solution that supports Fluentd. This option uses the Fluentd forward protocols. The fluentForward output can use a TCP or TLS connection and supports shared-key authentication by providing a shared_key field in a secret. Shared-key authentication can be used with or without TLS. Important The fluentdForward output is only supported if you are using the Fluentd collector. It is not supported if you are using the Vector collector. If you are using the Vector collector, you can forward logs to Fluentd by using the http output. syslog An external log aggregation solution that supports the syslog RFC3164 or RFC5424 protocols. The syslog output can use a UDP, TCP, or TLS connection. cloudwatch Amazon CloudWatch, a monitoring and log storage service hosted by Amazon Web Services (AWS). cloudlogging Google Cloud Logging, a monitoring and log storage service hosted by Google Cloud Platform (GCP). 9.3. Enabling JSON log forwarding You can configure the Log Forwarding API to parse JSON strings into a structured object. 9.3.1. Parsing JSON logs You can use a ClusterLogForwarder object to parse JSON logs into a structured object and forward them to a supported output. To illustrate how this works, suppose that you have the following structured JSON log entry: Example structured JSON log entry {"level":"info","name":"fred","home":"bedrock"} To enable parsing JSON log, you add parse: json to a pipeline in the ClusterLogForwarder CR, as shown in the following example: Example snippet showing parse: json pipelines: - inputRefs: [ application ] outputRefs: myFluentd parse: json When you enable parsing JSON logs by using parse: json , the CR copies the JSON-structured log entry in a structured field, as shown in the following example: Example structured output containing the structured JSON log entry {"structured": { "level": "info", "name": "fred", "home": "bedrock" }, "more fields..."} Important If the log entry does not contain valid structured JSON, the structured field is absent. 9.3.2. Configuring JSON log data for Elasticsearch If your JSON logs follow more than one schema, storing them in a single index might cause type conflicts and cardinality problems. To avoid that, you must configure the ClusterLogForwarder custom resource (CR) to group each schema into a single output definition. This way, each schema is forwarded to a separate index. Important If you forward JSON logs to the default Elasticsearch instance managed by OpenShift Logging, it generates new indices based on your configuration. To avoid performance issues associated with having too many indices, consider keeping the number of possible schemas low by standardizing to common schemas. Structure types You can use the following structure types in the ClusterLogForwarder CR to construct index names for the Elasticsearch log store: structuredTypeKey is the name of a message field. The value of that field is used to construct the index name. kubernetes.labels.<key> is the Kubernetes pod label whose value is used to construct the index name. openshift.labels.<key> is the pipeline.label.<key> element in the ClusterLogForwarder CR whose value is used to construct the index name. kubernetes.container_name uses the container name to construct the index name. structuredTypeName : If the structuredTypeKey field is not set or its key is not present, the structuredTypeName value is used as the structured type. When you use both the structuredTypeKey field and the structuredTypeName field together, the structuredTypeName value provides a fallback index name if the key in the structuredTypeKey field is missing from the JSON log data. Note Although you can set the value of structuredTypeKey to any field shown in the "Log Record Fields" topic, the most useful fields are shown in the preceding list of structure types. A structuredTypeKey: kubernetes.labels.<key> example Suppose the following: Your cluster is running application pods that produce JSON logs in two different formats, "apache" and "google". The user labels these application pods with logFormat=apache and logFormat=google . You use the following snippet in your ClusterLogForwarder CR YAML file. apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: # ... spec: # ... outputDefaults: elasticsearch: structuredTypeKey: kubernetes.labels.logFormat 1 structuredTypeName: nologformat pipelines: - inputRefs: - application outputRefs: - default parse: json 2 1 Uses the value of the key-value pair that is formed by the Kubernetes logFormat label. 2 Enables parsing JSON logs. In that case, the following structured log record goes to the app-apache-write index: And the following structured log record goes to the app-google-write index: A structuredTypeKey: openshift.labels.<key> example Suppose that you use the following snippet in your ClusterLogForwarder CR YAML file. outputDefaults: elasticsearch: structuredTypeKey: openshift.labels.myLabel 1 structuredTypeName: nologformat pipelines: - name: application-logs inputRefs: - application - audit outputRefs: - elasticsearch-secure - default parse: json labels: myLabel: myValue 2 1 Uses the value of the key-value pair that is formed by the OpenShift myLabel label. 2 The myLabel element gives its string value, myValue , to the structured log record. In that case, the following structured log record goes to the app-myValue-write index: Additional considerations The Elasticsearch index for structured records is formed by prepending "app-" to the structured type and appending "-write". Unstructured records are not sent to the structured index. They are indexed as usual in the application, infrastructure, or audit indices. If there is no non-empty structured type, forward an unstructured record with no structured field. It is important not to overload Elasticsearch with too many indices. Only use distinct structured types for distinct log formats , not for each application or namespace. For example, most Apache applications use the same JSON log format and structured type, such as LogApache . 9.3.3. Forwarding JSON logs to the Elasticsearch log store For an Elasticsearch log store, if your JSON log entries follow different schemas , configure the ClusterLogForwarder custom resource (CR) to group each JSON schema into a single output definition. This way, Elasticsearch uses a separate index for each schema. Important Because forwarding different schemas to the same index can cause type conflicts and cardinality problems, you must perform this configuration before you forward data to the Elasticsearch store. To avoid performance issues associated with having too many indices, consider keeping the number of possible schemas low by standardizing to common schemas. Procedure Add the following snippet to your ClusterLogForwarder CR YAML file. outputDefaults: elasticsearch: structuredTypeKey: <log record field> structuredTypeName: <name> pipelines: - inputRefs: - application outputRefs: default parse: json Use structuredTypeKey field to specify one of the log record fields. Use structuredTypeName field to specify a name. Important To parse JSON logs, you must set both the structuredTypeKey and structuredTypeName fields. For inputRefs , specify which log types to forward by using that pipeline, such as application, infrastructure , or audit . Add the parse: json element to pipelines. Create the CR object: USD oc create -f <filename>.yaml The Red Hat OpenShift Logging Operator redeploys the collector pods. However, if they do not redeploy, delete the collector pods to force them to redeploy. USD oc delete pod --selector logging-infra=collector 9.3.4. Forwarding JSON logs from containers in the same pod to separate indices You can forward structured logs from different containers within the same pod to different indices. To use this feature, you must configure the pipeline with multi-container support and annotate the pods. Logs are written to indices with a prefix of app- . It is recommended that Elasticsearch be configured with aliases to accommodate this. Important JSON formatting of logs varies by application. Because creating too many indices impacts performance, limit your use of this feature to creating indices for logs that have incompatible JSON formats. Use queries to separate logs from different namespaces, or applications with compatible JSON formats. Prerequisites Logging for Red Hat OpenShift: 5.5 Procedure Create or edit a YAML file that defines the ClusterLogForwarder CR object: apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: instance namespace: openshift-logging spec: outputDefaults: elasticsearch: structuredTypeKey: kubernetes.labels.logFormat 1 structuredTypeName: nologformat enableStructuredContainerLogs: true 2 pipelines: - inputRefs: - application name: application-logs outputRefs: - default parse: json 1 Uses the value of the key-value pair that is formed by the Kubernetes logFormat label. 2 Enables multi-container outputs. Create or edit a YAML file that defines the Pod CR object: apiVersion: v1 kind: Pod metadata: annotations: containerType.logging.openshift.io/heavy: heavy 1 containerType.logging.openshift.io/low: low spec: containers: - name: heavy 2 image: heavyimage - name: low image: lowimage 1 Format: containerType.logging.openshift.io/<container-name>: <index> 2 Annotation names must match container names Warning This configuration might significantly increase the number of shards on the cluster. Additional resources Kubernetes Annotations Additional resources About log forwarding 9.4. Configuring log forwarding In a logging deployment, container and infrastructure logs are forwarded to the internal log store defined in the ClusterLogging custom resource (CR) by default. Audit logs are not forwarded to the internal log store by default because this does not provide secure storage. You are responsible for ensuring that the system to which you forward audit logs is compliant with your organizational and governmental regulations, and is properly secured. If this default configuration meets your needs, you do not need to configure a ClusterLogForwarder CR. If a ClusterLogForwarder CR exists, logs are not forwarded to the internal log store unless a pipeline is defined that contains the default output. 9.4.1. About forwarding logs to third-party systems To send logs to specific endpoints inside and outside your OpenShift Dedicated cluster, you specify a combination of outputs and pipelines in a ClusterLogForwarder custom resource (CR). You can also use inputs to forward the application logs associated with a specific project to an endpoint. Authentication is provided by a Kubernetes Secret object. pipeline Defines simple routing from one log type to one or more outputs, or which logs you want to send. The log types are one of the following: application . Container logs generated by user applications running in the cluster, except infrastructure container applications. infrastructure . Container logs from pods that run in the openshift* , kube* , or default projects and journal logs sourced from node file system. audit . Audit logs generated by the node audit system, auditd , Kubernetes API server, OpenShift API server, and OVN network. You can add labels to outbound log messages by using key:value pairs in the pipeline. For example, you might add a label to messages that are forwarded to other data centers or label the logs by type. Labels that are added to objects are also forwarded with the log message. input Forwards the application logs associated with a specific project to a pipeline. In the pipeline, you define which log types to forward using an inputRef parameter and where to forward the logs to using an outputRef parameter. Secret A key:value map that contains confidential data such as user credentials. Note the following: If you do not define a pipeline for a log type, the logs of the undefined types are dropped. For example, if you specify a pipeline for the application and audit types, but do not specify a pipeline for the infrastructure type, infrastructure logs are dropped. You can use multiple types of outputs in the ClusterLogForwarder custom resource (CR) to send logs to servers that support different protocols. The following example forwards the audit logs to a secure external Elasticsearch instance, the infrastructure logs to an insecure external Elasticsearch instance, the application logs to a Kafka broker, and the application logs from the my-apps-logs project to the internal Elasticsearch instance. Sample log forwarding outputs and pipelines apiVersion: "logging.openshift.io/v1" kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: elasticsearch-secure 4 type: "elasticsearch" url: https://elasticsearch.secure.com:9200 secret: name: elasticsearch - name: elasticsearch-insecure 5 type: "elasticsearch" url: http://elasticsearch.insecure.com:9200 - name: kafka-app 6 type: "kafka" url: tls://kafka.secure.com:9093/app-topic inputs: 7 - name: my-app-logs application: namespaces: - my-project pipelines: - name: audit-logs 8 inputRefs: - audit outputRefs: - elasticsearch-secure - default labels: secure: "true" 9 datacenter: "east" - name: infrastructure-logs 10 inputRefs: - infrastructure outputRefs: - elasticsearch-insecure labels: datacenter: "west" - name: my-app 11 inputRefs: - my-app-logs outputRefs: - default - inputRefs: 12 - application outputRefs: - kafka-app labels: datacenter: "south" 1 In legacy implementations, the CR name must be instance . In multi log forwarder implementations, you can use any name. 2 In legacy implementations, the CR namespace must be openshift-logging . In multi log forwarder implementations, you can use any namespace. 3 The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace. 4 Configuration for an secure Elasticsearch output using a secret with a secure URL. A name to describe the output. The type of output: elasticsearch . The secure URL and port of the Elasticsearch instance as a valid absolute URL, including the prefix. The secret required by the endpoint for TLS communication. The secret must exist in the openshift-logging project. 5 Configuration for an insecure Elasticsearch output: A name to describe the output. The type of output: elasticsearch . The insecure URL and port of the Elasticsearch instance as a valid absolute URL, including the prefix. 6 Configuration for a Kafka output using a client-authenticated TLS communication over a secure URL: A name to describe the output. The type of output: kafka . Specify the URL and port of the Kafka broker as a valid absolute URL, including the prefix. 7 Configuration for an input to filter application logs from the my-project namespace. 8 Configuration for a pipeline to send audit logs to the secure external Elasticsearch instance: A name to describe the pipeline. The inputRefs is the log type, in this example audit . The outputRefs is the name of the output to use, in this example elasticsearch-secure to forward to the secure Elasticsearch instance and default to forward to the internal Elasticsearch instance. Optional: Labels to add to the logs. 9 Optional: String. One or more labels to add to the logs. Quote values like "true" so they are recognized as string values, not as a boolean. 10 Configuration for a pipeline to send infrastructure logs to the insecure external Elasticsearch instance. 11 Configuration for a pipeline to send logs from the my-project project to the internal Elasticsearch instance. A name to describe the pipeline. The inputRefs is a specific input: my-app-logs . The outputRefs is default . Optional: String. One or more labels to add to the logs. 12 Configuration for a pipeline to send logs to the Kafka broker, with no pipeline name: The inputRefs is the log type, in this example application . The outputRefs is the name of the output to use. Optional: String. One or more labels to add to the logs. Fluentd log handling when the external log aggregator is unavailable If your external logging aggregator becomes unavailable and cannot receive logs, Fluentd continues to collect logs and stores them in a buffer. When the log aggregator becomes available, log forwarding resumes, including the buffered logs. If the buffer fills completely, Fluentd stops collecting logs. OpenShift Dedicated rotates the logs and deletes them. You cannot adjust the buffer size or add a persistent volume claim (PVC) to the Fluentd daemon set or pods. Supported Authorization Keys Common key types are provided here. Some output types support additional specialized keys, documented with the output-specific configuration field. All secret keys are optional. Enable the security features you want by setting the relevant keys. You are responsible for creating and maintaining any additional configurations that external destinations might require, such as keys and secrets, service accounts, port openings, or global proxy configuration. Open Shift Logging will not attempt to verify a mismatch between authorization combinations. Transport Layer Security (TLS) Using a TLS URL ( http://... or ssl://... ) without a secret enables basic TLS server-side authentication. Additional TLS features are enabled by including a secret and setting the following optional fields: passphrase : (string) Passphrase to decode an encoded TLS private key. Requires tls.key . ca-bundle.crt : (string) File name of a customer CA for server authentication. Username and Password username : (string) Authentication user name. Requires password . password : (string) Authentication password. Requires username . Simple Authentication Security Layer (SASL) sasl.enable (boolean) Explicitly enable or disable SASL. If missing, SASL is automatically enabled when any of the other sasl. keys are set. sasl.mechanisms : (array) List of allowed SASL mechanism names. If missing or empty, the system defaults are used. sasl.allow-insecure : (boolean) Allow mechanisms that send clear-text passwords. Defaults to false. 9.4.1.1. Creating a Secret You can create a secret in the directory that contains your certificate and key files by using the following command: USD oc create secret generic -n <namespace> <secret_name> \ --from-file=ca-bundle.crt=<your_bundle_file> \ --from-literal=username=<your_username> \ --from-literal=password=<your_password> Note Generic or opaque secrets are recommended for best results. 9.4.2. Creating a log forwarder To create a log forwarder, you must create a ClusterLogForwarder CR that specifies the log input types that the service account can collect. You can also specify which outputs the logs can be forwarded to. If you are using the multi log forwarder feature, you must also reference the service account in the ClusterLogForwarder CR. If you are using the multi log forwarder feature on your cluster, you can create ClusterLogForwarder custom resources (CRs) in any namespace, using any name. If you are using a legacy implementation, the ClusterLogForwarder CR must be named instance , and must be created in the openshift-logging namespace. Important You need administrator permissions for the namespace where you create the ClusterLogForwarder CR. ClusterLogForwarder resource example apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 pipelines: - inputRefs: - <log_type> 4 outputRefs: - <output_name> 5 outputs: - name: <output_name> 6 type: <output_type> 7 url: <log_output_url> 8 # ... 1 In legacy implementations, the CR name must be instance . In multi log forwarder implementations, you can use any name. 2 In legacy implementations, the CR namespace must be openshift-logging . In multi log forwarder implementations, you can use any namespace. 3 The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace. 4 The log types that are collected. The value for this field can be audit for audit logs, application for application logs, infrastructure for infrastructure logs, or a named input that has been defined for your application. 5 7 The type of output that you want to forward logs to. The value of this field can be default , loki , kafka , elasticsearch , fluentdForward , syslog , or cloudwatch . Note The default output type is not supported in mutli log forwarder implementations. 6 A name for the output that you want to forward logs to. 8 The URL of the output that you want to forward logs to. 9.4.3. Tuning log payloads and delivery In logging 5.9 and newer versions, the tuning spec in the ClusterLogForwarder custom resource (CR) provides a means of configuring your deployment to prioritize either throughput or durability of logs. For example, if you need to reduce the possibility of log loss when the collector restarts, or you require collected log messages to survive a collector restart to support regulatory mandates, you can tune your deployment to prioritize log durability. If you use outputs that have hard limitations on the size of batches they can receive, you may want to tune your deployment to prioritize log throughput. Important To use this feature, your logging deployment must be configured to use the Vector collector. The tuning spec in the ClusterLogForwarder CR is not supported when using the Fluentd collector. The following example shows the ClusterLogForwarder CR options that you can modify to tune log forwarder outputs: Example ClusterLogForwarder CR tuning options apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: # ... spec: tuning: delivery: AtLeastOnce 1 compression: none 2 maxWrite: <integer> 3 minRetryDuration: 1s 4 maxRetryDuration: 1s 5 # ... 1 Specify the delivery mode for log forwarding. AtLeastOnce delivery means that if the log forwarder crashes or is restarted, any logs that were read before the crash but not sent to their destination are re-sent. It is possible that some logs are duplicated after a crash. AtMostOnce delivery means that the log forwarder makes no effort to recover logs lost during a crash. This mode gives better throughput, but may result in greater log loss. 2 Specifying a compression configuration causes data to be compressed before it is sent over the network. Note that not all output types support compression, and if the specified compression type is not supported by the output, this results in an error. The possible values for this configuration are none for no compression, gzip , snappy , zlib , or zstd . lz4 compression is also available if you are using a Kafka output. See the table "Supported compression types for tuning outputs" for more information. 3 Specifies a limit for the maximum payload of a single send operation to the output. 4 Specifies a minimum duration to wait between attempts before retrying delivery after a failure. This value is a string, and can be specified as milliseconds ( ms ), seconds ( s ), or minutes ( m ). 5 Specifies a maximum duration to wait between attempts before retrying delivery after a failure. This value is a string, and can be specified as milliseconds ( ms ), seconds ( s ), or minutes ( m ). Table 9.9. Supported compression types for tuning outputs Compression algorithm Splunk Amazon Cloudwatch Elasticsearch 8 LokiStack Apache Kafka HTTP Syslog Google Cloud Microsoft Azure Monitoring gzip X X X X X snappy X X X X zlib X X X zstd X X X lz4 X 9.4.4. Enabling multi-line exception detection Enables multi-line error detection of container logs. Warning Enabling this feature could have performance implications and may require additional computing resources or alternate logging solutions. Log parsers often incorrectly identify separate lines of the same exception as separate exceptions. This leads to extra log entries and an incomplete or inaccurate view of the traced information. Example java exception To enable logging to detect multi-line exceptions and reassemble them into a single log entry, ensure that the ClusterLogForwarder Custom Resource (CR) contains a detectMultilineErrors field, with a value of true . Example ClusterLogForwarder CR apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: instance namespace: openshift-logging spec: pipelines: - name: my-app-logs inputRefs: - application outputRefs: - default detectMultilineErrors: true 9.4.4.1. Details When log messages appear as a consecutive sequence forming an exception stack trace, they are combined into a single, unified log record. The first log message's content is replaced with the concatenated content of all the message fields in the sequence. Table 9.10. Supported languages per collector Language Fluentd Vector Java [✓] [✓] JS [✓] [✓] Ruby [✓] [✓] Python [✓] [✓] Golang [✓] [✓] PHP [✓] [✓] Dart [✓] [✓] 9.4.4.2. Troubleshooting When enabled, the collector configuration will include a new section with type: detect_exceptions Example vector configuration section Example fluentd config section 9.4.5. Forwarding logs to Google Cloud Platform (GCP) You can forward logs to Google Cloud Logging in addition to, or instead of, the internal default OpenShift Dedicated log store. Note Using this feature with Fluentd is not supported. Prerequisites Red Hat OpenShift Logging Operator 5.5.1 and later Procedure Create a secret using your Google service account key . USD oc -n openshift-logging create secret generic gcp-secret --from-file google-application-credentials.json= <your_service_account_key_file.json> Create a ClusterLogForwarder Custom Resource YAML using the template below: apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: gcp-1 type: googleCloudLogging secret: name: gcp-secret googleCloudLogging: projectId : "openshift-gce-devel" 4 logId : "app-gcp" 5 pipelines: - name: test-app inputRefs: 6 - application outputRefs: - gcp-1 1 In legacy implementations, the CR name must be instance . In multi log forwarder implementations, you can use any name. 2 In legacy implementations, the CR namespace must be openshift-logging . In multi log forwarder implementations, you can use any namespace. 3 The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace. 4 Set a projectId , folderId , organizationId , or billingAccountId field and its corresponding value, depending on where you want to store your logs in the GCP resource hierarchy . 5 Set the value to add to the logName field of the Log Entry . 6 Specify which log types to forward by using the pipeline: application , infrastructure , or audit . Additional resources Google Cloud Billing Documentation Google Cloud Logging Query Language Documentation 9.4.6. Forwarding logs to Splunk You can forward logs to the Splunk HTTP Event Collector (HEC) in addition to, or instead of, the internal default OpenShift Dedicated log store. Note Using this feature with Fluentd is not supported. Prerequisites Red Hat OpenShift Logging Operator 5.6 or later A ClusterLogging instance with vector specified as the collector Base64 encoded Splunk HEC token Procedure Create a secret using your Base64 encoded Splunk HEC token. USD oc -n openshift-logging create secret generic vector-splunk-secret --from-literal hecToken=<HEC_Token> Create or edit the ClusterLogForwarder Custom Resource (CR) using the template below: apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: splunk-receiver 4 secret: name: vector-splunk-secret 5 type: splunk 6 url: <http://your.splunk.hec.url:8088> 7 pipelines: 8 - inputRefs: - application - infrastructure name: 9 outputRefs: - splunk-receiver 10 1 In legacy implementations, the CR name must be instance . In multi log forwarder implementations, you can use any name. 2 In legacy implementations, the CR namespace must be openshift-logging . In multi log forwarder implementations, you can use any namespace. 3 The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace. 4 Specify a name for the output. 5 Specify the name of the secret that contains your HEC token. 6 Specify the output type as splunk . 7 Specify the URL (including port) of your Splunk HEC. 8 Specify which log types to forward by using the pipeline: application , infrastructure , or audit . 9 Optional: Specify a name for the pipeline. 10 Specify the name of the output to use when forwarding logs with this pipeline. 9.4.7. Forwarding logs over HTTP Forwarding logs over HTTP is supported for both the Fluentd and Vector log collectors. To enable, specify http as the output type in the ClusterLogForwarder custom resource (CR). Procedure Create or edit the ClusterLogForwarder CR using the template below: Example ClusterLogForwarder CR apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: httpout-app type: http url: 4 http: headers: 5 h1: v1 h2: v2 method: POST secret: name: 6 tls: insecureSkipVerify: 7 pipelines: - name: inputRefs: - application outputRefs: - 8 1 In legacy implementations, the CR name must be instance . In multi log forwarder implementations, you can use any name. 2 In legacy implementations, the CR namespace must be openshift-logging . In multi log forwarder implementations, you can use any namespace. 3 The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace. 4 Destination address for logs. 5 Additional headers to send with the log record. 6 Secret name for destination credentials. 7 Values are either true or false . 8 This value should be the same as the output name. 9.4.8. Forwarding to Azure Monitor Logs With logging 5.9 and later, you can forward logs to Azure Monitor Logs in addition to, or instead of, the default log store. This functionality is provided by the Vector Azure Monitor Logs sink . Prerequisites You are familiar with how to administer and create a ClusterLogging custom resource (CR) instance. You are familiar with how to administer and create a ClusterLogForwarder CR instance. You understand the ClusterLogForwarder CR specifications. You have basic familiarity with Azure services. You have an Azure account configured for Azure Portal or Azure CLI access. You have obtained your Azure Monitor Logs primary or the secondary security key. You have determined which log types to forward. To enable log forwarding to Azure Monitor Logs via the HTTP Data Collector API: Create a secret with your shared key: apiVersion: v1 kind: Secret metadata: name: my-secret namespace: openshift-logging type: Opaque data: shared_key: <your_shared_key> 1 1 Must contain a primary or secondary key for the Log Analytics workspace making the request. To obtain a shared key , you can use this command in Azure CLI: Get-AzOperationalInsightsWorkspaceSharedKey -ResourceGroupName "<resource_name>" -Name "<workspace_name>" Create or edit your ClusterLogForwarder CR using the template matching your log selection. Forward all logs apiVersion: "logging.openshift.io/v1" kind: "ClusterLogForwarder" metadata: name: instance namespace: openshift-logging spec: outputs: - name: azure-monitor type: azureMonitor azureMonitor: customerId: my-customer-id 1 logType: my_log_type 2 secret: name: my-secret pipelines: - name: app-pipeline inputRefs: - application outputRefs: - azure-monitor 1 Unique identifier for the Log Analytics workspace. Required field. 2 Azure record type of the data being submitted. May only contain letters, numbers, and underscores (_), and may not exceed 100 characters. Forward application and infrastructure logs apiVersion: "logging.openshift.io/v1" kind: "ClusterLogForwarder" metadata: name: instance namespace: openshift-logging spec: outputs: - name: azure-monitor-app type: azureMonitor azureMonitor: customerId: my-customer-id logType: application_log 1 secret: name: my-secret - name: azure-monitor-infra type: azureMonitor azureMonitor: customerId: my-customer-id logType: infra_log # secret: name: my-secret pipelines: - name: app-pipeline inputRefs: - application outputRefs: - azure-monitor-app - name: infra-pipeline inputRefs: - infrastructure outputRefs: - azure-monitor-infra 1 Azure record type of the data being submitted. May only contain letters, numbers, and underscores (_), and may not exceed 100 characters. Advanced configuration options apiVersion: "logging.openshift.io/v1" kind: "ClusterLogForwarder" metadata: name: instance namespace: openshift-logging spec: outputs: - name: azure-monitor type: azureMonitor azureMonitor: customerId: my-customer-id logType: my_log_type azureResourceId: "/subscriptions/111111111" 1 host: "ods.opinsights.azure.com" 2 secret: name: my-secret pipelines: - name: app-pipeline inputRefs: - application outputRefs: - azure-monitor 1 Resource ID of the Azure resource the data should be associated with. Optional field. 2 Alternative host for dedicated Azure regions. Optional field. Default value is ods.opinsights.azure.com . Default value for Azure Government is ods.opinsights.azure.us . 9.4.9. Forwarding application logs from specific projects You can forward a copy of the application logs from specific projects to an external log aggregator, in addition to, or instead of, using the internal log store. You must also configure the external log aggregator to receive log data from OpenShift Dedicated. To configure forwarding application logs from a project, you must create a ClusterLogForwarder custom resource (CR) with at least one input from a project, optional outputs for other log aggregators, and pipelines that use those inputs and outputs. Prerequisites You must have a logging server that is configured to receive the logging data using the specified protocol or format. Procedure Create or edit a YAML file that defines the ClusterLogForwarder CR: Example ClusterLogForwarder CR apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: instance 1 namespace: openshift-logging 2 spec: outputs: - name: fluentd-server-secure 3 type: fluentdForward 4 url: 'tls://fluentdserver.security.example.com:24224' 5 secret: 6 name: fluentd-secret - name: fluentd-server-insecure type: fluentdForward url: 'tcp://fluentdserver.home.example.com:24224' inputs: 7 - name: my-app-logs application: namespaces: - my-project 8 pipelines: - name: forward-to-fluentd-insecure 9 inputRefs: 10 - my-app-logs outputRefs: 11 - fluentd-server-insecure labels: project: "my-project" 12 - name: forward-to-fluentd-secure 13 inputRefs: - application 14 - audit - infrastructure outputRefs: - fluentd-server-secure - default labels: clusterId: "C1234" 1 The name of the ClusterLogForwarder CR must be instance . 2 The namespace for the ClusterLogForwarder CR must be openshift-logging . 3 The name of the output. 4 The output type: elasticsearch , fluentdForward , syslog , or kafka . 5 The URL and port of the external log aggregator as a valid absolute URL. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP address. 6 If using a tls prefix, you must specify the name of the secret required by the endpoint for TLS communication. The secret must exist in the openshift-logging project and have tls.crt , tls.key , and ca-bundle.crt keys that each point to the certificates they represent. 7 The configuration for an input to filter application logs from the specified projects. 8 If no namespace is specified, logs are collected from all namespaces. 9 The pipeline configuration directs logs from a named input to a named output. In this example, a pipeline named forward-to-fluentd-insecure forwards logs from an input named my-app-logs to an output named fluentd-server-insecure . 10 A list of inputs. 11 The name of the output to use. 12 Optional: String. One or more labels to add to the logs. 13 Configuration for a pipeline to send logs to other log aggregators. Optional: Specify a name for the pipeline. Specify which log types to forward by using the pipeline: application, infrastructure , or audit . Specify the name of the output to use when forwarding logs with this pipeline. Optional: Specify the default output to forward logs to the default log store. Optional: String. One or more labels to add to the logs. 14 Note that application logs from all namespaces are collected when using this configuration. Apply the ClusterLogForwarder CR by running the following command: USD oc apply -f <filename>.yaml 9.4.10. Forwarding application logs from specific pods As a cluster administrator, you can use Kubernetes pod labels to gather log data from specific pods and forward it to a log collector. Suppose that you have an application composed of pods running alongside other pods in various namespaces. If those pods have labels that identify the application, you can gather and output their log data to a specific log collector. To specify the pod labels, you use one or more matchLabels key-value pairs. If you specify multiple key-value pairs, the pods must match all of them to be selected. Procedure Create or edit a YAML file that defines the ClusterLogForwarder CR object. In the file, specify the pod labels using simple equality-based selectors under inputs[].name.application.selector.matchLabels , as shown in the following example. Example ClusterLogForwarder CR YAML file apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: pipelines: - inputRefs: [ myAppLogData ] 3 outputRefs: [ default ] 4 inputs: 5 - name: myAppLogData application: selector: matchLabels: 6 environment: production app: nginx namespaces: 7 - app1 - app2 outputs: 8 - <output_name> ... 1 In legacy implementations, the CR name must be instance . In multi log forwarder implementations, you can use any name. 2 In legacy implementations, the CR namespace must be openshift-logging . In multi log forwarder implementations, you can use any namespace. 3 Specify one or more comma-separated values from inputs[].name . 4 Specify one or more comma-separated values from outputs[] . 5 Define a unique inputs[].name for each application that has a unique set of pod labels. 6 Specify the key-value pairs of pod labels whose log data you want to gather. You must specify both a key and value, not just a key. To be selected, the pods must match all the key-value pairs. 7 Optional: Specify one or more namespaces. 8 Specify one or more outputs to forward your log data to. Optional: To restrict the gathering of log data to specific namespaces, use inputs[].name.application.namespaces , as shown in the preceding example. Optional: You can send log data from additional applications that have different pod labels to the same pipeline. For each unique combination of pod labels, create an additional inputs[].name section similar to the one shown. Update the selectors to match the pod labels of this application. Add the new inputs[].name value to inputRefs . For example: Create the CR object: USD oc create -f <file-name>.yaml Additional resources For more information on matchLabels in Kubernetes, see Resources that support set-based requirements . 9.4.11. Overview of API audit filter OpenShift API servers generate audit events for each API call, detailing the request, response, and the identity of the requester, leading to large volumes of data. The API Audit filter uses rules to enable the exclusion of non-essential events and the reduction of event size, facilitating a more manageable audit trail. Rules are checked in order, checking stops at the first match. How much data is included in an event is determined by the value of the level field: None : The event is dropped. Metadata : Audit metadata is included, request and response bodies are removed. Request : Audit metadata and the request body are included, the response body is removed. RequestResponse : All data is included: metadata, request body and response body. The response body can be very large. For example, oc get pods -A generates a response body containing the YAML description of every pod in the cluster. Note You can use this feature only if the Vector collector is set up in your logging deployment. In logging 5.8 and later, the ClusterLogForwarder custom resource (CR) uses the same format as the standard Kubernetes audit policy , while providing the following additional functions: Wildcards Names of users, groups, namespaces, and resources can have a leading or trailing * asterisk character. For example, namespace openshift-\* matches openshift-apiserver or openshift-authentication . Resource \*/status matches Pod/status or Deployment/status . Default Rules Events that do not match any rule in the policy are filtered as follows: Read-only system events such as get , list , watch are dropped. Service account write events that occur within the same namespace as the service account are dropped. All other events are forwarded, subject to any configured rate limits. To disable these defaults, either end your rules list with a rule that has only a level field or add an empty rule. Omit Response Codes A list of integer status codes to omit. You can drop events based on the HTTP status code in the response by using the OmitResponseCodes field, a list of HTTP status code for which no events are created. The default value is [404, 409, 422, 429] . If the value is an empty list, [] , then no status codes are omitted. The ClusterLogForwarder CR audit policy acts in addition to the OpenShift Dedicated audit policy. The ClusterLogForwarder CR audit filter changes what the log collector forwards, and provides the ability to filter by verb, user, group, namespace, or resource. You can create multiple filters to send different summaries of the same audit stream to different places. For example, you can send a detailed stream to the local cluster log store, and a less detailed stream to a remote site. Note The example provided is intended to illustrate the range of rules possible in an audit policy and is not a recommended configuration. Example audit policy apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: instance namespace: openshift-logging spec: pipelines: - name: my-pipeline inputRefs: audit 1 filterRefs: my-policy 2 outputRefs: default filters: - name: my-policy type: kubeAPIAudit kubeAPIAudit: # Don't generate audit events for all requests in RequestReceived stage. omitStages: - "RequestReceived" rules: # Log pod changes at RequestResponse level - level: RequestResponse resources: - group: "" resources: ["pods"] # Log "pods/log", "pods/status" at Metadata level - level: Metadata resources: - group: "" resources: ["pods/log", "pods/status"] # Don't log requests to a configmap called "controller-leader" - level: None resources: - group: "" resources: ["configmaps"] resourceNames: ["controller-leader"] # Don't log watch requests by the "system:kube-proxy" on endpoints or services - level: None users: ["system:kube-proxy"] verbs: ["watch"] resources: - group: "" # core API group resources: ["endpoints", "services"] # Don't log authenticated requests to certain non-resource URL paths. - level: None userGroups: ["system:authenticated"] nonResourceURLs: - "/api*" # Wildcard matching. - "/version" # Log the request body of configmap changes in kube-system. - level: Request resources: - group: "" # core API group resources: ["configmaps"] # This rule only applies to resources in the "kube-system" namespace. # The empty string "" can be used to select non-namespaced resources. namespaces: ["kube-system"] # Log configmap and secret changes in all other namespaces at the Metadata level. - level: Metadata resources: - group: "" # core API group resources: ["secrets", "configmaps"] # Log all other resources in core and extensions at the Request level. - level: Request resources: - group: "" # core API group - group: "extensions" # Version of group should NOT be included. # A catch-all rule to log all other requests at the Metadata level. - level: Metadata 1 The log types that are collected. The value for this field can be audit for audit logs, application for application logs, infrastructure for infrastructure logs, or a named input that has been defined for your application. 2 The name of your audit policy. Additional resources Logging for egress firewall and network policy rules 9.4.12. Forwarding logs to an external Loki logging system You can forward logs to an external Loki logging system in addition to, or instead of, the default log store. To configure log forwarding to Loki, you must create a ClusterLogForwarder custom resource (CR) with an output to Loki, and a pipeline that uses the output. The output to Loki can use the HTTP (insecure) or HTTPS (secure HTTP) connection. Prerequisites You must have a Loki logging system running at the URL you specify with the url field in the CR. Procedure Create or edit a YAML file that defines the ClusterLogForwarder CR object: apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: loki-insecure 4 type: "loki" 5 url: http://loki.insecure.com:3100 6 loki: tenantKey: kubernetes.namespace_name labelKeys: - kubernetes.labels.foo - name: loki-secure 7 type: "loki" url: https://loki.secure.com:3100 secret: name: loki-secret 8 loki: tenantKey: kubernetes.namespace_name 9 labelKeys: - kubernetes.labels.foo 10 pipelines: - name: application-logs 11 inputRefs: 12 - application - audit outputRefs: 13 - loki-secure 1 In legacy implementations, the CR name must be instance . In multi log forwarder implementations, you can use any name. 2 In legacy implementations, the CR namespace must be openshift-logging . In multi log forwarder implementations, you can use any namespace. 3 The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace. 4 Specify a name for the output. 5 Specify the type as "loki" . 6 Specify the URL and port of the Loki system as a valid absolute URL. You can use the http (insecure) or https (secure HTTP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP Address. Loki's default port for HTTP(S) communication is 3100. 7 For a secure connection, you can specify an https or http URL that you authenticate by specifying a secret . 8 For an https prefix, specify the name of the secret required by the endpoint for TLS communication. The secret must contain a ca-bundle.crt key that points to the certificates it represents. Otherwise, for http and https prefixes, you can specify a secret that contains a username and password. In legacy implementations, the secret must exist in the openshift-logging project. For more information, see the following "Example: Setting a secret that contains a username and password." 9 Optional: Specify a metadata key field to generate values for the TenantID field in Loki. For example, setting tenantKey: kubernetes.namespace_name uses the names of the Kubernetes namespaces as values for tenant IDs in Loki. To see which other log record fields you can specify, see the "Log Record Fields" link in the following "Additional resources" section. 10 Optional: Specify a list of metadata field keys to replace the default Loki labels. Loki label names must match the regular expression [a-zA-Z_:][a-zA-Z0-9_:]* . Illegal characters in metadata keys are replaced with _ to form the label name. For example, the kubernetes.labels.foo metadata key becomes Loki label kubernetes_labels_foo . If you do not set labelKeys , the default value is: [log_type, kubernetes.namespace_name, kubernetes.pod_name, kubernetes_host] . Keep the set of labels small because Loki limits the size and number of labels allowed. See Configuring Loki, limits_config . You can still query based on any log record field using query filters. 11 Optional: Specify a name for the pipeline. 12 Specify which log types to forward by using the pipeline: application, infrastructure , or audit . 13 Specify the name of the output to use when forwarding logs with this pipeline. Note Because Loki requires log streams to be correctly ordered by timestamp, labelKeys always includes the kubernetes_host label set, even if you do not specify it. This inclusion ensures that each stream originates from a single host, which prevents timestamps from becoming disordered due to clock differences on different hosts. Apply the ClusterLogForwarder CR object by running the following command: USD oc apply -f <filename>.yaml Additional resources Configuring Loki server 9.4.13. Forwarding logs to an external Elasticsearch instance You can forward logs to an external Elasticsearch instance in addition to, or instead of, the internal log store. You are responsible for configuring the external log aggregator to receive log data from OpenShift Dedicated. To configure log forwarding to an external Elasticsearch instance, you must create a ClusterLogForwarder custom resource (CR) with an output to that instance, and a pipeline that uses the output. The external Elasticsearch output can use the HTTP (insecure) or HTTPS (secure HTTP) connection. To forward logs to both an external and the internal Elasticsearch instance, create outputs and pipelines to the external instance and a pipeline that uses the default output to forward logs to the internal instance. Note If you only want to forward logs to an internal Elasticsearch instance, you do not need to create a ClusterLogForwarder CR. Prerequisites You must have a logging server that is configured to receive the logging data using the specified protocol or format. Procedure Create or edit a YAML file that defines the ClusterLogForwarder CR: Example ClusterLogForwarder CR apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: elasticsearch-example 4 type: elasticsearch 5 elasticsearch: version: 8 6 url: http://elasticsearch.example.com:9200 7 secret: name: es-secret 8 pipelines: - name: application-logs 9 inputRefs: 10 - application - audit outputRefs: - elasticsearch-example 11 - default 12 labels: myLabel: "myValue" 13 # ... 1 In legacy implementations, the CR name must be instance . In multi log forwarder implementations, you can use any name. 2 In legacy implementations, the CR namespace must be openshift-logging . In multi log forwarder implementations, you can use any namespace. 3 The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace. 4 Specify a name for the output. 5 Specify the elasticsearch type. 6 Specify the Elasticsearch version. This can be 6 , 7 , or 8 . 7 Specify the URL and port of the external Elasticsearch instance as a valid absolute URL. You can use the http (insecure) or https (secure HTTP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP Address. 8 For an https prefix, specify the name of the secret required by the endpoint for TLS communication. The secret must contain a ca-bundle.crt key that points to the certificate it represents. Otherwise, for http and https prefixes, you can specify a secret that contains a username and password. In legacy implementations, the secret must exist in the openshift-logging project. For more information, see the following "Example: Setting a secret that contains a username and password." 9 Optional: Specify a name for the pipeline. 10 Specify which log types to forward by using the pipeline: application, infrastructure , or audit . 11 Specify the name of the output to use when forwarding logs with this pipeline. 12 Optional: Specify the default output to send the logs to the internal Elasticsearch instance. 13 Optional: String. One or more labels to add to the logs. Apply the ClusterLogForwarder CR: USD oc apply -f <filename>.yaml Example: Setting a secret that contains a username and password You can use a secret that contains a username and password to authenticate a secure connection to an external Elasticsearch instance. For example, if you cannot use mutual TLS (mTLS) keys because a third party operates the Elasticsearch instance, you can use HTTP or HTTPS and set a secret that contains the username and password. Create a Secret YAML file similar to the following example. Use base64-encoded values for the username and password fields. The secret type is opaque by default. apiVersion: v1 kind: Secret metadata: name: openshift-test-secret data: username: <username> password: <password> # ... Create the secret: USD oc create secret -n openshift-logging openshift-test-secret.yaml Specify the name of the secret in the ClusterLogForwarder CR: kind: ClusterLogForwarder metadata: name: instance namespace: openshift-logging spec: outputs: - name: elasticsearch type: "elasticsearch" url: https://elasticsearch.secure.com:9200 secret: name: openshift-test-secret # ... Note In the value of the url field, the prefix can be http or https . Apply the CR object: USD oc apply -f <filename>.yaml 9.4.14. Forwarding logs using the Fluentd forward protocol You can use the Fluentd forward protocol to send a copy of your logs to an external log aggregator that is configured to accept the protocol instead of, or in addition to, the default Elasticsearch log store. You are responsible for configuring the external log aggregator to receive the logs from OpenShift Dedicated. To configure log forwarding using the forward protocol, you must create a ClusterLogForwarder custom resource (CR) with one or more outputs to the Fluentd servers, and pipelines that use those outputs. The Fluentd output can use a TCP (insecure) or TLS (secure TCP) connection. Prerequisites You must have a logging server that is configured to receive the logging data using the specified protocol or format. Procedure Create or edit a YAML file that defines the ClusterLogForwarder CR object: apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: instance 1 namespace: openshift-logging 2 spec: outputs: - name: fluentd-server-secure 3 type: fluentdForward 4 url: 'tls://fluentdserver.security.example.com:24224' 5 secret: 6 name: fluentd-secret - name: fluentd-server-insecure type: fluentdForward url: 'tcp://fluentdserver.home.example.com:24224' pipelines: - name: forward-to-fluentd-secure 7 inputRefs: 8 - application - audit outputRefs: - fluentd-server-secure 9 - default 10 labels: clusterId: "C1234" 11 - name: forward-to-fluentd-insecure 12 inputRefs: - infrastructure outputRefs: - fluentd-server-insecure labels: clusterId: "C1234" 1 The name of the ClusterLogForwarder CR must be instance . 2 The namespace for the ClusterLogForwarder CR must be openshift-logging . 3 Specify a name for the output. 4 Specify the fluentdForward type. 5 Specify the URL and port of the external Fluentd instance as a valid absolute URL. You can use the tcp (insecure) or tls (secure TCP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP address. 6 If you are using a tls prefix, you must specify the name of the secret required by the endpoint for TLS communication. The secret must exist in the openshift-logging project and must contain a ca-bundle.crt key that points to the certificate it represents. 7 Optional: Specify a name for the pipeline. 8 Specify which log types to forward by using the pipeline: application, infrastructure , or audit . 9 Specify the name of the output to use when forwarding logs with this pipeline. 10 Optional: Specify the default output to forward logs to the internal Elasticsearch instance. 11 Optional: String. One or more labels to add to the logs. 12 Optional: Configure multiple outputs to forward logs to other external log aggregators of any supported type: A name to describe the pipeline. The inputRefs is the log type to forward by using the pipeline: application, infrastructure , or audit . The outputRefs is the name of the output to use. Optional: String. One or more labels to add to the logs. Create the CR object: USD oc create -f <file-name>.yaml 9.4.14.1. Enabling nanosecond precision for Logstash to ingest data from fluentd For Logstash to ingest log data from fluentd, you must enable nanosecond precision in the Logstash configuration file. Procedure In the Logstash configuration file, set nanosecond_precision to true . Example Logstash configuration file input { tcp { codec => fluent { nanosecond_precision => true } port => 24114 } } filter { } output { stdout { codec => rubydebug } } 9.4.15. Forwarding logs using the syslog protocol You can use the syslog RFC3164 or RFC5424 protocol to send a copy of your logs to an external log aggregator that is configured to accept the protocol instead of, or in addition to, the default Elasticsearch log store. You are responsible for configuring the external log aggregator, such as a syslog server, to receive the logs from OpenShift Dedicated. To configure log forwarding using the syslog protocol, you must create a ClusterLogForwarder custom resource (CR) with one or more outputs to the syslog servers, and pipelines that use those outputs. The syslog output can use a UDP, TCP, or TLS connection. Prerequisites You must have a logging server that is configured to receive the logging data using the specified protocol or format. Procedure Create or edit a YAML file that defines the ClusterLogForwarder CR object: apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: rsyslog-east 4 type: syslog 5 syslog: 6 facility: local0 rfc: RFC3164 payloadKey: message severity: informational url: 'tls://rsyslogserver.east.example.com:514' 7 secret: 8 name: syslog-secret - name: rsyslog-west type: syslog syslog: appName: myapp facility: user msgID: mymsg procID: myproc rfc: RFC5424 severity: debug url: 'tcp://rsyslogserver.west.example.com:514' pipelines: - name: syslog-east 9 inputRefs: 10 - audit - application outputRefs: 11 - rsyslog-east - default 12 labels: secure: "true" 13 syslog: "east" - name: syslog-west 14 inputRefs: - infrastructure outputRefs: - rsyslog-west - default labels: syslog: "west" 1 In legacy implementations, the CR name must be instance . In multi log forwarder implementations, you can use any name. 2 In legacy implementations, the CR namespace must be openshift-logging . In multi log forwarder implementations, you can use any namespace. 3 The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace. 4 Specify a name for the output. 5 Specify the syslog type. 6 Optional: Specify the syslog parameters, listed below. 7 Specify the URL and port of the external syslog instance. You can use the udp (insecure), tcp (insecure) or tls (secure TCP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP address. 8 If using a tls prefix, you must specify the name of the secret required by the endpoint for TLS communication. The secret must contain a ca-bundle.crt key that points to the certificate it represents. In legacy implementations, the secret must exist in the openshift-logging project. 9 Optional: Specify a name for the pipeline. 10 Specify which log types to forward by using the pipeline: application, infrastructure , or audit . 11 Specify the name of the output to use when forwarding logs with this pipeline. 12 Optional: Specify the default output to forward logs to the internal Elasticsearch instance. 13 Optional: String. One or more labels to add to the logs. Quote values like "true" so they are recognized as string values, not as a boolean. 14 Optional: Configure multiple outputs to forward logs to other external log aggregators of any supported type: A name to describe the pipeline. The inputRefs is the log type to forward by using the pipeline: application, infrastructure , or audit . The outputRefs is the name of the output to use. Optional: String. One or more labels to add to the logs. Create the CR object: USD oc create -f <filename>.yaml 9.4.15.1. Adding log source information to message output You can add namespace_name , pod_name , and container_name elements to the message field of the record by adding the AddLogSource field to your ClusterLogForwarder custom resource (CR). spec: outputs: - name: syslogout syslog: addLogSource: true facility: user payloadKey: message rfc: RFC3164 severity: debug tag: mytag type: syslog url: tls://syslog-receiver.openshift-logging.svc:24224 pipelines: - inputRefs: - application name: test-app outputRefs: - syslogout Note This configuration is compatible with both RFC3164 and RFC5424. Example syslog message output without AddLogSource <15>1 2020-11-15T17:06:14+00:00 fluentd-9hkb4 mytag - - - {"msgcontent"=>"Message Contents", "timestamp"=>"2020-11-15 17:06:09", "tag_key"=>"rec_tag", "index"=>56} Example syslog message output with AddLogSource <15>1 2020-11-16T10:49:37+00:00 crc-j55b9-master-0 mytag - - - namespace_name=clo-test-6327,pod_name=log-generator-ff9746c49-qxm7l,container_name=log-generator,message={"msgcontent":"My life is my message", "timestamp":"2020-11-16 10:49:36", "tag_key":"rec_tag", "index":76} 9.4.15.2. Syslog parameters You can configure the following for the syslog outputs. For more information, see the syslog RFC3164 or RFC5424 RFC. facility: The syslog facility . The value can be a decimal integer or a case-insensitive keyword: 0 or kern for kernel messages 1 or user for user-level messages, the default. 2 or mail for the mail system 3 or daemon for system daemons 4 or auth for security/authentication messages 5 or syslog for messages generated internally by syslogd 6 or lpr for the line printer subsystem 7 or news for the network news subsystem 8 or uucp for the UUCP subsystem 9 or cron for the clock daemon 10 or authpriv for security authentication messages 11 or ftp for the FTP daemon 12 or ntp for the NTP subsystem 13 or security for the syslog audit log 14 or console for the syslog alert log 15 or solaris-cron for the scheduling daemon 16 - 23 or local0 - local7 for locally used facilities Optional: payloadKey : The record field to use as payload for the syslog message. Note Configuring the payloadKey parameter prevents other parameters from being forwarded to the syslog. rfc: The RFC to be used for sending logs using syslog. The default is RFC5424. severity: The syslog severity to set on outgoing syslog records. The value can be a decimal integer or a case-insensitive keyword: 0 or Emergency for messages indicating the system is unusable 1 or Alert for messages indicating action must be taken immediately 2 or Critical for messages indicating critical conditions 3 or Error for messages indicating error conditions 4 or Warning for messages indicating warning conditions 5 or Notice for messages indicating normal but significant conditions 6 or Informational for messages indicating informational messages 7 or Debug for messages indicating debug-level messages, the default tag: Tag specifies a record field to use as a tag on the syslog message. trimPrefix: Remove the specified prefix from the tag. 9.4.15.3. Additional RFC5424 syslog parameters The following parameters apply to RFC5424: appName: The APP-NAME is a free-text string that identifies the application that sent the log. Must be specified for RFC5424 . msgID: The MSGID is a free-text string that identifies the type of message. Must be specified for RFC5424 . procID: The PROCID is a free-text string. A change in the value indicates a discontinuity in syslog reporting. Must be specified for RFC5424 . 9.4.16. Forwarding logs to a Kafka broker You can forward logs to an external Kafka broker in addition to, or instead of, the default log store. To configure log forwarding to an external Kafka instance, you must create a ClusterLogForwarder custom resource (CR) with an output to that instance, and a pipeline that uses the output. You can include a specific Kafka topic in the output or use the default. The Kafka output can use a TCP (insecure) or TLS (secure TCP) connection. Procedure Create or edit a YAML file that defines the ClusterLogForwarder CR object: apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: app-logs 4 type: kafka 5 url: tls://kafka.example.devlab.com:9093/app-topic 6 secret: name: kafka-secret 7 - name: infra-logs type: kafka url: tcp://kafka.devlab2.example.com:9093/infra-topic 8 - name: audit-logs type: kafka url: tls://kafka.qelab.example.com:9093/audit-topic secret: name: kafka-secret-qe pipelines: - name: app-topic 9 inputRefs: 10 - application outputRefs: 11 - app-logs labels: logType: "application" 12 - name: infra-topic 13 inputRefs: - infrastructure outputRefs: - infra-logs labels: logType: "infra" - name: audit-topic inputRefs: - audit outputRefs: - audit-logs labels: logType: "audit" 1 In legacy implementations, the CR name must be instance . In multi log forwarder implementations, you can use any name. 2 In legacy implementations, the CR namespace must be openshift-logging . In multi log forwarder implementations, you can use any namespace. 3 The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace. 4 Specify a name for the output. 5 Specify the kafka type. 6 Specify the URL and port of the Kafka broker as a valid absolute URL, optionally with a specific topic. You can use the tcp (insecure) or tls (secure TCP) protocol. If the cluster-wide proxy using the CIDR annotation is enabled, the output must be a server name or FQDN, not an IP address. 7 If you are using a tls prefix, you must specify the name of the secret required by the endpoint for TLS communication. The secret must contain a ca-bundle.crt key that points to the certificate it represents. In legacy implementations, the secret must exist in the openshift-logging project. 8 Optional: To send an insecure output, use a tcp prefix in front of the URL. Also omit the secret key and its name from this output. 9 Optional: Specify a name for the pipeline. 10 Specify which log types to forward by using the pipeline: application, infrastructure , or audit . 11 Specify the name of the output to use when forwarding logs with this pipeline. 12 Optional: String. One or more labels to add to the logs. 13 Optional: Configure multiple outputs to forward logs to other external log aggregators of any supported type: A name to describe the pipeline. The inputRefs is the log type to forward by using the pipeline: application, infrastructure , or audit . The outputRefs is the name of the output to use. Optional: String. One or more labels to add to the logs. Optional: To forward a single output to multiple Kafka brokers, specify an array of Kafka brokers as shown in the following example: # ... spec: outputs: - name: app-logs type: kafka secret: name: kafka-secret-dev kafka: 1 brokers: 2 - tls://kafka-broker1.example.com:9093/ - tls://kafka-broker2.example.com:9093/ topic: app-topic 3 # ... 1 Specify a kafka key that has a brokers and topic key. 2 Use the brokers key to specify an array of one or more brokers. 3 Use the topic key to specify the target topic that receives the logs. Apply the ClusterLogForwarder CR by running the following command: USD oc apply -f <filename>.yaml 9.4.17. Forwarding logs to Amazon CloudWatch You can forward logs to Amazon CloudWatch, a monitoring and log storage service hosted by Amazon Web Services (AWS). You can forward logs to CloudWatch in addition to, or instead of, the default log store. To configure log forwarding to CloudWatch, you must create a ClusterLogForwarder custom resource (CR) with an output for CloudWatch, and a pipeline that uses the output. Procedure Create a Secret YAML file that uses the aws_access_key_id and aws_secret_access_key fields to specify your base64-encoded AWS credentials. For example: apiVersion: v1 kind: Secret metadata: name: cw-secret namespace: openshift-logging data: aws_access_key_id: QUtJQUlPU0ZPRE5ON0VYQU1QTEUK aws_secret_access_key: d0phbHJYVXRuRkVNSS9LN01ERU5HL2JQeFJmaUNZRVhBTVBMRUtFWQo= Create the secret. For example: USD oc apply -f cw-secret.yaml Create or edit a YAML file that defines the ClusterLogForwarder CR object. In the file, specify the name of the secret. For example: apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: cw 4 type: cloudwatch 5 cloudwatch: groupBy: logType 6 groupPrefix: <group prefix> 7 region: us-east-2 8 secret: name: cw-secret 9 pipelines: - name: infra-logs 10 inputRefs: 11 - infrastructure - audit - application outputRefs: - cw 12 1 In legacy implementations, the CR name must be instance . In multi log forwarder implementations, you can use any name. 2 In legacy implementations, the CR namespace must be openshift-logging . In multi log forwarder implementations, you can use any namespace. 3 The name of your service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace. 4 Specify a name for the output. 5 Specify the cloudwatch type. 6 Optional: Specify how to group the logs: logType creates log groups for each log type. namespaceName creates a log group for each application name space. It also creates separate log groups for infrastructure and audit logs. namespaceUUID creates a new log groups for each application namespace UUID. It also creates separate log groups for infrastructure and audit logs. 7 Optional: Specify a string to replace the default infrastructureName prefix in the names of the log groups. 8 Specify the AWS region. 9 Specify the name of the secret that contains your AWS credentials. 10 Optional: Specify a name for the pipeline. 11 Specify which log types to forward by using the pipeline: application, infrastructure , or audit . 12 Specify the name of the output to use when forwarding logs with this pipeline. Create the CR object: USD oc create -f <file-name>.yaml Example: Using ClusterLogForwarder with Amazon CloudWatch Here, you see an example ClusterLogForwarder custom resource (CR) and the log data that it outputs to Amazon CloudWatch. Suppose that you are running an OpenShift Dedicated cluster named mycluster . The following command returns the cluster's infrastructureName , which you will use to compose aws commands later on: USD oc get Infrastructure/cluster -ojson | jq .status.infrastructureName "mycluster-7977k" To generate log data for this example, you run a busybox pod in a namespace called app . The busybox pod writes a message to stdout every three seconds: USD oc run busybox --image=busybox -- sh -c 'while true; do echo "My life is my message"; sleep 3; done' USD oc logs -f busybox My life is my message My life is my message My life is my message ... You can look up the UUID of the app namespace where the busybox pod runs: USD oc get ns/app -ojson | jq .metadata.uid "794e1e1a-b9f5-4958-a190-e76a9b53d7bf" In your ClusterLogForwarder custom resource (CR), you configure the infrastructure , audit , and application log types as inputs to the all-logs pipeline. You also connect this pipeline to cw output, which forwards the logs to a CloudWatch instance in the us-east-2 region: apiVersion: "logging.openshift.io/v1" kind: ClusterLogForwarder metadata: name: instance namespace: openshift-logging spec: outputs: - name: cw type: cloudwatch cloudwatch: groupBy: logType region: us-east-2 secret: name: cw-secret pipelines: - name: all-logs inputRefs: - infrastructure - audit - application outputRefs: - cw Each region in CloudWatch contains three levels of objects: log group log stream log event With groupBy: logType in the ClusterLogForwarding CR, the three log types in the inputRefs produce three log groups in Amazon Cloudwatch: USD aws --output json logs describe-log-groups | jq .logGroups[].logGroupName "mycluster-7977k.application" "mycluster-7977k.audit" "mycluster-7977k.infrastructure" Each of the log groups contains log streams: USD aws --output json logs describe-log-streams --log-group-name mycluster-7977k.application | jq .logStreams[].logStreamName "kubernetes.var.log.containers.busybox_app_busybox-da085893053e20beddd6747acdbaf98e77c37718f85a7f6a4facf09ca195ad76.log" USD aws --output json logs describe-log-streams --log-group-name mycluster-7977k.audit | jq .logStreams[].logStreamName "ip-10-0-131-228.us-east-2.compute.internal.k8s-audit.log" "ip-10-0-131-228.us-east-2.compute.internal.linux-audit.log" "ip-10-0-131-228.us-east-2.compute.internal.openshift-audit.log" ... USD aws --output json logs describe-log-streams --log-group-name mycluster-7977k.infrastructure | jq .logStreams[].logStreamName "ip-10-0-131-228.us-east-2.compute.internal.kubernetes.var.log.containers.apiserver-69f9fd9b58-zqzw5_openshift-oauth-apiserver_oauth-apiserver-453c5c4ee026fe20a6139ba6b1cdd1bed25989c905bf5ac5ca211b7cbb5c3d7b.log" "ip-10-0-131-228.us-east-2.compute.internal.kubernetes.var.log.containers.apiserver-797774f7c5-lftrx_openshift-apiserver_openshift-apiserver-ce51532df7d4e4d5f21c4f4be05f6575b93196336be0027067fd7d93d70f66a4.log" "ip-10-0-131-228.us-east-2.compute.internal.kubernetes.var.log.containers.apiserver-797774f7c5-lftrx_openshift-apiserver_openshift-apiserver-check-endpoints-82a9096b5931b5c3b1d6dc4b66113252da4a6472c9fff48623baee761911a9ef.log" ... Each log stream contains log events. To see a log event from the busybox Pod, you specify its log stream from the application log group: USD aws logs get-log-events --log-group-name mycluster-7977k.application --log-stream-name kubernetes.var.log.containers.busybox_app_busybox-da085893053e20beddd6747acdbaf98e77c37718f85a7f6a4facf09ca195ad76.log { "events": [ { "timestamp": 1629422704178, "message": "{\"docker\":{\"container_id\":\"da085893053e20beddd6747acdbaf98e77c37718f85a7f6a4facf09ca195ad76\"},\"kubernetes\":{\"container_name\":\"busybox\",\"namespace_name\":\"app\",\"pod_name\":\"busybox\",\"container_image\":\"docker.io/library/busybox:latest\",\"container_image_id\":\"docker.io/library/busybox@sha256:0f354ec1728d9ff32edcd7d1b8bbdfc798277ad36120dc3dc683be44524c8b60\",\"pod_id\":\"870be234-90a3-4258-b73f-4f4d6e2777c7\",\"host\":\"ip-10-0-216-3.us-east-2.compute.internal\",\"labels\":{\"run\":\"busybox\"},\"master_url\":\"https://kubernetes.default.svc\",\"namespace_id\":\"794e1e1a-b9f5-4958-a190-e76a9b53d7bf\",\"namespace_labels\":{\"kubernetes_io/metadata_name\":\"app\"}},\"message\":\"My life is my message\",\"level\":\"unknown\",\"hostname\":\"ip-10-0-216-3.us-east-2.compute.internal\",\"pipeline_metadata\":{\"collector\":{\"ipaddr4\":\"10.0.216.3\",\"inputname\":\"fluent-plugin-systemd\",\"name\":\"fluentd\",\"received_at\":\"2021-08-20T01:25:08.085760+00:00\",\"version\":\"1.7.4 1.6.0\"}},\"@timestamp\":\"2021-08-20T01:25:04.178986+00:00\",\"viaq_index_name\":\"app-write\",\"viaq_msg_id\":\"NWRjZmUyMWQtZjgzNC00MjI4LTk3MjMtNTk3NmY3ZjU4NDk1\",\"log_type\":\"application\",\"time\":\"2021-08-20T01:25:04+00:00\"}", "ingestionTime": 1629422744016 }, ... Example: Customizing the prefix in log group names In the log group names, you can replace the default infrastructureName prefix, mycluster-7977k , with an arbitrary string like demo-group-prefix . To make this change, you update the groupPrefix field in the ClusterLogForwarding CR: cloudwatch: groupBy: logType groupPrefix: demo-group-prefix region: us-east-2 The value of groupPrefix replaces the default infrastructureName prefix: USD aws --output json logs describe-log-groups | jq .logGroups[].logGroupName "demo-group-prefix.application" "demo-group-prefix.audit" "demo-group-prefix.infrastructure" Example: Naming log groups after application namespace names For each application namespace in your cluster, you can create a log group in CloudWatch whose name is based on the name of the application namespace. If you delete an application namespace object and create a new one that has the same name, CloudWatch continues using the same log group as before. If you consider successive application namespace objects that have the same name as equivalent to each other, use the approach described in this example. Otherwise, if you need to distinguish the resulting log groups from each other, see the following "Naming log groups for application namespace UUIDs" section instead. To create application log groups whose names are based on the names of the application namespaces, you set the value of the groupBy field to namespaceName in the ClusterLogForwarder CR: cloudwatch: groupBy: namespaceName region: us-east-2 Setting groupBy to namespaceName affects the application log group only. It does not affect the audit and infrastructure log groups. In Amazon Cloudwatch, the namespace name appears at the end of each log group name. Because there is a single application namespace, "app", the following output shows a new mycluster-7977k.app log group instead of mycluster-7977k.application : USD aws --output json logs describe-log-groups | jq .logGroups[].logGroupName "mycluster-7977k.app" "mycluster-7977k.audit" "mycluster-7977k.infrastructure" If the cluster in this example had contained multiple application namespaces, the output would show multiple log groups, one for each namespace. The groupBy field affects the application log group only. It does not affect the audit and infrastructure log groups. Example: Naming log groups after application namespace UUIDs For each application namespace in your cluster, you can create a log group in CloudWatch whose name is based on the UUID of the application namespace. If you delete an application namespace object and create a new one, CloudWatch creates a new log group. If you consider successive application namespace objects with the same name as different from each other, use the approach described in this example. Otherwise, see the preceding "Example: Naming log groups for application namespace names" section instead. To name log groups after application namespace UUIDs, you set the value of the groupBy field to namespaceUUID in the ClusterLogForwarder CR: cloudwatch: groupBy: namespaceUUID region: us-east-2 In Amazon Cloudwatch, the namespace UUID appears at the end of each log group name. Because there is a single application namespace, "app", the following output shows a new mycluster-7977k.794e1e1a-b9f5-4958-a190-e76a9b53d7bf log group instead of mycluster-7977k.application : USD aws --output json logs describe-log-groups | jq .logGroups[].logGroupName "mycluster-7977k.794e1e1a-b9f5-4958-a190-e76a9b53d7bf" // uid of the "app" namespace "mycluster-7977k.audit" "mycluster-7977k.infrastructure" The groupBy field affects the application log group only. It does not affect the audit and infrastructure log groups. 9.4.18. Creating a secret for AWS CloudWatch with an existing AWS role If you have an existing role for AWS, you can create a secret for AWS with STS using the oc create secret --from-literal command. Procedure In the CLI, enter the following to generate a secret for AWS: USD oc create secret generic cw-sts-secret -n openshift-logging --from-literal=role_arn=arn:aws:iam::123456789012:role/my-role_with-permissions Example Secret apiVersion: v1 kind: Secret metadata: namespace: openshift-logging name: my-secret-name stringData: role_arn: arn:aws:iam::123456789012:role/my-role_with-permissions 9.4.19. Forwarding logs to Amazon CloudWatch from STS enabled clusters For clusters with AWS Security Token Service (STS) enabled, you can create an AWS service account manually or create a credentials request by using the Cloud Credential Operator (CCO) utility ccoctl . Prerequisites Logging for Red Hat OpenShift: 5.5 and later Procedure Create a CredentialsRequest custom resource YAML by using the template below: CloudWatch credentials request template apiVersion: cloudcredential.openshift.io/v1 kind: CredentialsRequest metadata: name: <your_role_name>-credrequest namespace: openshift-cloud-credential-operator spec: providerSpec: apiVersion: cloudcredential.openshift.io/v1 kind: AWSProviderSpec statementEntries: - action: - logs:PutLogEvents - logs:CreateLogGroup - logs:PutRetentionPolicy - logs:CreateLogStream - logs:DescribeLogGroups - logs:DescribeLogStreams effect: Allow resource: arn:aws:logs:*:*:* secretRef: name: <your_role_name> namespace: openshift-logging serviceAccountNames: - logcollector Use the ccoctl command to create a role for AWS using your CredentialsRequest CR. With the CredentialsRequest object, this ccoctl command creates an IAM role with a trust policy that is tied to the specified OIDC identity provider, and a permissions policy that grants permissions to perform operations on CloudWatch resources. This command also creates a YAML configuration file in /<path_to_ccoctl_output_dir>/manifests/openshift-logging-<your_role_name>-credentials.yaml . This secret file contains the role_arn key/value used during authentication with the AWS IAM identity provider. USD ccoctl aws create-iam-roles \ --name=<name> \ --region=<aws_region> \ --credentials-requests-dir=<path_to_directory_with_list_of_credentials_requests>/credrequests \ --identity-provider-arn=arn:aws:iam::<aws_account_id>:oidc-provider/<name>-oidc.s3.<aws_region>.amazonaws.com 1 1 <name> is the name used to tag your cloud resources and should match the name used during your STS cluster install Apply the secret created: USD oc apply -f output/manifests/openshift-logging-<your_role_name>-credentials.yaml Create or edit a ClusterLogForwarder custom resource: apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: clf-collector 3 outputs: - name: cw 4 type: cloudwatch 5 cloudwatch: groupBy: logType 6 groupPrefix: <group prefix> 7 region: us-east-2 8 secret: name: <your_secret_name> 9 pipelines: - name: to-cloudwatch 10 inputRefs: 11 - infrastructure - audit - application outputRefs: - cw 12 1 In legacy implementations, the CR name must be instance . In multi log forwarder implementations, you can use any name. 2 In legacy implementations, the CR namespace must be openshift-logging . In multi log forwarder implementations, you can use any namespace. 3 Specify the clf-collector service account. The service account is only required in multi log forwarder implementations if the log forwarder is not deployed in the openshift-logging namespace. 4 Specify a name for the output. 5 Specify the cloudwatch type. 6 Optional: Specify how to group the logs: logType creates log groups for each log type. namespaceName creates a log group for each application name space. Infrastructure and audit logs are unaffected, remaining grouped by logType . namespaceUUID creates a new log groups for each application namespace UUID. It also creates separate log groups for infrastructure and audit logs. 7 Optional: Specify a string to replace the default infrastructureName prefix in the names of the log groups. 8 Specify the AWS region. 9 Specify the name of the secret that contains your AWS credentials. 10 Optional: Specify a name for the pipeline. 11 Specify which log types to forward by using the pipeline: application, infrastructure , or audit . 12 Specify the name of the output to use when forwarding logs with this pipeline. Additional resources AWS STS API Reference 9.5. Configuring the logging collector Logging for Red Hat OpenShift collects operations and application logs from your cluster and enriches the data with Kubernetes pod and project metadata. All supported modifications to the log collector can be performed though the spec.collection stanza in the ClusterLogging custom resource (CR). 9.5.1. Configuring the log collector You can configure which log collector type your logging uses by modifying the ClusterLogging custom resource (CR). Note Fluentd is deprecated and is planned to be removed in a future release. Red Hat provides bug fixes and support for this feature during the current release lifecycle, but this feature no longer receives enhancements. As an alternative to Fluentd, you can use Vector instead. Prerequisites You have administrator permissions. You have installed the OpenShift CLI ( oc ). You have installed the Red Hat OpenShift Logging Operator. You have created a ClusterLogging CR. Procedure Modify the ClusterLogging CR collection spec: ClusterLogging CR example apiVersion: logging.openshift.io/v1 kind: ClusterLogging metadata: # ... spec: # ... collection: type: <log_collector_type> 1 resources: {} tolerations: {} # ... 1 The log collector type you want to use for the logging. This can be vector or fluentd . Apply the ClusterLogging CR by running the following command: USD oc apply -f <filename>.yaml 9.5.2. Creating a LogFileMetricExporter resource In logging version 5.8 and newer versions, the LogFileMetricExporter is no longer deployed with the collector by default. You must manually create a LogFileMetricExporter custom resource (CR) to generate metrics from the logs produced by running containers. If you do not create the LogFileMetricExporter CR, you may see a No datapoints found message in the OpenShift Dedicated web console dashboard for Produced Logs . Prerequisites You have administrator permissions. You have installed the Red Hat OpenShift Logging Operator. You have installed the OpenShift CLI ( oc ). Procedure Create a LogFileMetricExporter CR as a YAML file: Example LogFileMetricExporter CR apiVersion: logging.openshift.io/v1alpha1 kind: LogFileMetricExporter metadata: name: instance namespace: openshift-logging spec: nodeSelector: {} 1 resources: 2 limits: cpu: 500m memory: 256Mi requests: cpu: 200m memory: 128Mi tolerations: [] 3 # ... 1 Optional: The nodeSelector stanza defines which nodes the pods are scheduled on. 2 The resources stanza defines resource requirements for the LogFileMetricExporter CR. 3 Optional: The tolerations stanza defines the tolerations that the pods accept. Apply the LogFileMetricExporter CR by running the following command: USD oc apply -f <filename>.yaml Verification A logfilesmetricexporter pod runs concurrently with a collector pod on each node. Verify that the logfilesmetricexporter pods are running in the namespace where you have created the LogFileMetricExporter CR, by running the following command and observing the output: USD oc get pods -l app.kubernetes.io/component=logfilesmetricexporter -n openshift-logging Example output NAME READY STATUS RESTARTS AGE logfilesmetricexporter-9qbjj 1/1 Running 0 2m46s logfilesmetricexporter-cbc4v 1/1 Running 0 2m46s 9.5.3. Configure log collector CPU and memory limits The log collector allows for adjustments to both the CPU and memory limits. Procedure Edit the ClusterLogging custom resource (CR) in the openshift-logging project: USD oc -n openshift-logging edit ClusterLogging instance apiVersion: logging.openshift.io/v1 kind: ClusterLogging metadata: name: instance namespace: openshift-logging spec: collection: type: fluentd resources: limits: 1 memory: 736Mi requests: cpu: 100m memory: 736Mi # ... 1 Specify the CPU and memory limits and requests as needed. The values shown are the default values. 9.5.4. Configuring input receivers The Red Hat OpenShift Logging Operator deploys a service for each configured input receiver so that clients can write to the collector. This service exposes the port specified for the input receiver. The service name is generated based on the following: For multi log forwarder ClusterLogForwarder CR deployments, the service name is in the format <ClusterLogForwarder_CR_name>-<input_name> . For example, example-http-receiver . For legacy ClusterLogForwarder CR deployments, meaning those named instance and located in the openshift-logging namespace, the service name is in the format collector-<input_name> . For example, collector-http-receiver . 9.5.4.1. Configuring the collector to receive audit logs as an HTTP server You can configure your log collector to listen for HTTP connections and receive audit logs as an HTTP server by specifying http as a receiver input in the ClusterLogForwarder custom resource (CR). This enables you to use a common log store for audit logs that are collected from both inside and outside of your OpenShift Dedicated cluster. Prerequisites You have administrator permissions. You have installed the OpenShift CLI ( oc ). You have installed the Red Hat OpenShift Logging Operator. You have created a ClusterLogForwarder CR. Procedure Modify the ClusterLogForwarder CR to add configuration for the http receiver input: Example ClusterLogForwarder CR if you are using a multi log forwarder deployment apiVersion: logging.openshift.io/v1beta1 kind: ClusterLogForwarder metadata: # ... spec: serviceAccountName: <service_account_name> inputs: - name: http-receiver 1 receiver: type: http 2 http: format: kubeAPIAudit 3 port: 8443 4 pipelines: 5 - name: http-pipeline inputRefs: - http-receiver # ... 1 Specify a name for your input receiver. 2 Specify the input receiver type as http . 3 Currently, only the kube-apiserver webhook format is supported for http input receivers. 4 Optional: Specify the port that the input receiver listens on. This must be a value between 1024 and 65535 . The default value is 8443 if this is not specified. 5 Configure a pipeline for your input receiver. Example ClusterLogForwarder CR if you are using a legacy deployment apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: instance namespace: openshift-logging spec: inputs: - name: http-receiver 1 receiver: type: http 2 http: format: kubeAPIAudit 3 port: 8443 4 pipelines: 5 - inputRefs: - http-receiver name: http-pipeline # ... 1 Specify a name for your input receiver. 2 Specify the input receiver type as http . 3 Currently, only the kube-apiserver webhook format is supported for http input receivers. 4 Optional: Specify the port that the input receiver listens on. This must be a value between 1024 and 65535 . The default value is 8443 if this is not specified. 5 Configure a pipeline for your input receiver. Apply the changes to the ClusterLogForwarder CR by running the following command: USD oc apply -f <filename>.yaml Additional resources Overview of API audit filter 9.5.5. Advanced configuration for the Fluentd log forwarder Note Fluentd is deprecated and is planned to be removed in a future release. Red Hat provides bug fixes and support for this feature during the current release lifecycle, but this feature no longer receives enhancements. As an alternative to Fluentd, you can use Vector instead. Logging includes multiple Fluentd parameters that you can use for tuning the performance of the Fluentd log forwarder. With these parameters, you can change the following Fluentd behaviors: Chunk and chunk buffer sizes Chunk flushing behavior Chunk forwarding retry behavior Fluentd collects log data in a single blob called a chunk . When Fluentd creates a chunk, the chunk is considered to be in the stage , where the chunk gets filled with data. When the chunk is full, Fluentd moves the chunk to the queue , where chunks are held before being flushed, or written out to their destination. Fluentd can fail to flush a chunk for a number of reasons, such as network issues or capacity issues at the destination. If a chunk cannot be flushed, Fluentd retries flushing as configured. By default in OpenShift Dedicated, Fluentd uses the exponential backoff method to retry flushing, where Fluentd doubles the time it waits between attempts to retry flushing again, which helps reduce connection requests to the destination. You can disable exponential backoff and use the periodic retry method instead, which retries flushing the chunks at a specified interval. These parameters can help you determine the trade-offs between latency and throughput. To optimize Fluentd for throughput, you could use these parameters to reduce network packet count by configuring larger buffers and queues, delaying flushes, and setting longer times between retries. Be aware that larger buffers require more space on the node file system. To optimize for low latency, you could use the parameters to send data as soon as possible, avoid the build-up of batches, have shorter queues and buffers, and use more frequent flush and retries. You can configure the chunking and flushing behavior using the following parameters in the ClusterLogging custom resource (CR). The parameters are then automatically added to the Fluentd config map for use by Fluentd. Note These parameters are: Not relevant to most users. The default settings should give good general performance. Only for advanced users with detailed knowledge of Fluentd configuration and performance. Only for performance tuning. They have no effect on functional aspects of logging. Table 9.11. Advanced Fluentd Configuration Parameters Parameter Description Default chunkLimitSize The maximum size of each chunk. Fluentd stops writing data to a chunk when it reaches this size. Then, Fluentd sends the chunk to the queue and opens a new chunk. 8m totalLimitSize The maximum size of the buffer, which is the total size of the stage and the queue. If the buffer size exceeds this value, Fluentd stops adding data to chunks and fails with an error. All data not in chunks is lost. Approximately 15% of the node disk distributed across all outputs. flushInterval The interval between chunk flushes. You can use s (seconds), m (minutes), h (hours), or d (days). 1s flushMode The method to perform flushes: lazy : Flush chunks based on the timekey parameter. You cannot modify the timekey parameter. interval : Flush chunks based on the flushInterval parameter. immediate : Flush chunks immediately after data is added to a chunk. interval flushThreadCount The number of threads that perform chunk flushing. Increasing the number of threads improves the flush throughput, which hides network latency. 2 overflowAction The chunking behavior when the queue is full: throw_exception : Raise an exception to show in the log. block : Stop data chunking until the full buffer issue is resolved. drop_oldest_chunk : Drop the oldest chunk to accept new incoming chunks. Older chunks have less value than newer chunks. block retryMaxInterval The maximum time in seconds for the exponential_backoff retry method. 300s retryType The retry method when flushing fails: exponential_backoff : Increase the time between flush retries. Fluentd doubles the time it waits until the retry until the retry_max_interval parameter is reached. periodic : Retries flushes periodically, based on the retryWait parameter. exponential_backoff retryTimeOut The maximum time interval to attempt retries before the record is discarded. 60m retryWait The time in seconds before the chunk flush. 1s For more information on the Fluentd chunk lifecycle, see Buffer Plugins in the Fluentd documentation. Procedure Edit the ClusterLogging custom resource (CR) in the openshift-logging project: USD oc edit ClusterLogging instance Add or modify any of the following parameters: apiVersion: logging.openshift.io/v1 kind: ClusterLogging metadata: name: instance namespace: openshift-logging spec: collection: fluentd: buffer: chunkLimitSize: 8m 1 flushInterval: 5s 2 flushMode: interval 3 flushThreadCount: 3 4 overflowAction: throw_exception 5 retryMaxInterval: "300s" 6 retryType: periodic 7 retryWait: 1s 8 totalLimitSize: 32m 9 # ... 1 Specify the maximum size of each chunk before it is queued for flushing. 2 Specify the interval between chunk flushes. 3 Specify the method to perform chunk flushes: lazy , interval , or immediate . 4 Specify the number of threads to use for chunk flushes. 5 Specify the chunking behavior when the queue is full: throw_exception , block , or drop_oldest_chunk . 6 Specify the maximum interval in seconds for the exponential_backoff chunk flushing method. 7 Specify the retry type when chunk flushing fails: exponential_backoff or periodic . 8 Specify the time in seconds before the chunk flush. 9 Specify the maximum size of the chunk buffer. Verify that the Fluentd pods are redeployed: USD oc get pods -l component=collector -n openshift-logging Check that the new values are in the fluentd config map: USD oc extract configmap/collector-config --confirm Example fluentd.conf <buffer> @type file path '/var/lib/fluentd/default' flush_mode interval flush_interval 5s flush_thread_count 3 retry_type periodic retry_wait 1s retry_max_interval 300s retry_timeout 60m queued_chunks_limit_size "#{ENV['BUFFER_QUEUE_LIMIT'] || '32'}" total_limit_size "#{ENV['TOTAL_LIMIT_SIZE_PER_BUFFER'] || '8589934592'}" chunk_limit_size 8m overflow_action throw_exception disable_chunk_backup true </buffer> 9.6. Collecting and storing Kubernetes events The OpenShift Dedicated Event Router is a pod that watches Kubernetes events and logs them for collection by the logging. You must manually deploy the Event Router. The Event Router collects events from all projects and writes them to STDOUT . The collector then forwards those events to the store defined in the ClusterLogForwarder custom resource (CR). Important The Event Router adds additional load to Fluentd and can impact the number of other log messages that can be processed. 9.6.1. Deploying and configuring the Event Router Use the following steps to deploy the Event Router into your cluster. You should always deploy the Event Router to the openshift-logging project to ensure it collects events from across the cluster. Note The Event Router image is not a part of the Red Hat OpenShift Logging Operator and must be downloaded separately. The following Template object creates the service account, cluster role, and cluster role binding required for the Event Router. The template also configures and deploys the Event Router pod. You can either use this template without making changes or edit the template to change the deployment object CPU and memory requests. Prerequisites You need proper permissions to create service accounts and update cluster role bindings. For example, you can run the following template with a user that has the cluster-admin role. The Red Hat OpenShift Logging Operator must be installed. Procedure Create a template for the Event Router: apiVersion: template.openshift.io/v1 kind: Template metadata: name: eventrouter-template annotations: description: "A pod forwarding kubernetes events to OpenShift Logging stack." tags: "events,EFK,logging,cluster-logging" objects: - kind: ServiceAccount 1 apiVersion: v1 metadata: name: eventrouter namespace: USD{NAMESPACE} - kind: ClusterRole 2 apiVersion: rbac.authorization.k8s.io/v1 metadata: name: event-reader rules: - apiGroups: [""] resources: ["events"] verbs: ["get", "watch", "list"] - kind: ClusterRoleBinding 3 apiVersion: rbac.authorization.k8s.io/v1 metadata: name: event-reader-binding subjects: - kind: ServiceAccount name: eventrouter namespace: USD{NAMESPACE} roleRef: kind: ClusterRole name: event-reader - kind: ConfigMap 4 apiVersion: v1 metadata: name: eventrouter namespace: USD{NAMESPACE} data: config.json: |- { "sink": "stdout" } - kind: Deployment 5 apiVersion: apps/v1 metadata: name: eventrouter namespace: USD{NAMESPACE} labels: component: "eventrouter" logging-infra: "eventrouter" provider: "openshift" spec: selector: matchLabels: component: "eventrouter" logging-infra: "eventrouter" provider: "openshift" replicas: 1 template: metadata: labels: component: "eventrouter" logging-infra: "eventrouter" provider: "openshift" name: eventrouter spec: serviceAccount: eventrouter containers: - name: kube-eventrouter image: USD{IMAGE} imagePullPolicy: IfNotPresent resources: requests: cpu: USD{CPU} memory: USD{MEMORY} volumeMounts: - name: config-volume mountPath: /etc/eventrouter securityContext: allowPrivilegeEscalation: false capabilities: drop: ["ALL"] securityContext: runAsNonRoot: true seccompProfile: type: RuntimeDefault volumes: - name: config-volume configMap: name: eventrouter parameters: - name: IMAGE 6 displayName: Image value: "registry.redhat.io/openshift-logging/eventrouter-rhel9:v0.4" - name: CPU 7 displayName: CPU value: "100m" - name: MEMORY 8 displayName: Memory value: "128Mi" - name: NAMESPACE displayName: Namespace value: "openshift-logging" 9 1 Creates a Service Account in the openshift-logging project for the Event Router. 2 Creates a ClusterRole to monitor for events in the cluster. 3 Creates a ClusterRoleBinding to bind the ClusterRole to the service account. 4 Creates a config map in the openshift-logging project to generate the required config.json file. 5 Creates a deployment in the openshift-logging project to generate and configure the Event Router pod. 6 Specifies the image, identified by a tag such as v0.4 . 7 Specifies the minimum amount of CPU to allocate to the Event Router pod. Defaults to 100m . 8 Specifies the minimum amount of memory to allocate to the Event Router pod. Defaults to 128Mi . 9 Specifies the openshift-logging project to install objects in. Use the following command to process and apply the template: USD oc process -f <templatefile> | oc apply -n openshift-logging -f - For example: USD oc process -f eventrouter.yaml | oc apply -n openshift-logging -f - Example output serviceaccount/eventrouter created clusterrole.rbac.authorization.k8s.io/event-reader created clusterrolebinding.rbac.authorization.k8s.io/event-reader-binding created configmap/eventrouter created deployment.apps/eventrouter created Validate that the Event Router installed in the openshift-logging project: View the new Event Router pod: USD oc get pods --selector component=eventrouter -o name -n openshift-logging Example output pod/cluster-logging-eventrouter-d649f97c8-qvv8r View the events collected by the Event Router: USD oc logs <cluster_logging_eventrouter_pod> -n openshift-logging For example: USD oc logs cluster-logging-eventrouter-d649f97c8-qvv8r -n openshift-logging Example output {"verb":"ADDED","event":{"metadata":{"name":"openshift-service-catalog-controller-manager-remover.1632d931e88fcd8f","namespace":"openshift-service-catalog-removed","selfLink":"/api/v1/namespaces/openshift-service-catalog-removed/events/openshift-service-catalog-controller-manager-remover.1632d931e88fcd8f","uid":"787d7b26-3d2f-4017-b0b0-420db4ae62c0","resourceVersion":"21399","creationTimestamp":"2020-09-08T15:40:26Z"},"involvedObject":{"kind":"Job","namespace":"openshift-service-catalog-removed","name":"openshift-service-catalog-controller-manager-remover","uid":"fac9f479-4ad5-4a57-8adc-cb25d3d9cf8f","apiVersion":"batch/v1","resourceVersion":"21280"},"reason":"Completed","message":"Job completed","source":{"component":"job-controller"},"firstTimestamp":"2020-09-08T15:40:26Z","lastTimestamp":"2020-09-08T15:40:26Z","count":1,"type":"Normal"}} You can also use Kibana to view events by creating an index pattern using the Elasticsearch infra index.
apiVersion: logging.openshift.io/v1 kind: ClusterLogging metadata: name: instance namespace: openshift-logging spec: collection: logs: type: vector vector: {} oc adm policy add-cluster-role-to-user <cluster_role_name> system:serviceaccount:<namespace_name>:<service_account_name> {"level":"info","name":"fred","home":"bedrock"} pipelines: - inputRefs: [ application ] outputRefs: myFluentd parse: json {"structured": { "level": "info", "name": "fred", "home": "bedrock" }, "more fields..."} apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: spec: outputDefaults: elasticsearch: structuredTypeKey: kubernetes.labels.logFormat 1 structuredTypeName: nologformat pipelines: - inputRefs: - application outputRefs: - default parse: json 2 { "structured":{"name":"fred","home":"bedrock"}, "kubernetes":{"labels":{"logFormat": "apache", ...}} } { "structured":{"name":"wilma","home":"bedrock"}, "kubernetes":{"labels":{"logFormat": "google", ...}} } outputDefaults: elasticsearch: structuredTypeKey: openshift.labels.myLabel 1 structuredTypeName: nologformat pipelines: - name: application-logs inputRefs: - application - audit outputRefs: - elasticsearch-secure - default parse: json labels: myLabel: myValue 2 { "structured":{"name":"fred","home":"bedrock"}, "openshift":{"labels":{"myLabel": "myValue", ...}} } outputDefaults: elasticsearch: structuredTypeKey: <log record field> structuredTypeName: <name> pipelines: - inputRefs: - application outputRefs: default parse: json oc create -f <filename>.yaml oc delete pod --selector logging-infra=collector apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: instance namespace: openshift-logging spec: outputDefaults: elasticsearch: structuredTypeKey: kubernetes.labels.logFormat 1 structuredTypeName: nologformat enableStructuredContainerLogs: true 2 pipelines: - inputRefs: - application name: application-logs outputRefs: - default parse: json apiVersion: v1 kind: Pod metadata: annotations: containerType.logging.openshift.io/heavy: heavy 1 containerType.logging.openshift.io/low: low spec: containers: - name: heavy 2 image: heavyimage - name: low image: lowimage apiVersion: "logging.openshift.io/v1" kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: elasticsearch-secure 4 type: "elasticsearch" url: https://elasticsearch.secure.com:9200 secret: name: elasticsearch - name: elasticsearch-insecure 5 type: "elasticsearch" url: http://elasticsearch.insecure.com:9200 - name: kafka-app 6 type: "kafka" url: tls://kafka.secure.com:9093/app-topic inputs: 7 - name: my-app-logs application: namespaces: - my-project pipelines: - name: audit-logs 8 inputRefs: - audit outputRefs: - elasticsearch-secure - default labels: secure: "true" 9 datacenter: "east" - name: infrastructure-logs 10 inputRefs: - infrastructure outputRefs: - elasticsearch-insecure labels: datacenter: "west" - name: my-app 11 inputRefs: - my-app-logs outputRefs: - default - inputRefs: 12 - application outputRefs: - kafka-app labels: datacenter: "south" oc create secret generic -n <namespace> <secret_name> --from-file=ca-bundle.crt=<your_bundle_file> --from-literal=username=<your_username> --from-literal=password=<your_password> apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 pipelines: - inputRefs: - <log_type> 4 outputRefs: - <output_name> 5 outputs: - name: <output_name> 6 type: <output_type> 7 url: <log_output_url> 8 apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: spec: tuning: delivery: AtLeastOnce 1 compression: none 2 maxWrite: <integer> 3 minRetryDuration: 1s 4 maxRetryDuration: 1s 5 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) apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: instance namespace: openshift-logging spec: pipelines: - name: my-app-logs inputRefs: - application outputRefs: - default detectMultilineErrors: true [transforms.detect_exceptions_app-logs] type = "detect_exceptions" inputs = ["application"] languages = ["All"] group_by = ["kubernetes.namespace_name","kubernetes.pod_name","kubernetes.container_name"] expire_after_ms = 2000 multiline_flush_interval_ms = 1000 <label @MULTILINE_APP_LOGS> <match kubernetes.**> @type detect_exceptions remove_tag_prefix 'kubernetes' message message force_line_breaks true multiline_flush_interval .2 </match> </label> oc -n openshift-logging create secret generic gcp-secret --from-file google-application-credentials.json= <your_service_account_key_file.json> apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: gcp-1 type: googleCloudLogging secret: name: gcp-secret googleCloudLogging: projectId : "openshift-gce-devel" 4 logId : "app-gcp" 5 pipelines: - name: test-app inputRefs: 6 - application outputRefs: - gcp-1 oc -n openshift-logging create secret generic vector-splunk-secret --from-literal hecToken=<HEC_Token> apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: splunk-receiver 4 secret: name: vector-splunk-secret 5 type: splunk 6 url: <http://your.splunk.hec.url:8088> 7 pipelines: 8 - inputRefs: - application - infrastructure name: 9 outputRefs: - splunk-receiver 10 apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: httpout-app type: http url: 4 http: headers: 5 h1: v1 h2: v2 method: POST secret: name: 6 tls: insecureSkipVerify: 7 pipelines: - name: inputRefs: - application outputRefs: - 8 apiVersion: v1 kind: Secret metadata: name: my-secret namespace: openshift-logging type: Opaque data: shared_key: <your_shared_key> 1 Get-AzOperationalInsightsWorkspaceSharedKey -ResourceGroupName "<resource_name>" -Name "<workspace_name>" apiVersion: "logging.openshift.io/v1" kind: "ClusterLogForwarder" metadata: name: instance namespace: openshift-logging spec: outputs: - name: azure-monitor type: azureMonitor azureMonitor: customerId: my-customer-id 1 logType: my_log_type 2 secret: name: my-secret pipelines: - name: app-pipeline inputRefs: - application outputRefs: - azure-monitor apiVersion: "logging.openshift.io/v1" kind: "ClusterLogForwarder" metadata: name: instance namespace: openshift-logging spec: outputs: - name: azure-monitor-app type: azureMonitor azureMonitor: customerId: my-customer-id logType: application_log 1 secret: name: my-secret - name: azure-monitor-infra type: azureMonitor azureMonitor: customerId: my-customer-id logType: infra_log # secret: name: my-secret pipelines: - name: app-pipeline inputRefs: - application outputRefs: - azure-monitor-app - name: infra-pipeline inputRefs: - infrastructure outputRefs: - azure-monitor-infra apiVersion: "logging.openshift.io/v1" kind: "ClusterLogForwarder" metadata: name: instance namespace: openshift-logging spec: outputs: - name: azure-monitor type: azureMonitor azureMonitor: customerId: my-customer-id logType: my_log_type azureResourceId: "/subscriptions/111111111" 1 host: "ods.opinsights.azure.com" 2 secret: name: my-secret pipelines: - name: app-pipeline inputRefs: - application outputRefs: - azure-monitor apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: instance 1 namespace: openshift-logging 2 spec: outputs: - name: fluentd-server-secure 3 type: fluentdForward 4 url: 'tls://fluentdserver.security.example.com:24224' 5 secret: 6 name: fluentd-secret - name: fluentd-server-insecure type: fluentdForward url: 'tcp://fluentdserver.home.example.com:24224' inputs: 7 - name: my-app-logs application: namespaces: - my-project 8 pipelines: - name: forward-to-fluentd-insecure 9 inputRefs: 10 - my-app-logs outputRefs: 11 - fluentd-server-insecure labels: project: "my-project" 12 - name: forward-to-fluentd-secure 13 inputRefs: - application 14 - audit - infrastructure outputRefs: - fluentd-server-secure - default labels: clusterId: "C1234" oc apply -f <filename>.yaml apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: pipelines: - inputRefs: [ myAppLogData ] 3 outputRefs: [ default ] 4 inputs: 5 - name: myAppLogData application: selector: matchLabels: 6 environment: production app: nginx namespaces: 7 - app1 - app2 outputs: 8 - <output_name> - inputRefs: [ myAppLogData, myOtherAppLogData ] oc create -f <file-name>.yaml apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: instance namespace: openshift-logging spec: pipelines: - name: my-pipeline inputRefs: audit 1 filterRefs: my-policy 2 outputRefs: default filters: - name: my-policy type: kubeAPIAudit kubeAPIAudit: # Don't generate audit events for all requests in RequestReceived stage. omitStages: - "RequestReceived" rules: # Log pod changes at RequestResponse level - level: RequestResponse resources: - group: "" resources: ["pods"] # Log "pods/log", "pods/status" at Metadata level - level: Metadata resources: - group: "" resources: ["pods/log", "pods/status"] # Don't log requests to a configmap called "controller-leader" - level: None resources: - group: "" resources: ["configmaps"] resourceNames: ["controller-leader"] # Don't log watch requests by the "system:kube-proxy" on endpoints or services - level: None users: ["system:kube-proxy"] verbs: ["watch"] resources: - group: "" # core API group resources: ["endpoints", "services"] # Don't log authenticated requests to certain non-resource URL paths. - level: None userGroups: ["system:authenticated"] nonResourceURLs: - "/api*" # Wildcard matching. - "/version" # Log the request body of configmap changes in kube-system. - level: Request resources: - group: "" # core API group resources: ["configmaps"] # This rule only applies to resources in the "kube-system" namespace. # The empty string "" can be used to select non-namespaced resources. namespaces: ["kube-system"] # Log configmap and secret changes in all other namespaces at the Metadata level. - level: Metadata resources: - group: "" # core API group resources: ["secrets", "configmaps"] # Log all other resources in core and extensions at the Request level. - level: Request resources: - group: "" # core API group - group: "extensions" # Version of group should NOT be included. # A catch-all rule to log all other requests at the Metadata level. - level: Metadata apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: loki-insecure 4 type: "loki" 5 url: http://loki.insecure.com:3100 6 loki: tenantKey: kubernetes.namespace_name labelKeys: - kubernetes.labels.foo - name: loki-secure 7 type: "loki" url: https://loki.secure.com:3100 secret: name: loki-secret 8 loki: tenantKey: kubernetes.namespace_name 9 labelKeys: - kubernetes.labels.foo 10 pipelines: - name: application-logs 11 inputRefs: 12 - application - audit outputRefs: 13 - loki-secure oc apply -f <filename>.yaml apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: elasticsearch-example 4 type: elasticsearch 5 elasticsearch: version: 8 6 url: http://elasticsearch.example.com:9200 7 secret: name: es-secret 8 pipelines: - name: application-logs 9 inputRefs: 10 - application - audit outputRefs: - elasticsearch-example 11 - default 12 labels: myLabel: "myValue" 13 oc apply -f <filename>.yaml apiVersion: v1 kind: Secret metadata: name: openshift-test-secret data: username: <username> password: <password> oc create secret -n openshift-logging openshift-test-secret.yaml kind: ClusterLogForwarder metadata: name: instance namespace: openshift-logging spec: outputs: - name: elasticsearch type: "elasticsearch" url: https://elasticsearch.secure.com:9200 secret: name: openshift-test-secret oc apply -f <filename>.yaml apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: instance 1 namespace: openshift-logging 2 spec: outputs: - name: fluentd-server-secure 3 type: fluentdForward 4 url: 'tls://fluentdserver.security.example.com:24224' 5 secret: 6 name: fluentd-secret - name: fluentd-server-insecure type: fluentdForward url: 'tcp://fluentdserver.home.example.com:24224' pipelines: - name: forward-to-fluentd-secure 7 inputRefs: 8 - application - audit outputRefs: - fluentd-server-secure 9 - default 10 labels: clusterId: "C1234" 11 - name: forward-to-fluentd-insecure 12 inputRefs: - infrastructure outputRefs: - fluentd-server-insecure labels: clusterId: "C1234" oc create -f <file-name>.yaml input { tcp { codec => fluent { nanosecond_precision => true } port => 24114 } } filter { } output { stdout { codec => rubydebug } } apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: rsyslog-east 4 type: syslog 5 syslog: 6 facility: local0 rfc: RFC3164 payloadKey: message severity: informational url: 'tls://rsyslogserver.east.example.com:514' 7 secret: 8 name: syslog-secret - name: rsyslog-west type: syslog syslog: appName: myapp facility: user msgID: mymsg procID: myproc rfc: RFC5424 severity: debug url: 'tcp://rsyslogserver.west.example.com:514' pipelines: - name: syslog-east 9 inputRefs: 10 - audit - application outputRefs: 11 - rsyslog-east - default 12 labels: secure: "true" 13 syslog: "east" - name: syslog-west 14 inputRefs: - infrastructure outputRefs: - rsyslog-west - default labels: syslog: "west" oc create -f <filename>.yaml spec: outputs: - name: syslogout syslog: addLogSource: true facility: user payloadKey: message rfc: RFC3164 severity: debug tag: mytag type: syslog url: tls://syslog-receiver.openshift-logging.svc:24224 pipelines: - inputRefs: - application name: test-app outputRefs: - syslogout <15>1 2020-11-15T17:06:14+00:00 fluentd-9hkb4 mytag - - - {"msgcontent"=>"Message Contents", "timestamp"=>"2020-11-15 17:06:09", "tag_key"=>"rec_tag", "index"=>56} <15>1 2020-11-16T10:49:37+00:00 crc-j55b9-master-0 mytag - - - namespace_name=clo-test-6327,pod_name=log-generator-ff9746c49-qxm7l,container_name=log-generator,message={"msgcontent":"My life is my message", "timestamp":"2020-11-16 10:49:36", "tag_key":"rec_tag", "index":76} apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: app-logs 4 type: kafka 5 url: tls://kafka.example.devlab.com:9093/app-topic 6 secret: name: kafka-secret 7 - name: infra-logs type: kafka url: tcp://kafka.devlab2.example.com:9093/infra-topic 8 - name: audit-logs type: kafka url: tls://kafka.qelab.example.com:9093/audit-topic secret: name: kafka-secret-qe pipelines: - name: app-topic 9 inputRefs: 10 - application outputRefs: 11 - app-logs labels: logType: "application" 12 - name: infra-topic 13 inputRefs: - infrastructure outputRefs: - infra-logs labels: logType: "infra" - name: audit-topic inputRefs: - audit outputRefs: - audit-logs labels: logType: "audit" spec: outputs: - name: app-logs type: kafka secret: name: kafka-secret-dev kafka: 1 brokers: 2 - tls://kafka-broker1.example.com:9093/ - tls://kafka-broker2.example.com:9093/ topic: app-topic 3 oc apply -f <filename>.yaml apiVersion: v1 kind: Secret metadata: name: cw-secret namespace: openshift-logging data: aws_access_key_id: QUtJQUlPU0ZPRE5ON0VYQU1QTEUK aws_secret_access_key: d0phbHJYVXRuRkVNSS9LN01ERU5HL2JQeFJmaUNZRVhBTVBMRUtFWQo= oc apply -f cw-secret.yaml apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: <service_account_name> 3 outputs: - name: cw 4 type: cloudwatch 5 cloudwatch: groupBy: logType 6 groupPrefix: <group prefix> 7 region: us-east-2 8 secret: name: cw-secret 9 pipelines: - name: infra-logs 10 inputRefs: 11 - infrastructure - audit - application outputRefs: - cw 12 oc create -f <file-name>.yaml oc get Infrastructure/cluster -ojson | jq .status.infrastructureName "mycluster-7977k" oc run busybox --image=busybox -- sh -c 'while true; do echo "My life is my message"; sleep 3; done' oc logs -f busybox My life is my message My life is my message My life is my message oc get ns/app -ojson | jq .metadata.uid "794e1e1a-b9f5-4958-a190-e76a9b53d7bf" apiVersion: "logging.openshift.io/v1" kind: ClusterLogForwarder metadata: name: instance namespace: openshift-logging spec: outputs: - name: cw type: cloudwatch cloudwatch: groupBy: logType region: us-east-2 secret: name: cw-secret pipelines: - name: all-logs inputRefs: - infrastructure - audit - application outputRefs: - cw aws --output json logs describe-log-groups | jq .logGroups[].logGroupName "mycluster-7977k.application" "mycluster-7977k.audit" "mycluster-7977k.infrastructure" aws --output json logs describe-log-streams --log-group-name mycluster-7977k.application | jq .logStreams[].logStreamName "kubernetes.var.log.containers.busybox_app_busybox-da085893053e20beddd6747acdbaf98e77c37718f85a7f6a4facf09ca195ad76.log" aws --output json logs describe-log-streams --log-group-name mycluster-7977k.audit | jq .logStreams[].logStreamName "ip-10-0-131-228.us-east-2.compute.internal.k8s-audit.log" "ip-10-0-131-228.us-east-2.compute.internal.linux-audit.log" "ip-10-0-131-228.us-east-2.compute.internal.openshift-audit.log" aws --output json logs describe-log-streams --log-group-name mycluster-7977k.infrastructure | jq .logStreams[].logStreamName "ip-10-0-131-228.us-east-2.compute.internal.kubernetes.var.log.containers.apiserver-69f9fd9b58-zqzw5_openshift-oauth-apiserver_oauth-apiserver-453c5c4ee026fe20a6139ba6b1cdd1bed25989c905bf5ac5ca211b7cbb5c3d7b.log" "ip-10-0-131-228.us-east-2.compute.internal.kubernetes.var.log.containers.apiserver-797774f7c5-lftrx_openshift-apiserver_openshift-apiserver-ce51532df7d4e4d5f21c4f4be05f6575b93196336be0027067fd7d93d70f66a4.log" "ip-10-0-131-228.us-east-2.compute.internal.kubernetes.var.log.containers.apiserver-797774f7c5-lftrx_openshift-apiserver_openshift-apiserver-check-endpoints-82a9096b5931b5c3b1d6dc4b66113252da4a6472c9fff48623baee761911a9ef.log" aws logs get-log-events --log-group-name mycluster-7977k.application --log-stream-name kubernetes.var.log.containers.busybox_app_busybox-da085893053e20beddd6747acdbaf98e77c37718f85a7f6a4facf09ca195ad76.log { "events": [ { "timestamp": 1629422704178, "message": "{\"docker\":{\"container_id\":\"da085893053e20beddd6747acdbaf98e77c37718f85a7f6a4facf09ca195ad76\"},\"kubernetes\":{\"container_name\":\"busybox\",\"namespace_name\":\"app\",\"pod_name\":\"busybox\",\"container_image\":\"docker.io/library/busybox:latest\",\"container_image_id\":\"docker.io/library/busybox@sha256:0f354ec1728d9ff32edcd7d1b8bbdfc798277ad36120dc3dc683be44524c8b60\",\"pod_id\":\"870be234-90a3-4258-b73f-4f4d6e2777c7\",\"host\":\"ip-10-0-216-3.us-east-2.compute.internal\",\"labels\":{\"run\":\"busybox\"},\"master_url\":\"https://kubernetes.default.svc\",\"namespace_id\":\"794e1e1a-b9f5-4958-a190-e76a9b53d7bf\",\"namespace_labels\":{\"kubernetes_io/metadata_name\":\"app\"}},\"message\":\"My life is my message\",\"level\":\"unknown\",\"hostname\":\"ip-10-0-216-3.us-east-2.compute.internal\",\"pipeline_metadata\":{\"collector\":{\"ipaddr4\":\"10.0.216.3\",\"inputname\":\"fluent-plugin-systemd\",\"name\":\"fluentd\",\"received_at\":\"2021-08-20T01:25:08.085760+00:00\",\"version\":\"1.7.4 1.6.0\"}},\"@timestamp\":\"2021-08-20T01:25:04.178986+00:00\",\"viaq_index_name\":\"app-write\",\"viaq_msg_id\":\"NWRjZmUyMWQtZjgzNC00MjI4LTk3MjMtNTk3NmY3ZjU4NDk1\",\"log_type\":\"application\",\"time\":\"2021-08-20T01:25:04+00:00\"}", "ingestionTime": 1629422744016 }, cloudwatch: groupBy: logType groupPrefix: demo-group-prefix region: us-east-2 aws --output json logs describe-log-groups | jq .logGroups[].logGroupName "demo-group-prefix.application" "demo-group-prefix.audit" "demo-group-prefix.infrastructure" cloudwatch: groupBy: namespaceName region: us-east-2 aws --output json logs describe-log-groups | jq .logGroups[].logGroupName "mycluster-7977k.app" "mycluster-7977k.audit" "mycluster-7977k.infrastructure" cloudwatch: groupBy: namespaceUUID region: us-east-2 aws --output json logs describe-log-groups | jq .logGroups[].logGroupName "mycluster-7977k.794e1e1a-b9f5-4958-a190-e76a9b53d7bf" // uid of the "app" namespace "mycluster-7977k.audit" "mycluster-7977k.infrastructure" oc create secret generic cw-sts-secret -n openshift-logging --from-literal=role_arn=arn:aws:iam::123456789012:role/my-role_with-permissions apiVersion: v1 kind: Secret metadata: namespace: openshift-logging name: my-secret-name stringData: role_arn: arn:aws:iam::123456789012:role/my-role_with-permissions apiVersion: cloudcredential.openshift.io/v1 kind: CredentialsRequest metadata: name: <your_role_name>-credrequest namespace: openshift-cloud-credential-operator spec: providerSpec: apiVersion: cloudcredential.openshift.io/v1 kind: AWSProviderSpec statementEntries: - action: - logs:PutLogEvents - logs:CreateLogGroup - logs:PutRetentionPolicy - logs:CreateLogStream - logs:DescribeLogGroups - logs:DescribeLogStreams effect: Allow resource: arn:aws:logs:*:*:* secretRef: name: <your_role_name> namespace: openshift-logging serviceAccountNames: - logcollector ccoctl aws create-iam-roles --name=<name> --region=<aws_region> --credentials-requests-dir=<path_to_directory_with_list_of_credentials_requests>/credrequests --identity-provider-arn=arn:aws:iam::<aws_account_id>:oidc-provider/<name>-oidc.s3.<aws_region>.amazonaws.com 1 oc apply -f output/manifests/openshift-logging-<your_role_name>-credentials.yaml apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: <log_forwarder_name> 1 namespace: <log_forwarder_namespace> 2 spec: serviceAccountName: clf-collector 3 outputs: - name: cw 4 type: cloudwatch 5 cloudwatch: groupBy: logType 6 groupPrefix: <group prefix> 7 region: us-east-2 8 secret: name: <your_secret_name> 9 pipelines: - name: to-cloudwatch 10 inputRefs: 11 - infrastructure - audit - application outputRefs: - cw 12 apiVersion: logging.openshift.io/v1 kind: ClusterLogging metadata: spec: collection: type: <log_collector_type> 1 resources: {} tolerations: {} oc apply -f <filename>.yaml apiVersion: logging.openshift.io/v1alpha1 kind: LogFileMetricExporter metadata: name: instance namespace: openshift-logging spec: nodeSelector: {} 1 resources: 2 limits: cpu: 500m memory: 256Mi requests: cpu: 200m memory: 128Mi tolerations: [] 3 oc apply -f <filename>.yaml oc get pods -l app.kubernetes.io/component=logfilesmetricexporter -n openshift-logging NAME READY STATUS RESTARTS AGE logfilesmetricexporter-9qbjj 1/1 Running 0 2m46s logfilesmetricexporter-cbc4v 1/1 Running 0 2m46s oc -n openshift-logging edit ClusterLogging instance apiVersion: logging.openshift.io/v1 kind: ClusterLogging metadata: name: instance namespace: openshift-logging spec: collection: type: fluentd resources: limits: 1 memory: 736Mi requests: cpu: 100m memory: 736Mi apiVersion: logging.openshift.io/v1beta1 kind: ClusterLogForwarder metadata: spec: serviceAccountName: <service_account_name> inputs: - name: http-receiver 1 receiver: type: http 2 http: format: kubeAPIAudit 3 port: 8443 4 pipelines: 5 - name: http-pipeline inputRefs: - http-receiver apiVersion: logging.openshift.io/v1 kind: ClusterLogForwarder metadata: name: instance namespace: openshift-logging spec: inputs: - name: http-receiver 1 receiver: type: http 2 http: format: kubeAPIAudit 3 port: 8443 4 pipelines: 5 - inputRefs: - http-receiver name: http-pipeline oc apply -f <filename>.yaml oc edit ClusterLogging instance apiVersion: logging.openshift.io/v1 kind: ClusterLogging metadata: name: instance namespace: openshift-logging spec: collection: fluentd: buffer: chunkLimitSize: 8m 1 flushInterval: 5s 2 flushMode: interval 3 flushThreadCount: 3 4 overflowAction: throw_exception 5 retryMaxInterval: "300s" 6 retryType: periodic 7 retryWait: 1s 8 totalLimitSize: 32m 9 oc get pods -l component=collector -n openshift-logging oc extract configmap/collector-config --confirm <buffer> @type file path '/var/lib/fluentd/default' flush_mode interval flush_interval 5s flush_thread_count 3 retry_type periodic retry_wait 1s retry_max_interval 300s retry_timeout 60m queued_chunks_limit_size "#{ENV['BUFFER_QUEUE_LIMIT'] || '32'}" total_limit_size "#{ENV['TOTAL_LIMIT_SIZE_PER_BUFFER'] || '8589934592'}" chunk_limit_size 8m overflow_action throw_exception disable_chunk_backup true </buffer> apiVersion: template.openshift.io/v1 kind: Template metadata: name: eventrouter-template annotations: description: "A pod forwarding kubernetes events to OpenShift Logging stack." tags: "events,EFK,logging,cluster-logging" objects: - kind: ServiceAccount 1 apiVersion: v1 metadata: name: eventrouter namespace: USD{NAMESPACE} - kind: ClusterRole 2 apiVersion: rbac.authorization.k8s.io/v1 metadata: name: event-reader rules: - apiGroups: [""] resources: ["events"] verbs: ["get", "watch", "list"] - kind: ClusterRoleBinding 3 apiVersion: rbac.authorization.k8s.io/v1 metadata: name: event-reader-binding subjects: - kind: ServiceAccount name: eventrouter namespace: USD{NAMESPACE} roleRef: kind: ClusterRole name: event-reader - kind: ConfigMap 4 apiVersion: v1 metadata: name: eventrouter namespace: USD{NAMESPACE} data: config.json: |- { "sink": "stdout" } - kind: Deployment 5 apiVersion: apps/v1 metadata: name: eventrouter namespace: USD{NAMESPACE} labels: component: "eventrouter" logging-infra: "eventrouter" provider: "openshift" spec: selector: matchLabels: component: "eventrouter" logging-infra: "eventrouter" provider: "openshift" replicas: 1 template: metadata: labels: component: "eventrouter" logging-infra: "eventrouter" provider: "openshift" name: eventrouter spec: serviceAccount: eventrouter containers: - name: kube-eventrouter image: USD{IMAGE} imagePullPolicy: IfNotPresent resources: requests: cpu: USD{CPU} memory: USD{MEMORY} volumeMounts: - name: config-volume mountPath: /etc/eventrouter securityContext: allowPrivilegeEscalation: false capabilities: drop: ["ALL"] securityContext: runAsNonRoot: true seccompProfile: type: RuntimeDefault volumes: - name: config-volume configMap: name: eventrouter parameters: - name: IMAGE 6 displayName: Image value: "registry.redhat.io/openshift-logging/eventrouter-rhel9:v0.4" - name: CPU 7 displayName: CPU value: "100m" - name: MEMORY 8 displayName: Memory value: "128Mi" - name: NAMESPACE displayName: Namespace value: "openshift-logging" 9 oc process -f <templatefile> | oc apply -n openshift-logging -f - oc process -f eventrouter.yaml | oc apply -n openshift-logging -f - serviceaccount/eventrouter created clusterrole.rbac.authorization.k8s.io/event-reader created clusterrolebinding.rbac.authorization.k8s.io/event-reader-binding created configmap/eventrouter created deployment.apps/eventrouter created oc get pods --selector component=eventrouter -o name -n openshift-logging pod/cluster-logging-eventrouter-d649f97c8-qvv8r oc logs <cluster_logging_eventrouter_pod> -n openshift-logging oc logs cluster-logging-eventrouter-d649f97c8-qvv8r -n openshift-logging {"verb":"ADDED","event":{"metadata":{"name":"openshift-service-catalog-controller-manager-remover.1632d931e88fcd8f","namespace":"openshift-service-catalog-removed","selfLink":"/api/v1/namespaces/openshift-service-catalog-removed/events/openshift-service-catalog-controller-manager-remover.1632d931e88fcd8f","uid":"787d7b26-3d2f-4017-b0b0-420db4ae62c0","resourceVersion":"21399","creationTimestamp":"2020-09-08T15:40:26Z"},"involvedObject":{"kind":"Job","namespace":"openshift-service-catalog-removed","name":"openshift-service-catalog-controller-manager-remover","uid":"fac9f479-4ad5-4a57-8adc-cb25d3d9cf8f","apiVersion":"batch/v1","resourceVersion":"21280"},"reason":"Completed","message":"Job completed","source":{"component":"job-controller"},"firstTimestamp":"2020-09-08T15:40:26Z","lastTimestamp":"2020-09-08T15:40:26Z","count":1,"type":"Normal"}}