As a cluster administrator, you can deploy the logging subsystem to aggregate all the logs from your OKD cluster, such as node system audit logs, application container logs, and infrastructure logs. The logging subsystem aggregates these logs from throughout your cluster and stores them in a default log store. You can use the Kibana web console to visualize log data.
The logging subsystem aggregates the following types of logs:
application - Container logs generated by user applications running in the cluster, except infrastructure container applications.
infrastructure - Logs generated by infrastructure components running in the cluster and OKD nodes, such as journal logs. Infrastructure components are pods that run in the
audit - Logs generated by auditd, the node audit system, which are stored in the /var/log/audit/audit.log file, and the audit logs from the Kubernetes apiserver and the OpenShift apiserver.
Because the internal OKD Elasticsearch log store does not provide secure storage for audit logs, audit logs are not stored in the internal Elasticsearch instance by default. If you want to send the audit logs to the default internal Elasticsearch log store, for example to view the audit logs in Kibana, you must use the Log Forwarding API as described in Forward audit logs to the log store.
OKD cluster administrators can deploy the logging subsystem using
the OKD web console or CLI to install the OpenShift Elasticsearch
Operator and Red Hat OpenShift Logging Operator. When the operators are installed, you create
ClusterLogging custom resource (CR) to schedule logging subsystem pods and
other resources necessary to support the logging subsystem. The operators are
responsible for deploying, upgrading, and maintaining the logging subsystem.
ClusterLogging CR defines a complete logging subsystem environment that includes all the components
of the logging stack to collect, store and visualize logs. The Red Hat OpenShift Logging Operator watches the logging subsystem CR and adjusts the logging deployment accordingly.
Administrators and application developers can view the logs of the projects for which they have view access.
For information, see Configuring the log collector.
You can use JSON logging to configure the Log Forwarding API to parse JSON strings into a structured object. You can perform the following tasks:
Parse JSON logs
Configure JSON log data for Elasticsearch
Forward JSON logs to the Elasticsearch log store
For information, see About JSON Logging.
The OKD Event Router is a pod that watches Kubernetes events and logs them for collection by OKD Logging. You must manually deploy the Event Router.
For information, see About collecting and storing Kubernetes events.
OKD allows you to update OKD logging. You must update the following operators while updating OKD Logging:
Cluster Logging Operator
For information, see About updating OKD Logging.
The OKD Logging dashboard contains charts that show details about your Elasticsearch instance at the cluster level. These charts help you diagnose and anticipate problems.
For information, see About viewing the cluster dashboard.
You can troubleshoot the logging issues by performing the following tasks:
Viewing logging status
Viewing the status of the log store
Understanding logging alerts
Collecting logging data for Red Hat Support
Troubleshooting for critical alerts
You can stop log aggregation by deleting the ClusterLogging custom resource (CR). After deleting the CR, there are other cluster logging components that remain, which you can optionally remove.
For information, see About uninstalling OKD Logging.
The logging system exports fields. Exported fields are present in the log records and are available for searching from Elasticsearch and Kibana.
For information, see About exporting fields.
The logging subsystem components include a collector deployed to each node in the OKD cluster that collects all node and container logs and writes them to a log store. You can use a centralized web UI to create rich visualizations and dashboards with the aggregated data.
The major components of the logging subsystem are:
collection - This is the component that collects logs from the cluster, formats them, and forwards them to the log store. The current implementation is Fluentd.
log store - This is where the logs are stored. The default implementation is Elasticsearch. You can use the default Elasticsearch log store or forward logs to external log stores. The default log store is optimized and tested for short-term storage.
visualization - This is the UI component you can use to view logs, graphs, charts, and so forth. The current implementation is Kibana.
This document might refer to log store or Elasticsearch, visualization or Kibana, collection or Fluentd, interchangeably, except where noted.
The logging subsystem for Red Hat OpenShift collects container and node logs.
By default, the log collector uses the following sources:
journald for all system logs
/var/log/containers/*.log for all container logs
If you configure the log collector to collect audit logs, it gets them from
The logging collector is a daemon set that deploys pods to each OKD node. System and infrastructure logs are generated by journald log messages from the operating system, the container runtime, and OKD. Application logs are generated by the CRI-O container engine. Fluentd collects the logs from these sources and forwards them internally or externally as you configure in OKD.
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.
The available container runtimes provide minimal information to identify the source of log messages and do not guarantee unique individual log messages or that these messages can be traced to their source.
For information, see Configuring the log collector.
By default, OKD uses Elasticsearch (ES) to store log data. Optionally you can use the Log Forwarder API to forward logs to an external store. Several types of store are supported, including fluentd, rsyslog, kafka and others.
The logging subsystem Elasticsearch instance is optimized and tested for short term storage, approximately seven days. If you want to retain your logs over a longer term, it is recommended you move the data to a third-party storage system.
Elasticsearch organizes the log data from Fluentd into datastores, or indices, then subdivides each index into multiple pieces called shards, which it spreads across a set of Elasticsearch nodes in an Elasticsearch cluster. You can configure Elasticsearch to make copies of the shards, called replicas, which Elasticsearch also spreads across the Elasticsearch nodes. The
ClusterLogging custom resource (CR) allows you to specify how the shards are replicated to provide data redundancy and resilience to failure. You can also specify how long the different types of logs are retained using a retention policy in the
The number of primary shards for the index templates is equal to the number of Elasticsearch data nodes.
The Red Hat OpenShift Logging Operator and companion OpenShift Elasticsearch Operator ensure that each Elasticsearch node is deployed using a unique deployment that includes its own storage volume.
You can use a
ClusterLogging custom resource (CR) to increase the number of Elasticsearch nodes, as needed.
See the Elasticsearch documentation for considerations involved in configuring storage.
A highly-available Elasticsearch environment requires at least three Elasticsearch nodes, each on a different host.
Role-based access control (RBAC) applied on the Elasticsearch indices enables the controlled access of the logs to the developers. Administrators can access all logs and developers can access only the logs in their projects.
For information, see Configuring the log store.
OKD uses Kibana to display the log data collected by Fluentd and indexed by Elasticsearch.
Kibana is a browser-based console interface to query, discover, and visualize your Elasticsearch data through histograms, line graphs, pie charts, and other visualizations.
For information, see Configuring the log visualizer.
The Event Router is a pod that watches OKD events so they can be collected by the logging subsystem for Red Hat OpenShift.
The Event Router collects events from all projects and writes them to
STDOUT. Fluentd collects those events and forwards them into the OKD Elasticsearch instance. Elasticsearch indexes the events to the
You must manually deploy the Event Router.
For information, see Collecting and storing Kubernetes events.
By default, the logging subsystem for Red Hat OpenShift sends logs to the default internal Elasticsearch log store, defined in the
ClusterLogging custom resource (CR). If you want to forward logs to other log aggregators, you can use the log forwarding features to send logs to specific endpoints within or outside your cluster.
For information, see Forwarding logs to third-party systems.