Understanding Red Hat OpenShift Logging

As a cluster administrator, you can deploy OpenShift Logging to aggregate all the logs from your OKD cluster, such as node system audit logs, application container logs, and infrastructure logs. OpenShift Logging 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.

OpenShift Logging 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 openshift*, kube*, or default projects.

  • 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.

About deploying OpenShift Logging

OKD cluster administrators can deploy OpenShift Logging 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 a ClusterLogging custom resource (CR) to schedule OpenShift Logging pods and other resources necessary to support OpenShift Logging. The operators are responsible for deploying, upgrading, and maintaining OpenShift Logging.

The ClusterLogging CR defines a complete OpenShift Logging environment that includes all the components of the logging stack to collect, store and visualize logs. The Red Hat OpenShift Logging Operator watches the OpenShift Logging 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.

About JSON OKD Logging

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

  • Parse JSON logs

  • Configure JSON log data for Elasticsearch

  • Forward JSON logs to the Elasticsearch log store

For information, see About JSON Logging.

About collecting and storing Kubernetes events

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.

About updating OKD Logging

OKD allows you to update OKD logging. You must update the following operators while updating OKD Logging:

  • Elasticsearch Operator

  • Cluster Logging Operator

For information, see About updating OKD Logging.

About viewing the cluster dashboard

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.

About troubleshooting OKD Logging

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

  • Viewing logging status

  • Viewing the status of the log store

  • Understanding logging alerts

  • Collecting logging data for Red Hat Support

  • Troubleshooting for critical alerts

About uninstalling OKD Logging

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.

About exporting fields

The logging system exports fields. Exported fields are present in the log records and are available for searching from Elasticsearch and Kibana.

For information, see About exporting fields.

About OpenShift Logging components

The OpenShift Logging 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 OpenShift Logging 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.

About the logging collector

OKD uses Fluentd to collect 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 /var/log/audit/audit.log.

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.

About the log store

By default, OKD uses Elasticsearch (ES) to store log data. Optionally, you can use the log forwarding features to forward logs to external log stores using Fluentd protocols, syslog protocols, or the OKD Log Forwarding API.

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

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

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.

About logging visualization

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.

About event routing

The Event Router is a pod that watches OKD events so they can be collected by OpenShift Logging. 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 infra index.

You must manually deploy the Event Router.

For information, see Collecting and storing Kubernetes events.

About log forwarding

By default, OpenShift Logging 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.