Chapter 2. About deploying cluster logging
Before installing cluster logging into your OpenShift Container Platform cluster, review the following sections.
2.1. About deploying and configuring cluster logging
OpenShift Container Platform cluster logging is designed to be used with the default configuration, which is tuned for small to medium sized OpenShift Container Platform clusters.
The installation instructions that follow include a sample Cluster Logging Custom Resource (CR), which you can use to create a cluster logging instance and configure your cluster logging deployment.
If you want to use the default cluster logging install, you can use the sample CR directly.
If you want to customize your deployment, make changes to the sample CR as needed. The following describes the configurations you can make when installing your cluster logging instance or modify after installation. See the Configuring sections for more information on working with each component, including modifications you can make outside of the Cluster Logging Custom Resource.
2.1.1. Configuring and Tuning Cluster Logging
You can configure your cluster logging environment by modifying the Cluster Logging Custom Resource deployed in the openshift-logging
project.
You can modify any of the following components upon install or after install:
- Memory and CPU
-
You can adjust both the CPU and memory limits for each component by modifying the
resources
block with valid memory and CPU values:
spec: logStore: elasticsearch: resources: limits: cpu: memory: 16Gi requests: cpu: 500m memory: 16Gi type: "elasticsearch" collection: logs: fluentd: resources: limits: cpu: memory: requests: cpu: memory: type: "fluentd" visualization: kibana: resources: limits: cpu: memory: requests: cpu: memory: type: kibana curation: curator: resources: limits: memory: 200Mi requests: cpu: 200m memory: 200Mi type: "curator"
- Elasticsearch storage
-
You can configure a persistent storage class and size for the Elasticsearch cluster using the
storageClass
name
andsize
parameters. The Cluster Logging Operator creates aPersistentVolumeClaim
for each data node in the Elasticsearch cluster based on these parameters.
spec: logStore: type: "elasticsearch" elasticsearch: nodeCount: 3 storage: storageClassName: "gp2" size: "200G"
This example specifies each data node in the cluster will be bound to a PersistentVolumeClaim
that requests "200G" of "gp2" storage. Each primary shard will be backed by a single replica.
Omitting the storage
block results in a deployment that includes ephemeral storage only.
spec: logStore: type: "elasticsearch" elasticsearch: nodeCount: 3 storage: {}
- Elasticsearch replication policy
You can set the policy that defines how Elasticsearch shards are replicated across data nodes in the cluster:
-
FullRedundancy
. The shards for each index are fully replicated to every data node. -
MultipleRedundancy
. The shards for each index are spread over half of the data nodes. -
SingleRedundancy
. A single copy of each shard. Logs are always available and recoverable as long as at least two data nodes exist. -
ZeroRedundancy
. No copies of any shards. Logs may be unavailable (or lost) in the event a node is down or fails.
-
- Curator schedule
- You specify the schedule for Curator in the cron format.
spec: curation: type: "curator" resources: curator: schedule: "30 3 * * *"
2.1.2. Sample modified Cluster Logging Custom Resource
The following is an example of a Cluster Logging Custom Resource modified using the options previously described.
Sample modified Cluster Logging Custom Resource
apiVersion: "logging.openshift.io/v1" kind: "ClusterLogging" metadata: name: "instance" namespace: "openshift-logging" spec: managementState: "Managed" logStore: type: "elasticsearch" elasticsearch: nodeCount: 3 resources: limits: memory: 32Gi requests: cpu: 3 memory: 32Gi storage: {} redundancyPolicy: "SingleRedundancy" visualization: type: "kibana" kibana: resources: limits: memory: 1Gi requests: cpu: 500m memory: 1Gi replicas: 1 curation: type: "curator" curator: resources: limits: memory: 200Mi requests: cpu: 200m memory: 200Mi schedule: "*/5 * * * *" collection: logs: type: "fluentd" fluentd: resources: limits: memory: 1Gi requests: cpu: 200m memory: 1Gi
2.2. Storage considerations for cluster logging and OpenShift Container Platform
A persistent volume is required for each Elasticsearch deployment to have one data volume per data node. On OpenShift Container Platform this is achieved using Persistent Volume Claims.
The Elasticsearch Operator names the PVCs using the Elasticsearch resource name. Refer to Persistent Elasticsearch Storage for more details.
Fluentd ships any logs from systemd journal and /var/log/containers/ to Elasticsearch.
Therefore, consider how much data you need in advance and that you are aggregating application log data. Some Elasticsearch users have found that it is necessary to keep absolute storage consumption around 50% and below 70% at all times. This helps to avoid Elasticsearch becoming unresponsive during large merge operations.
By default, at 85% Elasticsearch stops allocating new data to the node, at 90% Elasticsearch attempts to relocate existing shards from that node to other nodes if possible. But if no nodes have free capacity below 85%, Elasticsearch effectively rejects creating new indices and becomes RED.
These low and high watermark values are Elasticsearch defaults in the current release. You can modify these values, but you also must apply any modifications to the alerts also. The alerts are based on these defaults.
2.3. Additional resources
For more information on installing operators,see Installing Operators from the OperatorHub.