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Once the upgrade is complete, the monitoring data is now available in Kibana. The monitoring dashboards present a wealth of information about the Elasticsearch cluster, including valuable information about how much log data is running through it. 

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Navigate to the Monitoring →  Elasticsearch (cluster) → > Nodes  section. All the nodes that make up the current Elasticsearch cluster are displayed, along with extremely useful data about JVM memory consumption and CPU usage per node. Assuming that the workload in the current cluster is realistic, it’s more clear to see if the current resource allocation for the management node CPU cores, or the JVM heap, is enough to handle the current workload plus additional spikes. 

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Currently, in the above cluster, JVM memory usage for the data nodes is around 60% which is suitable for the current environment, plus a 40% overhead to handle temporary excess log data. If this value was around 80% or 90% consistently, it would be beneficial to double the allocated resources in the logging-elk-data  StatefulSet. The current data nodes’ CPU usage is around 30% and the maximum value the nodes have hit is around 60%, so for the current workload, the CPU cores assigned to the management nodes are sufficient. In all cases, these values should be monitored for changes and adjusted as required to keep the logging services in a stable state. High CPU usage is an indicator that there is memory pressure in the data nodes due to garbage collection processes. 

