Kube eagle is a prometheus exporter which exports various metrics of kubernetes pod resource requests, limits and it's actual usages. It was created with the purpose to provide a better overview of your kubernetes cluster resources, so that you can optimize the resource allocation. You can easily build, or use our default grafana dashboard which will help you to achieve this goal:
Simply deploy a pod which runs kube-eagle inside the kubernetes cluster you would like to monitor. We recommend using our provided helm chart to deploy kube eagle in your cluster:
Kube eagle helm chart: https://github.com/google-cloud-tools/kube-eagle-helm-chart
Make sure the pod has a service account attached that has the required permissions. You can use our helm chart which is capable of creating the service account along with the required ClusterRole and ClusterRoleBinding.
|TELEMETRY_HOST||Host to bind socket on for the prometheus exporter||0.0.0.0|
|TELEMETRY_PORT||Port to listen on for the prometheus exporter||8080|
|METRICS_NAMESPACE||Prefix of exposed prometheus metrics||eagle|
|IS_IN_CLUSTER||Whether to use in cluster communication or to look for a kubeconfig in home directory||true|
|LOG_LEVEL||Logger's log granularity (debug, info, warn, error, fatal, panic)||info|
Configure Grafana dashboard
Import the dashboard: https://grafana.com/dashboards/9871 (Dashboard ID 9871)
Configure Dashboard variables: Open the Kube Eagle dashboard and click the gear icon at the top to configure the dashboard. On the left menu you should see a setting called "Variables". Since we don't have an explicit label for nodepools (yet) we rely on given node names which usually carry the nodepool name in it. Thus provide the full "node name prefix" including the nodepool name (e. g.
gke-brawlstats-k8s-highmem-.*where as highmem is the nodepool name).
|eagle_node_resource_allocatable_cpu_cores||Allocatable CPU cores in Kubernetes|
|eagle_node_resource_allocatable_memory_bytes||Allocatable RAM in Kubernetes in bytes|
|eagle_node_resource_limits_cpu_cores||Total limit CPU cores of all specified pod resources on a node|
|eagle_node_resource_limits_memory_bytes||Total limit of RAM bytes of all specified pod resources on a node|
|eagle_node_resource_requests_cpu_cores||Total request of CPU cores of all specified pod resources on a node|
|eagle_node_resource_requests_memory_bytes||Total request of RAM bytes all specified pod resources on a node|
|eagle_node_resource_usage_cpu_cores||Total number of used CPU cores on a node|
|eagle_node_resource_usage_memory_bytes||Total number of RAM bytes used on a node|
|eagle_pod_container_resource_limits_cpu_cores||Limit of CPU cores set for a specific container|
|eagle_pod_container_resource_limits_memory_bytes||Limit of RAM bytes set for a specific container|
|eagle_pod_container_resource_requests_cpu_cores||Requested CPU cores set for a specific container|
|eagle_pod_container_resource_requests_memory_bytes||Requested RAM bytes set for a specific container|
|eagle_pod_container_resource_usage_cpu_cores||CPU cores in use by a specific container|
|eagle_pod_container_resource_usage_memory_bytes||RAM bytes in use by a specific container|
How does it work
Kube eagle talks to the kubernetes master(s) using the official kubernetes go client. Every time the
/metrics endpoint is hit Kube Eagle sends requests to the k8s masters to get pod & node resource objects as well as the pod & node usage list. Kube eagle aggregates and brings together the collected data so that they can be attached as prometheus labels. This way it's easy to create grafana dashboards which help you to optimize your resource allocations.
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