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A Kubernetes cluster is a set of machines, called nodes, that run containerized applications managed by Kubernetes. A cluster has at least one worker node and at least one master node.

The worker node(s) host the pods that are the components of the application. The master node(s) manages the worker nodes and the pods in the cluster. Multiple master nodes are used to provide a cluster with failover and high availability.

The kube-scheduler schedules Pods to a node, based on the resource capacity of the node and the resource requirements of the containers in the Pods. To ensure that no Pod hogs the node's resources, resource requests and limits can be set per container.

At any given point in time, an administrator needs to have a macro view of the composition of their Kubernetes cluster - i.e., the number of nodes and Pods in the cluster - and the operational state of the nodes and Pods. This will help them quickly spot nodes and Pods that have failed - i.e., it will help them quickly detect a mismatch between the actual state of the cluster and its desired state. By taking appropriate action on such mismatches, administrators can prevent any adverse impact on the availability and performance of containerized applications. Additionally, administrators also need to track how the Pods are utilizing the cluster's compute resources. This way, they can proactively detect probable resource contentions / over-subscriptions, and rapidly initiate measures to right-size the cluster components (i.e., Pods and containers), so that application performance is not affected by resource crunches. Administrators also require an overview of Deployments across the cluster, so that they can easily locate problem areas. The Kube Cluster Overview test provides administrators with all these useful high-level insights!

This test monitors a Kubernetes cluster, reports the total count of nodes in the cluster, and also precisely pinpoints the master and worker nodes of the cluster. The test also tracks the Pod capacity of the cluster alongside Pod allocations, and additionally highlights Pods and nodes in an abnormal state. This enables administrators to rapidly detect any glaring mismatch between the desired state and actual state of the cluster and initiate appropriate remedial measures. Furthermore, the test reveals how the Pods in the cluster are utilizing the cluster's compute resource capacity. In the process, the test brings to light irregularities such as resource over-subscription and current/potential resource contention. Detailed diagnostics provided by the test lead administrators to the exact Pods that are hogging cluster resources, or have been poorly sized. This way, the test points administrators to those Pods for which resource allocations need to be fine-tuned to ensure optimal cluster performance. In addition, the test helps administrators easily compare the desired state of Deployments with the actual state, so that they can instantly capture and resolve discrepancies (if any).

Outputs of the test:One set of results for the Kubernetes cluster being monitored.

Measurement	Description	Measurement Unit	Interpretation
Tot_node_count	Indicates the total number of nodes in the cluster.	Number
Master_node	Indicates the count of master nodes in the cluster.	Number	Use the detailed diagnosis of this measure to know which are the master nodes in the cluster.
Worker_node	Indicates the number of worker nodes in the cluster.	Number	Use the detailed diagnosis of this measure to know which are the worker nodes in the cluster.
Added_node	Indicates the number of nodes that were added to the cluster since the last measurement period.	Number	Use the detailed diagnosis of this measure to know which nodes were recently added to the cluster.
Removed_node	Indicates the number of nodes that were removed from the cluster since the last measurement period.	Number	Use the detailed diagnosis of this measure to know which nodes were recently removed from the cluster.
Running_node	Indicates the number of nodes in the cluster that are currently running.	Number
NotRunning_node	Indicates the number of nodes in the cluster that are not running presently.	Number	Use the detailed diagnosis of this measure to know which nodes are not running and why.
Unknown_node	Indicates the number of nodes in the cluster that are in the Unknown presently.	Number	Use the detailed diagnosis of this measure to know which nodes are in an Unknown state and why.
Tot_pods_capacity	Indicates the maximum number of Pods that can be created on the nodes in the cluster.	Number
Tot_pods_allocation	Indicates the number of Pods that have been scheduled to nodes in the cluster.	Number	If the value of this measure is equal to or close to the value of the Pods capacity measure, it indicates that the cluster has or is about to exhaust its capacity. In such a situation, you may want to add more nodes to your cluster or increase the Pod capacity of your cluster.
Tot_running_pods	Indicates the number of Pods in the cluster that are in the Running state currently.	Number	If a Pod is in the Running state, it means that the Pod has been bound to a node, and all of the Containers have been created. At least one Container is still running, or is in the process of starting or restarting. Use the detailed diagnosis of this measure to know which Pods are in the Running state.
Tot_pending_pods	Indicates the number of Pods in the cluster that are in the Pending state currently.	Number	If a Pod is in the Pending state, it means that the Pod has been accepted by the Kubernetes system, but one or more of the Container images has not been created. This includes time before being scheduled as well as time spent downloading images over the network, which could take a while. If a pod is stuck in Pending it means that it can not be scheduled onto a node. Generally this is because there are insufficient resources of one type or another that prevent scheduling. If this is the case, do the following: Add more nodes to the cluster. Terminate unneeded pods to make room for pending pods. Check that the pod is not larger than your nodes. For example, if all nodes have a capacity of cpu:1, then a pod with a request of cpu: 1.1 will never be scheduled. Use the detailed diagnosis of this measure to know which Pods are in the Pending state.
Tot_succeeded_pods	Indicates the number of Pods in the cluster that are in the Succeeded state currently.	Number	If a Pod is in the Succeeded state, it means that all Containers in the Pod have terminated in success, and will not be restarted.
Tot_failed_pods	Indicates the number of Pods in the cluster that are in the Failed state currently.	Number	If a Pod is in the Failed state, it means that all Containers in the Pod have terminated, and at least one Container has terminated in failure. That is, the Container either exited with non-zero status or was terminated by the system. Use the detailed diagnosis of this measure to know which Pods are in the Failed state. Ideally, the value of this measure should be 0.
Tot_unknown_pods	Indicates the number of Pods in the cluster that are in the Unknown state currently.	Number	If a Pod is in the Unknown state, it means that the state of the Pod could not be obtained, probably due to an error in communicating with the host of the Pod. Ideally, the value of this measure should be 0.
Tot_pods_percent	Indicates the percentage of Pods in the cluster that are in a Running state currently.	Percent	The formula used for computing this measure is as follows: [Running pods/Pods capacity]*100 Ideally, the value of this measure should be high.
Tot_cpu_capacity	Indicates the total number of CPU cores supported by the cluster.	Number
Total_millicpu	Indicates the total CPU capacity of the cluster.	Millicpu
Tot_cpu_requests	Indicates the minimum CPU resources guaranteed to the Pods in the cluster.	Millicpu	This is the sum of CPU requests configured for all containers in all Pods across nodes in the cluster. A request is the amount of that resource that the system will guarantee to a Pod.
Tot_cpu_limits	Indicates that maximum amount of CPU resources that the Pods in the cluster can use.	Millicpu	This is the sum of CPU limits set for all containers in all Pods across nodes in the cluster. A limit is the maximum amount that the system will allow the Pod to use.
Percent_cpu_limits	Indicates what percentage of the CPU capacity of the cluster is allocated as CPU limits to containers. In other words, this is the percentage of a cluster's CPU capacity that the containers are allowed to use.	Percent	The formula used for computing this measure is as follows: (CPU limits/CPU capacity)*100 If the value of this measure exceeds 100%, it means that one/more Pods are probably over-subscribing to the capacity of one/more nodes.
Percent_cpu_request	Indicates what percentage of the total CPU capacity of the cluster is set as CPU requests for the containers in the cluster. In other words, this is the percentage of a cluster's CPU capacity that the containers on the cluster are guaranteed to receive.	Percent	The formula used for computing this measure is as follows: (CPU requests/CPU capacity )*100 If the value of this measure is unusually high, then you can use the detailed diagnosis of this measure to review the CPU requests configured for each Pod in the cluster. In the process, you can accurately identify the Pod for which the maximum amount of CPU resources in the cluster is guaranteed - i.e., the Pod that is hogging the CPU capacity of the cluster.
Tot_memory_capacity	Indicates the total memory capacity of the cluster.	GB
Tot_memory_request	Indicates the minimum memory resources guaranteed to the Pods in the cluster.	GB	This is the sum of memory requests configured for all containers in all Pods across nodes in the cluster. A request is the amount of that resource that the system will guarantee to the Pod.
Tot_memory_limits	Indicates the maximum amount of memory resources that the Pods in the cluster can use.	GB	This is the sum of memory limits set for all containers in all Pods across nodes in the cluster. A limit is the maximum amount that the system will allow the Pod to use.
Percent_memory_limits	Indicates what percentage of the memory capacity of the cluster is allocated as memory limits to containers in the cluster. In other words, this is the percentage of a cluster's memory capacity that the containers on the cluster are allowed to use.	Percent	The formula used for computing this measure is as follows: (Memory limits/Memory capacity)*100 If the value of this measure exceeds 100%, it means that one/more Pods are probably over-subscribing to the capacity of one/more nodes in the cluster.
Percent_memory_request	Indicates what percentage of the total memory capacity of the cluster is set as memory requests for the containers in the cluster. In other words, this is the percentage of a cluster's memory capacity that the containers in the cluster are guaranteed to receive.	Percent	The formula used for computing this measure is as follows: (Memory requests/Memory capacity)*100 If the value of this measure is unusually high, then you can use the detailed diagnosis of this measure to review the memory requests configured for each Pod in the cluster. In the process, you can accurately identify the Pod for which the maximum amount of memory resources in the cluster is guaranteed - i.e., the Pod that is hogging the memory capacity of the cluster.
Tot_replicas_count	Indicates the total number of non-terminated Pod replicas in the cluster that have been updated with changes (if any) made to Pod template specifications.	Number	Typically, whenever changes are made to a Deplopyment's Pod template - say, labels or container images of the template are changed - then a Deployment rollout is triggered. A new ReplicaSet is created and the Deployment manages moving the Pods from the old ReplicaSet to the new one at a controlled rate. Ideally, the value of this measure should be the same as the value of the Total pods with deployment measure. If not, then it means that the desired number of Pod replicas are not yet fully updated with the changes to the Pod template.
Tot_readyReplica_count	Indicates the number of ready Pods created in the cluster across Deployments.	Number
Tot_available_count	Indicates the number of available Pods created in the cluster across Deployments.	Number	A Pod is said to be Available, if it is ready without any containers crashing for at least the duration configured against minReadySeconds in the Pod specification. Ideally, the value of this measure should be the same as the value of the Total pods with deployment measure. This means that the desired state of the Deployments is not the same as their actual state.
Tot_unavailable_count	Indicates the total number of unavailable Pods created in the cluster across Deployments.	Number	Any Pod that is not ready, or is ready but has containers crashing for a period of time beyond the minReadySeconds duration, is automatically considered Unavailable. Ideally, the value of this measure should be 0. If this measure reports a non-zero value or a value equal to or close to the value of the Total pods with deployment measure, it means that the desired state of the Deployments is not the same as their actual state.