kubectl plugin for visualizing resource allocations across your cluster. Key differentiators:
- All resources — cpu, memory, gpu, and any custom resource; not limited to cpu/memory like most tools
- Tree view — group and aggregate by resource, node, namespace, or pod in any combination via
-g - Full picture — requested, limit, allocatable, free, and optionally live utilization (
-u, requires metrics-server)
kubectl view-allocations -u
Note: By default only nodes without taints are shown. If results look empty or incomplete, use
--ignore-taintsto include control-plane or tainted nodes — see Filter by Node Taints.
| Column | Description |
|---|---|
Requested |
Resources requested by containers in pod manifests, summed by pod/namespace/node. Includes percentage of the group's allocatable total. |
Limit |
Max resources (limit) set in pod manifests, summed by pod/namespace/node. Includes percentage of the group's allocatable total. |
Allocatable |
Allocatable resources defined (or detected) on nodes. |
Free |
Allocatable - max(Limit, Requested) by default — see --used-mode to change this. |
Utilization |
Actual cpu/memory usage from the Metrics API. Disabled by default; requires metrics-server on the cluster. |
- Install — krew · mise · binary · cargo
- Usage
- Alternatives
- Sponsors
Recommended: krew is the easiest way to install and keep the plugin up to date.
krew
kubectl krew install view-allocationsmise (GitHub releases backend)
mise use -g github:davidB/kubectl-view-allocationsbinary — download from GitHub releases or via script:
curl https://raw.githubusercontent.com/davidB/kubectl-view-allocations/master/scripts/getLatest.sh | bashcargo
cargo install kubectl-view-allocationsAll examples use kubectl-view-allocations directly; if installed via krew, substitute kubectl view-allocations.
> kubectl-view-allocations -g resource
Resource Requested Limit Allocatable Free
cpu (21%) 56.7 (65%) 176.1 272.0 95.9
ephemeral-storage (0%) __ (0%) __ 38.4T 38.4T
memory (8%) 52.7Gi (15%) 101.3Gi 675.6Gi 574.3Gi
nvidia.com/gpu (71%) 10.0 (71%) 10.0 14.0 4.0
pods (9%) 147.0 (9%) 147.0 1.6k 1.5k> kubectl-view-allocations -r gpu
Resource Requested Limit Allocatable Free
nvidia.com/gpu (71%) 10.0 (71%) 10.0 14.0 4.0
├─ node-gpu1 (0%) __ (0%) __ 2.0 2.0
├─ node-gpu2 (0%) __ (0%) __ 2.0 2.0
├─ node-gpu3 (100%) 2.0 (100%) 2.0 2.0 __
│ └─ fah-gpu-cpu-d29sc 2.0 2.0 __ __
├─ node-gpu4 (100%) 2.0 (100%) 2.0 2.0 __
│ └─ fah-gpu-cpu-hkg59 2.0 2.0 __ __
├─ node-gpu5 (100%) 2.0 (100%) 2.0 2.0 __
│ └─ fah-gpu-cpu-nw9fc 2.0 2.0 __ __
├─ node-gpu6 (100%) 2.0 (100%) 2.0 2.0 __
│ └─ fah-gpu-cpu-gtwsf 2.0 2.0 __ __
└─ node-gpu7 (100%) 2.0 (100%) 2.0 2.0 __
└─ fah-gpu-cpu-x7zfb 2.0 2.0 __ __- Utilization information are retrieved from metrics-server (should be setup on your cluster).
- Only reports cpu and memory utilization.
> kubectl-view-allocations -u
Resource Utilization Requested Limit Allocatable Free
cpu (0%) 31.0m (6%) 950.0m (1%) 100.0m 16.0 15.1
└─ demo-kube-control-plane (0%) 31.0m (6%) 950.0m (1%) 100.0m 16.0 15.1
├─ kube-apiserver-demo-kube-control-plane 13.0m 250.0m __ __ __
├─ etcd-demo-kube-control-plane 6.0m 100.0m __ __ __
├─ kube-controller-manager-demo-kube-control-plane 4.0m 200.0m __ __ __
├─ metrics-server-7c47898747-664mk 2.0m __ __ __ __
├─ kube-proxy-mnv5g 1.0m __ __ __ __
├─ local-path-provisioner-6bc4bddd6b-8q2bk 1.0m __ __ __ __
├─ coredns-5d78c9869d-955fw 1.0m 100.0m __ __ __
├─ coredns-5d78c9869d-9jcrv 1.0m 100.0m __ __ __
├─ kube-scheduler-demo-kube-control-plane 1.0m 100.0m __ __ __
└─ kindnet-fz7fb 1.0m 100.0m 100.0m __ __
ephemeral-storage __ __ __ 468.4Gi __
└─ demo-kube-control-plane __ __ __ 468.4Gi __
memory (2%) 487.1Mi (1%) 290.0Mi (1%) 390.0Mi 31.3Gi 30.9Gi
└─ demo-kube-control-plane (2%) 487.1Mi (1%) 290.0Mi (1%) 390.0Mi 31.3Gi 30.9Gi
├─ kube-apiserver-demo-kube-control-plane 296.8Mi __ __ __ __
├─ kube-controller-manager-demo-kube-control-plane 51.1Mi __ __ __ __
├─ etcd-demo-kube-control-plane 32.4Mi 100.0Mi __ __ __
├─ kube-scheduler-demo-kube-control-plane 25.2Mi __ __ __ __
├─ kube-proxy-mnv5g 17.2Mi __ __ __ __
├─ metrics-server-7c47898747-664mk 16.6Mi __ __ __ __
├─ coredns-5d78c9869d-955fw 14.9Mi 70.0Mi 170.0Mi __ __
├─ coredns-5d78c9869d-9jcrv 14.0Mi 70.0Mi 170.0Mi __ __
├─ kindnet-fz7fb 10.2Mi 50.0Mi 50.0Mi __ __
└─ local-path-provisioner-6bc4bddd6b-8q2bk 8.6Mi __ __ __ __
pods __ (9%) 10.0 (9%) 10.0 110.0 100.0
└─ demo-kube-control-plane __ (9%) 10.0 (9%) 10.0 110.0 100.0> kubectl-view-allocations -g namespace
Resource Requested Limit Allocatable Free
cpu (10%) 200.0m __ 2.0 1.8
└─ kube-system 200.0m __ __ __
ephemeral-storage __ __ 99.8G __
memory (2%) 140.0Mi (3%) 170.0Mi 5.8Gi 5.6Gi
└─ kube-system 140.0Mi 170.0Mi __ __
pods (5%) 5.0 (5%) 5.0 110.0 105.0
└─ kube-system 5.0 5.0 __ __Values are expanded as floats (with 2 decimal places).
kubectl-view-allocations -o csv
Date,Kind,resource,node,pod,Requested,%Requested,Limit,%Limit,Allocatable,Free
2020-08-19T19:12:48.326605746+00:00,resource,cpu,,,59.94,22%,106.10,39%,272.00,165.90
2020-08-19T19:12:48.326605746+00:00,node,cpu,node-gpu1,,2.31,19%,4.47,37%,12.00,7.53
2020-08-19T19:12:48.326605746+00:00,pod,cpu,node-gpu1,yyy-b8bd56fbd-5x8vq,1.00,,2.00,,,
2020-08-19T19:12:48.326605746+00:00,pod,cpu,node-gpu1,kube-flannel-ds-amd64-7dz9z,0.10,,0.10,,,
2020-08-19T19:12:48.326605746+00:00,pod,cpu,node-gpu1,node-exporter-gpu-b4w7s,0.11,,0.22,,,
2020-08-19T19:12:48.326605746+00:00,pod,cpu,node-gpu1,xxx-backend-7d84544458-46qnh,1.00,,2.00,,,
2020-08-19T19:12:48.326605746+00:00,pod,cpu,node-gpu1,weave-scope-agent-bbdnz,0.10,,0.15,,,
2020-08-19T19:12:48.326605746+00:00,node,cpu,node-gpu2,,0.31,1%,0.47,2%,24.00,23.53
2020-08-19T19:12:48.326605746+00:00,pod,cpu,node-gpu2,kube-flannel-ds-amd64-b5b4v,0.10,,0.10,,,
2020-08-19T19:12:48.326605746+00:00,pod,cpu,node-gpu2,node-exporter-gpu-796jz,0.11,,0.22,,,
2020-08-19T19:12:48.326605746+00:00,pod,cpu,node-gpu2,weave-scope-agent-8rhnd,0.10,,0.15,,,
2020-08-19T19:12:48.326605746+00:00,node,cpu,node-gpu3,,3.41,11%,6.67,21%,32.00,25.33
...Can be combined with --group-by:
kubectl-view-allocations -g resource -o csv
Date,Kind,resource,Requested,%Requested,Limit,%Limit,Allocatable,Free
2020-08-19T19:11:49.630864028+00:00,resource,cpu,59.94,22%,106.10,39%,272.00,165.90
2020-08-19T19:11:49.630864028+00:00,resource,ephemeral-storage,0.00,0%,0.00,0%,34462898618662.00,34462898618662.00
2020-08-19T19:11:49.630864028+00:00,resource,hugepages-1Gi,0.00,,0.00,,,
2020-08-19T19:11:49.630864028+00:00,resource,hugepages-2Mi,0.00,,0.00,,,
2020-08-19T19:11:49.630864028+00:00,resource,memory,69063409664.00,10%,224684670976.00,31%,722318667776.00,497633996800.00
2020-08-19T19:11:49.630864028+00:00,resource,nvidia.com/gpu,3.00,27%,3.00,27%,11.00,8.00
2020-08-19T19:11:49.630864028+00:00,resource,pods,0.00,0%,0.00,0%,1540.00,1540.00By default, kubectl-view-allocations only shows nodes without taints (workload nodes). The --ignore-taints option allows you to control which nodes are included based on their taints.
# Default: Only show nodes without taints (workload nodes)
kubectl-view-allocations
# Ignore all taints and show all nodes (including control-plane)
kubectl-view-allocations --ignore-taints
# Show untainted nodes + nodes with specific taints (ignore these taints)
kubectl-view-allocations --ignore-taints node-role.kubernetes.io/control-plane
# Show untainted nodes + nodes with specific taint key-value pairs
kubectl-view-allocations --ignore-taints dedicated=database
# Ignore multiple taint patterns
kubectl-view-allocations --ignore-taints node-role.kubernetes.io/control-plane,dedicated=database
# Combine with label selector
kubectl-view-allocations -l environment=production --ignore-taints dedicated=databaseUse cases:
- Default behavior: Show only workload nodes (nodes without taints) for typical application resource allocation
- Complete overview: Use
--ignore-taintswithout values to see all nodes in the cluster, including control-plane and specialized nodes - Include control-plane: Use
--ignore-taints node-role.kubernetes.io/control-planeto see workload + control-plane nodes - Include specialized nodes: Use
--ignore-taints dedicated,workloadto include dedicated or workload-specific nodes alongside regular workload nodes
> kubectl-view-allocations -h
kubectl plugin to list allocations (cpu, memory, gpu,...) X (utilization, requested, limit, allocatable,...)
Usage: kubectl-view-allocations [OPTIONS]
Options:
--kubeconfig <KUBECONFIG>
Path to the kubeconfig file to use for requests to kubernetes cluster
--context <CONTEXT>
The name of the kubeconfig context to use
-n, --namespace <NAMESPACE>...
Filter pods by namespace(s), by default pods in all namespaces are listed (comma separated list or multiple calls)
-l, --selector <SELECTOR>
Show only nodes match this label selector
--ignore-taints [<IGNORE_TAINTS>...]
Ignore nodes with specific taints; when not specified, only nodes without taints are shown; when used without values, show all nodes (comma-separated list)
-u, --utilization
Force to retrieve utilization (for cpu and memory), requires having metrics-server https://github.com/kubernetes-sigs/metrics-server
-z, --show-zero
Show lines with zero requested AND zero limit AND zero allocatable, OR pods with unset requested AND limit for `cpu` and `memory`
--used-mode <USED_MODE>
The way to compute the `used` part for free (`allocatable - used`) [default: max-request-limit] [possible values: max-request-limit, only-request]
--precheck
Pre-check access and refresh token on kubeconfig by running `kubectl cluster-info`
--accept-invalid-certs
Accept invalid certificates (dangerous)
-r, --resource-name <RESOURCE_NAME>...
Filter resources shown by name(s), by default all resources are listed (comma separated list or multiple calls)
-g, --group-by <GROUP_BY>...
Group information in a hierarchical manner; defaults to `-g resource,node,pod` (comma-separated list or multiple calls) [possible values: resource, node, pod, namespace]
-o, --output <OUTPUT>
Output format [default: table] [possible values: table, csv]
-s, --sort <SORT>
Sort rows by column(s), SQL-like syntax: 'col [ASC|DESC]' (comma-separated). Valid columns: usage/utilization, requested, limits/limit, allocatable, free, name. Direction is optional (default ASC). name ASC is always the implicit final tiebreaker [default: "usage DESC, requested DESC, limits DESC, name ASC"]
-h, --help
Print help
-V, --version
Print version
https://github.com/davidB/kubectl-view-allocations- see the discussion Need simple kubectl command to see cluster resource usage · Issue #17512 · kubernetes/kubernetes
- For CPU & Memory only
- ahmetb/kubectl-node_resource: Query node allocations/utilization in kubectl
- robscott/kube-capacity: A simple CLI that provides an overview of the resource requests, limits, and utilization in a Kubernetes cluster,
- hjacobs/kube-resource-report: Report Kubernetes cluster and pod resource requests vs usage and generate static HTML
- etopeter/kubectl-view-utilization: kubectl plugin to show cluster CPU and Memory requests utilization
- For CPU & Memory utilization only
kubectl top pods- LeastAuthority/kubetop: A top(1)-like tool for Kubernetes.
- As a Rust library
- kdash-rs/kdash embeds
kubectl-view-allocationsas a dependency. To use it in your own Rust project:kubectl-view-allocations = { version = "1.1.0", default-features = false }(addk8s-openapi/vX_YYto your features).
- kdash-rs/kdash embeds
- Via AI assistant (no binary needed)
- If your AI coding tool supports skills.sh, install the assistant workflow with
npx skills add davidB/kubectl-view-allocations. This lets your AI agent run and interpretkubectl view-allocationsfor you — it does not install the binary.
- If your AI coding tool supports skills.sh, install the assistant workflow with
Alchim312 | CDviz — SDLC observability platform built on CDEvents.
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