Kubernetes Monitoring

Cluster, workload, and span in one view

Install one Helm chart. Kubelet, host, and kube-state metrics flow in over OTLP. The OpenTelemetry Operator injects pod identity into every span your apps emit, so a slow request lands you on the exact replica that served it.

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In motion

The pod underneath the slow span.

Drop a Helm chart on your cluster. Kubelet, host, and kube-state metrics start flowing in. Every span your apps emit arrives carrying pod, node, and namespace, so a slow request lands you on the exact replica that served it.

pod heatmap · 192 pods · cluster cpu 44% LIVE

PODS 192

RUNNING 184

DEGRADED 6

FAILING 2

HOT >78% 11

default default 72

observability obs 32

kube-system kube-sys 40

ingress-nginx ingress 24

data data 24

CPU

0% 100% degraded failing

Cluster console

Workloads, pods, and nodes — one filter sidebar

The same view your platform team lives in. Facet down to a deployment in a namespace on a node, or scope to a cluster and watch it live.

cluster: prod-us-east-1 LIVE

Nodes 12

Workloads 8

Cluster CPU 42%

Cluster MEM 54%

Workloads

Aggregated pod metrics by deployment, statefulset, and daemonset.

Deployment StatefulSet DaemonSet

Namespace	Workload	Kind	Ready	CPU	Memory	Node
default	api-gateway	Deployment	3/3	47%	62%	ip-10-0-1-12
default	order-service	Deployment	4/4	71%	51%	ip-10-0-1-44
default	checkout-worker	StatefulSet	2/2	23%	38%	ip-10-0-2-08
observability	otel-collector	DaemonSet	6/6	39%	48%	per-node
default	payment-svc	Deployment	2/3 ⟳	86%	67%	ip-10-0-1-44
default	inventory-svc	Deployment	3/3	34%	49%	ip-10-0-2-08
kube-system	coredns	Deployment	2/2	12%	21%	ip-10-0-1-12
ingress-nginx	nginx-ingress	DaemonSet	3/3	28%	33%	per-node

Three views

Pods, nodes, and workloads — first-class

Same filters everywhere. Click through from a workload to its pods to the node that runs them.

/infra/kubernetes/pods 200 pods

Pods

Per-pod CPU and memory — against requests and limits.

Pod	CPU req	CPU lim	Mem lim
otel-gateway-5988fb47f… ns observability	147%	37%	30%
prd-artifacts-api-6688… ns default	0%	52%	10%
prd-warpstream-5bb84bb… ns warpstream	42%	42%	35%
prd-enrichment-api-769… ns default	0%	34%	21%

/infra/kubernetes/nodes 3 nodes

Nodes

Kubelet stats per node, with uptime and lifecycle.

Node	Status	CPU	Last seen
i-0ef7f77feb3e0a3eb	Active	3.00	14s ago
i-0293a9e7e2bac82a4	Active	2.65	30s ago
i-0a8c19f2d4e7b15c1	Active	1.84	22s ago

/infra/kubernetes/workloads 21 workloads

Workloads

Aggregated by deployment, statefulset, daemonset.

Workload	Pods	Avg CPU	NS
api-gateway	3	47%	default
order-service	4	71%	default
payment-svc	2	86%	default
inventory-svc	3	34%	default

Span → pod

From a slow span to the exact pod

The OpenTelemetry Operator stamps every span with k8s.pod.name, k8s.node.name, and k8s.namespace.name at admission. The slow trace already knows where it ran.

Trace: 7af1c204 1.18s

Waterfall

api-gateway

POST /checkout

1.18s

order-service

createOrder

425ms

payment-svc

processPayment

612ms

inventory-svc

reserveItems

98ms

k8s.pod.name injected at admission

Pod attribution Active

pod

payment-svc-78f4d6c89b-x7k2p

k8s.pod.name: payment-svc-78f4d6c89b-x7k2p
k8s.node.name: ip-10-0-1-44.ec2.internal
k8s.namespace.name: default

One Helm chart

Drop it on your cluster

Read the chart end-to-end. Three commands and the cluster is reporting kubelet, host, and kube-state metrics over OTLP.

helm install maple-k8s-infra 3 steps

1 Create the ingest-key secret

kubectl create namespace maple
kubectl -n maple create secret generic maple-ingest-key \
  --from-literal=ingest-key=$MAPLE_INGEST_KEY

2 Install the chart

helm upgrade --install maple-k8s-infra \
  oci://ghcr.io/makisuo/charts/maple-k8s-infra \
  --namespace maple \
  --set maple.ingestKey.existingSecret.name=maple-ingest-key \
  --set maple.ingestKey.existingSecret.key=ingest-key \
  --set global.clusterName=production

3 Watch the rollout

kubectl -n maple rollout status daemonset/maple-k8s-infra-agent

What it does

Cluster and application, one pipeline

Helm chart, not a black box: maple-k8s-infra is a small Helm chart you can read end-to-end. Kubelet stats per pod, host metrics per node, kube-state metrics across the cluster. OTLP receivers on every node for your apps.
Pod and span, joined: Spans arrive carrying k8s.pod.name, k8s.node.name, and k8s.namespace.name. Drill from a slow trace straight to the pod and node that ran it.
Kube-state metrics: Deployments, StatefulSets, DaemonSets, replica counts, and pod phase, all scraped via the standard kube-state-metrics exporter.
Workloads, pods, and nodes: Three first-class infrastructure views in the dashboard. Inspect a deployment, a single pod, or a single node, with the same filters you use everywhere else.
OpenTelemetry Operator: Pods get OTEL_EXPORTER_OTLP_ENDPOINT and pod/node identity injected at admission. Apps with an OTel SDK linked are instrumented without code changes.
Multi-cluster ingest: One ingest endpoint per organization. Send from prod, staging, and a developer's kind cluster, and split them with cluster-name resource attributes.

Cluster, workload, and span in one view

The pod underneath the slow span.

Workloads, pods, and nodes — one filter sidebar

Workloads

Pods, nodes, and workloads — first-class

Pods

Nodes

Workloads

From a slow span to the exact pod

Drop it on your cluster

Cluster and application, one pipeline

Surfaces that share this data

Service Catalog & Map

Metrics & Dashboards

Distributed Tracing

Microservices Debugging

Point OTLP at Maple.