Your cluster and your traces, together

Install one Helm chart. Cluster metrics flow in, and spans from pods in opted-in namespaces arrive tagged with their pod, node and namespace.

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In motion

The pod behind the slow span

Install a Helm chart and cluster metrics start flowing. Opt a namespace in, and a slow request takes you straight to the pod 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 panel

Filter down to one deployment, namespace or node, or watch a whole cluster live.

cluster: prod-us-east-1 LIVE
infra › kubernetes › workloads
Last 12 hours Reload Live 10s
Nodes 12
Workloads 8
Cluster CPU 42%
Cluster memory 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

Pods, nodes, workloads

Pods, nodes, and workloads

Same filters everywhere. Click from a workload to its pods to the node running 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

In opted-in namespaces, every span is tagged with its pod, node and namespace, so a 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
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

Install in three commands

Create the secret, install the chart, and metrics start arriving.

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/mapletechlabs/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 install
One Helm chart
A small chart you can read in full. Collects pod, node and cluster metrics, and receives OTLP from your apps.
k8s.pod.name
Pods linked to spans
Go from a slow trace to the pod and node that ran it.
k8s_cluster
Cluster-state metrics
Deployments, replicas and pod status, collected by the chart. No separate kube-state-metrics needed.
/infra/kubernetes
Workloads, pods, nodes and services
Four views that share the same filters, from a whole deployment down to one pod.
OTel Operator
OpenTelemetry Operator
Pods in an annotated namespace are configured automatically. Apps with an OTel SDK need no code changes.
one endpoint
Multi-cluster ingest
One endpoint for prod, staging and local clusters. Tell them apart by cluster name.

Keep going

Related features

01

Service Catalog & Map

Latency, errors and throughput per service, plus a live map

→ 02

Metrics & Dashboards

Browse every metric, build dashboards on them

→ 03

Distributed Tracing

Follow every request across services

→ 04

Microservices Debugging

Trace latency across service boundaries

→

Point OTLP at Maple.

One endpoint, one key. Traces, logs, metrics and sessions, linked from the first request.

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