Gatus on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

Gatus is an open-source, developer-oriented status page and health-check monitor: it polls configured HTTP, TCP, DNS, and other endpoints on independent schedules, evaluates simple pass/fail conditions, and serves a live public status page plus alerting — no external database required. This lab takes you through the full operational lifecycle of the Gatus on GKE Autopilot module on Google Cloud: deploy it, access and verify it, run it day-to-day, observe it, diagnose common problems, and tear it down.

The lab focuses on operating the GKE module and the Google Cloud platform, not on Gatus product features. For the complete list of provisioned services and every configuration input (organised by group), see the Configuration Guide — this lab deliberately does not duplicate that detail so it stays accurate over time.

Objectives

By the end of this lab you will be able to:

Deploy the module from the RAD platform and locate the resources it provisions.
Connect to the GKE cluster and access the running workload, including viewing the live status page.
Perform day-2 operations — inspect, scale considerations, update, and manage secrets and durable history storage.
Observe the workload with Cloud Logging and Cloud Monitoring.
Diagnose and resolve the most common deployment and runtime issues.
Tear the deployment down cleanly.

Prerequisites

Services_GCP deployed in the target project (provides the VPC, GKE Autopilot cluster, Artifact Registry, and shared service accounts this module depends on).
A Google Cloud project with billing enabled.
gcloud CLI and kubectl installed; gcloud auth login and gcloud auth application-default login completed.
Project Owner (or equivalent) IAM on the project.
RAD platform access with permission to deploy modules into the project.

Set these shell variables once; every task below reuses them:

export PROJECT="<your-gcp-project-id>"
export REGION="us-central1"           # the region you deploy into

Task 1 — Deploy the module [Automated]

Click Deploy in the RAD platform top navigation, open Gatus (GKE) from the Platform Modules list to start configuration, set project_id, and review the inputs. Configure only what you need — the Configuration Guide documents every input by group, with defaults. Review the estimated cost (if credits are enabled) and click Deploy, which opens the deployment status page with real-time logs. If you also plan to deploy Gatus_CloudRun on the same tenant, set tenant_deployment_id = "gke" here (and "cr" on the Cloud Run variant) to avoid a naming collision on shared secret names, GCS bucket names, and rotation topics.
The platform deploys a single Deployment workload into the GKE Autopilot cluster running the Gatus Go binary, and builds the container image (which bakes in a default config.yaml with one example HTTP check). No database, cache, or object-storage bucket is provisioned — Gatus's optional history store is a local SQLite file. There is no database-initialisation job to wait for, so a first deploy is typically much faster than a database-backed module (roughly 10–15 minutes, dominated by the image build and workload scheduling).

Connect to the cluster and discover the namespace with a name-agnostic filter:

CLUSTER=$(gcloud container clusters list --project="$PROJECT" --format="value(name)" --limit=1)
gcloud container clusters get-credentials "$CLUSTER" --region="$REGION" --project="$PROJECT"

NS=$(kubectl get ns -o name | grep gatus | head -1 | cut -d/ -f2)
echo "Cluster: $CLUSTER   Namespace: $NS"
kubectl get all -n "$NS"

Task 2 — Access & verify [Manual]

Confirm the workload is running and find its external address:

kubectl get pods,svc -n "$NS"
EXTERNAL_IP=$(kubectl get svc -n "$NS" \
  -o jsonpath='{.items[?(@.spec.type=="LoadBalancer")].status.loadBalancer.ingress[0].ip}')
echo "External IP: $EXTERNAL_IP"

Confirm the service is healthy. Gatus's health endpoint responds as soon as the server binds its port — there is no database dependency to wait on:
```
curl -s -o /dev/null -w '%{http_code}\n' "http://${EXTERNAL_IP}/health"   # expect 200
```
Open http://${EXTERNAL_IP}/ in a browser to view the live status page — it shows the baked-in example endpoint check and its up/down history as checks accumulate.
Gatus ships with no authentication on its status page by default — anyone with the external IP can view it. There is no admin account to create. If the page will list sensitive endpoint names, edit modules/Gatus_Common/scripts/config.yaml's security block (basic auth or OIDC) and redeploy — this requires a rebuild, not a runtime setting.
Gatus has no runtime API or UI for adding monitored endpoints. To monitor a real endpoint instead of (or alongside) the baked-in example, edit the endpoints list in modules/Gatus_Common/scripts/config.yaml and redeploy.

Task 3 — Operate & keep it running (Day-2) [Manual]

Inspect the workload — deployment and pods:

kubectl get deploy,pods -n "$NS"
kubectl describe deploy -n "$NS"

Do not scale beyond one replica. max_instance_count defaults to 1 and should stay there — Gatus's watchdog polling loop has no shared coordination between replicas, so scaling out would have each replica independently poll every endpoint and duplicate alert notifications. Any change to min/max instances is made via the RAD platform's deployment details page and applied via Update, not a manual kubectl scale (which would be reverted on the next apply).
Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds and a rolling update replaces the pod. Pin an explicit v5.x.y in production rather than relying on latest.
Manage secrets:
```
kubectl get secrets -n "$NS"
gcloud secrets list --project="$PROJECT" --filter="name~gatus"
```
Gatus generates no secrets of its own at deploy time — the Secret Manager list is only populated if you supplied entries via secret_environment_variables.
Enable durable check history, if the default ephemeral store is not acceptable. Set stateful_pvc_enabled = true (which auto-selects workload_type = "StatefulSet") with stateful_pvc_mount_path = "/data" and stateful_pvc_storage_class = "standard" (HDD) — this is the only option in this catalogue verified safe for Gatus's history store, since Gatus hardcodes SQLite WAL journal mode and SQLite's own documentation states WAL is unsupported on network filesystems (which rules out enable_nfs as a safe alternative).
```
kubectl get pvc -n "$NS"          # only present when stateful_pvc_enabled = true
```

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — from kubectl or the Logs Explorer:
```
kubectl logs -n "$NS" deploy/"$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')" --tail=50
```
Logs Explorer filter: resource.type="k8s_container" AND resource.labels.namespace_name="<namespace>". Gatus logs each endpoint check's result (success/failure, duration) as it runs — useful for confirming a newly-added endpoint is actually being polled.
Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and request metrics. The module can provision an uptime check (when enabled); review Monitoring → Uptime checks and Alerting → Policies.

Task 5 — Troubleshoot & debug [Manual]

Durable techniques for the failure modes you are most likely to hit. These are platform-level diagnostics and do not change with Gatus releases.

Pod not Ready / CrashLoopBackOff: inspect events and logs. The startup and liveness probes both target /health, which should return 200 within seconds of boot — Gatus has no database to wait on, so a slow or failing probe usually points at a container build or config issue rather than a downstream dependency.
```
kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container
```
Check history "disappears" after a pod restart: this is expected with the default stateless Deployment and ephemeral storage — a pod restart resets history by design (the configured endpoints themselves are unaffected, only their historical results/uptime percentages reset). If a PVC is enabled, confirm stateful_pvc_mount_path matches Gatus's baked-in storage.path directory (/data) exactly:
```
kubectl get pvc -n "$NS"
kubectl exec -n "$NS" <pod> -- ls -l /data
```
A newly-added endpoint isn't being checked: confirm you edited modules/Gatus_Common/scripts/config.yaml and redeployed — Gatus has no runtime API for adding checks, so an endpoint added anywhere else has no effect.
Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP:
```
kubectl get svc -n "$NS"
```
Status page unreachable / blocked unexpectedly: check whether enable_iap was turned on — IAP requires Google sign-in and blocks unauthenticated viewing, which is usually not what a public status page wants.
Image pull errors: confirm the image exists in Artifact Registry and the node service account can pull it.

See the Configuration Guide's Configuration Pitfalls & Sensible Defaults section for setting-specific gotchas (including keeping max_instance_count = 1, matching the PVC mount path to /data, and the SQLite WAL persistence caveat).

Task 6 — Tear down [Automated]

On the Deployments page, open the deployment and click the Trash icon (Delete). Delete runs terraform destroy and is irreversible (the deployment record is retained for history). If a deployment is stuck and the RAD platform can no longer manage it (for example after manual changes that conflict with the Terraform state), use Purge instead — it removes the deployment from RAD's records without destroying the cloud resources (it makes RAD forget the project). This removes everything the module created — the Kubernetes workload and namespace, any PVC, and Artifact Registry images. There is no Cloud SQL database, GCS bucket, or auto-generated secret to clean up (Gatus provisions none by default). Resources owned by Services_GCP (the VPC, GKE cluster, shared registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys a single GKE workload running Gatus; no database or storage bucket
2 — Access & verify	Manual	Connect to the cluster; health check passes; live status page renders with the baked-in example check
3 — Operate	Manual	Inspect workload, keep max instances at 1, update version, manage secrets, enable a block PVC for durable history
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, config-edit, history-persistence, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources

By Dr Shiyghan Emmanuel Navti · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

Overview​

Objectives​

Prerequisites​

Task 1 — Deploy the module [Automated]​

Task 2 — Access & verify [Manual]​

Task 3 — Operate & keep it running (Day-2) [Manual]​

Task 4 — Observe: Logging & Monitoring [Manual]​

Task 5 — Troubleshoot & debug [Manual]​

Task 6 — Tear down [Automated]​

Summary​