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Excalidraw on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

Excalidraw is an open-source virtual whiteboard for sketching hand-drawn-style diagrams, wireframes, and quick collaborative drawings. The self-hosted distribution is a static single-page application served by nginx — there is no backend, database, or user accounts. This lab takes you through the full operational lifecycle of the Excalidraw 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 Excalidraw 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.
  • Perform day-2 operations — inspect, scale, and update the deployment.
  • 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]​

  1. Click Deploy in the RAD platform top navigation, open Excalidraw (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.

  2. The platform builds a thin custom image (FROM excalidraw/excalidraw), mirrors it into Artifact Registry, and deploys the workload into the GKE Autopilot cluster as a plain Deployment behind a LoadBalancer Service. There is no Cloud SQL instance, no Secret Manager secret, no GCS bucket, no NFS, and no Redis — Excalidraw is a fully stateless static frontend, so this deploy is one of the fastest in the catalogue, typically 10–15 minutes (dominated by the image build and LoadBalancer provisioning).

  3. Connect to the cluster and discover the namespace with name-agnostic filters:

    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 excalidraw | head -1 | cut -d/ -f2)
    echo "Cluster: $CLUSTER   Namespace: $NS"
    kubectl get all -n "$NS"

Task 2 — Access & verify [Manual]​

  1. 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"

  2. Confirm the service is healthy. nginx answers the root path with 200 as soon as a pod is Ready — there is no database or backend to wait on:

    curl -sI "http://${EXTERNAL_IP}/" | head -1     # expect: HTTP/1.1 200 OK

  3. Open http://${EXTERNAL_IP} in a browser. The whiteboard loads immediately — there is no login, no admin account, and no first-run setup. Draw something and use Export (menu → Export) to save a .excalidraw, PNG, or SVG file; this is the only persistence mechanism, since drawings otherwise live only in the browser's local storage.

  4. Note that the live "shareable link" real-time collaboration feature is not available — it depends on a separate excalidraw-room WebSocket server that this module does not deploy. Single-user editing works fully out of the box.

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

  1. Inspect the workload — deployment, pods, and the horizontal autoscaler:

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

  2. Scale by changing the max-instance input and clicking Update on the deployment details page — the module owns the workload spec, so scaling is a configuration change, not a manual kubectl scale (a manual edit would be reverted on the next apply). min_instance_count is hardcoded to 1 in excalidraw.tf regardless of the input value — GKE has no scale-to-zero, so a resident pod always keeps the whiteboard reachable. Every pod is identical and stateless, so scaling out requires no session affinity or coordination.

  3. Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds from a new excalidraw/excalidraw tag and a rolling update replaces the pods. Note that unlike some sibling modules, latest here does not resolve to a pinned known-good tag — it tracks Docker Hub's rolling excalidraw/excalidraw:latest tag directly, so pin an explicit version (e.g. v1.11.86) for a reproducible production deploy. Because there is no server-side state, upgrades and rollbacks are trivial and non-destructive.

  4. Confirm there is nothing else to manage: unlike most modules, Excalidraw has no secrets, PVCs, or database to inspect:

    kubectl get secrets,pvc -n "$NS"                                            # no app secrets/PVCs
    gcloud secrets list --project="$PROJECT" --filter="name~excalidraw"          # (none)
    gcloud sql instances list --project="$PROJECT" --filter="name~excalidraw"    # (none)

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

  1. Logs — from kubectl or the Logs Explorer (nginx access/error logs):

    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>".

  2. Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and request metrics. Because the app is a static file server, resource usage should be consistently low. 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 Excalidraw releases.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. The liveness probe targets the root /, which nginx should answer within a second or two — a persistently failing probe almost always means a container/image problem, not an app dependency (there is no database to wait on). 
    kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
    kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container
  • Startup probe never passes / wrong port: confirm the pod's listening port matches the baked-in nginx port (80) — this is fixed in the image and should not be changed via container_port.
  • build_and_push_application_image fails with no Dockerfile / unbuilt image path: confirm container_image_source is custom (the default).
  • Pod running stale content after a rebuild: confirm imagePullPolicy: Always is set on the container (App_GKE sets this automatically for custom-built images) and compare the running image digest to the freshly built one: 
    kubectl get pod -n "$NS" -o jsonpath='{.items[0].status.containerStatuses[0].imageID}'
  • Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP: 
    kubectl get svc -n "$NS"
  • Real-time collaboration doesn't work: this is expected — the module does not deploy the separate excalidraw-room WebSocket server that the "shareable link" feature requires.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the fixed port, the hardcoded min_instance_count = 1, and the latest-tag caveat for production use).

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, namespace, Service, and the Artifact Registry image. There is no Cloud SQL database, Secret Manager secret, GCS bucket, or PVC to clean up, since none were created. Resources owned by Services_GCP (the VPC, GKE cluster, shared registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule builds and mirrors the static image, deploys a stateless Deployment + LoadBalancer — no database, secrets, or storage
2 — Access & verifyManualConnect to the cluster; health check passes instantly; whiteboard loads with no login or setup
3 — OperateManualInspect workload, scale (min=1 hardcoded), update/pin version — no secrets/PVCs/DB to manage
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose pod, port, image, and LoadBalancer issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

By · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

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