Penpot on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

Penpot is an open-source design and prototyping platform — a self-hosted alternative to Figma — that provides vector design editing, interactive prototyping, component libraries, and real-time multiplayer collaboration. This lab takes you through the full operational lifecycle of the Penpot 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 Penpot 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 workloads.
• Perform day-2 operations — inspect, scale, update, and manage secrets and storage.
• Observe the workloads 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, Cloud SQL, Artifact Registry, Redis/NFS, 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

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Task 1 — Deploy the module [Automated]

1. Click Deploy in the RAD platform top navigation, open Penpot (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 deploys three coordinated Kubernetes workloads (backend, frontend, exporter) into the GKE Autopilot cluster, provisions a Cloud SQL PostgreSQL database with its Secret Manager secrets, a GCS assets bucket, optional NFS/Redis, and builds the container images. The Penpot backend then runs its own PostgreSQL migrations on first boot — allow up to 60–120 seconds for JVM startup and migration. First deploys take roughly 25–40 minutes (Cloud SQL creation dominates).

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

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Task 2 — Access & verify [Manual]

1. Confirm all three workloads are running and find the 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 backend health endpoint responds:

   # Health endpoint on the backend pod — expect HTTP 200
   BACKEND_POD=$(kubectl get pods -n "$NS" -o name | grep backend | head -1 | cut -d/ -f2)
   kubectl exec -n "$NS" "$BACKEND_POD" -- \
     wget -qO- http://localhost:6060/api/health

3. Confirm the frontend is reachable externally:

   curl -s -o /dev/null -w "%{http_code}" "http://${EXTERNAL_IP}"

   Open http://$EXTERNAL_IP in a browser. If penpot_flags includes enable-registration (the default), self-registration is available. Otherwise, an administrator creates accounts directly inside Penpot. No admin credential is stored in Secret Manager — Penpot manages its own user accounts.

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

1. Inspect the workloads — deployments, pods, horizontal autoscalers, and (if enabled) persistent volumes:

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

2. Scale by changing the min/max instance inputs 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). Keep min_instance_count at 1 or higher; scale-to-zero terminates active WebSocket sessions and forces a 60–120 second JVM cold start on reconnect.

3. Update the application version by changing the version input via Update on the deployment details page; new images build for all three services and a rolling update replaces the pods. All three services must use the same version tag.

4. Manage secrets, storage, and jobs:

   kubectl get secrets -n "$NS"
   gcloud secrets list --project="$PROJECT" --filter="name~penpot"
   kubectl get jobs -n "$NS"

5. Open a database session for inspection or maintenance:

   INSTANCE=$(gcloud sql instances list --project="$PROJECT" \
     --format="value(name)" --limit=1)
   gcloud sql connect "$INSTANCE" --user=penpot --project="$PROJECT"

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Task 4 — Observe: Logging & Monitoring [Manual]

1. 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>".

2. Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and request metrics for all three workloads (backend, frontend, exporter). The module also provisions an uptime check against /api/health (when enabled); review Monitoring → Uptime checks and Alerting → Policies.

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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 Penpot releases.

• Pod not Ready / CrashLoopBackOff: the backend uses an HTTP startup probe on /api/health and can take 60–120 seconds to pass on first boot (JVM init + PostgreSQL migration). Inspect events and logs:

  kubectl describe pod -n "$NS" <pod>          # Events section shows probe/mount errors
  kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container

• Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and the init job completed. Penpot runs its own migrations at startup — a migration failure surfaces in the backend logs before the pod becomes Ready.
• WebSocket / real-time collaboration broken: Redis is mandatory for WebSocket fan-out between backend replicas. Check backend pod logs for Redis connection errors, confirm enable_redis = true, and verify session_affinity = "ClientIP" is set (prevents WebSocket frames from being routed to different replicas).
• Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP.
• Image pull errors: confirm all three images (backend, frontend, exporter) exist in Artifact Registry and the node service account can pull them.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including JVM heap sizing and quota_memory_requests binary suffix requirements).

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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 workloads and namespace, Cloud SQL PostgreSQL database, Secret Manager secrets, the GCS assets bucket, NFS, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL, registry) are managed separately and are not removed here.

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Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys three GKE workloads, Cloud SQL, GCS bucket, secrets, and NFS
2 — Access & verify	Manual	Connect to cluster; all workloads healthy; frontend reachable; /api/health returns 200
3 — Operate	Manual	Inspect workloads, scale, update version, manage secrets/storage, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database/migration, WebSocket/Redis, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources