Penpot on Cloud Run — 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 Cloud Run 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 Cloud Run 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.
• Access and verify all three running services (backend, frontend, exporter).
• Perform day-2 operations — inspect, scale, update, and manage secrets.
• Observe the services 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, Cloud SQL, Artifact Registry, Redis/NFS, and shared service accounts this module depends on).
• A Google Cloud project with billing enabled.
• gcloud CLI authenticated: gcloud auth login and gcloud auth application-default login.
• 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 (Cloud Run) 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 provisions three coordinated Cloud Run services (backend, frontend, exporter), a Cloud SQL PostgreSQL database with its Secret Manager secrets, a GCS assets bucket, optional NFS/Redis, builds the container images, and runs a one-shot database-initialisation job. 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. When it completes, discover the services with name-agnostic filters:

   # Frontend service — the user-facing entry point
   FRONTEND_SVC=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
     --filter="metadata.name~penpot AND metadata.name~frontend" \
     --format="value(metadata.name)" --limit=1)
   SERVICE_URL=$(gcloud run services describe "$FRONTEND_SVC" \
     --project="$PROJECT" --region="$REGION" --format="value(status.url)")
   
   # Backend and exporter services
   BACKEND_SVC=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
     --filter="metadata.name~penpot AND metadata.name~backend" \
     --format="value(metadata.name)" --limit=1)
   EXPORTER_SVC=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
     --filter="metadata.name~penpot AND metadata.name~exporter" \
     --format="value(metadata.name)" --limit=1)
   
   echo "Frontend: $FRONTEND_SVC  URL: $SERVICE_URL"
   echo "Backend:  $BACKEND_SVC"
   echo "Exporter: $EXPORTER_SVC"

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Task 2 — Access & verify [Manual]

1. Confirm all three services are healthy:

   gcloud run services list --project="$PROJECT" --region="$REGION" \
     --filter="metadata.name~penpot"

2. Confirm the backend API health endpoint responds:

   # The backend health endpoint — expect HTTP 200
   BACKEND_URL=$(gcloud run services describe "$BACKEND_SVC" \
     --project="$PROJECT" --region="$REGION" --format="value(status.url)")
   curl -s -o /dev/null -w "%{http_code}" "${BACKEND_URL}/api/health"

3. Confirm the frontend is reachable at the service URL:

   curl -s -o /dev/null -w "%{http_code}" "$SERVICE_URL"

   Open $SERVICE_URL 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 all three services and their revisions (each deploy creates an immutable revision; traffic shifts to the newest healthy one):

   gcloud run services list --project="$PROJECT" --region="$REGION" \
     --filter="metadata.name~penpot"
   gcloud run revisions list --service="$BACKEND_SVC" \
     --project="$PROJECT" --region="$REGION"

2. Scale by changing the min/max instance inputs and clicking Update on the deployment details page — the module owns the service spec, so scaling is a configuration change, not a manual gcloud edit (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 new revisions roll out. All three services must use the same version tag.

4. Manage secrets:

   gcloud secrets list --project="$PROJECT" --filter="name~penpot"

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"

---

Task 4 — Observe: Logging & Monitoring [Manual]

1. Logs — from the CLI or the Logs Explorer:

   gcloud run services logs read "$BACKEND_SVC" \
     --project="$PROJECT" --region="$REGION" --limit=50
   gcloud run services logs read "$EXPORTER_SVC" \
     --project="$PROJECT" --region="$REGION" --limit=20

   Logs Explorer filter: resource.type="cloud_run_revision" AND resource.labels.service_name="<service>".

2. Monitoring — open the Cloud Run dashboard and review each of the three services independently: request count, request latency (P50/P95/P99), instance count (scaling behaviour), and CPU/memory utilisation. The module also provisions an uptime check against /api/health; confirm it is green under Monitoring → Uptime checks, and review 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.

• Revision unhealthy / service won't serve: 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 logs and confirm the revision became healthy:

  gcloud run revisions list --service="$BACKEND_SVC" \
    --project="$PROJECT" --region="$REGION"
  gcloud run services logs read "$BACKEND_SVC" \
    --project="$PROJECT" --region="$REGION" --limit=100

• Database connection errors: confirm the Cloud SQL instance is RUNNABLE and the DB password secret exists. Penpot runs its own migrations at startup — a migration failure surfaces in the backend logs before the service becomes healthy.
• WebSocket / real-time collaboration broken: Redis is mandatory for WebSocket fan-out between backend replicas. Confirm Redis connectivity and that enable_redis = true. Check backend logs for Redis connection errors.
• Image build failed: review Cloud Build history for the failed build's log. All three service images must be present in Artifact Registry before Cloud Run can deploy.
• 403 / permission errors: verify the runtime service account's IAM roles and that the GCS assets bucket policy grants the Cloud Run SA storage.objectAdmin.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including container_protocol = "h2c" and JVM heap sizing).

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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 — all three Cloud Run services, the Cloud SQL PostgreSQL database, Secret Manager secrets, the GCS assets bucket, NFS, and Artifact Registry images. Resources owned by Services_GCP (the VPC, shared Cloud SQL, registry) are managed separately and are not removed here.

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Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions three Cloud Run services, Cloud SQL, GCS bucket, secrets, and NFS
2 — Access & verify	Manual	All three services healthy; frontend reachable; backend /api/health returns 200
3 — Operate	Manual	Inspect revisions, scale, update version, manage secrets, DB access
4 — Observe	Manual	Query Cloud Logging per service; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision, database/migration, WebSocket/Redis, build, and IAM issues
6 — Tear down	Automated	Delete (Trash) removes all module resources