Excalidraw on Cloud Run — Lab 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 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 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.
Access and verify the running service.
Perform day-2 operations — inspect, scale, and update the deployment.
Observe the service 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, Artifact Registry, 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

Task 1 — Deploy the module [Automated]

In the RAD platform, open Excalidraw (Cloud Run), 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.
The platform builds a thin custom image (FROM excalidraw/excalidraw), mirrors it into Artifact Registry, and provisions the Cloud Run service. There is no Cloud SQL instance, no Secret Manager secret, no GCS bucket, and no Redis — Excalidraw is a fully stateless static frontend, so this deploy is one of the fastest in the catalogue, typically 5–10 minutes (dominated by the image build).

When it completes, discover the resource with a name-agnostic filter (so the command keeps working regardless of the deployment suffix):

SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
  --filter="metadata.name~excalidraw" --format="value(metadata.name)" --limit=1)
SERVICE_URL=$(gcloud run services describe "$SERVICE" \
  --project="$PROJECT" --region="$REGION" --format="value(status.url)")
echo "Service: $SERVICE"
echo "URL:     $SERVICE_URL"

Task 2 — Access & verify [Manual]

Confirm the service is healthy. nginx answers the root path with 200 as soon as the revision is serving — there is no database or backend to wait on:
```
curl -sI "$SERVICE_URL/" | head -1     # expect: HTTP/2 200
```
Open $SERVICE_URL 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.
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]

Inspect the service and its revisions (each deploy creates an immutable revision; traffic shifts to the newest healthy one):

gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"

Scale by changing the max-instance input 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). min_instance_count is forced to 0 by the wrapper: Excalidraw has no background work to keep warm, so scale-to-zero is always on and idle deployments cost nothing. Because every request is served identically from static files, there is no session affinity to worry about when scaling out.
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 new revision rolls out. Because there is no server-side state, upgrades and rollbacks are trivial — traffic can be shifted back to a prior revision at any time with no data-consistency concerns.

Confirm there is nothing else to manage: unlike most modules, Excalidraw has no secrets, backup jobs, or database to inspect:

gcloud secrets list --project="$PROJECT" --filter="name~excalidraw"          # (none)
gcloud sql instances list --project="$PROJECT" --filter="name~excalidraw"    # (none)
gcloud run jobs list --project="$PROJECT" --region="$REGION" \
  --filter="metadata.name~excalidraw"                                       # (none)

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — from the CLI or the Logs Explorer (nginx access/error logs):
```
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=50
```
Logs Explorer filter: resource.type="cloud_run_revision" AND resource.labels.service_name="<service>".
Monitoring — open the Cloud Run dashboard for the service and review request count, request latency (P50/P95/P99), instance count (scaling behaviour), and CPU / memory utilisation. Because the app is a static file server, latency should be consistently low and CPU usage minimal. The module also provisions an uptime check; confirm it is green under Monitoring → Uptime checks, and review 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.

Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors. The startup 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).
```
gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100
```
Startup probe never passes / wrong port: confirm the running revision's listening port matches the baked-in nginx port (80) — this is fixed in the image and should not be changed via container_port:
```
gcloud run services describe "$SERVICE" --region="$REGION" \
  --format='value(spec.template.spec.containers[0].ports[0].containerPort, spec.template.spec.containers[0].image)'
```
Image not found: confirm container_image_source is custom (the default) and that the Cloud Build history shows a successful build/push into Artifact Registry.
Image build failed: review Cloud Build history for the failed build's log.
403 / permission errors: verify the runtime service account's IAM roles.
Whiteboard unreachable from a browser: confirm ingress_settings is all (the default) — internal restricts access to the VPC only.
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 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 Cloud Run service and its Artifact Registry image. There is no Cloud SQL database, Secret Manager secret, or GCS bucket to clean up, since none were created. Resources owned by Services_GCP (the VPC, shared registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module builds and mirrors the static image, provisions the Cloud Run service — no database, secrets, or storage
2 — Access & verify	Manual	Health check passes instantly; whiteboard loads with no login or setup
3 — Operate	Manual	Inspect revisions, scale (scale-to-zero forced), update version — no secrets/DB/backups to manage
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision, port, build, and ingress 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​