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Trilium on Cloud Run — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–60 minutes

Trilium Notes (the actively maintained TriliumNext fork) is a hierarchical, self-hosted note-taking application with an embedded SQLite database. This lab takes you through the full operational lifecycle of the Trilium 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 Trilium 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 the app, verify its health endpoint, and complete the first-run "Set Password" step.
  • Perform day-2 operations — inspect the service, keep single-instance scaling, and update the version.
  • Observe the service with Cloud Logging and Cloud Monitoring.
  • Diagnose and resolve the most common deployment and runtime issues, including the health-probe-path gotcha.
  • 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]​

  1. In the RAD platform, open Trilium (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.

  2. The platform builds a thin wrapper image over triliumnext/notes (via Cloud Build), provisions a dedicated Cloud Storage data bucket mounted at /home/node/trilium-data, and deploys a single Cloud Run service (port 8080, 1 vCPU / 1 GiB by default). There is no Cloud SQL instance and no Redis — Trilium's document store is entirely an embedded SQLite database on the mounted volume. First deploys typically take 5–10 minutes (the image build dominates; there is no database to wait for).

  3. Discover the deployed service with name-agnostic filters:

    SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
      --filter="metadata.name~trilium" --format="value(metadata.name)" --limit=1)
    SERVICE_URL=$(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]​

  1. Confirm the service is healthy. Trilium exposes an unauthenticated health endpoint — note this is not the root path:

    curl -s "$SERVICE_URL/api/health-check"   # expect {"status":"ok"}
    curl -s -o /dev/null -w '%{http_code}\n' "$SERVICE_URL/"   # expect 302 (redirect to setup)

  2. Open $SERVICE_URL in a browser. On first visit Trilium presents a "Set Password" screen — there is no pre-seeded admin credential in Secret Manager, unlike apps with an auto-generated password. Choose a strong password and complete the setup before sharing the URL with anyone else, especially if ingress_settings = "all" (the default, public).

  3. Verify persistence: create a note, then reload the page and confirm it's still there — everything lives in the mounted Cloud Storage bucket:

    gcloud storage buckets list --project="$PROJECT" --filter="name~trilium"

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

  1. Inspect the service and its revisions (each deploy creates an immutable revision):

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

  2. Do not scale out. The module deliberately pins min_instance_count = max_instance_count = 1: the embedded SQLite database has no multi-writer support — a second instance risks corrupting document.db. Resource changes (cpu_limit, memory_limit) go through Update on the deployment details page, not a manual gcloud run services update (a manual edit would be reverted on the next apply).

  3. Update the application version by changing the version input via Update on the deployment details page; a new image builds and a new revision rolls out. Trilium applies its own schema migrations on start, so no separate migration step is needed.

  4. There is no database session to open. database_type = "NONE" — no Cloud SQL instance, no db-init job, no database password. The only durable state is the data bucket.

  5. Back up the notes:

    DATA_BUCKET=$(gcloud storage buckets list --project="$PROJECT" \
      --filter="name~trilium" --format="value(name)" --limit=1)
    gcloud storage cp -r "gs://$DATA_BUCKET" "gs://<your-backup-bucket>/trilium-$(date +%F)"

    Trilium also has its own in-app export/backup feature (Menu → Export) for a single-note or whole-tree .zip export, independent of the infrastructure-level bucket copy above.

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

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

    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>".

  2. Monitoring — open the Cloud Run dashboard for the service and review request count, latency, and CPU/memory utilisation. The module can provision an uptime check (when uptime_check_config.enabled = true — it defaults to false) against /api/health-check; if enabled, confirm it is green under Monitoring → Uptime checks.

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 Trilium releases.

  • Revision never becomes Ready: check the probe path first. If a custom startup_probe/liveness_probe was set to / instead of the default /api/health-check, the 302 redirect it returns fails most HTTP health checks. 
    gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
    gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100
  • App boots but notes disappear after a restart: confirm the data bucket is actually mounted and its mount_options include uid=1000,gid=1000 — a mismatched uid/gid causes gcsfuse to mount the directory root-owned, which Trilium (running as uid 1000) cannot write to, and the app would fail to boot entirely rather than silently losing data — so this symptom instead points at a misconfigured or wrong bucket in gcs_volumes.
  • "Set Password" screen reappears on every visit: this means the SQLite database itself isn't persisting — confirm the bucket mount survived a revision update (check gcloud storage ls gs://<bucket>/ for a document.db file).
  • Image build failed: review Cloud Build history for the failed build's log; the image is a thin wrapper over triliumnext/notes.
  • 403 / permission errors: verify the runtime service account's IAM roles on the data bucket.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the critical rule to complete "Set Password" immediately for any publicly reachable deployment).

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, the Cloud Storage data bucket (all notes and attachments), and Artifact Registry images. Copy the notes out first (Task 3, step 5) if you want to keep them. Resources owned by Services_GCP (the VPC, Artifact Registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule builds the image and provisions the Cloud Run service and the data bucket (no DB, no Redis)
2 — Access & verifyManualHealth check passes on /api/health-check; complete the first-run "Set Password" step; verify note persistence
3 — OperateManualInspect revisions, keep single-instance scaling, update version, back up notes
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose probe-path, storage-mount, persistence, and build issues
6 — Tear downAutomatedDelete (Trash) removes all module resources including the data bucket

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

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