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Ntfy on Cloud Run — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

ntfy is an open-source pub/sub push-notification server: applications publish messages over a simple REST/HTTP API and clients receive them instantly over WebSocket or Server-Sent-Events streams, with no external database required. This lab takes you through the full operational lifecycle of the ntfy 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 ntfy 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, including a publish/subscribe smoke test.
  • Perform day-2 operations — inspect, scale considerations, update, and manage secrets.
  • 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]​

  1. In the RAD platform, open Ntfy (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 provisions a single Cloud Run v2 service running the ntfy Go binary and builds the container image. No database, cache, or object-storage bucket is provisioned — ntfy keeps its message cache in a local SQLite file. There is no database-initialisation job to wait for, so a first deploy is typically much faster than a database-backed module (roughly 5–10 minutes, dominated by the image build).

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

    SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
      --filter="metadata.name~ntfy" --format="value(metadata.name)" --limit=1)
    SERVICE_URL=$(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. ntfy's health endpoint responds as soon as the server binds its port — there is no database dependency to wait on:

    curl -s "$SERVICE_URL/v1/health"   # expect {"healthy":true}

  2. Run a publish/subscribe smoke test:

    curl -d "hello from ntfy" "$SERVICE_URL/mytopic"     # publish
    curl -s "$SERVICE_URL/mytopic/json"                   # subscribe (streaming JSON; Ctrl-C to stop)

    Open $SERVICE_URL/mytopic in a browser to see the built-in web UI receive the message in real time.

  3. ntfy ships with open access — any client can publish to or subscribe from any topic on the public URL. There is no admin account to create. If you need access control, configure users and per-topic ACLs post-deploy via ntfy's CLI (ntfy user add, ntfy access) or by setting NTFY_AUTH_* environment variables in environment_variables and applying via Update. If you plan to use attachments or browser web-push, also set NTFY_BASE_URL to $SERVICE_URL.

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

  1. 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"

  2. Do not scale beyond one instance. max_instance_count defaults to 1 and should stay there — a subscriber's WebSocket/SSE stream is anchored to the instance holding it, and ntfy has no shared message bus. Scaling out silently splits subscribers across instances, so a message published against one instance is never delivered to a subscriber pinned to another. Any change to min/max instances is made via the RAD platform's deployment details page and applied via Update, not a manual gcloud edit (which would be reverted on the next apply).

  3. Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds and a new revision rolls out. Pin an explicit v2.x.y in production rather than relying on latest.

  4. Manage secrets:

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

    ntfy generates no secrets of its own at deploy time — this list is only populated if you supplied entries via secret_environment_variables.

  5. Enable durable message history, if the default ephemeral cache is not acceptable: set enable_nfs = true and point NTFY_CACHE_FILE's directory at the NFS mount, then apply via Update. Without this, the SQLite cache is lost on every restart or redeploy.

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>". ntfy logs its listen address and resolved cache path on startup — check here first if the cache fell back to /tmp/ntfy.

  2. Monitoring — open the Cloud Run dashboard for the service and review request count, request latency, instance count, and CPU / memory utilisation. Because cpu_always_allocated = true by default, expect a steady CPU baseline even at low traffic — this is required to keep subscriber streams alive, not a misconfiguration. If a Cloud Monitoring uptime check is enabled, 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 ntfy releases.

  • Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors. The startup and liveness probes both target /v1/health, which should return 200 within seconds of boot — ntfy has no database to wait on, so a slow or failing probe usually points at a container build or config issue rather than a downstream dependency. 
    gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
    gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100
  • Messages "disappear" or subscribers don't see history: check max_instance_count (should be 1) and whether enable_nfs is set — with the default ephemeral cache, a restart or redeploy wipes message history by design, which is easy to mistake for a delivery bug.
  • Attachments or web-push links are broken: confirm NTFY_BASE_URL is set to the service's actual public URL in environment_variables.
  • Publish/subscribe blocked unexpectedly: check ingress_settings (must be all for public traffic) and whether enable_iap was turned on — IAP requires Google sign-in and blocks unauthenticated publish/subscribe calls, which is usually not what a notification endpoint wants.
  • Image build failed: review Cloud Build history for the failed build's log.
  • 403 / permission errors: verify the runtime service account's IAM roles.

See the Configuration Guide's Configuration Pitfalls & Sensible Defaults section for setting-specific gotchas (including keeping max_instance_count = 1 and cpu_always_allocated = true for correct real-time delivery).

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 Artifact Registry images. There is no Cloud SQL database, GCS bucket, or auto-generated secret to clean up (ntfy provisions none by default). Resources owned by Services_GCP (the VPC, shared registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule provisions a single Cloud Run service running ntfy; no database or storage bucket
2 — Access & verifyManualHealth check passes; publish/subscribe smoke test confirms real-time delivery
3 — OperateManualInspect revisions, keep max instances at 1, update version, manage secrets, enable NFS for durability
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose revision, cache-persistence, access, and build issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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

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