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LubeLogger on Cloud Run — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–60 minutes

LubeLogger is a free, open-source vehicle maintenance and fuel-mileage tracker (ASP.NET Core, embedded LiteDB database). This lab takes you through the full operational lifecycle of the LubeLogger 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 LubeLogger 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 the self-service first-run registration flow.
  • Perform day-2 operations — inspect, scale (understanding why it's fixed at one instance), update, and manage storage.
  • 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 LubeLogger (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 the Cloud Run service (no Cloud SQL — LubeLogger's default mode uses an internal embedded LiteDB database file), two Cloud Storage buckets (storage for app data, dpkeys for ASP.NET Core Data Protection keys), and mirrors the official prebuilt image into Artifact Registry. There is no build step and no database-initialisation job, so first deploys are fast — typically 5–10 minutes.

  3. When it completes, discover the resources with name-agnostic filters (so the commands keep working regardless of the deployment suffix):

    SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
      --filter="metadata.name~lubelogger" --format="value(metadata.name)" --limit=1)
    SERVICE_URL=$(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. LubeLogger exposes its public, unauthenticated /Login page — the same path the platform's own health probes use:

    curl -s -o /dev/null -w '%{http_code}\n' "$SERVICE_URL/Login"   # expect 200

  2. Open $SERVICE_URL/Login in a browser. There is no pre-seeded admin credential — click Register and create the first account (name, email, password). Because EnableAuth = "true" is on by default, this is the ONLY way to gain access; the app root / redirects unauthenticated visitors to /Login. Complete this step immediately after deploy, since the Register form itself is reachable by anyone with the URL until a first account exists.

  3. After logging in, add a vehicle and a maintenance/fuel record to confirm the database write path (embedded LiteDB, persisted on the storage GCS volume) is working. Refresh the page and confirm the record is still there.

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. Scaling is intentionally fixed at one instance. min_instance_count = 1 and max_instance_count = 1 are enforced by a plan-time validation guard — LubeLogger's default mode serves one shared embedded database file from one volume, so running multiple replicas risks corruption. There is no supported way to horizontally scale this module in its default (embedded LiteDB) configuration.

  3. Update the application version tag by changing application_version in the RAD platform and applying it via Update; since the image is prebuilt (not custom-built), this directly selects the corresponding ghcr.io/hargata/lubelogger release tag and a new revision rolls out.

  4. Inspect storage:

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

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, request latency (P50/P95/P99), and CPU / memory utilisation (expect a flat single instance, no scaling activity). The module can provision an uptime check (when uptime_check_config.enabled = true — it defaults to false); 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 LubeLogger releases.

  • Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors. The startup probe targets /Login and should pass within seconds of the container starting — a persistent failure here usually means the container isn't listening on port 8080, not a slow first-boot migration (there is none). 
    gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
    gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100
  • Data not persisting across revisions/restarts: confirm the storage GCS volume is actually mounted at /App/data — check the revision's volume mounts in gcloud run revisions describe.
  • Logged out unexpectedly after a redeploy: confirm the dpkeys bucket exists and is mounted at /root/.aspnet/DataProtection-Keys — if it was ever deleted/recreated, all existing sessions are invalidated (not fatal, just requires re-login).
  • / returns a redirect/401 instead of the app: expected behaviour when EnableAuth = "true" and you are not logged in — the app root is [Authorize]-gated. Go to /Login directly.
  • Image build failed: review Cloud Build history — the module mirrors the official image; a failure here usually indicates a GHCR rate limit or transient network issue, not an application bug.
  • 403 / permission errors: verify the runtime service account's IAM roles.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the critical rule to keep max_instance_count = 1).

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 both Cloud Storage buckets (all vehicle records and uploaded documents are lost). Resources owned by Services_GCP (the VPC, Artifact Registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule provisions Cloud Run, two Cloud Storage buckets, and mirrors the prebuilt image (no database, no build step)
2 — Access & verifyManualHealth check passes; register the first account and confirm a record persists
3 — OperateManualInspect revisions, understand the fixed single-instance constraint, update version, inspect storage
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose revision, storage/persistence, session, and build issues
6 — Tear downAutomatedDelete (Trash) removes all module resources, including all data

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

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