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SparkyFitness on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–75 minutes

SparkyFitness is a self-hosted, AI-assisted family food, fitness, water, and health tracker. This lab takes you through the full operational lifecycle of the SparkyFitness on GKE Autopilot 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 GKE module and the Google Cloud platform, not on SparkyFitness 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 application (backend + frontend as separate Deployments/Services).
  • Perform day-2 operations — inspect, scale, update, and manage secrets.
  • Observe the workloads 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, GKE Autopilot cluster, Cloud SQL, 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
export NAMESPACE="<namespace-from-outputs>"
gcloud container clusters get-credentials <cluster-name> --region "$REGION" --project "$PROJECT"

Task 1 — Deploy the module [Automated]​

  1. In the RAD platform, open SparkyFitness (GKE), 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 backend as the main Deployment/Service, the frontend as a separate additional_services Deployment/Service with a reserved static external LoadBalancer IP, a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (SPARKY_FITNESS_API_ENCRYPTION_KEY, BETTER_AUTH_SECRET, SPARKY_FITNESS_APP_DB_PASSWORD, and the database password), and runs a one-shot db-init job. First deploys take roughly 20–30 minutes (Cloud SQL and cluster provisioning dominate). Both container images are prebuilt — no application build step runs.

  3. When it completes, discover the resources with name-agnostic filters:

    kubectl get deployments -n "$NAMESPACE"
    FRONTEND_IP=$(kubectl get service -n "$NAMESPACE" \
      -l app.kubernetes.io/component=frontend -o jsonpath='{.items[0].status.loadBalancer.ingress[0].ip}' 2>/dev/null || \
      kubectl get services -n "$NAMESPACE" -o wide | grep frontend)
    echo "Frontend: http://$FRONTEND_IP"

Task 2 — Access & verify [Manual]​

  1. Confirm the frontend Service has a real external IP and serves:

    kubectl get service -n "$NAMESPACE" -o wide
    curl -s -o /dev/null -w '%{http_code}\n' "http://$FRONTEND_IP"   # expect 200

  2. Open http://$FRONTEND_IP in a browser. On first visit, sign up to create the first user account — SparkyFitness has no pre-seeded admin credential in Secret Manager. admin_email only ELEVATES an existing account, it does not create one, so signup must happen first.

  3. After creating the account, set admin_email in the RAD platform and click Update to grant it admin privileges. Consider setting disable_signup = true afterward.

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

  1. Inspect both Deployments and their pods:

    kubectl get deployments -n "$NAMESPACE"
    kubectl get pods -n "$NAMESPACE" -o wide
    kubectl describe deployment <backend-deployment> -n "$NAMESPACE"

  2. Scale by changing the min/max instance inputs and clicking Update on the deployment details page — the module owns the workload spec, so scaling is a configuration change, not a manual kubectl scale (a manual edit would be reverted on the next apply, though kubectl scale --replicas=0 is fine temporarily for cost-saving between sessions).

  3. Update the application version tag by changing application_version in the RAD platform and applying it via Update. It tags BOTH the frontend and backend images identically — use the exact upstream tag format (e.g. v0.17.3, not a bare 0.17.3).

  4. Manage secrets:

    gcloud secrets list --project="$PROJECT" --filter="name~sparkyfitness"
    kubectl get secret -n "$NAMESPACE"

  5. Open a database session for inspection or maintenance (connect as the admin role — the app-level role is managed internally by the backend):

    INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
    gcloud sql connect "$INSTANCE" --user=sparky --project="$PROJECT"

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

  1. Logs — from the CLI or the Logs Explorer, per Deployment:

    kubectl logs -n "$NAMESPACE" deployment/<backend-deployment> --tail=100
    kubectl logs -n "$NAMESPACE" deployment/<frontend-deployment> --tail=100

    Logs Explorer filter: resource.type="k8s_container" AND resource.labels.namespace_name="<namespace>".

  2. Monitoring — open the GKE Workloads dashboard and review pod restarts, CPU/memory utilisation, and request latency (if configured). Review Monitoring → Uptime checks / Alerting → Policies if enabled.

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 SparkyFitness releases.

  • Frontend pod healthy but /api/* calls hang: the frontend proxies to the backend's Service on port 80 (the fixed App_GKE Service port), not container_port (3010) — verify with kubectl get service <backend-service> -n "$NAMESPACE" that port 80 exists and maps to the backend's targetPort. 
    kubectl get pods -n "$NAMESPACE"
    kubectl logs -n "$NAMESPACE" deployment/<frontend-deployment> --tail=50
  • Backend pod CrashLoopBackOff: check for a database connection failure — confirm the Cloud SQL Auth Proxy sidecar is healthy and the instance is RUNNABLE. 
    kubectl describe pod <backend-pod> -n "$NAMESPACE"
    kubectl logs -n "$NAMESPACE" <backend-pod> -c cloud-sql-proxy
  • db-init job failed: 
    kubectl get jobs -n "$NAMESPACE"
    kubectl logs -n "$NAMESPACE" job/db-init
  • Frontend LoadBalancer has no external IP: the reserved static IP (google_compute_address) may be exhausted against the project's global IN_USE_ADDRESSES quota — check gcloud compute addresses list --project "$PROJECT".
  • 403 / permission errors: verify the workload service account's IAM roles.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the critical rule never to rotate BETTER_AUTH_SECRET after users enable 2FA).

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). Delete removes everything the module created — both Deployments/Services, the reserved frontend static IP, Cloud SQL database, and Secret Manager secrets. Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL, registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule provisions backend + frontend Deployments/Services, Cloud SQL (PostgreSQL 15), secrets, and runs DB init
2 — Access & verifyManualFrontend LB serves 200; sign up to create the first account, then set admin_email
3 — OperateManualInspect Deployments/pods, scale, update version, manage secrets, DB access
4 — ObserveManualQuery Cloud Logging per Deployment; review GKE Workloads dashboard
5 — TroubleshootManualDiagnose frontend/backend pods, database, init-job, and IP-quota issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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

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