SparkyFitness on Cloud Run — Lab 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 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 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, including its two-container architecture.
Perform day-2 operations — inspect, scale, 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, 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

Task 1 — Deploy the module [Automated]

In the RAD platform, open SparkyFitness (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 provisions a single multi-container Cloud Run service (frontend as the ingress container, backend as an in-pod sidecar), 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 15–25 minutes (Cloud SQL creation dominates). Both container images are prebuilt — no application build step runs.

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="$REGION" \
  --filter="metadata.name~sparkyfitness" --format="value(metadata.name)" --limit=1)
SERVICE_URL=$(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 frontend (ingress container) is serving:

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

Open $SERVICE_URL in a browser. On first visit, sign up to create the first user account — SparkyFitness has no pre-seeded admin credential in Secret Manager. SPARKY_FITNESS_ADMIN_EMAIL only ELEVATES an existing account, it does not create one, so signup must happen first.
After creating the account, set admin_email to that user's email in the RAD platform and click Update to grant it admin privileges. Consider setting disable_signup = true afterward to prevent further open registration.

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

Inspect the service, its revisions, and both containers (each deploy creates an immutable revision containing both the frontend and backend containers):

gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run revisions describe "$(gcloud run revisions list --service="$SERVICE" \
  --project="$PROJECT" --region="$REGION" --format='value(name)' --limit=1)" \
  --project="$PROJECT" --region="$REGION" --format='value(spec.containers[].name)'

Scale by changing the min/max instance inputs 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).
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).

Manage secrets:

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

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]

Logs — from the CLI or the Logs Explorer. Both containers' logs flow to the same stream, tagged by container name:
```
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=50
```
Logs Explorer filter for the backend sidecar only: resource.type="cloud_run_revision" AND resource.labels.service_name="<service>" AND labels."k8s-pod/app"="backend" (verify the exact label key against a sample log entry — Cloud Run's multi-container log labeling may vary by revision).
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. 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 SparkyFitness releases.

Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors on BOTH containers.

gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100

502/504 on /api/* calls but the frontend loads fine: the backend sidecar likely hasn't passed its startup_tcp_port check yet (its first-boot migrations can take longer than expected), or the sidecar crashed — check the backend container's logs specifically.
Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret exists, and the db-init job completed successfully. Also verify the backend can actually reach the Cloud SQL private IP over TCP — Cloud Run's additional_containers/inherit_app_env mechanism always injects the raw IP (not a Unix socket) for a sidecar, so a Cloud SQL instance that enforces SSL may need a follow-up investigation.

db-init job failed: list executions and read the failed one's logs:

gcloud run jobs executions list --job="${SERVICE}-db-init" \
  --project="$PROJECT" --region="$REGION"

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 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 — the Cloud Run service, Cloud SQL database, and Secret Manager secrets. Resources owned by Services_GCP (the VPC, shared Cloud SQL, registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions a multi-container Cloud Run service, Cloud SQL (PostgreSQL 15), secrets, and runs DB init
2 — Access & verify	Manual	Frontend serves 200; sign up to create the first account, then set `admin_email`
3 — Operate	Manual	Inspect revisions/containers, scale, update version, manage secrets, DB access
4 — Observe	Manual	Query Cloud Logging for both containers; review Cloud Monitoring metrics
5 — Troubleshoot	Manual	Diagnose revision, sidecar, database, init-job, and IAM 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​