Tandoor on Cloud Run — Lab Guide

Overview

Estimated time: 45–90 minutes

Tandoor Recipes is an open-source, self-hosted recipe manager and meal planner with URL-import recipe scraping. This lab takes you through the full operational lifecycle of the Tandoor 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 Tandoor 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 retrieving the generated superuser credential.
Perform day-2 operations — inspect, scale, update, and manage secrets and backups.
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 Tandoor (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 the Cloud Run service, a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (SECRET_KEY, DJANGO_SUPERUSER_PASSWORD, and the database password), a Cloud Storage data bucket, and runs two one-shot jobs: db-init (creates the database and user) and create-superuser (bootstraps the initial admin account). First deploys take roughly 20–35 minutes (Cloud SQL creation dominates).

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="$REGION" \
  --filter="metadata.name~tandoor" --format="value(metadata.name)" --limit=1)
SERVICE_URL=$(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 service is healthy. Tandoor has no dedicated unauthenticated health endpoint, so the platform probes (and this check) target Django's public login page:
```
curl -s -o /dev/null -w '%{http_code}\n' "$SERVICE_URL/accounts/login/"   # expect 200
```

Retrieve the generated superuser credential — Tandoor has no self-registration flow and no fixed default credential, unlike some apps in this catalogue:

SECRET_NAME=$(gcloud secrets list --project="$PROJECT" \
  --filter="name~tandoor-superuser-password" --format="value(name)" --limit=1)
gcloud secrets versions access latest --secret="$SECRET_NAME" --project="$PROJECT"

Open $SERVICE_URL/accounts/login/ in a browser and log in with username admin (or your configured admin_username) and the password retrieved above. Consider changing the password immediately after first login as a good security practice.

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

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"

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). Tandoor has no background worker, so scaling beyond one instance needs no special coordination.
Update the application version tag by changing the version input in the RAD platform and applying it via Update; a new revision rolls out. Tandoor publishes a genuine latest tag upstream, so this reflects real upstream releases (unlike some apps in this catalogue whose version tag is cosmetic only).

Manage secrets and backups:

gcloud secrets list --project="$PROJECT" --filter="name~tandoor"
gcloud run jobs list --project="$PROJECT" --region="$REGION"   # init + scheduled backup jobs

Open a database session for inspection or maintenance:

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

Task 4 — Observe: Logging & Monitoring [Manual]

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>".
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, 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 Tandoor releases.

Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors, and confirm env vars and secrets resolved. The startup probe targets /accounts/login/ and requires Postgres connectivity plus applied migrations to return 200.
```
gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100
```
Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret exists, and the db-init job completed successfully.
Can't log in / no credential known: retrieve DJANGO_SUPERUSER_PASSWORD from Secret Manager (Task 2, step 2) — there is no fixed fallback credential to fall back on.
create-superuser job failed: list executions and read the failed one's logs — a common cause is the job running before db-init finished (it should be listed as a dependency; the job retries up to twice):
```
gcloud run jobs executions list --job="${SERVICE}-create-superuser" \
  --project="$PROJECT" --region="$REGION"
```
Image build failed: review Cloud Build history for the failed build's log (Tandoor is prebuilt, so this only applies if container_image_source was overridden to custom).
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 SECRET_KEY after first boot).

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, Secret Manager secrets, GCS buckets, and Artifact Registry images. 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 Cloud Run, Cloud SQL (PostgreSQL 15), secrets, storage bucket, and runs `db-init` + `create-superuser`
2 — Access & verify	Manual	Health check passes; retrieve the generated superuser credential and log in
3 — Operate	Manual	Inspect revisions, scale, update version, manage secrets/backups, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision, database, init-job, build, 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​