Saleor on Cloud Run — Lab Guide

Overview

Estimated time: 60–90 minutes

Saleor is an open-source, GraphQL-first headless e-commerce platform (product catalog, checkout, orders, payment plugins) built on Python/Django. This lab takes you through the full operational lifecycle of the Saleor 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 Saleor 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 both the Saleor API and the separate Dashboard service.
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 Saleor (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 two Cloud Run services (the main Saleor API and a separate Dashboard), a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (SECRET_KEY, RSA_PRIVATE_KEY, DJANGO_SUPERUSER_PASSWORD, and the database password), a Cloud Storage media bucket, builds the custom container image, and runs two sequential database-initialization jobs (db-init then db-migrate). 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="$REGION" \
  --filter="metadata.name~saleor AND NOT metadata.name~dashboard" --format="value(metadata.name)" --limit=1)
SERVICE_URL=$(gcloud run services describe "$SERVICE" \
  --project="$PROJECT" --region="$REGION" --format="value(status.url)")
DASHBOARD=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
  --filter="metadata.name~saleor AND metadata.name~dashboard" --format="value(metadata.name)" --limit=1)
DASHBOARD_URL=$(gcloud run services describe "$DASHBOARD" \
  --project="$PROJECT" --region="$REGION" --format="value(status.url)")
echo "API:       $SERVICE ($SERVICE_URL)"
echo "Dashboard: $DASHBOARD ($DASHBOARD_URL)"

Task 2 — Access & verify [Manual]

Confirm the API is healthy:

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

Run a real GraphQL query to confirm the schema and database wiring work end to end:

curl -s -X POST "$SERVICE_URL/graphql/" \
  -H 'Content-Type: application/json' \
  -d '{"query":"{ shop { name } }"}'

Retrieve the bootstrap superuser credential and log in through the Dashboard:
```
gcloud secrets versions access latest \
  --secret="$(gcloud secrets list --project="$PROJECT" --filter="name~saleor-admin-password" --format='value(name)')" \
  --project="$PROJECT"
```
Open $DASHBOARD_URL in a browser and sign in with admin@example.com and the retrieved password (the default SALEOR_SUPERUSER_EMAIL — override via environment_variables on the wiring file before deploying if a different address is needed).

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

Inspect the API 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). cpu_always_allocated = true remains on regardless of instance count — the co-located Celery worker needs continuous CPU on every running instance.
Update the application version tag by changing application_version in the RAD platform and applying it via Update; a new image builds (mapped to the SALEOR_VERSION build ARG) and a new revision rolls out.

Manage secrets and backups:

gcloud secrets list --project="$PROJECT" --filter="name~saleor"
gcloud run jobs list --project="$PROJECT" --region="$REGION"   # db-init, db-migrate, 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 "$INSTANCE" --user=saleor_user --project="$PROJECT"

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — from the CLI or the Logs Explorer, for both services:

gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=50
gcloud run services logs read "$DASHBOARD" --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 each service and review request count, request latency (P50/P95/P99), instance count (scaling behaviour), and CPU / memory utilisation — the API service's CPU floor stays non-zero even between requests because cpu_always_allocated = true. 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 Saleor 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 /health/ with a 20-second initial delay and a 20-failure threshold.
```
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 both db-init and db-migrate completed successfully (in order — db-migrate depends on db-init).

Initialisation job failed: list executions and read the failed one's logs:

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

GraphQL query fails with a database error even though the API is Ready: usually means db-migrate did not complete — check its execution logs before assuming an application bug.
Dashboard loads but can't reach the API: the Dashboard's API_URL is baked into its static bundle at container start from the main API's predicted URL — if the API's actual run.app URL differs (e.g. after a service rename), the Dashboard needs to be rebuilt/redeployed to pick up the corrected URL.
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 section for setting-specific gotchas (including the critical rule never to rotate RSA_PRIVATE_KEY outside a maintenance window).

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 Cloud Run services (API and Dashboard), the Cloud SQL database, Secret Manager secrets, the GCS media bucket, and Artifact Registry images. Resources owned by Services_GCP (the VPC, shared Cloud SQL instance, registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions two Cloud Run services (API + Dashboard), Cloud SQL (PostgreSQL 15), secrets, `media` bucket, and runs `db-init` → `db-migrate`
2 — Access & verify	Manual	Health check and GraphQL query pass; log into the Dashboard with the bootstrap admin credential
3 — Operate	Manual	Inspect revisions, scale, update version, manage secrets/backups, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics for both services and uptime check
5 — Troubleshoot	Manual	Diagnose revision, database, init-job, Dashboard-linkage, 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​