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Saleor on GKE Autopilot — Lab Guide

📖 Configuration 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 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 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 workload.
  • Perform day-2 operations — inspect, scale, update, and manage secrets and backups.
  • Observe the workload 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, 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 Saleor (GKE), set project_id, and review the inputs. Configure only what you need — the Configuration Guide documents every input by group, with defaults.

    IP quota note: the module defaults service_type = "LoadBalancer". If the project's external IP quota (IN_USE_ADDRESSES) is exhausted, set service_type = "ClusterIP" in the deployment inputs (or config/deploy.tfvars for a maintainer apply) — this is exactly what the live reference deployment for this module currently runs. Switch back to LoadBalancer (with reserve_static_ip = true) once quota is available.

  2. Review the estimated cost (if credits are enabled) and click Deploy, which opens the deployment status page with real-time logs.

  3. The platform provisions two Kubernetes workloads (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 and GKE cluster provisioning dominate).

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

    NAMESPACE=$(kubectl get ns -o name | grep saleor | sed 's|namespace/||' | head -1)
    SERVICE=$(kubectl get svc -n "$NAMESPACE" -o name | grep -v dashboard | grep saleor | sed 's|service/||' | head -1)
    DASHBOARD=$(kubectl get svc -n "$NAMESPACE" -o name | grep dashboard | sed 's|service/||' | head -1)
    echo "Namespace: $NAMESPACE"
    echo "API svc:   $SERVICE"
    echo "Dashboard: $DASHBOARD"

Task 2 — Access & verify [Manual]​

  1. If service_type = "ClusterIP" (the current default on the reference deployment because of exhausted IP quota — see Task 1), reach the services via port-forward:

    kubectl port-forward -n "$NAMESPACE" svc/"$SERVICE" 18080:8000 &
    kubectl port-forward -n "$NAMESPACE" svc/"$DASHBOARD" 18081:80 &
    curl -s -o /dev/null -w '%{http_code}\n' "http://localhost:18080/health/"   # expect 200
    curl -s -X POST "http://localhost:18080/graphql/" \
      -H 'Content-Type: application/json' -d '{"query":"{ shop { name } }"}'

    If service_type = "LoadBalancer", use the external IP from kubectl get svc -n "$NAMESPACE" instead.

  2. 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 the Dashboard (via port-forward at http://localhost:18081 or the external URL) and sign in with admin@example.com and the retrieved password.

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

  1. Inspect the workload and its pods:

    kubectl get deploy,pods,svc -n "$NAMESPACE"
    kubectl describe deploy "$SERVICE" -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, and would not persist Celery worker CPU guarantees).

  3. 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 rolling update deploys.

  4. Manage secrets and backups:

    gcloud secrets list --project="$PROJECT" --filter="name~saleor"
    kubectl get jobs -n "$NAMESPACE"   # db-init, db-migrate, scheduled backup jobs

  5. 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]​

  1. Logs — from the CLI or the Logs Explorer, for both workloads:

    kubectl logs -n "$NAMESPACE" deploy/"$SERVICE" --tail=100
    kubectl logs -n "$NAMESPACE" deploy/"$DASHBOARD" --tail=100

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

  2. Monitoring — review GKE workload CPU/memory utilisation in the Console (Kubernetes Engine → Workloads). The module can provision an uptime check (when uptime_check_config.enabled = true — it defaults to false, and requires a publicly reachable endpoint, i.e. service_type = "LoadBalancer"); confirm it is green under Monitoring → Uptime checks 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 Saleor releases.

  • Pod not Ready: inspect events and logs; the startup probe targets /health/ with a 90-second initial delay (giving db-migrate time to complete first). 
    kubectl describe pod -n "$NAMESPACE" -l app="$SERVICE"
    kubectl logs -n "$NAMESPACE" deploy/"$SERVICE" --tail=200
  • Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the Cloud SQL Auth Proxy sidecar is healthy, and both db-init and db-migrate completed successfully (in order — db-migrate depends on db-init).
  • Initialisation job failed: 
    kubectl get jobs -n "$NAMESPACE"
    kubectl logs -n "$NAMESPACE" job/db-init
    kubectl logs -n "$NAMESPACE" job/db-migrate
  • GraphQL query fails with a database error even though the pod is Ready: usually means db-migrate did not complete — check its job logs before assuming an application bug.
  • Service unreachable from a browser: confirm service_type — if it is ClusterIP (the current state on the reference deployment due to exhausted IP quota), you must use kubectl port-forward; there is no external IP by design until it is switched to LoadBalancer.
  • Dashboard loads but can't reach the API: the Dashboard's API_URL is baked into its static bundle at container start from $(GKE_SERVICE_URL) — if the Service was recreated with a different name/IP, the Dashboard needs to be 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 workload's Workload Identity bindings.

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 Kubernetes workloads (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, GKE Autopilot cluster, shared Cloud SQL instance, registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule provisions two GKE workloads (API + Dashboard), Cloud SQL (PostgreSQL 15), secrets, media bucket, and runs db-init → db-migrate
2 — Access & verifyManualHealth check and GraphQL query pass (via port-forward if ClusterIP); log into the Dashboard with the bootstrap admin credential
3 — OperateManualInspect workload, scale, update version, manage secrets/backups, DB access
4 — ObserveManualQuery Cloud Logging; review GKE workload metrics and uptime check
5 — TroubleshootManualDiagnose pod, database, init-job, Dashboard-linkage, build, and Workload Identity issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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

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