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Medusa on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 60–90 minutes — this is one of the longest labs in this catalogue. Unlike almost every other application module here, Medusa_GKE builds its container image from source (there is no official Medusa Docker image), and it runs a four-stage initialization chain instead of the usual one or two jobs. Both add real, observable time to a first deploy on top of normal Cloud SQL provisioning.

Medusa is an open-source, headless e-commerce platform — API-first, with full programmatic control over products, carts, orders, customers, and payments, plus a built-in Admin UI served by the same process. This lab takes you through the full operational lifecycle of the Medusa 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 Medusa's e-commerce 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 understand why it takes longer than most modules in this catalogue.
  • Connect to the GKE cluster and access the running workload, including the built-in Admin UI.
  • Perform day-2 operations — inspect, scale, update, and manage secrets.
  • Observe the workload with Cloud Logging and Cloud Monitoring.
  • Diagnose and resolve the most common deployment and runtime issues, including image build failures — a class of failure this module is uniquely exposed to.
  • 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 and kubectl installed; gcloud auth login and gcloud auth application-default login completed.
  • 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]​

  1. Click Deploy in the RAD platform, open Medusa (GKE) from the Platform Modules list, 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. This deploy takes noticeably longer than most modules in this catalogue. Three phases run mostly in sequence:

    • Image build (~10 minutes): Cloud Build clones medusajs/dtc-starter, runs pnpm install and medusa build, then packages a runtime image — a genuine git clone + dependency install + application build, not just a docker pull.
    • Cloud SQL provisioning (~20–35 minutes on a first deploy): standard for any PostgreSQL-backed module in this catalogue, but dominates the overall timeline.
    • The four-stage init chain (Kubernetes Jobs): db-init → medusa-migrate → medusa-verify → medusa-admin-create, each waiting on the previous and each showing real multi-minute latency in practice (medusa-migrate alone is allotted up to 30 minutes).

    Altogether, budget 30–45+ minutes for a first deploy — this is expected, not a sign of a stuck deployment. Watch the live log stream for progress through each phase rather than assuming a hang.

  3. Connect to the cluster and discover the namespace with name-agnostic filters:

    CLUSTER=$(gcloud container clusters list --project="$PROJECT" --format="value(name)" --limit=1)
    gcloud container clusters get-credentials "$CLUSTER" --region="$REGION" --project="$PROJECT"

    NS=$(kubectl get ns -o name | grep medusa | head -1 | cut -d/ -f2)
    echo "Cluster: $CLUSTER   Namespace: $NS"
    kubectl get all -n "$NS"

Task 2 — Access & verify [Manual]​

  1. Confirm the workload is running and find its external address:

    kubectl get pods,svc -n "$NS"
    EXTERNAL_IP=$(kubectl get svc -n "$NS" \
      -o jsonpath='{.items[?(@.spec.type=="LoadBalancer")].status.loadBalancer.ingress[0].ip}')
    echo "External IP: $EXTERNAL_IP"

  2. Confirm the service is healthy:

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

  3. Retrieve the auto-created admin credentials from Secret Manager — unlike many apps in this catalogue, Medusa's first admin user is bootstrapped automatically by the medusa-admin-create init job, not created interactively on first visit:

    ADMIN_SECRET=$(gcloud secrets list --project="$PROJECT" \
      --filter="name~medusa-admin-password" --format="value(name)")
    ADMIN_PASSWORD=$(gcloud secrets versions access latest \
      --secret="$ADMIN_SECRET" --project="$PROJECT")
    echo "Admin password: $ADMIN_PASSWORD"
    # Admin email is whatever admin_email was set to at deploy time
    # (default: admin@techequity.cloud)

  4. Open http://${EXTERNAL_IP}/app in a browser — Medusa serves its built-in Admin UI from the same process and port as the API. Sign in with the retrieved email/password.

  5. Browse the seeded demo data. On a fresh install, medusa-migrate seeds sample store/region/product/inventory data as part of Medusa's own migration process — you should see a demo product catalogue already populated in the Admin UI (Products, Regions) without any manual setup.

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

  1. Inspect the workload — deployment and pods:

    kubectl get deploy,pods -n "$NS"
    kubectl describe deploy -n "$NS"

  2. Scale by changing the min/max instance inputs and clicking Update on the deployment details page. Note the scaling implication specific to this module: MEDUSA_WORKER_MODE = "shared" means every running pod handles both API requests and Medusa's background jobs/subscribers/ workflows — there is no separate worker tier to scale independently. Scaling up adds redundant capacity for both request serving and background work together, not one or the other. Unlike Cloud Run, GKE has no request-based CPU throttling — each pod's CPU limit is available continuously regardless of inbound traffic.

  3. Update the application version. Changing the version input and applying via Update does not simply swap an image tag — because there is no upstream image, this triggers a full Cloud Build rebuild (~10 minutes for the build step) from source, followed by a rolling update of the pods. application_version itself doesn't even select what gets rebuilt (the Dockerfile has no ARG consuming it); only MEDUSA_STARTER_REF (fixed to the main branch of dtc-starter) determines what code is pulled.

  4. Manage secrets and jobs:

    kubectl get secrets -n "$NS"
    gcloud secrets list --project="$PROJECT" --filter="name~medusa"
    kubectl get jobs -n "$NS"          # the four init 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 "$INSTANCE" --user=medusa --project="$PROJECT"

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

  1. Logs — from kubectl or the Logs Explorer:

    kubectl logs -n "$NS" deploy/"$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')" --tail=50

    Look for "Server is ready on port: 9000" confirming a healthy boot. Logs Explorer filter: resource.type="k8s_container" AND resource.labels.namespace_name="<namespace>".

  2. Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation and restart counts. If a pod is being OOM-killed under combined API + background-workflow load, consider raising container_resources.memory_limit above its 1Gi default.

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 Medusa releases.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. 
    kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
    kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container
  • Database or Redis connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and that Redis is actually reachable — remember Medusa logs the deceptively calm "redisUrl not found. A fake redis instance will be used." and boots anyway rather than failing outright, so a missing Redis connection can look like a healthy deploy that misbehaves under load.
  • Migration or verification failures: inspect the job and its pod logs. medusa-verify is designed to fail the apply loudly (rather than silently shipping a healthy-looking pod against an empty database) — if it failed, the error message names the table count it found: 
    kubectl get jobs -n "$NS"
    kubectl logs -n "$NS" job/<service-name>-medusa-migrate
    kubectl logs -n "$NS" job/<service-name>-medusa-verify
  • Image build failures — the failure mode most distinctive to this module. Because the image is built from source on every deploy, this module is the one in the batch most likely to hit a genuine Cloud Build failure — e.g. an upstream change to the dtc-starter repository breaking the clone, pnpm install, or medusa build step. Review the Cloud Build history for the failed build's full log: 
    gcloud builds list --project="$PROJECT" --limit=5
    gcloud builds log <build-id> --project="$PROJECT"
    A build failing with "medusa: not found" at container start time (rather than at build time) is the pnpm workspace-isolation bug documented in the Configuration Guide's Configuration Pitfalls — the fix already lives in the shipped Dockerfile, but is worth recognising if you ever modify it. When a Cloud-Run-only version of this kind of issue looks like a permanent hang with no diagnosable cause from logs alone, GKE's kubectl exec shell access is a genuine advantage — kubectl exec <pod> -- ps aux can show whether a slow boot step is still genuinely progressing or has stalled.
  • Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas.

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). This removes everything the module created — the Kubernetes workload and namespace, Cloud SQL database, Secret Manager secrets, and Artifact Registry images (plus any GCS bucket, if enable_gcs_storage was enabled). 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 builds the Medusa image from source (~10 min), deploys onto GKE Autopilot with Cloud SQL (PostgreSQL 15), secrets, and runs the four-stage init chain (~30–45+ min total)
2 — Access & verifyManualConnect to the cluster; health check passes; retrieve auto-created admin credentials from Secret Manager; log into the built-in Admin UI at /app; browse seeded demo products
3 — OperateManualInspect workload, scale (shared worker mode — every pod does both API + background work), update version (triggers a full source rebuild), manage secrets, DB access
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics
5 — TroubleshootManualDiagnose pod, database/Redis, migration/verify-job, scheduling, and image build issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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

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