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Medusa on Cloud Run — 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_CloudRun 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 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 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.
  • Access and verify the running service, including the built-in Admin UI.
  • 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, 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, 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]​

  1. In the RAD platform, open Medusa (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.

  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: 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. 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="$REGION" \
      --filter="metadata.name~medusa" --format="value(metadata.name)" --limit=1)
    SERVICE_URL=$(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]​

  1. Confirm the service is healthy:

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

  2. 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)

  3. Open $SERVICE_URL/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.

  4. 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 service and its revisions:

    gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION"
    gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"

  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 instance 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.

  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. 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:

    gcloud secrets list --project="$PROJECT" --filter="name~medusa"
    gcloud run jobs list --project="$PROJECT" --region="$REGION"   # 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 the CLI or the Logs Explorer:

    gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=50

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

  2. Monitoring — open the Cloud Run dashboard for the service and review request count, request latency, instance count, and CPU/memory utilisation. If cpu_always_allocated = false (the default), remember that CPU is throttled between inbound requests — if you see Medusa background workflows behaving sluggishly at low request volume, that is the likely cause (see the Configuration Guide's Configuration Pitfalls table).

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.

  • Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors. 
    gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
    gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100
  • Database or Redis connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret exists, 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: list job executions and read logs. medusa-verify is designed to fail the apply loudly (rather than silently shipping a healthy-looking service against an empty database) — if it failed, the error message names the table count it found: 
    gcloud run jobs executions list --job="${SERVICE}-medusa-migrate" \
      --project="$PROJECT" --region="$REGION"
    gcloud run jobs executions list --job="${SERVICE}-medusa-verify" \
      --project="$PROJECT" --region="$REGION"
  • 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.
  • 403 / permission errors: verify the runtime service account's IAM roles.

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 Cloud Run service, 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, shared Cloud SQL, registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule builds the Medusa image from source (~10 min), provisions Cloud Run + Cloud SQL (PostgreSQL 15), secrets, and runs the four-stage init chain (~30–45+ min total)
2 — Access & verifyManualHealth 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 revisions, scale (shared worker mode — every instance 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 revision, database/Redis, migration/verify-job, 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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