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Azimutt on Cloud Run — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

Azimutt is an open-source, next-generation database-schema explorer and ERD (entity relationship diagram) tool for real-world databases, built with Elixir/Phoenix. This lab takes you through the full operational lifecycle of the Azimutt 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 Azimutt 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 and create the first Azimutt account.
  • Perform day-2 operations — inspect, scale, update, and manage secrets and the database.
  • 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]​

  1. In the RAD platform, open Azimutt (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. The platform provisions the Cloud Run service, a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (SECRET_KEY_BASE and the database password), a Cloud Storage bucket, builds the container image (a thin wrapper FROM ghcr.io/azimuttapp/azimutt), and runs a one-shot database-initialisation job that creates the application role and database. First deploys take roughly 20–35 minutes (Cloud SQL creation dominates).

  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="$REGION" \
      --filter="metadata.name~azimutt" --format="value(metadata.name)" --limit=1)
    SERVICE_URL=$(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 and connected to its database. Azimutt has no dedicated health JSON endpoint — the startup and readiness probes target the Phoenix root /, which only returns 200 once the server has booted, applied its migrations, and connected to Postgres:

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

  2. Open $SERVICE_URL in a browser. On first visit Azimutt shows its sign-up page — no pre-seeded admin credential exists in Secret Manager. Create your first account with an email and password. Sign-up is open by default, so after creating your account, restrict further access (custom domain + IAP, or Azimutt's own auth settings via environment_variables).

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

  1. 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"

  2. 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). Unlike apps with an in-memory job queue, Azimutt uses PostgreSQL (Oban) for background work, so scaling beyond one instance needs no Redis. Note that min_instance_count = 0 (the default) enables scale-to-zero; set 1 to avoid the few seconds of cold-start latency after idle.

  3. Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds and a new revision rolls out. Migrations run automatically on every boot (/app/bin/migrate && /app/bin/server), so an upgrade applies its schema changes on start — allow extra time on the first boot after a version bump. Azimutt publishes no :latest tag (application_version = "latest" maps to its main tag); pin to a specific release in production.

  4. Manage secrets:

    gcloud secrets list --project="$PROJECT" --filter="name~azimutt"

    Never rotate SECRET_KEY_BASE outside a maintenance window — rotating it invalidates every active session cookie and signs out all users.

  5. Open a database session for inspection or maintenance:

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

  6. File uploads are ephemeral by default. With the default FILE_STORAGE_ADAPTER = local, uploads are written to the container's local disk, not the provisioned Cloud Storage bucket — they do not survive a redeploy or a scale-to-zero cold start. Project data itself (schemas, diagrams, layouts, users) lives safely in Postgres.

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

  1. Logs — from the CLI or the Logs Explorer. The cloud-entrypoint lines show the resolved DATABASE_URL path, PHX_HOST, and PORT:

    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>".

  2. 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 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 Azimutt 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 / with a 60-second initial delay — allow ~1–2 minutes on first boot for migrations to finish before the endpoint binds. 
    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. Azimutt connects over the instance's private IP with SSL (DATABASE_ENABLE_SSL=true) — Ecto/postgrex cannot parse the Cloud SQL socket DSN, so the socket mount (enable_cloudsql_volume = true) exists solely for the db-init job, not the running app.
  • 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"
  • Image build failed: review Cloud Build history for the failed build's log — the base tag comes from the AZIMUTT_VERSION build arg (latest maps to main).
  • 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_BASE after first boot, and why db_name/db_user are immutable after first deploy).

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, GCS bucket, and Artifact Registry images. Resources owned by Services_GCP (the VPC, shared Cloud SQL, registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule provisions Cloud Run, Cloud SQL (PostgreSQL 15), secrets, storage bucket, and runs DB init
2 — Access & verifyManualHealth check (/) passes; create the first Azimutt account in the UI
3 — OperateManualInspect revisions, scale, update version, manage secrets, DB access
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose revision, database, init-job, build, and IAM issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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

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