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Azimutt on GKE Autopilot — 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 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 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.
  • Connect to the GKE cluster and access the running workload.
  • Perform day-2 operations — inspect, scale, update, and manage secrets and storage.
  • 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, 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 top navigation, open Azimutt (GKE) from the Platform Modules list to start configuration, 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 deploys the workload into the GKE Autopilot cluster, provisions a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (SECRET_KEY_BASE and the database password), a Cloud Filestore (NFS) share for Azimutt's attachment storage, 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. 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"

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

Task 2 — Access & verify [Manual]​

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

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

  2. Confirm the service is healthy. Azimutt has no dedicated health JSON endpoint — the startup and liveness 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" "http://${EXTERNAL_IP}/"   # expect 200

  3. Open http://${EXTERNAL_IP} 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 workload — deployment, pods, and the horizontal autoscaler:

    kubectl get deploy,pods,hpa,pvc -n "$NAMESPACE"
    kubectl describe deploy -n "$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). Unlike apps with an in-memory job queue, Azimutt uses PostgreSQL (Oban) for background work, so scaling beyond one replica needs no Redis. GKE does not support scale-to-zero, so min_instance_count stays at its default of 1. session_affinity = ClientIP is set by default to keep a client pinned to one pod.

  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 rolling update replaces the pods. 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, storage, and jobs:

    kubectl get secrets -n "$NAMESPACE"
    gcloud secrets list --project="$PROJECT" --filter="name~azimutt"
    kubectl get jobs -n "$NAMESPACE"           # db-init job
    gcloud filestore instances list --project="$PROJECT"

    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"

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

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

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

    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, restart counts, and request metrics. The module can provision an uptime check (when enabled); review Monitoring → Uptime checks and 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.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. The startup and liveness probes target / with a 60-second initial delay — allow ~1–2 minutes on first boot for migrations to finish before the endpoint binds. container_port and the Service/probe ports must all be 4000 (GKE does not auto-inject PORT; the entrypoint defaults it) — a mismatch leaves the pod stuck Running but never Ready. 
    kubectl describe pod -n "$NAMESPACE" <pod>          # Events section shows scheduling/probe/mount errors
    kubectl logs -n "$NAMESPACE" <pod> --previous       # logs from the crashed container
  • Database connection errors: confirm the Cloud SQL instance is RUNNABLE and enable_cloudsql_volume = true. Azimutt connects through the Cloud SQL Auth Proxy sidecar on 127.0.0.1 (TLS terminated by the proxy, so DATABASE_ENABLE_SSL=false) — disabling the sidecar leaves Azimutt with no database.
  • Initialisation job failed: inspect the job and its pod logs. The job signals the proxy sidecar to shut down (/quitquitquit) once it completes: 
    kubectl get jobs -n "$NAMESPACE"
    kubectl logs -n "$NAMESPACE" job/<job-name>
  • Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP.
  • Image pull errors: confirm the image exists in Artifact Registry and the node service account can pull it. Because Azimutt's image is a rebuilt/mirrored wrapper, imagePullPolicy = Always is set so nodes never serve a stale cached layer.

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 application_database_name/ application_database_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 Kubernetes workload and namespace, Cloud SQL database, Secret Manager secrets, Cloud Filestore share, GCS bucket, and Artifact Registry images. 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 deploys the GKE workload, Cloud SQL (PostgreSQL 15), NFS share, secrets, storage bucket, and runs DB init
2 — Access & verifyManualConnect to the cluster; health check (/) passes; create the first Azimutt account in the UI
3 — OperateManualInspect workload, scale, update version, manage secrets/storage, DB access
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose pod, database, init-job, scheduling, and image-pull issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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

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