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Certification track: Professional Cloud Developer (PCD)

Django on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

Django is a battle-tested Python web framework that encourages rapid development and clean, pragmatic design. This lab takes you through the full operational lifecycle of the Django 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 Django 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 Django (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) database with its Secret Manager secrets, a Filestore NFS share and Cloud Storage media bucket, builds the container image, and runs two one-shot jobs: db-init (creates the database, user, and PostgreSQL extensions) and db-migrate (runs manage.py migrate and collectstatic). 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"

    NS=$(kubectl get ns -o name | grep django | 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: $EXTERNAL_IP"
    curl -s "http://${EXTERNAL_IP}/"   # expect HTTP 200

  2. If a Django superuser was configured at deploy time via DJANGO_SUPERUSER_PASSWORD, retrieve the password from Secret Manager and sign in to the admin panel at http://${EXTERNAL_IP}/admin:

    ADMIN_SECRET=$(gcloud secrets list --project="$PROJECT" \
      --filter="name~django" --format="value(name)" --limit=1)
    gcloud secrets versions access latest --secret="$ADMIN_SECRET" --project="$PROJECT"

    The auto-generated SECRET_KEY secret follows the pattern secret-<resource_prefix>-django-key. Django's product documentation covers the admin panel and application features.

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

  1. Inspect the workload — deployment, pods, and (if enabled) the horizontal autoscaler and persistent volumes:

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

  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).

  3. Update the application version by changing the version input via Update on the deployment details page; a new image builds and a rolling update replaces the pods.

  4. Manage secrets, storage, and jobs:

    kubectl get secrets -n "$NS"
    gcloud secrets list --project="$PROJECT" --filter="name~django"
    kubectl get jobs -n "$NS"          # db-init, db-migrate, and any scheduled jobs
    kubectl get cronjobs -n "$NS"      # any cron_jobs configured at deploy time

  5. Open a database session for inspection or maintenance:

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

    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 also provisions 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 Django 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 connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and both init jobs completed.
  • Initialisation job failed: inspect the jobs and their pod logs: 
    kubectl get jobs -n "$NS"
    kubectl logs -n "$NS" job/db-init
    kubectl logs -n "$NS" job/db-migrate
  • Pending pod / no external IP: check kubectl describe pod events for resource or quota issues (remember that quota_memory_requests / quota_memory_limits must use binary unit suffixes such as 4Gi), 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.

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, GCS buckets, Filestore NFS share, 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, NFS, GCS, secrets, and runs db-init + db-migrate
2 — Access & verifyManualConnect to the cluster; health check passes; sign in to the admin panel
3 — OperateManualInspect workload, scale, update version, manage secrets/jobs, DB access
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose pod, health probe, database, init-job, scheduling, and image-pull issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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