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WriteFreely on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

WriteFreely is an open-source, minimalist, federated blogging platform written in Go — a lightweight Medium alternative for publishing clean, distraction-free writing. This lab takes you through the full operational lifecycle of the WriteFreely 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 WriteFreely 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 blog.
  • Perform day-2 operations — inspect, scale, update, and manage secrets and the database.
  • 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 WriteFreely (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 (MySQL 8.0) database with its Secret Manager secrets (three AES-256 keys — cookies-auth, cookies-enc, email-key — plus the database password), a dedicated writefreely-uploads Cloud Storage bucket, builds the custom config-gen container image, an NFS filesystem (enabled by default), and runs a one-shot database-initialisation job. First deploys take roughly 15–25 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 writefreely | 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 up. WriteFreely has no dedicated /health endpoint — the liveness probe is an HTTP GET / that expects a 200 from the home page:

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

  3. Open http://${EXTERNAL_IP} in a browser to confirm the blog's home page renders. Registration is closed by default (open_registration = false) and no admin account is seeded, so create the first account now, either:

    • Temporarily set WF_OPEN_REGISTRATION = "true" in environment_variables and apply via Update, register through the UI, then set it back to "false" and apply again; or

    • exec WriteFreely's built-in admin creation command inside a running pod:

      SERVICE=$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')
      kubectl exec -n "$NS" deploy/"$SERVICE" -- \
        /usr/local/bin/writefreely --create-admin <user>:<password>

  4. Do not rotate the AES-256 keys (cookies-auth, cookies-enc, email-key) after this first boot — doing so logs out every user and makes previously encrypted email addresses undecryptable.

  5. After the LoadBalancer IP is assigned, set WF_PUBLIC_URL (via environment_variables) to http://<external-ip> or a custom domain, so generated links and federation use the reachable host.

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

  1. Inspect the workload — deployment, pods, and the horizontal autoscaler:

    kubectl get deploy,pods,hpa -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). WriteFreely defaults to min_instance_count = 1 and max_instance_count = 1 (GKE does not support scale-to-zero). Session affinity (ClientIP) is set by default to keep requests from the same client sticking to the same 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. Pin a specific release rather than leaving application_version = "latest" in production, so rebuilds stay reproducible.

  4. Manage secrets and storage:

    kubectl get secrets -n "$NS"
    gcloud secrets list --project="$PROJECT" \
      --filter="name~cookies-auth OR name~cookies-enc OR name~email-key"
    kubectl get jobs -n "$NS"          # db-init job

  5. Open a database session for inspection or maintenance:

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

  6. Confirm the injected DB host in the running pod (should be 127.0.0.1 — the Cloud SQL Auth Proxy sidecar — not the private IP):

    kubectl exec -n "$NS" deploy/"$SERVICE" -- env | grep -E 'DB_HOST|WF_'

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

  1. Logs — from kubectl or the Logs Explorer. On first boot look for the entrypoint's progress lines (WriteFreely: rendered config.ini …, … seeded stable encryption keys …, … starting server …):

    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 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 WriteFreely releases.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. The startup probe is TCP (Ready as soon as port 8080 is bound); the liveness probe is HTTP GET /. A connection failure to MySQL via the Auth Proxy sidecar will keep the pod from becoming Ready. 
    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 the init job completed. WriteFreely on GKE reaches MySQL through the Cloud SQL Auth Proxy sidecar on 127.0.0.1:3306 (enable_cloudsql_volume = true) — do not confuse this with the Cloud Run variant's private-IP TCP path.
  • Initialisation job failed: inspect the job and its pod logs: 
    kubectl get jobs -n "$NS"
    kubectl logs -n "$NS" 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 — container_image_source must stay custom since the config-gen entrypoint is not present in any prebuilt upstream image.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the critical rule never to rotate the AES-256 keys 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, GCS buckets, 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 (MySQL 8.0), 3 AES-256 key secrets, storage bucket, NFS, and runs DB init
2 — Access & verifyManualConnect to the cluster; home page returns 200; create the initial account
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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