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Linkwarden on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

Linkwarden is an open-source, self-hosted bookmark manager with full-page archiving (screenshot, PDF, and single-file "monolith" snapshots via a bundled headless Chrome). This lab takes you through the full operational lifecycle of the Linkwarden 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 Linkwarden 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 Linkwarden (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 (NEXTAUTH_SECRET and the database password), a Cloud Storage bucket mounted at /data/data for archived content, builds the custom container image (a thin wrapper around ghcr.io/linkwarden/linkwarden), and runs a one-shot database-initialisation job. 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 linkwarden | 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 responds (Linkwarden has no confirmed dedicated health endpoint, so the root page is the best signal):

    curl -s -o /dev/null -w '%{http_code} %{size_download}\n' "http://${EXTERNAL_IP}"
    # expect 200 and a non-trivial byte size (a rendered page, not an empty body)

  3. Open http://${EXTERNAL_IP} in a browser. On first visit Linkwarden shows the registration page — no pre-seeded admin credential exists in Secret Manager. Register the first account; it automatically becomes the instance owner.

  4. Verify archiving end-to-end (the real stateful test). Log in, add a bookmark (any public URL), and wait 10–30 seconds for the background archiving worker to process it. Refresh the link's detail view and confirm a screenshot/preview has been generated — this proves the DB write, the background worker, headless Chrome, and the GCS-backed storage mount are all correctly wired.

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

  1. Inspect the workload — deployment and pods:

    kubectl get deploy,pods -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). Keep min_instance_count >= 1 — GKE has no scale-to-zero, and the in-container background archiving worker needs to keep running. Session affinity (ClientIP) is set by default to keep NextAuth session cookies stable.

  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. Linkwarden publishes a genuine latest tag upstream, so latest tracks the real upstream release.

  4. Manage secrets, storage, and jobs:

    kubectl get secrets -n "$NS"
    gcloud secrets list --project="$PROJECT" --filter="name~linkwarden"
    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 "$INSTANCE" --user=linkwarden --project="$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 (watch for spikes during archive-worker batches), 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 Linkwarden releases.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. The startup probe defaults to / with a generous window for Next.js cold start plus headless Chrome/Playwright initialization; a connection failure to PostgreSQL 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. Linkwarden's DATABASE_URL connects over the cloud-sql-proxy sidecar's loopback (127.0.0.1) on GKE with sslmode=disable — confirm the sidecar container in the pod is healthy if connections fail.
  • Archiving never completes / links stay un-previewed: first confirm the pod itself is Ready (Task 2). If it is, use kubectl logs to check for a headless Chrome/Playwright launch failure in the worker's output. Unlike Cloud Run's gVisor sandbox, GKE runs on real Linux nodes, so this class of platform-sandbox incompatibility is far less likely here — a genuine archiving failure is more likely a memory constraint (container_resources. memory_limit too low) or a disable_browser misconfiguration.
  • 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 (reserve_static_ip = true by default, so the IP should be stable across redeploys).
  • 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 (including the critical rule never to rotate NEXTAUTH_SECRET after first boot).

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 (PostgreSQL 15), secrets, GCS storage bucket, and runs DB init
2 — Access & verifyManualConnect to the cluster; service responds; register the first account (becomes owner); confirm archiving completes end-to-end
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, archiving, 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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