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Memos on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 30–60 minutes

Memos is an open-source, self-hosted, markdown-native note-taking service for quick capture. This lab takes you through the full operational lifecycle of the Memos 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 Memos 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 workload, and create the first (admin) account.
  • Perform day-2 operations — inspect, scale, update, and manage backups.
  • 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 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
export NAMESPACE="<deployment-namespace>"   # reported in the deployment Outputs
gcloud container clusters get-credentials <cluster-name> --region "$REGION" --project "$PROJECT"

Task 1 — Deploy the module [Automated]​

  1. In the RAD platform, open Memos (GKE), 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 Kubernetes workload, a Cloud SQL (PostgreSQL) database with its Secret Manager password secret, builds the container image, and runs a one-shot database-initialisation Job. First deploys take roughly 15–25 minutes (Cloud SQL creation dominates; Memos's own image build and boot are fast — a single small Go binary).

  3. When it completes, discover the resources with name-agnostic filters (so the commands keep working regardless of the deployment suffix):

    SERVICE=$(kubectl get svc -n "$NAMESPACE" -o name | grep memos | head -1 | cut -d/ -f2)
    EXTERNAL_IP=$(kubectl get svc "$SERVICE" -n "$NAMESPACE" -o jsonpath='{.status.loadBalancer.ingress[0].ip}')
    echo "Service: $SERVICE"
    echo "IP:      $EXTERNAL_IP"

Task 2 — Access & verify [Manual]​

  1. Confirm the pod is healthy and serving:

    kubectl get pods -n "$NAMESPACE" -l app="$SERVICE"    # expect N/N Running, 0 restarts
    curl -s "http://$EXTERNAL_IP/" -o /dev/null -w '%{http_code} %{size_download}\n'   # expect 200 and >0 bytes

  2. Open http://$EXTERNAL_IP/ in a browser (or kubectl port-forward if service_type = "ClusterIP"). Memos shows its sign-up/login page. Create the first account — whoever registers first automatically becomes the host/admin. After creating it, write your first note to confirm the database round-trip (the note persists on refresh — proof the MEMOS_DSN wiring and db-init job worked). Consider disabling public self-registration from within Memos's own settings afterward.

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

  1. Inspect the workload and its rollout history:

    kubectl get deploy "$SERVICE" -n "$NAMESPACE"
    kubectl rollout status deploy/"$SERVICE" -n "$NAMESPACE"
    kubectl describe hpa -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, unless used only as a temporary teardown measure — see Task 6).

  3. Update the application version tag by changing the version input in the RAD platform and applying it via Update; a new image builds and a rolling update deploys. Memos_Common maps "latest" to a pinned MEMOS_VERSION build arg, so set an explicit version (e.g. 0.28.0) to track a specific upstream release.

  4. Manage backups:

    kubectl get jobs -n "$NAMESPACE"   # init + scheduled backup jobs

  5. Open a database session for inspection or maintenance:

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

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

  1. Logs:

    kubectl logs -n "$NAMESPACE" deploy/"$SERVICE" --tail=100

    Logs Explorer filter: resource.type="k8s_container" AND resource.labels.namespace_name="<namespace>".

  2. Monitoring — open the GKE Workloads dashboard for the deployment and review request count, CPU / memory utilisation, and replica count (scaling behaviour). The module can provision an uptime check (when uptime_check_config.enabled = true, only meaningful with a publicly reachable LoadBalancer Service); if enabled, confirm it is green under Monitoring → Uptime checks.

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 Memos releases.

  • Pod unhealthy / CrashLoopBackOff: inspect pod events and logs for startup errors. The startup probe targets / with a 30-second initial delay. 
    kubectl describe pod -n "$NAMESPACE" -l app="$SERVICE"
    kubectl logs -n "$NAMESPACE" deploy/"$SERVICE" --tail=200
  • Database connection errors (MEMOS_DSN parse failures, auth failures): confirm the Cloud SQL instance is RUNNABLE, the DB password secret synced via SecretSync, and the initialisation Job completed. Check the container logs for the memos-entrypoint.sh startup banner (DB host:/DB name:/DB user:) — on GKE, DB host should resolve to 127.0.0.1.
  • Initialisation Job failed: 
    kubectl get jobs -n "$NAMESPACE"
    kubectl logs -n "$NAMESPACE" job/<job-name>
  • Image build failed: review Cloud Build history for the failed build's log — a common cause is MEMOS_VERSION resolving to a tag that doesn't exist upstream.
  • 403 / permission errors: verify the Workload Identity binding and the runtime service account's IAM roles.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the "first account becomes admin" behaviour and the container_image_source trade-off).

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, Service, Cloud SQL database, Secret Manager secret, 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 provisions the GKE workload, Cloud SQL (PostgreSQL), DB password secret, and runs DB init
2 — Access & verifyManualPod Ready 0 restarts; create the first (admin) account in the UI and write a note
3 — OperateManualInspect rollout, scale, update version, manage backups, DB access
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose pod, database, init-job, build, and IAM issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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