Outline on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

Outline is a fast, collaborative, Notion-style team knowledge base and wiki with real-time editing, rich markdown documents, and powerful full-text search. Unlike most modules in this catalogue, Outline has no built-in username/password store — it authenticates exclusively through an external identity provider (OIDC, Google, Slack, etc.), which makes wiring up auth a required, not optional, part of this lab. This lab takes you through the full operational lifecycle of the Outline on GKE Autopilot module on Google Cloud: deploy it, connect to the cluster, configure the required authentication provider, 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 Outline 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.
Configure the required OIDC authentication provider so login actually works.
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, Filestore NFS, 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]

Click Deploy in the RAD platform top navigation, open Outline (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.
The platform deploys the workload into the GKE Autopilot cluster, provisions a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (auto-generated SECRET_KEY and UTILS_SECRET, plus the database password), a Cloud Filestore (NFS) share mounted at /var/lib/outline/data for uploaded files, two Cloud Storage buckets (created but unused by default — Outline is configured for local/NFS storage, not object storage), builds the custom container image, and runs a one-shot database-initialisation job. Outline also requires Redis, which is enabled by default and points at the co-hosted Redis process on the shared NFS VM — no separate Memorystore instance is created unless you configure one. First deploys take roughly 20–35 minutes (Cloud SQL and Filestore creation dominate).

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 outline | head -1 | cut -d/ -f2)
echo "Cluster: $CLUSTER   Namespace: $NS"
kubectl get all -n "$NS"

Task 2 — Access & verify [Manual]

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: $EXTERNAL_IP"
```
Allow 60+ seconds after the pod goes Running for the entrypoint to connect to PostgreSQL, run the Sequelize migrations, and connect to Redis before the startup probe (GET /) passes.
Open http://${EXTERNAL_IP} in a browser. Expect an empty login page — this is the module's most important first-run fact, not a failure. The OIDC_* environment variables ship intentionally blank, so with no identity provider configured Outline registers zero auth methods. The service is healthy; sign-in requires the next step.
Configure the required OIDC provider. First check what URL the workload is already using — on GKE, App_GKE unconditionally injects the computed address as GKE_SERVICE_URL, and the entrypoint sets URL to it automatically whenever URL isn't already set, so a fresh deploy has a working URL with no manual override needed (unless you want to force a hostname ahead of DNS/cert provisioning):
```
kubectl exec -n "$NS" deploy/<service-name> -- env | grep -E '^(URL|GKE_SERVICE_URL)'
```
Create an OAuth client at your IdP (e.g. Google: APIs & Services → Credentials) with <URL>/auth/oidc.callback as an authorized redirect URI — the callback must be on the same host as the injected URL. Then, on the deployment's configuration page in the RAD platform, set the plain endpoint values in environment_variables and click Update:
```
OIDC_AUTH_URI      = https://accounts.google.com/o/oauth2/v2/auth
OIDC_TOKEN_URI      = https://oauth2.googleapis.com/token
OIDC_USERINFO_URI   = https://openidconnect.googleapis.com/v1/userinfo
OIDC_USERNAME_CLAIM = email
```
Bind the client credentials as secrets in secret_environment_variables (OIDC_CLIENT_ID, OIDC_CLIENT_SECRET) pointing at Secret Manager secrets holding your OAuth client ID/secret, then Update again. Unlike the Cloud Run variant — where gcloud run services update refuses to convert a plain env var straight to a secret ref in one call ("already set with a different type"), forcing a --remove-env-vars pass before --update-secrets — GKE's path is declarative: on GKE, Secret-Manager-backed values are materialised into the cluster as native Kubernetes Secrets by the SecretSync controller (the secretsyncs.secret-sync.gke.io CRD), and a single tofu apply (the platform's Update) renders the whole desired pod env list in one pass — no two-step gcloud dance needed. The one thing to get right yourself: remove OIDC_CLIENT_ID/OIDC_CLIENT_SECRET from environment_variables in the same apply that adds them to secret_environment_variables. Leaving the same key in both maps puts two env entries of the same name in the Pod spec (one value, one valueFrom.secretKeyRef) — Kubernetes accepts this without erroring, but which value the running process actually observes is not something to rely on; keep exactly one source per key.

Confirm the secret materialised and reload the login page — your provider button should appear; the first user to sign in creates the workspace.
```
kubectl get secret -n "$NS"                       # look for the SecretSync-materialised secret object
gcloud secrets list --project="$PROJECT" --filter="name~outline"
```
Before you enable auth at all, make sure the LoadBalancer is behind HTTPS. The default service_type = LoadBalancer is plain HTTP with no TLS terminator. Outline's OAuth flow sets the state cookie with secure: true; over plain HTTP, /auth/<provider> returns 500 — Cannot send secure cookie over unencrypted connection even though the landing page loads fine. Enable enable_custom_domain with application_domains set to a real hostname (plus DNS pointed at the reserved static IP) before wiring up the IdP, or expect the 500 above.

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

Inspect the workload — deployment, pods, and persistent volumes:

kubectl get deploy,pods,pvc -n "$NS"
kubectl describe deploy -n "$NS"
kubectl get pvc -n "$NS"          # the Filestore-backed NFS claim

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). Outline defaults to a higher ceiling than most NFS-backed modules here (min_instance_count = 1, max_instance_count = 3) because Redis coordinates realtime/session state across replicas. Session affinity (ClientIP) is set by default to keep a client routed to the same pod. Because enable_nfs = true, rollouts use the Recreate strategy — all running replicas are stopped before the replacement set starts, so expect a brief full outage on every redeploy, not a rolling zero-downtime one.
Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds and the Recreate rollout replaces the pods (the entrypoint runs any pending Sequelize migrations on start).

Manage secrets, storage, and jobs:

kubectl get secrets -n "$NS"
gcloud secrets list --project="$PROJECT" --filter="name~outline"    # DB password, SECRET_KEY, UTILS_SECRET
kubectl get jobs -n "$NS"                                           # db-init and any scheduled jobs
gcloud storage buckets list --project="$PROJECT" --filter="name~outline"

Open a database session for inspection or maintenance:

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

Task 4 — Observe: Logging & Monitoring [Manual]

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>". On startup, look for the entrypoint's DATABASE_URL assembly log, the Sequelize migration output, and the Redis connection confirmation.
Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and request metrics. uptime_check_config is disabled by default for this module — leave it off until the app is reachable over HTTPS with auth configured (until then the app is expected to be effectively unusable), then enable it and 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 Outline releases.

Pod not Ready / CrashLoopBackOff: inspect events and logs. The startup probe allows roughly 120 seconds (60s initial delay, 6 retries) for the entrypoint to reach PostgreSQL and run migrations before Kubernetes gives up.
```
kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container
```
Empty login page (the app-specific one): this is not a deployment failure — the OIDC_* placeholders are blank until you configure an IdP (Task 2). If a provider is configured but login loops or errors, verify the redirect URI is <URL>/auth/oidc.callback on the exact host in the injected URL, and that the LoadBalancer is behind HTTPS (plain HTTP 500s on /auth/<provider> with a secure cookie error):
```
kubectl exec -n "$NS" deploy/<service-name> -- env | grep -E '^(URL|OIDC_)'
```
Database connection errors: confirm the Cloud SQL (PostgreSQL 15) instance is RUNNABLE, the DB password secret materialised into the namespace via SecretSync, and the db-init job completed. The connection uses the Cloud SQL Auth Proxy sidecar (loopback 127.0.0.1:5432) — enable_cloudsql_volume must stay true.

Initialisation job failed: inspect the job and its pod logs:

kubectl get jobs -n "$NS"
kubectl logs -n "$NS" job/<db-init-job-name>

Redis errors / reconnect loop in logs: Outline requires Redis even at a single replica — it is not an optional cache tier here. Verify enable_redis = true and that the shared NFS VM (which co-hosts Redis) is RUNNING, or that redis_host points at a reachable endpoint:
```
kubectl exec -n "$NS" deploy/<service-name> -- env | grep -E '^REDIS_'
```
Uploads not persisting / NFS mount issues: verify enable_nfs = true, nfs_mount_path is /var/lib/outline/data, and the Filestore instance and PVC are healthy:
```
gcloud filestore instances list --project="$PROJECT"
kubectl get pvc -n "$NS"
```
Rollout appears stuck on update: because enable_nfs = true forces the Recreate strategy, an update briefly shows zero running pods (all replicas torn down together) before the replacement set starts — this is expected, not a hang, though with max_instance_count = 3 it is a longer visible gap than a single-replica app.
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; this is a custom-build module, so also check Cloud Build history for a failed image build.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas — including the critical rules never to rotate SECRET_KEY after first boot, never to disable enable_redis, and to put HTTPS in front of the Service before enabling any auth provider.

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 (database password, SECRET_KEY, UTILS_SECRET), the Filestore NFS share, both Cloud Storage buckets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL, the NFS/Redis VM, registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys the GKE workload, Cloud SQL (PostgreSQL 15), Filestore NFS, GCS buckets, secrets, builds the image, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; health check passes; configure the required OIDC provider (endpoints + secret-backed client ID/secret via SecretSync) and complete first sign-in
3 — Operate	Manual	Inspect workload, scale, update version, manage secrets/storage, DB access; note the `Recreate` rollout outage
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and optional uptime check
5 — Troubleshoot	Manual	Diagnose pod, OIDC, database, init-job, Redis, NFS, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources

By Dr Shiyghan Emmanuel Navti · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

Overview​

Objectives​

Prerequisites​

Task 1 — Deploy the module [Automated]​

Task 2 — Access & verify [Manual]​

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

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

Task 5 — Troubleshoot & debug [Manual]​

Task 6 — Tear down [Automated]​

Summary​