Logto on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

Logto is an open-source identity provider — an Auth0 alternative that speaks OIDC and OAuth 2.0, with sign-in flows, social/enterprise connectors, multi-tenancy, and an admin console. This lab takes you through the full operational lifecycle of the Logto 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 Logto 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, access the running workload, and reach the admin console for first-run setup.
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]

Click Deploy in the RAD platform top navigation, open Logto (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 secret (the database password — Logto has no external application secret; its OIDC signing keys are generated into the database on first boot), a Cloud Storage bucket, builds the container image, and runs a one-shot database-initialisation job that creates the application role (with CREATEROLE) and database. A LoadBalancer Service with a reserved static IP and nip.io custom domain is provisioned by default. First deploys take roughly 20–35 minutes (Cloud SQL creation dominates).

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 logto | 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"

Confirm the service is healthy. Logto exposes an unauthenticated status endpoint that responds only once the core has booted and seeded its schema:
```
curl -s "http://${EXTERNAL_IP}/api/status"   # expect HTTP 200
```
The admin console is not reachable at the external IP. The Service exposes only the core (port 3001, OIDC); the admin console — where you create the first administrator and register OIDC applications — runs on port 3002. Reach it via a port-forward directly to the pod:
```
kubectl port-forward -n "$NS" deploy/"$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')" 3002:3002
# then open http://localhost:3002
```
Create the first administrator account and register your first OIDC application. Note the registered redirect URI must use the same host as ENDPOINT (see Task 5) — a mismatch breaks every OAuth callback.

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

Inspect the workload — deployment, pods, and the horizontal autoscaler:
```
kubectl get deploy,pods,hpa -n "$NS"
kubectl describe deploy -n "$NS"
```
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). GKE requires min_instance_count >= 1 (no scale-to-zero); session affinity (ClientIP) is set by default so a client consistently reaches the same pod.
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 application_version to a specific release (e.g. 1.33) in production rather than tracking latest.

Manage secrets, storage, and jobs:

kubectl get secrets -n "$NS"
gcloud secrets list --project="$PROJECT" --filter="name~logto"
kubectl get jobs -n "$NS"          # db-init job

Open a database session for inspection or maintenance:
```
INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
gcloud sql connect "$INSTANCE" --user=logto --database=logto --project="$PROJECT"
```
Never wipe or reset this database outside of an intentional restore — Logto's OIDC signing keys live only in it, and wiping it invalidates every issued token and registered client.

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — from kubectl or the Logs Explorer. The entrypoint prints a [cloud-entrypoint] line reporting the resolved DB connection mode and ENDPOINT, which is the first thing to check when diagnosing a connection or issuer-URL problem:
```
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>".
Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and request metrics. An uptime check is disabled by default; the module can provision one against the LoadBalancer host 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 Logto releases.

Pod not Ready / CrashLoopBackOff: inspect events and logs. The liveness and startup probes target /api/status, with a wide first-boot window for the schema/OIDC-key seed step; 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
```
OIDC / login callback errors: confirm ENDPOINT matches the exact external LoadBalancer or custom-domain host the browser used to reach Logto — Logto builds its OIDC issuer and every absolute redirect URL from this value.
Database connection errors: confirm the Cloud SQL instance is RUNNABLE and the DB password secret materialised into the namespace via the Secret Store CSI driver. On GKE the Auth Proxy sidecar listens on 127.0.0.1; the entrypoint connects over plain TCP loopback with SSL disabled (the proxy terminates TLS) — this differs from the private-IP path used on Cloud Run.

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

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

Admin console unreachable: this is expected at the external IP — see Task 2. The admin console (3002) is never published on the LoadBalancer Service; use a port-forward instead.
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.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the critical rule never to wipe the Cloud SQL database, since Logto's only copy of its OIDC signing keys lives there).

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 (and with it Logto's only copy of its OIDC signing keys), Secret Manager secret, GCS bucket, 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

Task	Type	Outcome
1 — Deploy	Automated	Module deploys the GKE workload, Cloud SQL (PostgreSQL 15), a DB-password secret, a storage bucket, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; health check passes; reach the admin console via port-forward to create the first administrator
3 — Operate	Manual	Inspect workload, scale, update version, manage secrets/storage, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, OIDC/callback, database, init-job, 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​