Forgejo on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

Forgejo is a lightweight, community-managed, self-hosted Git service — a fork of Gitea — providing repository hosting, issue tracking, pull requests, a built-in CI/CD (Actions) runner, code review, and a package registry from a single Go binary. This lab takes you through the full operational lifecycle of the Forgejo 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 Forgejo 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.
Bootstrap the first Forgejo administrator account (no admin is auto-created).
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, Cloud 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 Forgejo (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. Leave database_type at its default POSTGRES_15 — it is the only engine the module's database-init script supports, even though MySQL/NONE appear as dropdown options. 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 reached through a Cloud SQL Auth Proxy sidecar, mounts Cloud Filestore (NFS) for repository/LFS/attachment storage, generates the SECRET_KEY and INTERNAL_TOKEN secrets in Secret Manager, builds the container image, and runs a one-shot database-initialisation job. 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 forgejo | 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: $EXTERNAL_IP"
```
The Service is fronted by a Gateway with a reserved static IP; if no custom domain was supplied, an auto-provisioned nip.io HTTPS hostname is also reachable (see the service_url output).
Confirm the service is healthy. Forgejo serves an unauthenticated health endpoint that only responds correctly once its first-boot schema migrations finish:
```
curl -s "http://${EXTERNAL_IP}/api/healthz"   # a healthy instance returns {"status":"pass"}
```
Bootstrap the first administrator. Unlike the Cloud Run variant, this module skips Forgejo's web installer entirely (GITEA__security__INSTALL_LOCK = "true") and no init job creates an admin account — nothing pre-seeds a privileged user. Self-registration is open by default (GITEA__service__DISABLE_REGISTRATION = "false"), so the practical path is: register a normal account through the UI at http://${EXTERNAL_IP}/, then promote it to admin from inside the running pod using Forgejo's own CLI:
```
kubectl exec -n "$NS" deploy/<service-name> -- forgejo admin user create --help
# then, once the exact flags are confirmed against your deployed version:
kubectl exec -n "$NS" deploy/<service-name> -- forgejo admin user create \
  --username <admin-user> --email <admin-email> --password '<strong-password>' --admin
```
The exact CLI invocation and whether the forgejo binary is on PATH inside the container were not re-verified against a live pod for this guide — run the --help variant first to confirm before scripting it.
Set public_domain (and optionally public_url) to the real external hostname and apply via Update — both default to localhost, which produces wrong Git clone URLs and broken links until overridden. After the admin account exists, consider setting GITEA__service__DISABLE_REGISTRATION = "true" (via environment_variables) if the instance should not be open to public sign-up.

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

Inspect the workload — deployment, pods, and PVC/NFS mounts:

kubectl get deploy,pods,pvc -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). max_instance_count defaults to 3, but every replica shares the same NFS-backed repository data and the same Postgres database — concurrent-write correctness across replicas isn't documented for this module, so treat scaling beyond a single steady-state replica with the same caution as any shared-filesystem workload. Session affinity (ClientIP) is set by default to keep a client routed to the same pod.
Update the application version by changing the version input in the RAD platform and applying it via Update. Because Forgejo is NFS-backed by default, the Deployment uses the Recreate rollout strategy rather than a rolling update: the old pod is fully terminated before the new one starts. Expect a brief service interruption during an update — this is expected, safe behaviour (it prevents two pods writing to the same repository data and database simultaneously), not a stuck rollout.

Manage secrets, storage, and jobs:

kubectl get secrets -n "$NS"
gcloud secrets list --project="$PROJECT" --filter="name~forgejo"
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 "$INSTANCE" --user=forgejo --project="$PROJECT"

Redis caution. enable_redis = true by default and REDIS_HOST/ REDIS_PORT are injected into the container, but Forgejo is not configured to consume them (GITEA__cache__*/GITEA__session__* are not set) — it falls back to its built-in in-memory cache/session defaults regardless. If you don't intend to add that wiring yourself via environment_variables, there's no functional benefit to leaving Redis provisioned.

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>".
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 Forgejo releases.

Pod not Ready / CrashLoopBackOff: inspect events and logs. Both the startup probe (GET /api/healthz, initial_delay_seconds=0, period_seconds=30, failure_threshold=10 — roughly 5 minutes of tolerance) and the liveness probe (initial_delay_seconds=60, period_seconds=30, failure_threshold=3) target the same unauthenticated health endpoint; 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. Forgejo reaches it through a Cloud SQL Auth Proxy sidecar on 127.0.0.1:5432 (SSL_MODE=disable for that hop); the platform entrypoint logs the resolved wiring (Forgejo DB wired: host=... sslmode=... name=... user=...) — check it with:
```
kubectl exec -n "$NS" deploy/<service-name> -- env | grep GITEA__
```

Initialisation job failed: inspect the db-init job and its pod logs:

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

Update rollout appears stuck: because the Deployment uses Recreate (NFS-backed), you will briefly see zero Ready pods between the old pod terminating and the new one starting — this is expected and resolves once the new pod passes its startup probe, not a genuine hang. If it persists well past the ~5-minute startup-probe tolerance, treat it as a real failure and inspect the new pod's events/logs as above.
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 keeps the address stable across redeploys).
Image pull errors: confirm the image exists in Artifact Registry and the node service account can pull it.
No admin user / anyone can sign up: expected out of the box — see Task 2, step 3, and consider disabling GITEA__service__DISABLE_REGISTRATION once an admin account exists.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the critical rules never to rotate SECRET_KEY/INTERNAL_TOKEN after first boot, never to change db_name/ db_user after first deploy, and to keep database_type at POSTGRES_15).

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, the Cloud SQL database, Secret Manager secrets (SECRET_KEY, INTERNAL_TOKEN, and the database password), the unused Cloud Storage bucket, and Artifact Registry images. A destroy-time NFS app-volume cleanup Job also removes Forgejo's repository data from the shared Filestore volume on a best-effort basis (it skips if the namespace is already gone). Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL instance, the Filestore NFS server itself, and the 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), NFS storage, secrets, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; health check passes; register and promote the first admin account via CLI
3 — Operate	Manual	Inspect workload, scale, update version (Recreate rollout), manage secrets/storage, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database, init-job, rollout, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources, including NFS app data (best-effort)

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​