Gitea on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

Gitea is a lightweight, self-hosted Git service and software forge — repository hosting, issues, pull requests, code review, and a package registry from a single Go binary. This lab takes you through the full operational lifecycle of the Gitea 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 Gitea 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.
Access the forge, claim the first-registrant admin account, and verify health.
Perform day-2 operations — inspect, scale, update, and manage secrets and NFS-backed 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 Gitea (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 INTERNAL_TOKEN, plus the database password), a Cloud Filestore (NFS) share mounted at /mnt/nfs for repositories, Git LFS objects, and attachments (no GCS buckets are created for this module), builds the thin custom image over gitea/gitea via Cloud Build, and runs a one-shot, PostgreSQL-only database-initialisation job. 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 gitea | 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. Both the startup and liveness probes target GET /api/healthz, which Gitea serves unauthenticated with HTTP 200 once it has finished booting:
```
curl -s -o /dev/null -w "%{http_code}\n" "http://${EXTERNAL_IP}/api/healthz"
```
Open http://${EXTERNAL_IP} in a browser. The first-run web installer is skipped (GITEA__security__INSTALL_LOCK = "true" — configuration is entirely env-driven), and the first user to register becomes the administrator — there is no Terraform-managed job that bootstraps an admin account. Click Register, create your admin account immediately, and sign in.
Immediate hardening: self-registration is enabled by default (GITEA__service__DISABLE_REGISTRATION = "false"). For a private forge, disable it right after claiming the admin account by adding GITEA__service__DISABLE_REGISTRATION = "true" to environment_variables via the RAD Update flow. Also set public_domain / public_url to the assigned external IP or your real hostname — both default to localhost, which otherwise breaks clone URLs, webhook callbacks, and OAuth redirects.
Create a test repository in the UI and clone it over HTTPS — only the HTTP port is wired into the Kubernetes Service; the image's own sshd is not exposed, so SSH clone URLs are not reachable:
```
git clone "http://${EXTERNAL_IP}/<your-user>/<test-repo>.git"
```

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

Inspect the workload — deployment, pods, and persistent volume claims:
```
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). Defaults are min 0 / max 3. Because repository data lives on shared NFS rather than per-pod storage, running more than one replica is generally safe for stateless HTTP requests, but Git's own locking and any in-flight background jobs are not explicitly coordinated across replicas by this module — keep max_instance_count conservative unless verified. Session affinity (ClientIP) is set by default to keep a client routed to the same pod.
Update the application version by changing application_version in the RAD platform and applying it via Update; Cloud Build rebuilds the image and a new rollout replaces the pods. Expect a brief outage during the rollout, not a hang: because this app is NFS-backed, the Deployment's rollout strategy is Recreate rather than the default RollingUpdate — the foundation deliberately terminates the existing pod before starting the replacement rather than running both against the same NFS volume and Cloud SQL database simultaneously (a surge pod under RollingUpdate would never become Ready, wedging the rollout indefinitely). A short gap in availability during an update is therefore expected, safe behaviour — watch it complete with:
```
kubectl rollout status deploy/<service-name> -n "$NS"
```

Manage secrets, storage, and jobs:

kubectl get secrets -n "$NS"
gcloud secrets list --project="$PROJECT" --filter="name~gitea"
kubectl get jobs -n "$NS"          # db-init and any scheduled backup jobs
gcloud filestore instances list --project="$PROJECT"
kubectl exec -n "$NS" deploy/<service-name> -- df -h /mnt/nfs

Open a database session for inspection or maintenance:

INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
gcloud sql connect "$INSTANCE" --user=gitea --database=gitea --project="$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>".
Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation, restart counts, and request metrics, plus the Cloud SQL and Filestore dashboards for the database and NFS share. 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 Gitea releases.

Pod not Ready / CrashLoopBackOff: inspect events and logs. The startup and liveness probes both target /api/healthz; 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
```
lookup $(DB_HOST): no such host or unresolved database vars in logs: the stock upstream image was deployed instead of the custom build. container_image_source must be custom — the platform entrypoint that composes GITEA__database__* from the injected DB_HOST/DB_IP only exists in the custom build.
Database connection errors: confirm the Cloud SQL (PostgreSQL 15) instance is RUNNABLE, the DB password secret materialised into the namespace, and the db-init job completed. database_type must stay on a Postgres value — the bundled db-init.sh is hard-coded to psql, so a MySQL value passes validation but leaves the database uninitialized.

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

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

Update/rollout appears stuck: confirm the rollout strategy is Recreate (expected for this NFS-backed app) and that the previous pod actually terminated before assuming a real hang:
```
kubectl get deploy -n "$NS" -o jsonpath='{.spec.strategy.type}'
kubectl get pods -n "$NS" -w
```
A genuine hang (pod stuck Terminating past its grace period, or the new pod stuck Pending/Init) points at a different cause — check NFS mount health and node scheduling, not the update mechanism itself.
Broken clone URLs / redirects to localhost: public_domain / public_url still hold their defaults — set them to the external IP or your real hostname via Update.
SSH clone fails: expected — only the HTTP port is wired into the Kubernetes Service. Use HTTPS remotes with a Gitea access token.
NFS mount / repositories missing: verify enable_nfs = true, that the Filestore instance is available, and that the pod's PVC is Bound:
```
kubectl get pvc -n "$NS"
kubectl exec -n "$NS" deploy/<service-name> -- df -h /mnt/nfs
```
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 change SECRET_KEY or INTERNAL_TOKEN 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, and the Filestore (NFS)-held repository data. 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), NFS (Filestore), secrets, builds the image, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; health check passes; first registrant claims the admin account; registration hardened
3 — Operate	Manual	Inspect workload, scale, update version (Recreate rollout — brief downtime, not a hang), manage secrets/NFS, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database, init-job, rollout, clone-URL, NFS, 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​