Gitea on Cloud Run — 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 Cloud Run 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 Cloud Run 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.
Access the forge, claim the first-registrant admin account, and verify health.
Perform day-2 operations — inspect, scale, update, and manage secrets and backups.
Observe the service 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, 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

Task 1 — Deploy the module [Automated]

Click Deploy in the RAD platform top navigation, open Gitea (Cloud Run) 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 provisions the Cloud Run service, a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (DB password, Gitea SECRET_KEY and INTERNAL_TOKEN), an NFS share holding repositories/LFS/attachments, a GCS data bucket, builds the thin custom image over gitea/gitea via Cloud Build, and runs a one-shot database-initialisation job. First deploys take roughly 20–35 minutes (Cloud SQL creation dominates).

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

SERVICE=$(gcloud run services list --project="$PROJECT" --region="$REGION" \
  --filter="metadata.name~gitea" --format="value(metadata.name)" --limit=1)
SERVICE_URL=$(gcloud run services describe "$SERVICE" \
  --project="$PROJECT" --region="$REGION" --format="value(status.url)")
echo "Service: $SERVICE"
echo "URL:     $SERVICE_URL"

Task 2 — Access & verify [Manual]

Confirm the service is healthy. Gitea's health path is /api/healthz, which returns HTTP 200 without authentication once the server is up (first boot also runs schema migrations — allow up to ~60 seconds on a fresh deploy):
```
curl -s -o /dev/null -w "%{http_code}\n" "$SERVICE_URL/api/healthz"
```
Open $SERVICE_URL in a browser. The first-run web installer is skipped (INSTALL_LOCK=true — configuration is env-driven), and the first user to register becomes the administrator. Click Register, create your admin account immediately, and sign in.
Immediate hardening: self-registration is enabled by default. For a private forge, disable it right after claiming the admin account by adding GITEA__service__DISABLE_REGISTRATION = "true" to environment_variables via the RAD Update flow. Also set public_domain / public_url to the service's real host so clone URLs are correct (the localhost default breaks them).
Create a test repository in the UI and clone it over HTTPS (Cloud Run routes only the HTTP port, so SSH clone URLs are not reachable on this platform):
```
git clone "$SERVICE_URL/<your-user>/<test-repo>.git"
```

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

Inspect the service and its revisions (each deploy creates an immutable revision; traffic shifts to the newest healthy one):

gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"

Scale by changing the min/max instance inputs and clicking Update on the deployment details page — the module owns the service spec, so scaling is a configuration change, not a manual gcloud edit (a manual edit would be reverted on the next apply). Defaults are min 0 / max 1; set min 1 for teams that notice cold starts, and cpu_always_allocated = true only if you rely on scheduled mirror sync or timed webhook delivery.
Update the application version by changing the application_version input via Update on the deployment details page; Cloud Build rebuilds the image from the matching gitea/gitea tag and a new revision rolls out (Gitea migrates its schema automatically on startup).

Manage secrets, storage, and jobs:

gcloud secrets list --project="$PROJECT" --filter="name~gitea"
gcloud run jobs list --project="$PROJECT" --region="$REGION"   # db-init + backup jobs
gcloud storage buckets list --project="$PROJECT" --filter="name~gitea"

Open a database session for inspection or maintenance. The application role and database are tenant-prefixed — read the real names from the service env rather than assuming gitea:

INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
DB_USER=$(gcloud run services describe "$SERVICE" --project="$PROJECT" --region="$REGION" \
  --format="json" | grep -A1 '"name": "DB_USER"' | grep value | cut -d'"' -f4)
gcloud sql connect "$INSTANCE" --user="$DB_USER" --project="$PROJECT"

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — from the CLI or the Logs Explorer:
```
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=50
```
Logs Explorer filter: resource.type="cloud_run_revision" AND resource.labels.service_name="<service>". On every start the platform entrypoint logs a Gitea DB wired: host=… sslmode=… line — useful confirmation of the database wiring.
Monitoring — open the Cloud Run dashboard for the service and review request count, request latency (P50/P95/P99), instance count (scaling behaviour), and CPU / memory utilisation. The module's uptime check is disabled by default — enable uptime_check_config for production and confirm it is green under Monitoring → Uptime checks; review 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.

Revision unhealthy / service won't serve: inspect the latest revision and its logs; the startup probe targets /api/healthz with a 30-second initial delay:

gcloud run revisions list --service="$SERVICE" --project="$PROJECT" --region="$REGION"
gcloud run services logs read "$SERVICE" --project="$PROJECT" --region="$REGION" --limit=100

Database connection errors: confirm the Cloud SQL (PostgreSQL 15) instance is RUNNABLE, the DB password secret exists, and the db-init job completed. Check the Gitea DB wired: log line — the user/name must be the tenant-prefixed values, never a hardcoded gitea.

Initialisation job failed: list executions and read the failed one's logs:

gcloud run jobs executions list --job="${SERVICE}-db-init" \
  --project="$PROJECT" --region="$REGION"

lookup $(DB_HOST): no such host in logs: the stock upstream image was deployed instead of the custom build. container_image_source must be custom — Cloud Run does not interpolate $(VAR) env references, so the platform entrypoint in the custom image is required to compose the DB connection.
Broken clone URLs / redirects to localhost: public_domain / public_url still hold their defaults — set them to the service host via Update.
SSH clone fails: expected — Cloud Run exposes only the HTTP port. Use HTTPS remotes with a Gitea access token.
NFS mount / repositories missing: verify enable_nfs = true and that the execution environment is gen2 (required for NFS mounts in Cloud Run).
Image build failed: review Cloud Build history for the failed build's log.
403 / permission errors: verify the runtime service account's IAM roles.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas.

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 Cloud Run service, Cloud SQL database, Secret Manager secrets (DB password, SECRET_KEY, INTERNAL_TOKEN), GCS buckets, the NFS-held repository data, and Artifact Registry images. Resources owned by Services_GCP (the VPC, shared Cloud SQL, registry) are managed separately and are not removed here.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module provisions Cloud Run, Cloud SQL (PostgreSQL 15), NFS, GCS, secrets, builds the image, and runs DB init
2 — Access & verify	Manual	Health check passes; first registrant claims the admin account; registration hardened
3 — Operate	Manual	Inspect revisions, scale, update version, manage secrets/backups/storage, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision, database, init-job, image-source, clone-URL, NFS, build, and IAM 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​