Kimai on Cloud Run — Lab Guide

Overview

Estimated time: 45–60 minutes

Kimai is a free, open-source time-tracking application used by freelancers and agencies for billable-hours tracking, timesheets, and reporting that feeds into invoicing. This lab takes you through the full operational lifecycle of the Kimai 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 Kimai 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 and verify the running service, and log in with the bootstrapped administrator account.
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]

In the RAD platform, open Kimai (Cloud Run), 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 (MySQL 8.0) database with its Secret Manager secrets (APP_SECRET, ADMINPASS, and the database password), the storage Cloud Storage bucket, builds the custom DATABASE_URL-composing wrapper image, and runs the db-init initialization job (creates the database, user, and grants). First deploys take roughly 15–25 minutes (Cloud SQL creation and the image build dominate).

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="$REGION" \
  --filter="metadata.name~kimai" --format="value(metadata.name)" --limit=1)
SERVICE_URL=$(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 — Kimai's login page returns HTTP 200:

curl -s -o /dev/null -w "%{http_code}\n" "$SERVICE_URL/en/login"   # expect 200

Retrieve the bootstrapped administrator credentials from Secret Manager — the username is always admin (hardcoded by the vendor image), and the password is the auto-generated ADMINPASS secret:

ADMINPASS_SECRET=$(gcloud secrets list --project="$PROJECT" \
  --filter="name~admin-password" --format="value(name)" --limit=1)
gcloud secrets versions access latest --secret="$ADMINPASS_SECRET" --project="$PROJECT"

Open $SERVICE_URL in a browser and log in with admin and the password retrieved above.
Create a test project, activity, and timesheet entry to confirm end-to-end write/read against the real database: Administration → Projects (create one), Administration → Activities (create one), then log a timesheet entry against them from the main timesheet view. Reload the page — or redeploy — and confirm the entry is still there.

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).
Update the application version by changing application_version in the RAD platform and applying it via Update; a new image builds FROM kimai/kimai2:<version>-apache and a new revision rolls out. kimai:install re-runs safely against the existing schema on the new container's first boot — no manual migration step is needed.

Manage secrets and backups:

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

Open a database session for inspection or maintenance:

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

Set up an API token or additional users. With the admin account logged in, go to Profile → API access to generate an API token for time-tracking integrations, or Administration → Users to invite teammates (self-service registration is off by default).

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>".
Monitoring — open the Cloud Run dashboard for the service and review request count, request latency, instance count (scaling behaviour), and CPU / memory utilisation. The module can provision an uptime check (uptime_check_config.enabled = true); if enabled, confirm it is green under Monitoring → Uptime checks, and 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 Kimai releases.

Revision unhealthy / service won't serve: inspect the latest revision and its logs for startup errors, and confirm env vars and secrets resolved. The startup probe targets GET /en/login with a generous 20-retry threshold to cover the first-boot kimai:install run.
```
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 on first boot. Confirm the db-init job completed successfully before the app's own kimai:install (which runs on every container start) had a chance to run:
```
gcloud run jobs executions list --job="${SERVICE}-db-init" \
  --project="$PROJECT" --region="$REGION"
```
If the app container is failing to start with a database error, verify DB_IP resolved correctly — it should be the Cloud SQL instance's private IP on Cloud Run (this module does not use the Auth Proxy socket).
Wrong port assumption. If you're comparing this deployment against documentation or another Kimai install that assumes port 80, note this module's :apache image variant serves on 8001 — confirmed via local testing and live deployment. container_port should read 8001.
Image build failed: review Cloud Build history for the failed build's log — the build compiles the thin wrapper image FROM kimai/kimai2.
403 / permission errors: verify the runtime service account's IAM roles.
Forgot the admin password: it's not lost — ADMINPASS is a persistent Secret Manager secret, re-injected and re-applied to the admin account on every container boot (idempotent, so re-fetching the secret and restarting the service, if needed, always yields a working login):
```
gcloud secrets versions access latest --secret="$ADMINPASS_SECRET" --project="$PROJECT"
```

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, GCS buckets, 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 (MySQL 8.0), secrets, storage bucket, and runs the `db-init` job
2 — Access & verify	Manual	Health check returns 200 at `/en/login`; log in as `admin` with the generated `ADMINPASS` secret; create a test timesheet entry
3 — Operate	Manual	Inspect revisions, scale, update version, manage secrets/backups, DB access, API/user setup
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose revision, database, port, and build 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​