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Matomo on Cloud Run — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–90 minutes

Matomo is the leading open-source web analytics platform — a privacy-focused, self-hosted alternative to Google Analytics. This lab takes you through the full operational lifecycle of the Matomo 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 Matomo 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 running service and complete Matomo's first-run web installer.
  • 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]​

  1. Click Deploy in the RAD platform top navigation, open Matomo (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.

  2. The platform provisions the Cloud Run service, a Cloud SQL (MySQL 8.0) database with its Secret Manager password secret, a Filestore NFS share that persists Matomo's document root (/var/www/html), a dedicated matomo-data GCS bucket, mirrors the official matomo:5-apache image into Artifact Registry (no build step — this is a prebuilt module), and runs a one-shot db-init job that creates the empty database and user. First deploys take roughly 20–35 minutes (Cloud SQL creation dominates).

  3. 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="$REGION" \
      --filter="metadata.name~matomo" --format="value(metadata.name)" --limit=1)
    SERVICE_URL=$(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]​

  1. Confirm the service is up. Matomo's health path is /, which returns HTTP 200 — or 302 to the installer on a fresh deploy — once Apache and PHP are running. With the default scale-to-zero, allow a 10–30 second cold start on the first request:

    curl -s -o /dev/null -w "%{http_code}\n" "$SERVICE_URL/"

  2. Open $SERVICE_URL in a browser. On a fresh deploy Matomo presents its web installer: the database screen is pre-filled from the injected MATOMO_DATABASE_* environment variables (the db-init job already created the empty database and user), so click through, create the superuser account, and register your first tracked website. The installer writes config.ini.php to the NFS-persisted document root, so setup survives restarts. If you ever need the database password:

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

  3. Immediate hardening: the installer URL is public until setup completes — finish the wizard right after deploying. Then copy the tracking snippet from Administration → Websites → Tracking Code into a test page and confirm the visit appears under Visitors → Visits Log.

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

  1. 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"

  2. 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). Note the module defaults to min = 0, max = 1: raise min to 1 for an always-warm production tracker, and keep max = 1 unless multi-instance safety over the shared NFS document root has been confirmed.

  3. Update the application version by changing the application_version input (use an Apache variant tag, e.g. 5.x-apache) via Update on the deployment details page; the new image is mirrored and a new revision rolls out. Matomo runs its own schema migrations from the persistent document root.

  4. Manage secrets, storage, and jobs:

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

  5. Open a database session for inspection or maintenance (analytics tables use the matomo_ prefix):

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

Task 4 — Observe: Logging & Monitoring [Manual]​

  1. 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>".

  2. 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 (uptime_check_config.enabled = false) — enable it via Update for production, then 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 Matomo releases.

  • Revision unhealthy / service won't serve: the startup probe is TCP with a generous 20-failure threshold to cover the first-boot copy of the application from /usr/src/matomo into the empty NFS volume. Inspect the latest revision and its logs before concluding the service has failed: 
    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: Matomo connects over TCP to the Cloud SQL private IP (enable_cloudsql_volume = false; the IP is injected as MATOMO_DATABASE_HOST). Confirm the MySQL 8.0 instance is RUNNABLE, the DB password secret exists, and the db-init job completed — it verifies the app user's credentials, so a green db-init rules out most auth issues.
  • 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"
  • NFS mount / installer reappears after restart: verify enable_nfs = true, nfs_mount_path = /var/www/html, and that the execution environment is gen2 (required for Filestore mounts in Cloud Run). If the document root is not persisted, config.ini.php is lost on every restart and Matomo re-enters setup.
  • Image pull failed: this is a prebuilt module — there is no Cloud Build step. Check that the mirrored image exists in Artifact Registry and that application_version is a real Apache variant tag (5-apache, 5.x-apache); fpm/alpine tags don't serve HTTP on port 80.
  • Slow pages under heavy traffic (app-specific): Matomo defaults to browser-triggered report archiving, which runs inside visitor requests. For busy sites, add a cron_jobs entry running console core:archive so archiving runs as a scheduled Cloud Run Job instead.
  • 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, GCS buckets (including the matomo-data bucket), Filestore NFS share, and Artifact Registry images. Resources owned by Services_GCP (the VPC, shared Cloud SQL, registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule provisions Cloud Run, Cloud SQL (MySQL 8.0), NFS, GCS bucket, secrets, and runs DB init
2 — Access & verifyManualHealth check passes; complete Matomo's web installer and verify tracking
3 — OperateManualInspect revisions, scale, update version, manage secrets/backups/storage, DB access
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics; enable the uptime check
5 — TroubleshootManualDiagnose revision, database, init-job, NFS, image, archiving, and IAM issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

By · Google Cloud certified: ACE · PCA · PCD · PDE · PSE · CDL

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