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Speedtest Tracker on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 45–60 minutes

Speedtest Tracker is an open-source, self-hosted internet speed test monitoring tool that runs automated speed tests on a schedule and charts the results over time. This lab takes you through the full operational lifecycle of the Speedtest Tracker 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 Speedtest Tracker 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.
  • Perform day-2 operations — inspect, scale (correctly, given the cron scheduler), update, and manage secrets.
  • Observe the workload with Cloud Logging and Cloud Monitoring.
  • Diagnose and resolve the most common deployment and runtime issues, including the "looks healthy but the schedule never fires" failure mode.
  • Tear the deployment down cleanly.

Prerequisites​

  • Services_GCP deployed in the target project (provides the VPC, GKE Autopilot cluster, Cloud SQL, 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]​

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

  2. The platform deploys the workload into the GKE Autopilot cluster (a single always-running replica), provisions a Cloud SQL (MySQL 8.0) database with its Secret Manager secrets (APP_KEY and the database password), and runs a one-shot database-initialisation job. First deploys take roughly 15–25 minutes (Cloud SQL creation dominates).

  3. 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 speedtesttracker | head -1 | cut -d/ -f2)
    echo "Cluster: $CLUSTER   Namespace: $NS"
    kubectl get all -n "$NS"

Task 2 — Access & verify [Manual]​

  1. 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"

  2. Confirm the service is healthy. Speedtest Tracker exposes an unauthenticated health endpoint:

    curl -s "http://${EXTERNAL_IP}/api/healthcheck"   # expect a 200 JSON message

  3. Open http://${EXTERNAL_IP} in a browser. On first visit Speedtest Tracker's setup wizard walks you through creating the initial administrator account — no pre-seeded admin credential exists in Secret Manager. After the admin account is created, review Settings → General and confirm the speed test schedule (SPEEDTEST_SCHEDULE) matches what you expect; trigger an on-demand test from the dashboard to confirm end-to-end connectivity works before relying on the schedule.

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

  1. Inspect the workload — deployment and pods:

    kubectl get deploy,pods -n "$NS"
    kubectl describe deploy -n "$NS"

  2. Scale carefully — this app is NOT a typical scale-out candidate. Speedtest Tracker's in-process Laravel scheduler has no cross-pod coordination, so max_instance_count must stay at 1 while speedtest_schedule is set (a plan-time validation enforces this). Do not raise max_instance_count unless you disable the schedule and use this deployment purely as a multi-replica dashboard.

  3. Update the application version by changing the version input in the RAD platform and applying it via Update; a new image is pulled and a rolling update replaces the pod.

  4. Manage secrets and jobs:

    kubectl get secrets -n "$NS"
    gcloud secrets list --project="$PROJECT" --filter="name~speedtesttracker"
    kubectl get jobs -n "$NS"          # db-init job

  5. Open a database session for inspection or maintenance:

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

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

  1. 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>".

  2. Monitoring — open the GKE / Kubernetes dashboards and review pod CPU and memory utilisation and restart counts. The module can provision an uptime check (when enabled); review Monitoring → Uptime checks.

  3. Confirm the schedule is actually firing — check the dashboard's results history for new entries appearing at the expected cadence. A pod that is Ready (1/1 Running, 0 restarts) is not, on its own, proof the schedule is producing results — the results history is the definitive signal.

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 Speedtest Tracker releases.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. 
    kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
    kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container
  • "Healthy but no new results ever appear": unlike the Cloud Run variant (where this points at CPU throttling), on GKE this usually means the schedule itself is misconfigured, or the deployment was scaled to more than 1 replica without disabling the schedule (duplicate/racing runs can produce inconsistent-looking history). Use kubectl exec to get a shell and inspect the running process: 
    kubectl exec -n "$NS" deploy/<service-name> -- ps aux
    kubectl exec -n "$NS" deploy/<service-name> -- env | grep SPEEDTEST_SCHEDULE
  • Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and the init job completed.
  • Initialisation job failed: inspect the job and its pod logs: 
    kubectl get jobs -n "$NS"
    kubectl logs -n "$NS" job/<job-name>
  • 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 rotate APP_KEY 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, and Secret Manager secrets. Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL, registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule deploys the GKE workload (single replica), Cloud SQL (MySQL 8.0), secrets, and runs DB init
2 — Access & verifyManualConnect to the cluster; health check passes; create the initial admin account in the UI; trigger a test test
3 — OperateManualInspect workload, scale carefully (max=1), update version, manage secrets, DB access
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics; confirm the schedule is actually producing new results
5 — TroubleshootManualDiagnose pod, "healthy but no results," database, init-job, and image-pull issues
6 — Tear downAutomatedDelete (Trash) removes all module resources

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

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