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Hoppscotch on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview​

Estimated time: 20–40 minutes

Hoppscotch is an open-source, Postman-style API development platform for designing, sending, and inspecting HTTP, GraphQL, and WebSocket requests from the browser. This lab takes you through the full operational lifecycle of the Hoppscotch 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 Hoppscotch 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, and update the deployment.
  • 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, 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 Hoppscotch (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 builds a thin custom container image (FROM hoppscotch/hoppscotch-frontend) with Cloud Build, mirrors it into Artifact Registry, and deploys it as a stateless Deployment on the GKE Autopilot cluster, fronted by a LoadBalancer Service with a reserved static IP. Hoppscotch is deliberately stateless — no Cloud SQL instance, no Secret Manager secrets, and no Cloud Storage bucket are created (database_type = "NONE" is enforced by a plan-time guard). With no database to provision, a first deploy typically completes in well under the time a stateful module needs.

  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 hoppscotch | 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 serving. Hoppscotch has no backend to be reachable from — the root path returns the app UI as soon as Caddy binds port 3000:

    curl -sS -o /dev/null -w '%{http_code}\n' "http://${EXTERNAL_IP}/"   # expect 200

  3. Open http://${EXTERNAL_IP} in a browser. Unlike most modules, Hoppscotch has no first-run admin account to create — the self-hosted frontend has no login or user management of its own. You can start building requests immediately. Collections, environments, and history are kept in the browser's local storage on each user's machine, not on the server.

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

  1. Inspect the workload — deployment, pods, and the horizontal autoscaler:

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

  2. 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). Because Hoppscotch keeps no shared queue or database, scaling is unconstrained — raise max_instance_count freely as a throughput ceiling. Note that GKE has no scale-to-zero, so min_instance_count must stay at least 1 (the default). session_affinity defaults to None because the static bundle is identical on every pod, so sticky routing is unnecessary.

  3. Update the application version by changing the version input in the RAD platform and applying it via Update; a new image builds and a rolling update replaces the pods (safe here — the SPA is stateless, so there is no shared NFS or database lock to deadlock on). HOPPSCOTCH_VERSION (not the generic APP_VERSION) pins the upstream hoppscotch-frontend tag, so application_version = "latest" resolves to a pinned, known-good tag at build time rather than the literal string latest.

  4. Check secrets — Hoppscotch provisions none by design; confirm nothing unexpected shows up:

    kubectl get secrets -n "$NS"
    gcloud secrets list --project="$PROJECT" --filter="name~hoppscotch"

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 against the LoadBalancer host (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 Hoppscotch releases.

  • Pod not Ready / CrashLoopBackOff: inspect events and logs. The liveness probe targets the root /, which returns HTTP 200 within seconds of Caddy binding port 3000 — a failing probe almost always means the image tag is invalid, not that a backend is unreachable (there is no backend). 
    kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
    kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container
  • Image pull errors: confirm the image exists in Artifact Registry and the node service account can pull it. Custom/mirrored images use imagePullPolicy=Always, so a rebuilt tag is never served stale from a node cache. 
    kubectl get deploy -n "$NS" -o jsonpath='{.items[0].spec.template.spec.containers[0].image}'
  • Pending pod / no external IP: check kubectl describe pod events for resource or quota issues, and confirm the LoadBalancer Service has an assigned IP.
  • Plan fails with a database_type error: this module enforces database_type = "NONE" at plan time — Hoppscotch has no backend to connect to a database. Leave the default rather than trying to select an engine.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including why container_image_source must stay custom and why min_instance_count cannot be 0 on GKE).

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, namespace, LoadBalancer, and reserved static IP, plus the Artifact Registry image (Hoppscotch provisions no database, secrets, or storage buckets, so there is nothing else to clean up). Resources owned by Services_GCP (the VPC, GKE cluster, shared registry) are managed separately and are not removed here.

Summary​

TaskTypeOutcome
1 — DeployAutomatedModule builds the custom image and deploys the GKE workload + LoadBalancer — no database, secrets, or storage bucket
2 — Access & verifyManualConnect to the cluster; health check passes; open the external IP and start using Hoppscotch immediately (no admin account)
3 — OperateManualInspect workload, scale (unconstrained, min ≥ 1), update version, confirm no secrets
4 — ObserveManualQuery Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — TroubleshootManualDiagnose pod, image-pull, scheduling, and database_type guard issues
6 — Tear downAutomatedDelete (Trash) removes the workload, LoadBalancer, and image

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

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