Supabase on GKE Autopilot — Lab Guide

📖 Configuration Guide

Overview

Estimated time: 45–90 minutes

Supabase is an open-source Firebase alternative that provides PostgreSQL 15, a Kong API gateway, GoTrue authentication, PostgREST REST APIs, real-time subscriptions, and an S3-compatible storage service — all deployed as Kubernetes workloads behind a single external LoadBalancer IP. This lab takes you through the full operational lifecycle of the Supabase 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 Supabase 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.

GKE only. Supabase is available in the GKE variant only. Its multi-service architecture requires persistent connections and Kubernetes primitives that Cloud Run does not support.

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.
• Retrieve the JWT signing secret and replace the placeholder API keys.
• Perform day-2 operations — inspect, scale, update, and manage secrets and storage.
• 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, 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

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Task 1 — Deploy the module [Automated]

1. Click Deploy in the RAD platform top navigation, open Supabase (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. Note that supabase_db_password is required; there is no default. Click Deploy.

2. The platform deploys the Kong gateway workload into the GKE Autopilot cluster, provisions a Cloud SQL (PostgreSQL 15) database with pgvector support, creates six Secret Manager secrets (JWT secret, anon key, service role key, and others), provisions a Cloud Storage bucket for file uploads, builds the container image, and runs a one-shot database-initialisation job. First deploys take roughly 20–35 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 supabase | head -1 | cut -d/ -f2)
   echo "Cluster: $CLUSTER   Namespace: $NS"
   kubectl get all -n "$NS"

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Task 2 — Access & verify [Manual]

1. Confirm the Kong gateway 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"

   If the IP shows <pending>, wait until the LoadBalancer is provisioned:

   kubectl get svc -n "$NS" --watch

2. Confirm the Kong gateway is healthy:

   curl -s -o /dev/null -w "%{http_code}" "http://${EXTERNAL_IP}:8000/health"
   # expect 200

3. Retrieve the JWT signing secret from Secret Manager. The anon key and service role key are stored as placeholders on first deploy — they must be replaced with valid JWTs signed by this secret before Supabase clients can authenticate:

   JWT_SECRET_NAME=$(gcloud secrets list --project="$PROJECT" \
     --filter="name~supabase.*jwt-secret" --format="value(name)" --limit=1)
   gcloud secrets versions access latest --secret="$JWT_SECRET_NAME" --project="$PROJECT"

   Use the returned value with jwt.io or the Supabase JWT generator to generate a signed anon JWT and a signed service role JWT, then upload them:

   ANON_SECRET=$(gcloud secrets list --project="$PROJECT" \
     --filter="name~supabase.*anon-key" --format="value(name)" --limit=1)
   SERVICE_SECRET=$(gcloud secrets list --project="$PROJECT" \
     --filter="name~supabase.*service-role-key" --format="value(name)" --limit=1)
   
   echo -n "<signed-anon-jwt>" | gcloud secrets versions add "$ANON_SECRET" \
     --data-file=- --project="$PROJECT"
   echo -n "<signed-service-role-jwt>" | gcloud secrets versions add "$SERVICE_SECRET" \
     --data-file=- --project="$PROJECT"

   Restart the Kong pod to pick up the updated secrets:

   kubectl rollout restart deployment -n "$NS"
   kubectl rollout status deployment -n "$NS"

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

1. Inspect the workload — deployments, pods, and (if enabled) the horizontal autoscaler and persistent volumes:

   kubectl get deploy,pods,hpa,pvc -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).

3. Update the application version by changing the version input via Update on the deployment details page; a new Kong image builds and a rolling update replaces the pods.

4. Manage secrets, storage, and jobs:

   kubectl get secrets -n "$NS"
   gcloud secrets list --project="$PROJECT" --filter="name~supabase"
   kubectl get jobs -n "$NS"          # db-init and any additional jobs
   gcloud storage buckets list --project="$PROJECT"   # supabase-storage bucket

5. Open a database session for inspection or maintenance:

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

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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, restart counts, and request metrics. The module also provisions an uptime check targeting the Kong /health endpoint (when enabled); review Monitoring → Uptime checks and Alerting → Policies.

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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 Supabase 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

• Kong startup failure (401 on all requests): the anon key or service role key secrets still contain placeholder values. Replace them with valid signed JWTs (see Task 2, step 3) and restart the deployment.
• Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and the db-init job completed successfully.
• Initialisation job failed: inspect the job and its pod logs:

  kubectl get jobs -n "$NS"
  kubectl logs -n "$NS" job/db-init

• 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 Kong image exists in Artifact Registry (image mirroring is always enabled) and the node service account can pull it.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas, including the mandatory supabase_db_password, the immutability of the JWT secret set, and the binary-unit requirement for memory quota values.

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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, Secret Manager secrets, Cloud Storage bucket, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL, registry) are managed separately and are not removed here.

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Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys Kong gateway, Cloud SQL (PostgreSQL 15 + pgvector), secrets, storage bucket, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; health check passes; JWT placeholders replaced with signed keys
3 — Operate	Manual	Inspect workload, scale, update version, manage secrets/storage/jobs, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, JWT/auth, database, init-job, scheduling, and image-pull issues
6 — Tear down	Automated	Delete (Trash) removes all module resources