Hasura on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

Hasura is an open-source engine that gives you an instant, realtime GraphQL and REST API over a PostgreSQL database, with role-based authorization and a built-in admin console. This lab takes you through the full operational lifecycle of the Hasura on GKE Autopilot module on Google Cloud: deploy it, access and verify it, open the console and run a GraphQL query, 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 Hasura product internals. 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.
Retrieve the admin secret, open the console, track a table, and run a GraphQL query.
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

Task 1 — Deploy the module [Automated]

Click Deploy in the RAD platform top navigation, open Hasura (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.
The platform deploys the workload into the GKE Autopilot cluster, provisions a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (HASURA_GRAPHQL_ADMIN_SECRET and the database password), builds the container image (a thin wrapper over hasura/graphql-engine), and runs a one-shot database-initialisation job. First deploys take roughly 20–35 minutes (Cloud SQL creation dominates).

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

Task 2 — Access & verify [Manual]

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"

Confirm the service is healthy. Hasura exposes a public health endpoint that returns 200 only when the engine is up and connected to PostgreSQL:
```
curl -s -o /dev/null -w '%{http_code}\n' "http://${EXTERNAL_IP}/healthz"   # expect 200
```

Retrieve the admin secret from Secret Manager — you need it for the console and for every GraphQL/metadata API call:

ADMIN_SECRET_NAME=$(gcloud secrets list --project="$PROJECT" \
  --filter="name~admin-secret" --format="value(name)" --limit=1)
ADMIN=$(gcloud secrets versions access latest --secret="$ADMIN_SECRET_NAME" --project="$PROJECT")
echo "Admin secret: $ADMIN"

Open http://${EXTERNAL_IP}/console in a browser. Hasura prompts for the admin secret — paste the value from step 3. The console opens on the Data tab, connected to your Cloud SQL database (the default source).

Task 3 — Worked example: track a table and run a GraphQL query [Manual]

Create a table. In the console, go to Data → default → public → Create Table. Name it todos with columns id (Integer, auto-increment, primary key) and title (Text). Click Add Table. (Prefer SQL? Open Data → SQL, run CREATE TABLE todos (id serial primary key, title text);, and tick Track this table.)
Track the table. If you created it via SQL, Hasura lists it under Untracked tables — click Track. Tracking is what exposes the table through the GraphQL API; it writes an entry into Hasura's metadata catalog (stored in Postgres, so it survives pod restarts and rolling updates).

Insert a row via the API (using the admin secret as the x-hasura-admin-secret header):

curl -s "http://${EXTERNAL_IP}/v1/graphql" \
  -H "x-hasura-admin-secret: $ADMIN" \
  -H 'Content-Type: application/json' \
  -d '{"query":"mutation { insert_todos_one(object: {title: \"Ship the docs\"}) { id title } }"}'

Run a GraphQL query to read it back:

curl -s "http://${EXTERNAL_IP}/v1/graphql" \
  -H "x-hasura-admin-secret: $ADMIN" \
  -H 'Content-Type: application/json' \
  -d '{"query":"query { todos { id title } }"}'
# => {"data":{"todos":[{"id":1,"title":"Ship the docs"}]}}

You can also run the same query interactively in the console's API (GraphiQL) tab — it sends the admin-secret header for you.

Task 4 — Operate & keep it running (Day-2) [Manual]

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

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

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). Hasura scales horizontally safely because all state is in Postgres and session_affinity = "None"; a rolling update replaces pods without dropping requests.
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. Your tracked-table metadata persists in the database across the upgrade.

Manage secrets, storage, and jobs:

kubectl get secrets -n "$NS"
gcloud secrets list --project="$PROJECT" --filter="name~hasura"
kubectl get jobs -n "$NS"          # DB-init and any scheduled jobs

Open a database session for inspection or maintenance:

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

Task 5 — Observe: Logging & Monitoring [Manual]

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

Task 6 — 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 Hasura releases.

Pod not Ready / CrashLoopBackOff: inspect events and logs. The liveness probe targets /healthz; a connection failure to PostgreSQL keeps the pod from becoming Ready.

kubectl describe pod -n "$NS" <pod>          # Events section shows scheduling/probe/mount errors
kubectl logs -n "$NS" <pod> --previous       # logs from the crashed container

/console or /v1/graphql returns 401: you are missing or mis-sending the admin secret. Re-fetch it (Task 2 step 3) and send it as x-hasura-admin-secret. Never point health probes at these paths — use /healthz.
Database connection errors: confirm the Cloud SQL instance is RUNNABLE, the DB password secret materialised into the namespace, and the init job completed. On GKE the Auth Proxy sidecar listens on 127.0.0.1; the DSN is plain loopback (no SSL).

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 keeping probes on /healthz and never exposing the admin secret).

Task 7 — 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, 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.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module deploys the GKE workload, Cloud SQL (PostgreSQL 15), secrets, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; health check passes; retrieve the admin secret; open the console
3 — Worked example	Manual	Track a table and run an insert + GraphQL query end to end
4 — Operate	Manual	Inspect workload, scale, update version, manage secrets/storage, DB access
5 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
6 — Troubleshoot	Manual	Diagnose pod, auth (401), database, init-job, scheduling, and image-pull issues
7 — 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 — Worked example: track a table and run a GraphQL query [Manual]​

Task 4 — Operate & keep it running (Day-2) [Manual]​

Task 5 — Observe: Logging & Monitoring [Manual]​

Task 6 — Troubleshoot & debug [Manual]​

Task 7 — Tear down [Automated]​

Summary​