Element on GKE Autopilot — Lab Guide

Overview

Estimated time: 30–60 minutes

Element is the leading open-source Matrix web client — a self-hosted, end-to-end-encrypted messaging app that runs as a static single-page application and connects to a Matrix homeserver you specify. This lab takes you through the full operational lifecycle of the Element on GKE Autopilot module on Google Cloud: deploy it, point it at a homeserver, 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 Element 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.
Point Element at a Matrix homeserver and verify the running workload.
Perform day-2 operations — inspect pods, scale replicas, update the version, and re-point the homeserver.
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 authenticated: gcloud auth login, gcloud auth application-default login.
Project Owner (or equivalent) IAM on the project.
RAD platform access with permission to deploy modules into the project.
A Matrix homeserver to connect to — either the public matrix.org (the default) or your own Synapse/Dendrite instance.

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
export NAMESPACE="<namespace>"       # from the deployment Outputs

Task 1 — Deploy the module [Automated]

In the RAD platform, open Element (GKE), set project_id, and set homeserver_url / homeserver_name to your Matrix homeserver (or leave them blank to use the public matrix.org). Review the remaining inputs — 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 builds the custom Element image (a thin layer over vectorim/element-web that generates config.json at start-up), pushes it to Artifact Registry, and provisions the GKE Deployment plus an external LoadBalancer Service. There is no database, no secret, and no storage bucket to create. First deploys take roughly 10–20 minutes (Autopilot node provisioning and LoadBalancer IP assignment dominate).

When it completes, get cluster credentials and discover the workload:

CLUSTER=$(gcloud container clusters list --project="$PROJECT" --format="value(name)" --limit=1)
gcloud container clusters get-credentials "$CLUSTER" --region="$REGION" --project="$PROJECT"
kubectl get pods,svc -n "$NAMESPACE"
EXTERNAL_IP=$(kubectl get svc -n "$NAMESPACE" -o jsonpath='{.items[?(@.spec.type=="LoadBalancer")].status.loadBalancer.ingress[0].ip}')
echo "External IP: $EXTERNAL_IP"

Task 2 — Access & verify [Manual]

Confirm the pod is serving and its runtime config.json points at your homeserver:

kubectl exec -n "$NAMESPACE" deploy/element -- env | grep HOMESERVER
curl -s "http://$EXTERNAL_IP/config.json" | grep -E 'base_url|server_name'
curl -s -o /dev/null -w '%{http_code}\n' "http://$EXTERNAL_IP/"       # expect 200

Open http://$EXTERNAL_IP (or your custom domain) in a browser. Element loads its sign-in screen showing the configured homeserver. Log in with an account on that homeserver — authentication happens between your browser and the homeserver, not in the pod.

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

Inspect pods, the Service, and the autoscaler:

kubectl get pods,svc,hpa -n "$NAMESPACE"
kubectl describe deploy/element -n "$NAMESPACE"
kubectl logs -n "$NAMESPACE" deploy/element --tail=100

Scale by changing the min/max instance inputs and clicking Update — 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). Element is stateless with no session affinity, so replicas are freely interchangeable; GKE keeps a minimum of 1.
Re-point the homeserver by changing homeserver_url / homeserver_name in the RAD platform and clicking Update — the entrypoint rewrites config.json on the new pods. No image rebuild is required.
Update the application version by changing the version input and applying it via Update; a new image builds and, because imagePullPolicy=Always is set for the reused custom tag, the rollout pulls the fresh layers.

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — pod stdout/stderr (nginx access/error), from the CLI or the Logs Explorer:

gcloud logging read 'resource.type="k8s_container" AND resource.labels.namespace_name="'"$NAMESPACE"'"' \
  --project="$PROJECT" --limit=50

Monitoring — open the GKE workload dashboard and review pod count, CPU / memory utilisation, and restart counts. Review any provisioned uptime check under 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 Element releases.

Pod not Ready / CrashLoopBackOff: inspect the pod events and nginx logs. The startup probe targets /, which nginx answers as soon as it binds port 80.
```
kubectl describe pod -n "$NAMESPACE" -l app=element
kubectl logs -n "$NAMESPACE" deploy/element --tail=100
```
Login screen shows the wrong homeserver: the entrypoint writes config.json from HOMESERVER_URL / HOMESERVER_NAME; confirm the env on the running pod (Task 2, step 1) and re-point via Update.
Users can load the UI but cannot log in: the homeserver is unreachable or incorrect — verify it serves the Matrix client-server API (curl -s <homeserver_url>/_matrix/client/versions).
No external IP assigned: confirm the LoadBalancer Service and that a static IP is reserved:
```
kubectl get svc -n "$NAMESPACE"
gcloud compute addresses list --project="$PROJECT"
```
Rebuild runs stale: verify the pod's image digest matches the freshly built one; imagePullPolicy=Always should pull the new layers on rollout.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the binary-unit requirement for quota_memory_*).

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, use Purge instead — it removes the deployment from RAD's records without destroying the cloud resources. This removes everything the module created — the GKE Deployment, Service, LoadBalancer IP, and Artifact Registry images. Resources owned by Services_GCP (the VPC, cluster, registry) are managed separately and are not removed here.

Because Element is stateless, there is no database, secret, PVC, or storage bucket to clean up — teardown is clean and fast.

Summary

Task	Type	Outcome
1 — Deploy	Automated	Module builds the Element image and provisions the GKE Deployment + LoadBalancer (no DB/secret/storage)
2 — Access & verify	Manual	`config.json` points at your homeserver; log in via the browser-to-homeserver flow
3 — Operate	Manual	Inspect pods, scale replicas, re-point the homeserver, update the version
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, homeserver-config, login, LoadBalancer, and image-pull issues
6 — 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 — Operate & keep it running (Day-2) [Manual]​

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

Task 5 — Troubleshoot & debug [Manual]​

Task 6 — Tear down [Automated]​

Summary​