Docmost on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

Docmost is an open-source, real-time collaborative wiki and documentation platform — a self-hosted Confluence/Notion alternative built on NestJS. This lab takes you through the full operational lifecycle of the Docmost 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 Docmost 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.
Access and verify the running service and create the first workspace and admin account.
Perform day-2 operations — inspect, scale, update, and manage secrets and storage.
Understand the roles of PostgreSQL, Redis, and NFS in a collaborative-editing workload.
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 Docmost (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 (port 3000, 1–3 replicas via HPA), provisions a Cloud SQL (PostgreSQL 15) database with its Secret Manager secrets (the auto-generated APP_SECRET and the database password), Redis for real-time collaboration and job queues (co-located on the NFS server VM by default), an NFS volume mounted at /app/data/storage for uploaded attachments, and a GCS data bucket. It builds a custom container image (wrapping docmost/docmost:latest) via Cloud Build 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 docmost | 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,hpa -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. Docmost's health path is /api/health, which returns HTTP 200 once the app has booted and run its schema migrations (allow up to ~2 minutes on a fresh deploy — the startup probe uses a 60-second initial delay plus a retry window):
```
curl -s -o /dev/null -w "%{http_code}\n" "http://${EXTERNAL_IP}/api/health"
```
Open http://${EXTERNAL_IP} in a browser. Docmost ships with no default credentials — the first visitor completes the setup form and creates the initial workspace and administrator account. Do this promptly after deploy so no one else can claim the workspace. The auto-generated secrets can be inspected if needed:
```
gcloud secrets list --project="$PROJECT" --filter="name~docmost"
kubectl get secret -n "$NS"
```
Set APP_URL to the external address once it is known, so absolute links and the collaboration WebSocket resolve correctly (the module injects the internal/predicted URL by default):
```
kubectl set env deploy/$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}') \
  -n "$NS" APP_URL="http://${EXTERNAL_IP}"
```
Verify the collaboration wiring: create a page and open it in two browser tabs — edits should appear live in both (real-time sync runs over the APP_URL WebSocket endpoint, coordinated through Redis).

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

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

kubectl get deploy,pods,hpa,pvc -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). min_instance_count defaults to 1 (GKE does not support scale-to-zero, unlike the Cloud Run variant); max_instance_count defaults to 3. Redis is enabled by default, so multiple replicas stay coordinated for real-time editing and background queues. Session affinity (ClientIP) is set by default to keep a client's collaboration WebSocket on one pod.
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 deployment replaces the pods. Because attachments live on the shared NFS volume, the Deployment uses the Recreate strategy (not rolling update) to avoid two pods writing the same NFS/DB state during the transition. Docmost runs its schema migrations automatically on boot — there is no separate migration step.

Manage secrets, storage, and jobs. Treat APP_SECRET as immutable — rotating it after first boot logs everyone out and makes data encrypted under the old value unrecoverable:

kubectl get secrets -n "$NS"
gcloud secrets list --project="$PROJECT" --filter="name~docmost"
kubectl get jobs -n "$NS"          # db-init and any scheduled jobs
kubectl get pvc -n "$NS"
gcloud filestore instances list --project="$PROJECT"

Open a database session for inspection or maintenance:

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

Task 4 — 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; Cloud SQL metrics live under the SQL page. The module can provision an uptime check (when the endpoint is publicly reachable); 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 Docmost releases.

Pod not Ready / CrashLoopBackOff: inspect events and logs. The startup and liveness probes target /api/health; a connection failure to PostgreSQL (via the Cloud SQL Auth Proxy sidecar on 127.0.0.1) will keep 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
```
Database connection errors: confirm the Cloud SQL (PostgreSQL 15) instance is RUNNABLE, the DB password secret materialised into the namespace, and the db-init job completed. Note the pod connects through the Auth Proxy sidecar over plaintext loopback (sslmode=disable) — this differs from the Cloud Run variant, which connects over private-IP TCP with SSL.

Initialisation job failed: inspect the job and its pod logs:

kubectl get jobs -n "$NS"
kubectl logs -n "$NS" job/<job-name>

Real-time editing broken / edits don't sync: verify Redis is reachable (enable_redis = true; with redis_host empty the NFS server VM co-hosts Redis — it must be RUNNING), and check that APP_URL in the running pod matches the URL users actually browse to (a mismatch breaks the collaboration WebSocket and absolute links):
```
kubectl exec -n "$NS" deploy/<service-name> -- env | grep -E 'APP_URL|REDIS_URL'
```
Attachments disappear after a restart: verify enable_nfs = true (the default) and that the NFS volume is mounted at /app/data/storage; with NFS off, uploads land on ephemeral pod disk and are lost on restart / not shared across replicas.
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 custom-built image exists in Artifact Registry and the node service account can pull it. The image is built with the DOCMOST_VERSION build ARG (so application_version = "latest" maps to a pinned release); rebuilt images deploy with imagePullPolicy=Always so nodes don't serve a stale cached layer.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the critical rule never to rotate APP_SECRET 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, Secret Manager secrets (including APP_SECRET), GCS buckets, the NFS-backed attachment volume, 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), Redis, NFS, GCS bucket, secrets, and runs DB init
2 — Access & verify	Manual	Connect to the cluster; `/api/health` passes; create the first workspace and admin account; verify real-time editing
3 — Operate	Manual	Inspect workload, scale (Redis-coordinated), update version (Recreate strategy), manage secrets/storage, DB access
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database, init-job, Redis/collaboration, NFS, scheduling, 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​