AFFiNE on GKE Autopilot — Lab Guide

Overview

Estimated time: 45–90 minutes

AFFiNE is an open-source, privacy-first knowledge base that unifies docs, whiteboards, and databases in one workspace — a self-hostable alternative to Notion and Miro. This lab takes you through the full operational lifecycle of the AFFiNE 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 AFFiNE 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, 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, the shared NFS/Redis VM, 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 AFFiNE (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 password secret, a Filestore NFS mount for blob persistence (the same shared NFS VM also serves as the default Redis endpoint AFFiNE requires), a dedicated storage GCS bucket, builds the custom container image (a thin wrapper over ghcr.io/toeverything/affine), and runs two one-shot init Jobs: db-init (database + user) and affine-migrate (AFFiNE's own self-host-predeploy schema migration and signing-key generation). 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 affine | 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. AFFiNE's startup, liveness, and readiness probes all target a plain HTTP GET /, which returns 200 once the server is ready — no authentication required (the startup probe allows up to ~510 seconds, but a healthy pod typically becomes Ready well before that since schema migration already ran in the affine-migrate job, not at boot):
```
curl -s -o /dev/null -w "%{http_code}\n" "http://${EXTERNAL_IP}/"
```
Open http://${EXTERNAL_IP} in a browser (or http://<EXTERNAL_IP>.nip.io if you prefer a hostname) and create the first account — on a fresh AFFiNE self-host instance the first registered user becomes the server administrator (the admin panel is at /admin). Do this immediately after deploying: until an admin account exists, anyone who reaches the URL can register it. AFFiNE has no pre-seeded admin credential in Secret Manager — the only secret stored there is the database password, retrievable if needed:
```
DB_SECRET=$(gcloud secrets list --project="$PROJECT" \
  --filter="name~affine" --format="value(name)" --limit=1)
gcloud secrets versions access latest --secret="$DB_SECRET" --project="$PROJECT"
```

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

Inspect the workload — deployment, pods, and persistent volumes:

kubectl get deploy,pods,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). AFFiNE's real-time collaboration and job queue run through Redis, so multiple replicas depend on enable_redis = true (the default) and the shared NFS VM. Session affinity (ClientIP) is set by default to keep WebSocket-based collaboration sessions stable on the same pod.
Update the application version by changing the application_version input (e.g. stable → a pinned release tag) in the RAD platform and applying it via Update; a new image builds and a rolling update replaces the pods. The affine-migrate job re-runs idempotently on the new version.

Manage secrets, storage, and jobs:

kubectl get secrets -n "$NS"
gcloud secrets list --project="$PROJECT" --filter="name~affine"
kubectl get jobs -n "$NS"          # db-init and affine-migrate
gcloud storage buckets list --project="$PROJECT" --filter="name~affine"
kubectl get pvc -n "$NS"           # NFS-backed blob storage claims

Open a database session for inspection or maintenance (PostgreSQL 15, reached from the workload through the Cloud SQL Auth Proxy sidecar; from your own shell, gcloud sql connect opens its own tunnel):
```
INSTANCE=$(gcloud sql instances list --project="$PROJECT" --format="value(name)" --limit=1)
gcloud sql connect "$INSTANCE" --user=affine --project="$PROJECT"
```

Check Redis connectivity — AFFiNE requires Redis for Yjs real-time document sync and its background job queue; by default it resolves to the shared NFS/Redis VM's IP:

kubectl exec -n "$NS" deploy/"$(kubectl get deploy -n "$NS" -o jsonpath='{.items[0].metadata.name}')" \
  -- env | grep -i REDIS
gcloud compute instances list --project="$PROJECT" --filter="name~nfs"

Task 4 — Observe: Logging & Monitoring [Manual]

Logs — from kubectl or the Logs Explorer. Startup log lines show which database host and Redis endpoint the cloud entrypoint resolved:
```
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 affine-migrate init job alone requests 2Gi, so watch for OOM events on the server container too, especially under real-time collaboration load. The module can provision an uptime check (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 AFFiNE releases.

Pod not Ready / CrashLoopBackOff: inspect events and logs. The liveness probe targets /; a connection failure to PostgreSQL (via the Cloud SQL Auth Proxy sidecar on 127.0.0.1:5432) or to Redis 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 via the Secret Store CSI driver, and the db-init job completed.
Schema / signing-key not present: the affine-migrate job (not the server container) creates the schema and generates AFFiNE's signing key in PostgreSQL. If it failed or is still retrying (max_retries = 3), the server will never reach a healthy state:
```
kubectl get jobs -n "$NS"
kubectl logs -n "$NS" job/<affine-migrate-job-name>
kubectl logs -n "$NS" job/<db-init-job-name>
```
Real-time collaboration not syncing: Redis is mandatory, not optional. Verify the shared NFS/Redis VM is RUNNING and that the pod's environment shows a non-empty REDIS_SERVER_HOST. Disabling enable_nfs without supplying an external redis_host silently removes Redis connectivity as well as blob persistence.
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. container_image_source must be custom — the upstream image lacks the entrypoint that assembles DATABASE_URL and the REDIS_SERVER_* variables.

See the Configuration Guide's Configuration Pitfalls section for setting-specific gotchas (including the critical rule that application_database_name and application_database_user are immutable after first deploy, and that disabling NFS without an external Redis host breaks collaboration silently).

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, GCS buckets, and Artifact Registry images. Resources owned by Services_GCP (the VPC, GKE cluster, shared Cloud SQL, the shared NFS/Redis VM, 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), NFS blob storage, GCS bucket, secrets, and runs db-init + affine-migrate
2 — Access & verify	Manual	Connect to the cluster; health check passes; first registered account becomes the server admin
3 — Operate	Manual	Inspect workload, scale, update version, manage secrets/storage/jobs, DB and Redis checks
4 — Observe	Manual	Query Cloud Logging; review Cloud Monitoring metrics and uptime check
5 — Troubleshoot	Manual	Diagnose pod, database, init-job, Redis, 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​