Warmbly Deployment
Two distinct planes, deployed differently.
| Plane | Services | How |
|---|---|---|
| Control | backend, consumer, tracking, realtime, web | Container hosting in one region (Railway in production). Stable region-pinning so KMS/S3 calls stay local. |
| Execution | worker | One process per VPS, anywhere with a public IPv4. Managed from the admin dashboard over SSH. |
Directory layout
deploy/
├── docker/
│ ├── backend.Dockerfile
│ ├── consumer.Dockerfile
│ ├── worker.Dockerfile
│ └── realtime.Dockerfile
└── config/
└── env.example
The tracking Dockerfile lives at tracking/Dockerfile. The local-dev compose is docker-compose.yml at the repo root.
Building images
docker build -f deploy/docker/backend.Dockerfile -t warmbly/backend .
docker build -f deploy/docker/consumer.Dockerfile -t warmbly/consumer .
docker build -f deploy/docker/worker.Dockerfile -t warmbly/worker .
docker build -f deploy/docker/realtime.Dockerfile -t warmbly/realtime .
docker build -f tracking/Dockerfile -t warmbly/tracking tracking/
GitHub Actions publishes these to GHCR automatically. See the deployment guide.
Local development
make infra # postgres, redis, kafka, etc. (leave running, shared across worktrees)
make app # backend, consumer, worker, tracking, realtime, web (hot reload)
make sim # adds premium + dedicated workers (prod-image flow)
make seed # rich fixture
make tools # kafka-ui at :18090
make reset # nuke volumes
Full reference: local development.
Deploying the control plane
The Dockerfiles in deploy/docker/ are the deployment unit. Production runs on Railway. Other valid targets: Fly.io, ECS Fargate, single-VPS systemd. Migrations run automatically on backend boot.
Configuration is env-driven — see deploy/config/env.example for the full env reference, or the deployment guide for a step-by-step.
Realtime transport
Backend, consumer, and the Elixir realtime service all pick their event transport from one flag, PUBSUB_ENABLED, so they cannot disagree:
PUBSUB_ENABLED=false(default): events bridge over Redis (REDIS_URL). No GCP needed. This is the local-dev and simple self-host path.PUBSUB_ENABLED=true: events flow through Google Pub/Sub. Also setGCP_PROJECT_IDandGOOGLE_APPLICATION_CREDENTIALS_JSONon every service. The backend and consumer auto-provision the realtime topics and their<topic>-subpull subscriptions on boot (idempotent), so there is no manualgcloudstep. The service account needsroles/pubsub.editor.
Set the flag the same on all three services. A publisher on Pub/Sub with a subscriber on Redis silently drops every realtime event.
Worker deployment
Workers run on per-VPS machines so cold-mail traffic spreads across many IPs. Worker identity is a deterministic UUIDv5 derived from the VPS's public IPv4 — same IP, same worker.
Add a worker from the admin dashboard:
- Provision a VPS, note its public IP + root user
- Admin → Workers → Add Worker
- Copy the generated enrollment command
- Run it on the VPS as root
The installer is served by the backend at /worker-install.sh. It exchanges the one-time token for worker config, writes /etc/warmbly/worker.env, configures systemd, enables a daily randomized self-update timer, and starts the worker container. The worker then heartbeats back to the backend and marks itself installed.
The older SSH-managed path is still supported: paste the generated SSH public key into the VPS's ~/.ssh/authorized_keys, then click Test and Install. From then on, lifecycle operations (restart, update, system updates, reboot, rotate keys, logs, uninstall) can happen from the dashboard.
Manual install on the VPS is also supported:
curl -fsSL https://api.example.com/worker-install.sh | sudo bash -s -- \
--enroll wmenroll_... \
--api-base https://api.example.com
# or fully manual:
sudo bash scripts/install-worker.sh \
--kafka kafka.example.com:9092 \
--schema-registry https://schema.example.com \
--redis redis://cache.example.com:6379 \
--aws-region us-east-1 --aws-key ... --aws-secret ...
Why per-VPS instead of Kubernetes DaemonSet
Cold-mail reputation lives at the IP level. K8s nodes typically NAT pods through a small set of egress IPs, so a per-node DaemonSet does not deliver IP diversity. Workers don't depend on Postgres, so cluster-level service discovery isn't needed. Spreading across VPS providers and regions is the only thing that actually moves the deliverability needle.
Worker env reference
Workers in production should be assigned to a worker profile in the dashboard. The profile bundles all of these:
| Env var | Source | Notes |
|---|---|---|
APP_ENV |
profile | prod selects alias/master-key; otherwise alias/master-key-dev |
AWS_REGION |
profile (via AWS credentials row) | |
AWS_ACCESS_KEY_ID |
profile (via AWS credentials row) | |
AWS_SECRET_ACCESS_KEY |
profile (via AWS credentials row) | encrypted at rest |
KAFKA_BOOTSTRAP_SERVERS |
profile | |
KAFKA_SASL_USERNAME |
profile | |
KAFKA_SASL_PASSWORD |
profile | encrypted at rest |
SCHEMA_REGISTRY_URL |
profile | |
SCHEMA_REGISTRY_KEY |
profile | |
SCHEMA_REGISTRY_SECRET |
profile | encrypted at rest |
REDIS |
profile | full URL with embedded password; encrypted at rest |
WORKER_TIER |
(worker row) | shared or dedicated |
The worker does not open a Postgres connection. Do not add one.
Auto-update
Each worker profile picks a release channel (pinned / stable / dev) and an auto_update toggle. When a GitHub release fires the webhook, the backend resolves the channel and (if auto_update=true) rolls every assigned worker. See the deployment guide.
Health checks
curl http://localhost:8080/health # backend
curl http://localhost:3000/health # tracking
curl http://localhost:4000/health # realtime