# Deployment notes

Operational decisions and dependency configuration for staging/production. Keep this updated as we make infra choices; cross-link from feature plans when a deploy-time setting matters.

## Infrastructure summary

| Component | Service | Tier / Notes |
|---|---|---|
| Backend (public + internal) | GCP Cloud Run | Horizontal scaling; SIGTERM trapped for graceful drain ([server.js](../backend/src/server.js)) |
| Database | GCP Cloud SQL (PostgreSQL) | Source of truth for all durable state |
| Pub/sub + cache | Valkey | Self-hosted on VM today; Memorystore Standard (HA) recommended for prod (see [§ Valkey](#valkey)) |
| Networking | GCP VPC | Internal listener (port 3001) never exposed; CC reaches it via VPN |
| Payment | Xendit | See [phase5-xendit-plan.md](phase5-xendit-plan.md) for keys / webhook URL setup |
| Auth | Self-managed JWT + FCM-only Firebase | See [backend/CLAUDE.md](../backend/CLAUDE.md) |

## Valkey

Valkey is used for two distinct purposes:

1. **Pub/sub** — cross-instance event fan-out (chat messages, session lifecycle, config invalidation). See [backend/src/plugins/valkey.js](../backend/src/plugins/valkey.js).
2. **Availability mirror** — `mitras:online`, `mitras:deactivated`, `mitra:capacity:<id>`, `mitra:heartbeat:<id>`, and `availability:snapshot` per [valkey-online-mirror-plan.md](valkey-online-mirror-plan.md). Postgres remains the durable source of truth; Valkey is the hot read path.

### Persistence — required or optional?

**Not required.** All durable state lives in Postgres; Valkey is a cache + ephemeral liveness layer that fully rebuilds via `seedFromPostgres()` on backend reconnect.

What's actually in Valkey, and what happens if it's wiped:

| Key | Derivable from Postgres? | Cost of loss |
|---|---|---|
| `mitras:online` | yes | reseeded on reconnect |
| `mitras:deactivated` | yes | reseeded on reconnect |
| `mitra:capacity:<id>` | yes (`COUNT(*) FROM chat_sessions`) | reseeded on reconnect |
| `mitra:heartbeat:<id>` | **no** — pure transient liveness | seed writes `NOW`; ≤ a few seconds of fuzz on `last_heartbeat_at` forensics |
| `availability:snapshot` | recomputable | next beacon poll repopulates |

Reader code in services/* has explicit Postgres fallbacks for every Valkey op, so the cold-cache window during a restart degrades performance, not correctness.

### Persistence recommendation by environment

| Environment | Setting | Reason |
|---|---|---|
| **Dev / local** | No persistence (`--save "" --appendonly no` or just default) | Restarts wipe state; reseed handles it cleanly; zero disk overhead |
| **Staging** | AOF on (`--appendonly yes`) | Verifies prod-like behavior; tiny disk cost |
| **Production** | AOF on, optionally RDB too (`--appendonly yes --save 60 1000`) | Eliminates cold-cache window after restart; trivial disk footprint (few MB) |

The application code is identical across all three — persistence is a deploy-time knob, not a code-level concern.

### Self-hosted Valkey (current state, dev/staging)

Docker container on the existing VM. Reference config:

```yaml
valkey:
  image: valkey/valkey:7-alpine
  command: valkey-server --appendonly yes --save 60 1000
  volumes:
    - valkey-data:/data
  ports:
    - "6379:6379"
  restart: unless-stopped
```

Backend reaches it via `VALKEY_URL=redis://<vm-ip>:6379` in `backend/.env` (or Cloud Run env var).

### Memorystore migration (when going to prod)

The reseed-from-Postgres flow makes migration trivial — Valkey state is never load-bearing:

1. Provision **Memorystore for Valkey, Standard tier** (HA with replica) in the same VPC + region as Cloud Run.
   - Smallest available size (~1 GB) is plenty; actual data footprint is well under 1 MB.
   - Cost: ~$50/month at minimum sizing in asia-southeast2.
2. Update Cloud Run env: `VALKEY_URL=redis://<memorystore-internal-ip>:6379`.
3. Deploy new revision. Cloud Run rolling deploy → new instances seed Memorystore from Postgres; old instances drain on old Valkey.
4. Shut down old Valkey once traffic has migrated.

**Zero downtime.** No data migration needed (state is derivable). The cold-cache window on new instances is handled by the existing Postgres-fallback reader paths.

### Tier choice rationale

| Tier | When to use | Failover behavior |
|---|---|---|
| Self-hosted Docker | Dev, staging | Manual restart; backend reseeds when Valkey comes back |
| Memorystore Basic | Cost-sensitive single-AZ staging | ~1–5 min outage per maintenance event; backend handles via Postgres fallback |
| Memorystore Standard (HA) | **Production** | ~30s automatic failover; replica keeps data live |

The system is correct on any tier — HA reduces customer-visible latency spikes during Valkey events from minutes to seconds.

## Cloud Run

(Placeholder — fill in as we make decisions about region, min/max instances, concurrency, secrets manager wiring.)

## Cloud SQL

(Placeholder — pool size, machine type, HA flag, backup retention.)

## Xendit

See [phase5-xendit-plan.md](phase5-xendit-plan.md) for credential setup and webhook URL configuration. Stage 8 (live E2E) is currently blocked on test-mode keys.

## Open ops decisions

- [ ] Confirm Memorystore Standard tier for prod deploy (recommended in [§ Valkey](#valkey)).
- [ ] Pin GCP region for backend + Cloud SQL + Memorystore (all must match for sub-ms internal latency).
- [ ] Secrets manager (GCP Secret Manager vs Cloud Run env vars) for `AUTH_JWT_SECRET`, `XENDIT_SECRET_KEY`, etc.
- [ ] Backup retention policy for Cloud SQL.
- [ ] CI/CD pipeline for Cloud Run deploys.
