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Phase 6: Valkey availability mirror — move read path off Postgres

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Mitra-availability state (online flag, deactivated flag, per-mitra session
count, heartbeat liveness) mirrored into Valkey so the customer beacon
+ pairing blast + dashboard counts no longer hit Postgres on the hot path.
Postgres remains the durable source of truth; Valkey state is fully
derivable via seedFromPostgres on startup + reconnect.

Schema
- mitras:online           SET    — mirror of is_online
- mitras:deactivated      SET    — mirror of is_active=false
- mitra:capacity:<id>     STRING — active+pending_payment session count
- mitra💓<id>    STRING — ISO timestamp of last ping
- availability:snapshot   JSON   — beacon cache, TTL 10s, cluster-shared

Write paths (Postgres first, best-effort Valkey)
- setOnline/setOffline mirror SADD/SREM + heartbeat SET/DEL
- updateMitraStatus mirrors mitras:deactivated AND revokes auth_sessions
  on deactivate (bounds the "ghost online" window to access-token TTL)
- heartbeat is Valkey-only on the hot path; the per-ping Postgres UPDATE
  on last_heartbeat_at is eliminated (was 1,200 ops/min at prod scale)
- chat_session lifecycle (accept/end/reroute/extension/expiry) calls
  recomputeCapacityForMitra after each UPDATE — derive-from-truth avoids
  the bookkeeping risk of per-transition INCR/DECR

Read paths (Valkey-first, Postgres fallback on Valkey error)
- isMitraReachable: SISMEMBER mitras:online + heartbeat freshness
- findAvailableMitras: SDIFF + pipelined GETs, filter by capacity + heartbeat
- countAvailableMitrasFromCache: Valkey-driven, cached cluster-wide 10s TTL
- dashboard online count: SCARD
- Each reader wraps Valkey ops in try/catch → Postgres fallback on outage

Heartbeat path on /api/mitra/status/heartbeat
- resolveMitra preHandler replaced with heartbeatGuard: SISMEMBER on
  mitras:deactivated (~0 DB hits per ping). Falls back to full DB
  resolveMitra if Valkey is unreachable so a Valkey outage doesn't
  silently accept heartbeats from deactivated mitras.

Three sweeps, env-configurable cadences
- MITRA_AUTO_OFFLINE_SWEEP_SECONDS (30) — Valkey-driven stale detection
- HEARTBEAT_MIRROR_INTERVAL_SECONDS (60) — batched UPSERT writes
  Valkey timestamps to Postgres last_heartbeat_at via UNNEST (1 statement
  per cycle, idempotent across instances)
- VALKEY_ONLINE_MIRROR_SWEEP_SECONDS (300) — periodic reseed heals drift

Startup
- restoreActiveTimers → seedFromPostgres → bind listeners
- onValkeyReady re-runs the seed on every reconnect (cold start + reseed
  on Valkey restart, no manual intervention)

Failure semantics
- Read fallback: every Valkey read wrapped, falls back to existing
  Postgres JOIN query — system stays correct during Valkey outage,
  performance degrades not breaks
- Write best-effort: Postgres write commits before Valkey is touched;
  Valkey errors log + continue; reconciliation sweep heals drift
- Auto-offline sweep aborts entirely on Valkey error (does NOT mass-
  offline via Postgres scan during Valkey hiccup)

Tests
- New: 32 integration tests in mitra-status.valkey-mirror.test.js
  covering seed, write-through, fallbacks, capacity lifecycle,
  auto-offline sweep, heartbeat mirror, deactivation flow, beacon cache
- Updated: fixtures.js seeds Valkey alongside Postgres when isOnline=true
- Updated: helpers/db.js resetDb also flushes test Valkey
- Fixed 2 pre-existing session-timer flakes (string IDs failed uuid
  parse; vi.advanceTimersByTimeAsync raced real Postgres I/O)
- All 124/124 backend tests pass (was 90/92)

Docs
- requirement/valkey-online-mirror-plan.md — canonical plan
- requirement/valkey-online-mirror-testing.md — manual E2E checklist
- requirement/deployment.md — infra + Valkey persistence guidance for
  prod (Memorystore Standard tier recommended; migration from
  self-hosted Valkey is zero-downtime via reseed-from-Postgres)

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
Ramadhan Sjamsani
2026-05-25 18:07:55 +08:00
parent 3fff4b1c6e
commit 553dbac52f
20 changed files with 1839 additions and 82 deletions
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# 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.