Executive Summary -Event-driven ingestion (Go+NATS) feeding TimescaleDB hypertables; Svelte web dashboard + offline-first mobile via CRDT-based sync queue. -Stack fits: Go handles high-throughput ping ingestion, TimescaleDB natively suits time-series GPS data, Svelte keeps bundle small for low-bandwidth driver areas. -Top risks: 50k pings/min ingestion bottleneck, offline sync conflict resolution, route optimization latency under load. System Architecture -Components: Ingestion service, Route Optimizer service, Sync service, Dashboard API, Notification worker. -Frontend: Svelte dashboard (dispatcher view) + separate mobile app (Capacitor/Svelte) with local SQLite queue. -Backend: Go microservices behind API Gateway; NATS JetStream for ping buffering/backpressure. -DB: TimescaleDB hypertable location_pings(driver_id,ts,lat,lng,geom), relational tables for routes/drivers/orders. -Auth: JWT (short-lived) + refresh tokens; role-based (dispatcher/driver/admin). -Infra: DigitalOcean Kubernetes (DOKS), managed TimescaleDB, Spaces for static assets, Load Balancer. Feature Breakdown & Prioritization Feature MoSCoW Dependency Live GPS ingestion Must Ingestion service Driver mobile app + offline sync Must Local SQLite, Sync API Route optimization engine Must Ping data, OSRM/VROOM Dispatcher dashboard Must Auth, ingestion Push notifications Should Notification worker Historical analytics reports Could Hypertable rollups Phased Implementation Roadmap 1-Foundation (Wks 1-3): auth, DB schema, ingestion pipeline skeleton — deliverable: pings flowing end-to-end at scale. 2-Core Features (Wks 4-7): route optimizer, dispatcher dashboard, mobile offline sync — deliverable: functional MVP loop. 3-Hardening (Wks 8-10): load testing, security pass, notifications, deployment — deliverable: production-ready launch. Data & API Contracts -POST /pings (batched array, driver_id, ts, lat, lng) → 202 Accepted. -GET /routes/:driverId/optimized → ordered stop list + ETA. -POST /sync/queue (idempotent, client-generated UUID) → conflict-resolved ack. -Model: Route{id, driver_id, stops[], status, optimized_at}. Security & Compliance -JWT+mTLS between services; TLS everywhere; GDPR-style data retention for location history. -Top risks: ping spoofing (HMAC-signed device payloads), JWT replay (short TTL+rotation), SQL injection (parameterized queries only). Testing Strategy -Unit+integration tests per service (Go testing, Playwright for Svelte); load-test ingestion at 2x target (100k/min) before launch. DevOps & Deployment -CI/CD via GitHub Actions → DOKS; blue-green deploys for backend, canary for mobile OTA updates. -IaC with Terraform; centralized logging/metrics (Grafana+Loki+Prometheus). Scalability & Performance -Horizontal-scale ingestion pods behind NATS consumer groups; TimescaleDB continuous aggregates for dashboard queries. -Cache hot routes/driver status in Redis; partition hypertables by time+region. Team & Resource Allocation -2 backend (Go/ingestion+optimizer), 1 frontend (Svelte dashboard), 1 mobile (offline sync), 1 DevOps/QA hybrid. -Weekly sprint reviews; optimizer dev pairs with backend on ingestion contract early. Risk Register & Mitigation Risk Impact Mitigation Ingestion overload High NATS backpressure, autoscale pods Sync conflicts Med Last-write-wins + vector clocks Optimizer latency Med Precompute + async recompute Timeline slip High Cut Should/Could features first Post-Launch Considerations -Monitor ping loss rate and sync conflict frequency; iterate optimizer with real driver feedback loops.