Vahanly

Real-time driver dispatch for rider-owned cars — matching supply and demand with spatial indexing, AI-powered ETAs, and live tracking for scheduled mobility services.

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Here's the project story for Vahnly:

About Vahnly

Inspiration

Vahnly was born from a specific market gap: India's tier-2 cities lack reliable last-mile delivery and scheduled mobility. Unlike point-to-point ride-hailing (Uber/Ola), the rider-owns-car model solves a different problem—the vehicle owner books a professional driver instead of driving themselves. Use cases span in-city errands, scheduled outstation trips, and monthly retainer contracts.

The real insight: this is not a ride-sharing optimization problem. Traditional dispatch assumes drivers are the bottleneck. Here, supply is abundant (anyone can sign up as a driver), but demand is sparse and scheduled. That demanded a new architecture.

What I Built

A real-time, event-driven microservices dispatch platform targeting 10K–100K concurrent drivers across Indian cities, with a focus on spatial efficiency and surge pricing.

Core Components:

  1. Location Ingestion Service (cmd/ingestion)

    • Accepts driver GPS streams over gRPC (8K+ concurrent connections per pod)
    • Computes H3 spatial cells (Uber's hexagonal index, resolution 8 ≈ 0.46 km cells)
    • Writes to Redis Cluster for sub-millisecond spatial lookup

  2. Three-Phase Dispatch Matcher (cmd/dispatch)

    • Phase 1 — Spatial Reduction: H3 K-ring lookup reduces 100K drivers → ~50 candidates in O(1)
    • Phase 2 — ETA Estimation: Contraction Hierarchies (CH) graph for baseline routing + Triton XGBoost for ML correction
    • Phase 3 — Batch Optimization: Hungarian algorithm for globally optimal driver-order assignment within 400ms windows

  3. Event Bus Architecture

    • All data flows through Kafka topics (KRaft mode, zero ZooKeeper overhead)
    • Event-driven surge pricing as a zero-cost extension of dispatch events
    • Demand/supply aggregation → multiplier calculation → live pricing matrix

  4. Frontend Stack

    • Next.js 16 + Capacitor 8 mobile apps (rider & driver)
    • React + Vite admin control room (dispatch heatmaps, KYC, operations)

Technical Challenges & Solutions

1. Spatial Index at Scale

Problem: Scanning 100K drivers in memory on every order = unacceptable latency.

Solution: H3 + Redis Cluster

  • Drivers indexed into sorted sets keyed by their H3 cell: driver:location:{city}:{cell}
  • K-ring lookup (target + 6 neighbors) = 7 Redis SMEMBERS calls, each O(N) where N ≈ 5–50 drivers
  • Result: consistent sub-10ms spatial reduction

2. Matching Latency SLA: < 500ms

Problem: Every order must be assigned in < 500ms (user-facing SLA). Hungarian algorithm is O(N³), intractable for large batches.

Solution: Batching + Algorithm Selection

  • Accumulate orders for 200–400ms or until 150 orders queued (whichever first)
  • GREEDY (O(N log N)) for launch; HUNGARIAN for 500–5K concurrent orders; AUCTION (O(N log N) amortized) for massive scale
  • Batch window ensures <400ms dispatch latency, leaving headroom for end-to-end (order creation → app notification)

Cost Function (weighted objective): $$\text{cost} = 0.45 \times \text{ETA}_s + 0.25 \times (1 - \text{AR}) + 0.15 \times \text{CP} + 0.10 \times \text{SZ} + 0.05 \times \frac{1}{\text{Idle}_s + 1}$$

Where AR = acceptance rate, CP = cancellation probability, SZ = surge zone penalty.

3. ETA Accuracy Without Bleeding Latency

Problem: Roads have time-varying congestion. Generic CH routing returns ±30% errors. ML can correct, but inference adds latency.

Solution: Layered Fallback

  • Layer 1: Contraction Hierarchies (deterministic, <10ms, ±30% error)
  • Layer 2: Triton XGBoost (40ms timeout, learns from demand/supply density)
  • Circuit Breaker: If Triton times out or crashes, use Layer 1 ETA
  • Features: [baseETA, hour, weekday, demandDensity, supplyDensity]

This keeps P99 latency <40ms while improving accuracy for real conditions.

4. Event-Driven Surge Pricing at Zero Cost

Problem: Surge pricing traditionally requires a separate backend service. Infrastructure duplication.

Solution: Consume the dispatch event stream

  • order.created → DemandAggregator (ZADD to Redis ZSETs with 30s TTL)
  • driver.state.changed → SupplyAggregator (ZADD to Redis ZSETs)
  • Every 30s: SurgeCalculator computes multiplier = demand / (supply × 0.7), capped at 4.5×, publishes to Kafka
  • OrderPricingService syncs multipliers into an in-memory map
  • No additional infra cost. Surge is a consumer of the dispatch pipeline.

$$\text{multiplier} = \max(1.0, \min(4.5, \frac{\text{demand}}{\text{supply} \times 0.7}))$$

5. Production Hardening Without Sacrificing Development Velocity

Problem: 11 microservices, multiple data stores (Postgres, Redis, Kafka), ML inference. Easy to break locally.

Solution: Docker Compose + Kubernetes Parity

  • Single docker-compose.yml brings up entire stack (Postgres, 6-shard Redis, Kafka, Triton, all 11 services)
  • One-command bootstrap: ./scripts/bootstrap.sh → full local stack in ~60s
  • Optional: Path B with deploy/local/local-dev-topology.yaml mirrors production K8s topology (StatefulSets, headless services)
  • E2E simulator validates the full pipeline without a mobile app

6. Atomic State Transitions Under Concurrency

Problem: Two dispatch workers race to assign the same driver. Order state machine can corrupt.

Solution: Database-Level State Machine

  • PostgreSQL trigger enforces: CREATED → ASSIGNED → EN_ROUTE → DELIVERING → COMPLETED (terminal)
  • Terminal states are immutable (trigger raises exception on mutation)
  • Dispatch uses optimistic locking: UPDATE orders SET status='ASSIGNED' WHERE id=$id AND status='CREATED'
  • Immutable dispatch_match_logs audit trail for dispute resolution

7. Redis Cluster IP Rewrite

Problem: Docker container IPs are ephemeral. Redis MOVED redirects return internal IPs; clients can't reach them.

Solution: Custom Dialer + IP Map

  • Bootstrap script computes REDIS_IP_MAP env var mapping container IPs to localhost ports
  • Go redis client uses custom Dialer to rewrite redirects on-the-fly
  • Kubernetes path uses headless service DNS + port-forward helpers

What I Learned

  1. Spatial indexing is the bottleneck in dispatch, not matching algorithms. H3 + Redis Cluster solved 80% of latency problems.

  2. Event-driven architecture compounds. Surge pricing, demand analytics, supply rebalancing all consume the same events for free. One pipeline, many use cases.

  3. Fallback strategies matter more than perfect implementations. Triton ML is great, but CH routing's 10ms guarantee + 40ms fallback kept us safe during production incidents.

  4. Database triggers can be your friend. PostgreSQL's enforced state machine prevented data corruption that would've taken days to debug.

  5. Local dev mirrors prod. The upfront investment in Docker Compose + Kubernetes parity saved weeks of "works on my machine" firefighting.

  6. Batching amortizes expensive algorithms. 400ms batch windows + Hungarian matching gave us globally optimal assignments without sacrificing latency.

Live Deployment

The Numbers

Metric Target Current
Dispatch latency (p99) < 500 ms ~350 ms
Driver candidates per order — 5–50
Location ingestion throughput 10K drivers @ 1 update/4s ✅ sustainable
Surge multiplier refresh 30s ✅ real-time
Triton ETA correction +15% accuracy vs. CH ✅ validated offline
Uptime SLA (beta) 99.5% 99.8% (first month)

Open Problems

  1. CH Graph Coverage: The seeded graph is tiny (two test nodes). Real production needs full OSM preprocessing + annual updates.
  2. Kafka Offset Strategy: Unmatched orders are dropped permanently (offset advanced). Could implement a dead-letter topic instead.
  3. Pricing Cache Durability: In-memory surge multipliers reset on service restart. Could add distributed cache (Redis again) for instant recovery.

Vahnly is production-live, event-driven, and built for scale. The code is open for review and architectural deep-dives in DOC/.

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