Create metrics.md

Author: Trojan3877

metrics.md

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+# 📊 System Metrics & Performance Characteristics  
+### Scalable Event-Driven Ride-Sharing Platform  
+### Author: Corey Leath
+
+This document summarizes the engineering metrics, performance characteristics,
+scalability targets, and reliability expectations of the distributed
+ride-sharing platform. These metrics mirror real-world targets used by
+companies such as Uber, Lyft, and DoorDash.
+
+---
+
+# 🚦 1. Surge Pricing Engine Metrics
+
+| Metric | Target | Notes |
+|--------|--------|-------|
+| Surge computation latency | **< 10 ms** | Pydantic validation + pure Python math operations. |
+| Event→surge propagation | **< 50 ms** | From incoming supply/demand event to API-visible multiplier. |
+| Cache update frequency | **1–2 updates/sec per zone** | Realistic for city-scale rider load. |
+| Supported zones | **50–200 active zones** | Expandable with sharding. |
+
+---
+
+# 📡 2. Event Bus Throughput
+
+| Metric | Target | Notes |
+|--------|--------|-------|
+| Events processed per second | **300–1,000 msg/sec** | AsyncIO concurrency + in-memory routing. |
+| Producer publish latency | **< 5 ms** | No network overhead for dev environment. |
+| Subscriber fan-out time | **< 20 ms** | Concurrent tasks via asyncio.gather. |
+
+---
+
+# 🧠 3. Dispatch & Matching Engine Metrics
+
+| Metric | Target | Notes |
+|--------|--------|-------|
+| Driver match latency | **< 100 ms** | Includes distance estimate + surge pricing lookup. |
+| ETA estimation time | **< 30 ms** | Lightweight heuristic without ML. |
+| Match throughput | **50–200 matches/sec** | Scales horizontally with worker count. |
+| Driver selection depth | **10–50 candidates** | Configurable ranking window. |
+
+---
+
+# 📍 4. Driver Location Service Metrics
+
+| Metric | Target | Notes |
+|--------|--------|-------|
+| Location update rate | **1 update/sec per driver** | Matches real-world telematics freq. |
+| Max active drivers | **1,000–5,000** | In-memory storage for dev. |
+| Query latency (driver lookup) | **< 10 ms** | Dict-based lookup + zone calculation. |
+
+---
+
+# 🧾 5. Trip Management Metrics
+
+| Metric | Target | Notes |
+|--------|--------|-------|
+| Trip creation time | **< 25 ms** | Includes ID generation + initial store. |
+| Trip completion processing | **< 40 ms** | Emits PaymentEvent. |
+| Trip lookup time | **< 5 ms** | In-memory store for dev. |
+
+---
+
+# ⚖️ 6. System Scalability Goals
+
+| Dimension | Target |
+|-----------|--------|
+| Horizontal scaling | **Unlimited** with stateless microservices |
+| Load shedding | Supported via event queue backpressure |
+| Service instances | **1–50 replicas** depending on load |
+| Event throughput scaling | Optimized by partitioning topics |
+
+---
+
+# 🔒 7. Reliability & Fault Tolerance
+
+| Guarantee | Value |
+|-----------|--------|
+| API uptime target | **99.9%** |
+| Event loss tolerance | **Zero for core topics** (in production with Kafka) |
+| Graceful failover | Yes — independent microservices |
+| Circuit-breaker style isolation | Achieved through EventBus decoupling |
+
+---
+
+# 🧪 8. Testing & CI Metrics
+
+| Category | Status |
+|----------|--------|
+| Static typing | Pydantic model enforcement |
+| Unit tests | Architecture supports pytest |
+| Integration tests | Enabled through EventBus simulation |
+| Load testing | Achieved via PricingProducer event loops |
+
+---
+
+# 🚀 9. Future Metric Enhancements
+
+- Replace EventBus with **Kafka or Redis Streams** for durability  
+- Add **Prometheus + Grafana dashboards**  
+- Add **percentile-based latency tracking (P95, P99)**  
+- Add **driver heatmaps using H3 geospatial indexing**  
+- Add **ML-based ETA prediction**  
+
+---
+
+This metrics document is designed to match Big Tech expectations for
+system-design clarity, performance transparency, and operational readiness.