1. Executive Summary
Objective: Design a globally scalable ride-sharing platform (Uber/Lyft competitor) supporting:
1M+ concurrent rides
<100ms ETAs during peak load
99.99% availability
Real-time driver/rider matching
Key Challenges:
Geospatial queries at scale
Surge pricing computations
Fraud detection
Multi-region resilience
2. High-Level Architecture
![Architecture Diagram: Microservices + Event-Driven + CQRS]
Core Components:
Frontend:
React Native (iOS/Android) with offline-first PWA fallback
Google Maps SDK (optimized route rendering)
API Layer:
Envoy Proxy (L7 load balancing)
GraphQL Apollo Federation (aggregates microservices)
Backend Services:
Driver/Rider Matching: Go (low-latency geospatial queries)
Pricing Engine: Python (ML-based surge pricing)
Payments: Java (PCI-compliant with Stripe/PayPal)
Data Layer:
Spanner (globally distributed ACID transactions)
Bigtable (real-time driver locations)
Pub/Sub (event bus for ride updates)
Analytics:
BigQuery (historical ride analysis)
Vertex AI (predictive ETAs)
3. Tech Stack Justification
Component | Technology | Rationale |
---|---|---|
Geospatial Queries | Google S2 Geometry + Go | S2’s hierarchical geohashing outperforms PostGIS at Uber-scale workloads |
Real-Time Events | Pub/Sub + Dataflow | Exactly-once processing for ride-state changes (no duplicates/lost messages) |
Payments | Java + Cloud KMS | JVM’s thread safety + KMS for PCI DSS compliance |
Caching | Memorystore (Redis) | Sub-millisecond latency for fare estimates |
Observability | OpenTelemetry + GCP Logs | Unified tracing across 50+ microservices |
4. Scaling Strategies
Hotspot Mitigation
Sharding: Drivers partitioned by S2 cell (e.g.,
s2cell_id=892e71
)Backpressure: Rate limiting in Envoy (gRPC max concurrent streams)
Multi-Region Resilience
Data: Spanner multi-region writes with 99.999% SLA
Compute: GKE Autopilot pods distributed across 3+ zones
Cost Optimization
Spot VMs: Stateless services (e.g., ETA predictions)
Cold Storage: Infrequent ride archives → Cloud Storage Nearline
5. Risk Analysis
Risk | Mitigation | Fallback |
---|---|---|
Geospatial query latency | S2 indexing + in-memory caching | Fallback to grid-based approximation |
Payment failures | Idempotent API + 72-hour retry queue | Manual review via Stripe Dashboard |
Driver app offline | Local SQLite cache + eventual sync | SMS-based ride confirmations |
6. Phase 1 Milestones
Q1: Core matching engine (MVP in 1 city)
Q2: Surge pricing + multi-region Spanner
Q3: Fraud detection with TensorFlow
7. Ask from Stakeholders
Budget Approval: $2.8M/year for GCP resources
Headcount: 3 SREs for reliability engineering
Partnerships: Google Maps API rate-limit exemptions
Final Note: This architecture leverages Google’s core competencies in distributed systems (Spanner, Borg) while avoiding vendor lock-in via OpenTelemetry and GraphQL standards.
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