Scalable Event-Driven Ride-Sharing Platform: Architecture Proposal

 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

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2. High-Level Architecture

![Architecture Diagram: Microservices + Event-Driven + CQRS]

Core Components:

1. Frontend:

   • React Native (iOS/Android) with offline-first PWA fallback

   • Google Maps SDK (optimized route rendering)

2. API Layer:

   • Envoy Proxy (L7 load balancing)

   • GraphQL Apollo Federation (aggregates microservices)

3. Backend Services:

   • Driver/Rider Matching: Go (low-latency geospatial queries)

   • Pricing Engine: Python (ML-based surge pricing)

   • Payments: Java (PCI-compliant with Stripe/PayPal)

4. Data Layer:

   • Spanner (globally distributed ACID transactions)

   • Bigtable (real-time driver locations)

   • Pub/Sub (event bus for ride updates)

5. Analytics:

   • BigQuery (historical ride analysis)

   • Vertex AI (predictive ETAs)

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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

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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

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6. Phase 1 Milestones

1. Q1: Core matching engine (MVP in 1 city)

2. Q2: Surge pricing + multi-region Spanner

3. Q3: Fraud detection with TensorFlow

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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

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Final Note: This architecture leverages Google’s core competencies in distributed systems (Spanner, Borg) while avoiding vendor lock-in via OpenTelemetry and GraphQL standards.