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


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)


3. Tech Stack Justification

ComponentTechnologyRationale
Geospatial QueriesGoogle S2 Geometry + GoS2’s hierarchical geohashing outperforms PostGIS at Uber-scale workloads
Real-Time EventsPub/Sub + DataflowExactly-once processing for ride-state changes (no duplicates/lost messages)
PaymentsJava + Cloud KMSJVM’s thread safety + KMS for PCI DSS compliance
CachingMemorystore (Redis)Sub-millisecond latency for fare estimates
ObservabilityOpenTelemetry + GCP LogsUnified 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

RiskMitigationFallback
Geospatial query latencyS2 indexing + in-memory cachingFallback to grid-based approximation
Payment failuresIdempotent API + 72-hour retry queueManual review via Stripe Dashboard
Driver app offlineLocal SQLite cache + eventual syncSMS-based ride confirmations

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


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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