Code-Memo

✅ STEP 5: High-Level Architecture — Uber-like Ride Sharing System

🔧 1. Core Architectural Principles

Principle Purpose
Microservices Decouple services (auth, trip, pricing, location, etc.)
Pub/Sub (Kafka) Decouple high-volume real-time streams (e.g. GPS updates)
Low Latency Location-sensitive services, geo-sharding
Strong Consistency Required for payments, trip state
Eventual Consistency Used where acceptable (e.g. map history)
Horizontal Scalability Key for location tracking, trip processing

🧩 2. System Component Overview

We’ll categorize the system into:

📦 3. Component Breakdown

✅ 3.1. Client Layer

Client App Description
Rider App User interface to book, pay, track rides
Driver App Interface for trip requests, navigation, earnings
Web Admin Panel For support, driver ops, fraud monitoring

✅ 3.2. Edge/API Gateway

✅ 3.3. Core Backend Microservices

Service Description
Auth Service Sign-up/login with phone/email/social
User Profile Service Manages rider/driver data
Trip Service Manages trip lifecycle: request → assign → start → end
Driver Service Handles driver state (available, busy, offline)
Matching Service Matches rider with best driver (within X km)
Pricing Service Computes trip fare, surge pricing
Payment Service Payment gateway integration, wallet, receipts
Rating Service Stores and analyzes ratings & feedback
Notifications SMS, push, email alerts
Ride History Trip logs for users, analytics, export
Fraud Service Detects fake accounts, collusion, abuse

✅ 3.4. Real-Time Services

Component Purpose
Location Service Ingests and stores real-time GPS updates
Geo Index Spatial index (Uber’s H3, or Quadtrees in Redis)
Live Trip Tracker Stream GPS, detect delays/deviations
Kafka / Pulsar Streams GPS and trip events
WebSocket Gateway Bi-directional comms for live tracking

✅ 3.5. Geo Services

Component Description
Map Service Uses Google Maps, OpenStreetMap, or own tiles
Route Engine Computes ETA, optimal route
Surge Engine Calculates real-time demand/supply for surge zones
Geo-Fencing Region restrictions, dynamic pricing boundaries

✅ 3.6. Infra & Storage

Data Type Tech Stack
User Data PostgreSQL / MySQL (sharded by user ID)
Trips Cassandra / DynamoDB for high write throughput
Locations Kafka stream → Redis (for current location) → S3/archive (history)
Geo-Index Redis + H3/Quadtree or Google S2
Payments Relational DB + integration with Stripe, PayPal, etc.
Caches Redis (driver lookup, surge zones, fare estimates)
Blob Storage S3 for receipts, profile images, documents
Analytics Snowflake / BigQuery / ClickHouse

🧪 4. Data Flow: Trip Request

1. Rider hits "Request Ride"
2. Trip Service creates a trip (state: PENDING)
3. Matching Service queries nearby drivers (Geo index in Redis)
4. Push trip offer to selected driver via WebSocket
5. Driver accepts: Trip state changes → ASSIGNED
6. Real-time updates flow via Kafka:
   - Driver location, trip events, status changes
7. On trip end:
   - Trip saved to DB
   - Pricing calculated
   - Payment processed
   - Rating triggered

🌍 5. Geo-Sharded Deployment Strategy

Strategy Use Case
Geo-Sharding Split users/drivers based on geohash prefix
Region-Based Clusters Run separate clusters per continent
Global Kafka Cluster Kafka clusters per region (async replication)
Redis Partitioning Driver location indexed by geohash key ranges
Failover DNS Geo DNS routes to closest region with fallback

🔐 6. Security Components

Layer Tooling
Authentication JWT tokens, device fingerprinting
Rate Limiting API Gateway or Redis token buckets
Encryption TLS for all comms, AES for stored data
KYC Verification Third-party services for driver documents
Fraud Detection Heuristic + ML-based anomaly detection

📊 Text-Based System Diagram (Highly Expanded)

+-------------------+             +------------------+
|  Rider App (iOS)  | <---------> |   API Gateway    |
+-------------------+             +--------+---------+
                                           |
                                           v
                           +-------------------------------+
                           |       Core Backend Services   |
                           |-------------------------------|
                           | Auth Service                  |
                           | Trip Service                  |
                           | Matching Service              |
                           | Pricing Service               |
                           | Driver Management             |
                           | Rating Service                |
                           +-------------------------------+
                                           |
                                           v
               +--------------------------------------------------+
               |          Real-Time & Event Processing            |
               |--------------------------------------------------|
               | Kafka (Trip Events, GPS)                         |
               | Location Updater Service                         |
               | WebSocket Gateway (push to riders & drivers)     |
               | Stream Processor (e.g. Flink/Spark Streaming)    |
               +--------------------------------------------------+
                                           |
                                           v
        +------------------+   +-------------------+   +------------------+
        |     Redis        |   |     Cassandra     |   |     Postgres     |
        | (driver geohash) |   | (trip events)     |   | (user/payment)   |
        +------------------+   +-------------------+   +------------------+
                                           |
                                           v
                        +-------------------------------+
                        |     External Integrations      |
                        |-------------------------------|
                        | Payment Gateway (Stripe, etc) |
                        | SMS / Email Notification      |
                        | Map Routing API               |
                        | KYC Services                  |
                        +-------------------------------+

🧠 Additional Notes

🔄 Retry & Idempotency

📉 Downsampling GPS

🌐 Offline/Low Connectivity