Design a Ride-Sharing Service like Uber - System Design Interview

Introduction

Uber is a ride-sharing platform that connects riders with nearby drivers in real-time. The system handles millions of rides daily, processes real-time location updates, matches riders with drivers efficiently, implements dynamic pricing, and processes payments seamlessly.

This post provides a detailed walkthrough of designing a ride-sharing service like Uber, covering key architectural decisions, real-time matching algorithms, geospatial data structures, dynamic pricing models, payment processing, and scalability challenges. This is one of the most common system design interview questions that tests your understanding of real-time systems, geospatial queries, microservices architecture, and handling complex business logic at scale.

Problem Statement
Requirements
- Functional Requirements
- Non-Functional Requirements
Capacity Estimation
Core Entities
API
Data Flow
Database Design
- Schema Design
- Database Sharding Strategy
High-Level Design
Deep Dive
Summary

Problem Statement

Design a ride-sharing service like Uber with the following features:

Rider registration and authentication
Driver registration, verification, and onboarding
Real-time driver-rider matching
Real-time location tracking (drivers and riders)
Dynamic pricing (surge pricing)
Ride booking and cancellation
Trip tracking and navigation
Payment processing
Rating and review system
Driver earnings and payouts
Ride history
Multi-city support

Scale Requirements:

100 million+ users (riders and drivers)
20 million+ daily active users
15 million+ rides per day
Peak: 1 million concurrent rides
500K+ active drivers
Real-time location updates every 4 seconds
Average ride: 20 minutes, 5 miles
Global presence: 10,000+ cities

Requirements

Functional Requirements

Core Features:

User Management:
- Rider registration and authentication
- Driver registration, background checks, vehicle verification
- Profile management
Ride Booking:
- Request a ride with pickup and dropoff locations
- Select ride type (UberX, UberXL, UberBlack, etc.)
- See estimated fare and wait time
- Cancel ride (with cancellation fees if applicable)
Driver-Rider Matching:
- Match rider with nearest available driver
- Consider driver availability, location, rating, vehicle type
- Handle multiple concurrent ride requests
- Reassign driver if needed
Real-Time Location Tracking:
- Track driver location in real-time
- Track rider location (for pickup)
- Show ETA (Estimated Time of Arrival)
- Update location every 4 seconds
Dynamic Pricing:
- Base fare calculation
- Surge pricing based on demand/supply ratio
- Time and distance-based pricing
- Show price estimate before booking
Trip Management:
- Start trip when driver arrives
- Track trip progress
- End trip at destination
- Handle trip cancellation
Payment Processing:
- Multiple payment methods (credit card, PayPal, etc.)
- Automatic payment after trip completion
- Split fare (multiple riders)
- Refund processing
Rating and Reviews:
- Rate driver after trip
- Rate rider (driver perspective)
- Review and feedback
Notifications:
- Push notifications for ride status
- SMS notifications
- Email receipts
Driver Features:
- Driver earnings dashboard
- Payout processing
- Driver availability toggle
- Accept/reject ride requests

Out of Scope:

Food delivery (UberEats)
Freight (Uber Freight)
Advanced route optimization
Multi-stop trips
Scheduled rides
Carpool/ridesharing between riders

Non-Functional Requirements

Availability: 99.9% uptime
Reliability: No ride loss, guaranteed matching
Performance:
- Ride matching: < 5 seconds
- Location update latency: < 1 second
- Payment processing: < 2 seconds
- API response time: < 200ms (p95)
Scalability: Handle 1 million concurrent rides
Consistency: Strong consistency for payments, eventual consistency for ratings
Durability: All ride data must be persisted
Real-Time: Location updates every 4 seconds
Geographic Distribution: Global presence with regional data centers
Security: Secure payment processing, user data protection, driver verification

Capacity Estimation

Traffic Estimates

Assumptions:

100 million total users
20 million daily active users (DAU)
15 million rides per day
Peak traffic: 3x average (45 million rides/day)
Average ride duration: 20 minutes

Read Traffic:

Location updates: 4 seconds per update
- Active drivers: 500K
- Active riders (during trip): 1M (peak)
- Location updates per second: (500K + 1M) / 4 = 375K updates/sec
API calls per ride: ~50 calls (booking, matching, tracking, payment)
Total API calls per day: 15M rides × 50 = 750M calls/day
Average QPS: 750M / 86400 = ~8,700 QPS
Peak QPS: 8,700 × 3 = ~26,000 QPS

Write Traffic:

Ride creation: 15M rides/day = ~174 rides/sec
Location updates: 375K updates/sec
Payment transactions: 15M/day = ~174 transactions/sec
Total writes: ~375K writes/sec (dominated by location updates)

Storage Estimates

Assumptions:

User data: 1KB per user
Ride data: 5KB per ride
Location data: 100 bytes per update
Location updates per ride: 20 min × 60 sec / 4 sec = 300 updates
Store location history for 30 days
Store ride history indefinitely

Storage Calculations:

User data: 100M users × 1KB = 100GB
Ride data: 15M rides/day × 365 days × 5KB = ~27TB/year
Location data: 15M rides/day × 300 updates × 100 bytes × 30 days = ~1.35TB
Total storage: ~100GB + 27TB + 1.35TB = ~28.5TB (first year)
With replication (3x): ~85TB

Bandwidth Estimates

Assumptions:

Location update: 200 bytes (including headers)
API request/response: 2KB average
Real-time updates: WebSocket or long polling

Bandwidth Calculations:

Location updates: 375K updates/sec × 200 bytes = 75MB/sec = 600Mbps
API traffic: 26K QPS × 2KB = 52MB/sec = 416Mbps
Total bandwidth: ~1Gbps (peak)
Per region: ~100-200Mbps average

Core Entities

User (Rider/Driver)

User {
    user_id: UUID (Primary Key)
    email: String
    phone: String
    name: String
    user_type: Enum (RIDER, DRIVER)
    created_at: Timestamp
    updated_at: Timestamp
    status: Enum (ACTIVE, INACTIVE, SUSPENDED)
}

Driver

Driver {
    driver_id: UUID (Primary Key, Foreign Key -> User)
    license_number: String
    vehicle_make: String
    vehicle_model: String
    vehicle_year: Integer
    vehicle_license_plate: String
    vehicle_type: Enum (UBERX, UBERXL, UBERBLACK, etc.)
    rating: Float (average rating)
    total_rides: Integer
    is_available: Boolean
    current_location: Point (latitude, longitude)
    status: Enum (ONLINE, OFFLINE, ON_TRIP)
    verified: Boolean
    created_at: Timestamp
}

Ride

Ride {
    ride_id: UUID (Primary Key)
    rider_id: UUID (Foreign Key -> User)
    driver_id: UUID (Foreign Key -> Driver, nullable)
    pickup_location: Point
    dropoff_location: Point
    pickup_address: String
    dropoff_address: String
    ride_type: Enum (UBERX, UBERXL, etc.)
    status: Enum (REQUESTED, MATCHED, DRIVER_ARRIVED, IN_PROGRESS, COMPLETED, CANCELLED)
    fare: Decimal
    base_fare: Decimal
    distance_fare: Decimal
    time_fare: Decimal
    surge_multiplier: Float
    estimated_fare: Decimal
    actual_fare: Decimal
    distance: Float (miles)
    duration: Integer (seconds)
    requested_at: Timestamp
    matched_at: Timestamp
    started_at: Timestamp
    completed_at: Timestamp
    cancelled_at: Timestamp
    cancellation_reason: String
    payment_status: Enum (PENDING, PROCESSING, COMPLETED, FAILED)
    payment_method_id: UUID
}

Location Update

LocationUpdate {
    update_id: UUID (Primary Key)
    user_id: UUID (Foreign Key -> User)
    user_type: Enum (RIDER, DRIVER)
    ride_id: UUID (Foreign Key -> Ride, nullable)
    latitude: Float
    longitude: Float
    heading: Float (direction in degrees)
    speed: Float (mph)
    accuracy: Float (meters)
    timestamp: Timestamp
    created_at: Timestamp
}

Payment

Payment {
    payment_id: UUID (Primary Key)
    ride_id: UUID (Foreign Key -> Ride)
    rider_id: UUID (Foreign Key -> User)
    amount: Decimal
    currency: String (USD, EUR, etc.)
    payment_method: Enum (CREDIT_CARD, PAYPAL, etc.)
    payment_method_id: UUID
    status: Enum (PENDING, PROCESSING, COMPLETED, FAILED, REFUNDED)
    transaction_id: String
    processed_at: Timestamp
    created_at: Timestamp
}

Rating

Rating {
    rating_id: UUID (Primary Key)
    ride_id: UUID (Foreign Key -> Ride)
    rated_user_id: UUID (Foreign Key -> User)  # Driver or Rider
    rating_user_id: UUID (Foreign Key -> User)  # Who gave the rating
    rating: Integer (1-5)
    review: String (optional)
    created_at: Timestamp
}

API

Rider APIs

# Request a ride
POST /api/v1/rides/request
Request Body:
{
    "rider_id": "uuid",
    "pickup_location": {
        "latitude": 37.7749,
        "longitude": -122.4194
    },
    "dropoff_location": {
        "latitude": 37.7849,
        "longitude": -122.4094
    },
    "pickup_address": "123 Main St, San Francisco, CA",
    "dropoff_address": "456 Market St, San Francisco, CA",
    "ride_type": "UBERX"
}
Response:
{
    "ride_id": "uuid",
    "estimated_fare": 15.50,
    "estimated_wait_time": 5,  // minutes
    "surge_multiplier": 1.2,
    "status": "REQUESTED"
}

# Get ride status
GET /api/v1/rides/{ride_id}
Response:
{
    "ride_id": "uuid",
    "status": "MATCHED",
    "driver": {
        "driver_id": "uuid",
        "name": "John Doe",
        "rating": 4.8,
        "vehicle": {
            "make": "Toyota",
            "model": "Camry",
            "license_plate": "ABC123"
        },
        "current_location": {
            "latitude": 37.7750,
            "longitude": -122.4195
        },
        "eta": 3  // minutes
    },
    "pickup_location": {...},
    "dropoff_location": {...}
}

# Cancel ride
POST /api/v1/rides/{ride_id}/cancel
Request Body:
{
    "reason": "Changed my mind"
}
Response:
{
    "ride_id": "uuid",
    "status": "CANCELLED",
    "cancellation_fee": 5.00
}

# Rate driver
POST /api/v1/rides/{ride_id}/rate
Request Body:
{
    "rating": 5,
    "review": "Great driver!"
}
Response:
{
    "rating_id": "uuid",
    "status": "success"
}

# Get ride history
GET /api/v1/rides/history?rider_id={rider_id}&page=1&limit=20
Response:
{
    "rides": [...],
    "total": 150,
    "page": 1,
    "limit": 20
}

Driver APIs

# Update driver availability
PUT /api/v1/drivers/{driver_id}/availability
Request Body:
{
    "is_available": true
}
Response:
{
    "driver_id": "uuid",
    "is_available": true,
    "status": "ONLINE"
}

# Update driver location
POST /api/v1/drivers/{driver_id}/location
Request Body:
{
    "latitude": 37.7749,
    "longitude": -122.4194,
    "heading": 45.0,
    "speed": 25.5
}
Response:
{
    "status": "success",
    "timestamp": "2025-11-20T10:30:00Z"
}

# Accept ride request
POST /api/v1/rides/{ride_id}/accept
Request Body:
{
    "driver_id": "uuid"
}
Response:
{
    "ride_id": "uuid",
    "status": "MATCHED",
    "rider": {
        "rider_id": "uuid",
        "name": "Jane Smith",
        "rating": 4.9,
        "pickup_location": {...}
    }
}

# Reject ride request
POST /api/v1/rides/{ride_id}/reject
Request Body:
{
    "driver_id": "uuid",
    "reason": "Too far"
}
Response:
{
    "status": "success"
}

# Start trip
POST /api/v1/rides/{ride_id}/start
Request Body:
{
    "driver_id": "uuid"
}
Response:
{
    "ride_id": "uuid",
    "status": "IN_PROGRESS",
    "started_at": "2025-11-20T10:35:00Z"
}

# Complete trip
POST /api/v1/rides/{ride_id}/complete
Request Body:
{
    "driver_id": "uuid",
    "dropoff_location": {
        "latitude": 37.7849,
        "longitude": -122.4094
    }
}
Response:
{
    "ride_id": "uuid",
    "status": "COMPLETED",
    "fare": 18.50,
    "distance": 5.2,
    "duration": 1200
}

# Get driver earnings
GET /api/v1/drivers/{driver_id}/earnings?start_date=2025-11-01&end_date=2025-11-20
Response:
{
    "driver_id": "uuid",
    "total_earnings": 1250.50,
    "total_rides": 85,
    "period": {
        "start": "2025-11-01",
        "end": "2025-11-20"
    },
    "daily_earnings": [...]
}

Common APIs

# Get fare estimate
GET /api/v1/fare/estimate?pickup_lat=37.7749&pickup_lng=-122.4194&dropoff_lat=37.7849&dropoff_lng=-122.4094&ride_type=UBERX
Response:
{
    "estimated_fare": 15.50,
    "estimated_distance": 5.2,  // miles
    "estimated_duration": 20,  // minutes
    "surge_multiplier": 1.2,
    "base_fare": 2.50,
    "distance_fare": 1.50,
    "time_fare": 0.25
}

# Get nearby drivers
GET /api/v1/drivers/nearby?latitude=37.7749&longitude=-122.4194&radius=5&limit=10
Response:
{
    "drivers": [
        {
            "driver_id": "uuid",
            "distance": 0.5,  // miles
            "eta": 2,  // minutes
            "rating": 4.8
        },
        ...
    ]
}

Data Flow

Ride Request Flow

1. Rider opens app and requests ride
   └─> Rider App
       └─> API Gateway
           └─> Ride Service
               ├─> Validates request
               ├─> Calculates fare estimate
               │   └─> Pricing Service
               ├─> Creates ride record
               │   └─> Database (Ride table)
               ├─> Publishes ride request event
               │   └─> Message Queue (Kafka)
               └─> Returns ride_id and estimate

2. Matching Service processes ride request
   └─> Matching Service (consumes from Kafka)
       ├─> Queries nearby available drivers
       │   └─> Location Service (Geospatial Index)
       ├─> Applies matching algorithm
       │   ├─> Distance
       │   ├─> Driver rating
       │   ├─> Driver availability
       │   └─> Vehicle type match
       ├─> Selects best driver
       ├─> Sends ride request to driver
       │   └─> Notification Service
       │       └─> Push Notification
       └─> Waits for driver acceptance (30 seconds timeout)

3. Driver accepts ride
   └─> Driver App
       └─> API Gateway
           └─> Ride Service
               ├─> Updates ride status to MATCHED
               ├─> Updates driver_id in ride record
               ├─> Updates driver status to ON_TRIP
               ├─> Sends notifications
               │   ├─> Rider: "Driver matched"
               │   └─> Driver: "Ride details"
               └─> Starts real-time location tracking

4. Real-time tracking
   └─> Location Service
       ├─> Receives location updates (every 4 seconds)
       │   ├─> From Driver App
       │   └─> From Rider App (during pickup)
       ├─> Stores location updates
       │   └─> Time-Series Database
       ├─> Calculates ETA
       └─> Pushes updates to clients
           └─> WebSocket/Server-Sent Events

5. Driver arrives at pickup
   └─> Driver App
       └─> API Gateway
           └─> Ride Service
               ├─> Updates ride status to DRIVER_ARRIVED
               └─> Sends notification to rider

6. Trip starts
   └─> Driver App (starts trip)
       └─> API Gateway
           └─> Ride Service
               ├─> Updates ride status to IN_PROGRESS
               ├─> Records start time and location
               └─> Continues location tracking

7. Trip completes
   └─> Driver App (completes trip)
       └─> API Gateway
           └─> Ride Service
               ├─> Updates ride status to COMPLETED
               ├─> Calculates final fare
               │   └─> Pricing Service
               ├─> Records end time, location, distance, duration
               ├─> Triggers payment processing
               │   └─> Payment Service
               │       ├─> Charges rider
               │       └─> Updates driver earnings
               └─> Sends notifications
                   ├─> Rider: "Trip completed, receipt"
                   └─> Driver: "Earnings updated"

8. Rating flow
   └─> Rider App (rates driver)
       └─> API Gateway
           └─> Rating Service
               ├─> Creates rating record
               ├─> Updates driver average rating
               └─> Stores review

Database Design

Schema Design

Users Table

CREATE TABLE users (
    user_id UUID PRIMARY KEY,
    email VARCHAR(255) UNIQUE NOT NULL,
    phone VARCHAR(20) UNIQUE NOT NULL,
    name VARCHAR(255) NOT NULL,
    user_type ENUM('RIDER', 'DRIVER') NOT NULL,
    status ENUM('ACTIVE', 'INACTIVE', 'SUSPENDED') DEFAULT 'ACTIVE',
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
    INDEX idx_email (email),
    INDEX idx_phone (phone),
    INDEX idx_user_type (user_type)
);

Drivers Table

CREATE TABLE drivers (
    driver_id UUID PRIMARY KEY,
    user_id UUID NOT NULL,
    license_number VARCHAR(50) UNIQUE NOT NULL,
    vehicle_make VARCHAR(100) NOT NULL,
    vehicle_model VARCHAR(100) NOT NULL,
    vehicle_year INT NOT NULL,
    vehicle_license_plate VARCHAR(20) UNIQUE NOT NULL,
    vehicle_type ENUM('UBERX', 'UBERXL', 'UBERBLACK', 'UBERLUX') NOT NULL,
    rating DECIMAL(3,2) DEFAULT 0.00,
    total_rides INT DEFAULT 0,
    is_available BOOLEAN DEFAULT FALSE,
    current_latitude DECIMAL(10,8),
    current_longitude DECIMAL(11,8),
    status ENUM('ONLINE', 'OFFLINE', 'ON_TRIP') DEFAULT 'OFFLINE',
    verified BOOLEAN DEFAULT FALSE,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
    FOREIGN KEY (user_id) REFERENCES users(user_id),
    INDEX idx_status (status),
    INDEX idx_is_available (is_available),
    INDEX idx_vehicle_type (vehicle_type),
    SPATIAL INDEX idx_location (current_latitude, current_longitude)
);

Rides Table

CREATE TABLE rides (
    ride_id UUID PRIMARY KEY,
    rider_id UUID NOT NULL,
    driver_id UUID,
    pickup_latitude DECIMAL(10,8) NOT NULL,
    pickup_longitude DECIMAL(11,8) NOT NULL,
    dropoff_latitude DECIMAL(10,8) NOT NULL,
    dropoff_longitude DECIMAL(11,8) NOT NULL,
    pickup_address TEXT NOT NULL,
    dropoff_address TEXT NOT NULL,
    ride_type ENUM('UBERX', 'UBERXL', 'UBERBLACK', 'UBERLUX') NOT NULL,
    status ENUM('REQUESTED', 'MATCHED', 'DRIVER_ARRIVED', 'IN_PROGRESS', 'COMPLETED', 'CANCELLED') NOT NULL,
    fare DECIMAL(10,2),
    base_fare DECIMAL(10,2),
    distance_fare DECIMAL(10,2),
    time_fare DECIMAL(10,2),
    surge_multiplier DECIMAL(4,2) DEFAULT 1.00,
    estimated_fare DECIMAL(10,2),
    actual_fare DECIMAL(10,2),
    distance DECIMAL(10,2),  -- miles
    duration INT,  -- seconds
    requested_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    matched_at TIMESTAMP,
    started_at TIMESTAMP,
    completed_at TIMESTAMP,
    cancelled_at TIMESTAMP,
    cancellation_reason TEXT,
    payment_status ENUM('PENDING', 'PROCESSING', 'COMPLETED', 'FAILED') DEFAULT 'PENDING',
    payment_method_id UUID,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
    FOREIGN KEY (rider_id) REFERENCES users(user_id),
    FOREIGN KEY (driver_id) REFERENCES drivers(driver_id),
    INDEX idx_rider_id (rider_id),
    INDEX idx_driver_id (driver_id),
    INDEX idx_status (status),
    INDEX idx_requested_at (requested_at),
    INDEX idx_completed_at (completed_at)
);

Location Updates Table

CREATE TABLE location_updates (
    update_id UUID PRIMARY KEY,
    user_id UUID NOT NULL,
    user_type ENUM('RIDER', 'DRIVER') NOT NULL,
    ride_id UUID,
    latitude DECIMAL(10,8) NOT NULL,
    longitude DECIMAL(11,8) NOT NULL,
    heading DECIMAL(5,2),
    speed DECIMAL(5,2),
    accuracy DECIMAL(5,2),
    timestamp TIMESTAMP NOT NULL,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY (user_id) REFERENCES users(user_id),
    FOREIGN KEY (ride_id) REFERENCES rides(ride_id),
    INDEX idx_user_id (user_id),
    INDEX idx_ride_id (ride_id),
    INDEX idx_timestamp (timestamp),
    INDEX idx_created_at (created_at)
) PARTITION BY RANGE (YEAR(created_at), MONTH(created_at));

Payments Table

CREATE TABLE payments (
    payment_id UUID PRIMARY KEY,
    ride_id UUID NOT NULL,
    rider_id UUID NOT NULL,
    amount DECIMAL(10,2) NOT NULL,
    currency VARCHAR(3) DEFAULT 'USD',
    payment_method ENUM('CREDIT_CARD', 'PAYPAL', 'DEBIT_CARD', 'WALLET') NOT NULL,
    payment_method_id UUID NOT NULL,
    status ENUM('PENDING', 'PROCESSING', 'COMPLETED', 'FAILED', 'REFUNDED') DEFAULT 'PENDING',
    transaction_id VARCHAR(255),
    processed_at TIMESTAMP,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
    FOREIGN KEY (ride_id) REFERENCES rides(ride_id),
    FOREIGN KEY (rider_id) REFERENCES users(user_id),
    INDEX idx_ride_id (ride_id),
    INDEX idx_rider_id (rider_id),
    INDEX idx_status (status),
    INDEX idx_created_at (created_at)
);

Ratings Table

CREATE TABLE ratings (
    rating_id UUID PRIMARY KEY,
    ride_id UUID NOT NULL,
    rated_user_id UUID NOT NULL,  -- Driver or Rider being rated
    rating_user_id UUID NOT NULL,  -- User giving the rating
    rating INT NOT NULL CHECK (rating >= 1 AND rating <= 5),
    review TEXT,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    FOREIGN KEY (ride_id) REFERENCES rides(ride_id),
    FOREIGN KEY (rated_user_id) REFERENCES users(user_id),
    FOREIGN KEY (rating_user_id) REFERENCES users(user_id),
    INDEX idx_ride_id (ride_id),
    INDEX idx_rated_user_id (rated_user_id),
    INDEX idx_rating (rating)
);

Database Sharding Strategy

Sharding by Geography (City/Region):

Strategy: Shard by city or geographic region
Shard Key: city_id or region_id
Benefits:
- Locality: Most queries are city-specific
- Reduced cross-shard queries
- Easier data management per region
Challenges:
- Cross-city rides (rare but possible)
- Driver movement between cities

Sharding by User ID:

Strategy: Hash-based sharding on user_id
Shard Key: Hash of user_id
Benefits:
- Even distribution
- Simple routing
Challenges:
- Cross-shard queries for matching
- Location queries require querying all shards

Hybrid Approach:

Primary Sharding: By geography (city/region)
Secondary Indexing: User-based indexes for cross-shard queries
Location Service: Separate service with geospatial indexes (Redis Geo, Elasticsearch)

Recommended Approach:

Rides Table: Shard by city_id (geographic sharding)
Users Table: Shard by user_id hash (even distribution)
Location Updates: Use time-series database (InfluxDB, TimescaleDB) with city-based partitioning
Location Service: Use Redis Geo or Elasticsearch for real-time location queries

High-Level Design

┌─────────────────────────────────────────────────────────────────┐
│                         Client Applications                      │
│  ┌──────────────┐                    ┌──────────────┐          │
│  │  Rider App   │                    │  Driver App  │          │
│  │  (Mobile)    │                    │  (Mobile)    │          │
│  └──────┬───────┘                    └──────┬───────┘          │
└─────────┼────────────────────────────────────┼──────────────────┘
          │                                    │
          │ HTTPS/WebSocket                    │ HTTPS/WebSocket
          │                                    │
┌─────────┴────────────────────────────────────┴──────────────────┐
│                         API Gateway                             │
│                    (Load Balancer + Routing)                     │
└─────────┬────────────────────────────────────┬──────────────────┘
          │                                    │
          ├────────────────────────────────────┤
          │                                    │
┌─────────┴────────────────────────────────────┴──────────────────┐
│                      Application Services                        │
│                                                                  │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐        │
│  │   Ride       │  │  Matching    │  │  Location    │        │
│  │   Service    │  │  Service     │  │  Service     │        │
│  └──────┬───────┘  └──────┬───────┘  └──────┬───────┘        │
│         │                 │                 │                  │
│  ┌──────┴─────────────────┴─────────────────┴───────┐        │
│  │              Message Queue (Kafka)                │        │
│  └──────────────────────────────────────────────────┘        │
│                                                                  │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐        │
│  │   Pricing    │  │   Payment    │  │ Notification │        │
│  │   Service    │  │   Service    │  │   Service    │        │
│  └──────────────┘  └──────┬───────┘  └──────┬───────┘        │
│                           │                 │                  │
│  ┌─────────────────────────┴─────────────────┴───────┐        │
│  │              External Services                     │        │
│  │  ┌──────────┐  ┌──────────┐  ┌──────────┐        │        │
│  │  │ Payment  │  │   SMS    │  │   Push   │        │        │
│  │  │ Gateway  │  │ Gateway  │  │ Service │        │        │
│  │  └──────────┘  └──────────┘  └──────────┘        │        │
│  └──────────────────────────────────────────────────┘        │
└──────────────────────────────────────────────────────────────┘
          │                                    │
          ├────────────────────────────────────┤
          │                                    │
┌─────────┴────────────────────────────────────┴──────────────────┐
│                         Data Layer                                │
│                                                                   │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐         │
│  │  PostgreSQL  │  │   Redis      │  │  Elasticsearch│        │
│  │  (Primary DB)│  │  (Cache +    │  │  (Location    │        │
│  │              │  │   Geo Index) │  │   Search)    │        │
│  └──────────────┘  └──────────────┘  └──────────────┘         │
│                                                                   │
│  ┌──────────────┐  ┌──────────────┐                            │
│  │  TimescaleDB │  │   S3         │                            │
│  │  (Location   │  │  (Archived   │                            │
│  │   History)   │  │   Data)      │                            │
│  └──────────────┘  └──────────────┘                            │
└───────────────────────────────────────────────────────────────────┘

Deep Dive

Driver-Rider Matching Service

Problem

Match a rider with the best available driver in real-time, considering:

Proximity (distance)
Driver availability
Driver rating
Vehicle type match
Driver preferences (if any)

Solution: Geospatial Matching Algorithm

Approach 1: Radius-Based Search

def find_nearby_drivers(pickup_location, radius_miles=5, ride_type='UBERX'):
    """
    Find drivers within radius using geospatial index
    """
    # Query Redis Geo or Elasticsearch for nearby drivers
    nearby_drivers = location_service.find_within_radius(
        latitude=pickup_location.latitude,
        longitude=pickup_location.longitude,
        radius_miles=radius_miles,
        filters={
            'status': 'ONLINE',
            'is_available': True,
            'vehicle_type': ride_type
        }
    )
    
    # Score and rank drivers
    scored_drivers = []
    for driver in nearby_drivers:
        score = calculate_match_score(driver, pickup_location)
        scored_drivers.append((driver, score))
    
    # Sort by score (higher is better)
    scored_drivers.sort(key=lambda x: x[1], reverse=True)
    
    return [driver for driver, score in scored_drivers[:10]]

Scoring Function:

def calculate_match_score(driver, pickup_location):
    """
    Calculate match score based on multiple factors
    """
    # Distance score (closer is better)
    distance = calculate_distance(
        driver.current_location,
        pickup_location
    )
    distance_score = max(0, 100 - (distance * 10))  # Max 100 points
    
    # Rating score (higher rating is better)
    rating_score = driver.rating * 20  # Max 100 points (5.0 * 20)
    
    # Availability score (recently available is better)
    availability_score = calculate_availability_score(driver)
    
    # Total rides score (more experience is better, but diminishing returns)
    experience_score = min(50, driver.total_rides / 100)
    
    # Weighted combination
    total_score = (
        distance_score * 0.4 +      # 40% weight on distance
        rating_score * 0.3 +         # 30% weight on rating
        availability_score * 0.2 +   # 20% weight on availability
        experience_score * 0.1       # 10% weight on experience
    )
    
    return total_score

Approach 2: Grid-Based Matching

For very high scale, use grid-based approach:

class GridBasedMatching:
    def __init__(self, grid_size_miles=1):
        self.grid_size = grid_size_miles
        self.grid_cache = {}  # grid_id -> [driver_ids]
    
    def get_grid_id(self, latitude, longitude):
        """Convert lat/lng to grid ID"""
        lat_grid = int(latitude / self.grid_size)
        lng_grid = int(longitude / self.grid_size)
        return f"{lat_grid}_{lng_grid}"
    
    def find_drivers_in_grid(self, pickup_location):
        """Find drivers in same and adjacent grids"""
        grid_id = self.get_grid_id(
            pickup_location.latitude,
            pickup_location.longitude
        )
        
        # Check current grid and 8 adjacent grids
        drivers = []
        for lat_offset in [-1, 0, 1]:
            for lng_offset in [-1, 0, 1]:
                check_grid_id = self.get_grid_id(
                    pickup_location.latitude + lat_offset * self.grid_size,
                    pickup_location.longitude + lng_offset * self.grid_size
                )
                drivers.extend(self.grid_cache.get(check_grid_id, []))
        
        return drivers

Matching Flow:

1. Ride request received
   └─> Matching Service

2. Query nearby drivers
   └─> Location Service (Redis Geo/Elasticsearch)
       └─> Returns drivers within 5 miles radius

3. Filter drivers
   ├─> Available (is_available = true)
   ├─> Online (status = ONLINE)
   ├─> Vehicle type match
   └─> Not on trip

4. Score drivers
   ├─> Distance (closer = better)
   ├─> Rating (higher = better)
   ├─> Availability (recently available = better)
   └─> Experience (more rides = better)

5. Select top driver
   └─> Send ride request notification

6. Wait for acceptance (30 seconds timeout)
   ├─> If accepted: Match confirmed
   ├─> If rejected: Select next driver
   └─> If timeout: Select next driver or expand radius

Optimization:

Caching: Cache available drivers per grid/city
Pre-computation: Pre-score drivers when they come online
Batch Processing: Process multiple ride requests in batch
Load Balancing: Distribute matching across multiple instances

Location Tracking Service

Problem

Track real-time location of drivers and riders, update every 4 seconds, support millions of concurrent users.

Solution: Real-Time Location Tracking

Architecture:

Client App (Mobile)
    │
    │ Location Update (every 4 seconds)
    │
    ▼
API Gateway
    │
    │
    ▼
Location Service
    ├─> Validates location
    ├─> Stores in cache (Redis)
    │   └─> Key: user_id, Value: {lat, lng, timestamp}
    ├─> Updates geospatial index (Redis Geo)
    ├─> Stores in time-series DB (TimescaleDB)
    └─> Publishes location update event
        └─> Kafka (for other services)

Redis Geo Index:

# Add driver location to Redis Geo
redis.geoadd(
    "drivers:geo",
    longitude,
    latitude,
    driver_id
)

# Find nearby drivers
nearby_drivers = redis.georadius(
    "drivers:geo",
    longitude,
    latitude,
    radius=5,  # miles
    unit="mi",
    withdist=True,  # Include distance
    withcoord=True  # Include coordinates
)

Time-Series Storage:

-- TimescaleDB hypertable for location history
CREATE TABLE location_updates (
    time TIMESTAMPTZ NOT NULL,
    user_id UUID NOT NULL,
    ride_id UUID,
    latitude DOUBLE PRECISION NOT NULL,
    longitude DOUBLE PRECISION NOT NULL,
    heading DOUBLE PRECISION,
    speed DOUBLE PRECISION
);

SELECT create_hypertable('location_updates', 'time');

-- Query location history for a ride
SELECT time, latitude, longitude
FROM location_updates
WHERE ride_id = 'uuid'
  AND time >= '2025-11-20 10:00:00'
ORDER BY time;

Real-Time Updates to Clients:

# WebSocket connection for real-time updates
class LocationWebSocket:
    def on_connect(self, client):
        # Subscribe to location updates for active ride
        ride_id = client.get_ride_id()
        client.subscribe(f"ride:{ride_id}:location")
    
    def on_location_update(self, user_id, location):
        # Publish to subscribed clients
        redis.publish(
            f"ride:{ride_id}:location",
            json.dumps({
                "user_id": user_id,
                "latitude": location.latitude,
                "longitude": location.longitude,
                "timestamp": location.timestamp
            })
        )

Optimization:

Batching: Batch location updates (reduce write load)
Compression: Compress location data
Throttling: Throttle updates if client is slow
Caching: Cache recent locations in Redis
Partitioning: Partition by time (daily/monthly partitions)

Dynamic Pricing Service

Problem

Calculate ride fare dynamically based on:

Base fare
Distance
Time
Surge pricing (demand/supply ratio)
Ride type

Solution: Multi-Factor Pricing Model

Base Fare Calculation:

def calculate_fare(ride):
    """
    Calculate ride fare
    """
    # Base fare (varies by ride type)
    base_fare = get_base_fare(ride.ride_type)  # e.g., $2.50 for UberX
    
    # Distance fare
    distance_fare = ride.distance * get_per_mile_rate(ride.ride_type)  # e.g., $1.50/mile
    
    # Time fare
    time_fare = (ride.duration / 60) * get_per_minute_rate(ride.ride_type)  # e.g., $0.25/minute
    
    # Surge multiplier
    surge_multiplier = calculate_surge_multiplier(
        ride.pickup_location,
        ride.requested_at
    )
    
    # Total fare
    total_fare = (base_fare + distance_fare + time_fare) * surge_multiplier
    
    # Minimum fare
    min_fare = get_minimum_fare(ride.ride_type)
    
    return max(total_fare, min_fare)

Surge Pricing Algorithm:

def calculate_surge_multiplier(location, timestamp):
    """
    Calculate surge multiplier based on demand/supply ratio
    """
    # Get demand (active ride requests in area)
    demand = get_active_requests_count(location, radius_miles=2)
    
    # Get supply (available drivers in area)
    supply = get_available_drivers_count(location, radius_miles=2)
    
    # Calculate demand/supply ratio
    if supply == 0:
        return 3.0  # Maximum surge if no drivers
    
    ratio = demand / supply
    
    # Map ratio to surge multiplier
    if ratio < 0.5:
        return 1.0  # No surge
    elif ratio < 1.0:
        return 1.0 + (ratio - 0.5) * 0.4  # 1.0 to 1.2
    elif ratio < 1.5:
        return 1.2 + (ratio - 1.0) * 0.6  # 1.2 to 1.5
    elif ratio < 2.0:
        return 1.5 + (ratio - 1.5) * 0.5  # 1.5 to 1.75
    else:
        return min(3.0, 1.75 + (ratio - 2.0) * 0.25)  # 1.75 to 3.0

Demand/Supply Calculation:

def get_active_requests_count(location, radius_miles):
    """
    Count active ride requests in area
    """
    # Query Redis for active requests in geospatial index
    active_requests = redis.georadius(
        "requests:geo",
        location.longitude,
        location.latitude,
        radius=radius_miles,
        unit="mi"
    )
    
    # Filter by time (requests in last 5 minutes)
    recent_requests = [
        req for req in active_requests
        if (time.now() - req.timestamp).seconds < 300
    ]
    
    return len(recent_requests)

def get_available_drivers_count(location, radius_miles):
    """
    Count available drivers in area
    """
    # Query Redis Geo for available drivers
    available_drivers = redis.georadius(
        "drivers:geo:available",
        location.longitude,
        location.latitude,
        radius=radius_miles,
        unit="mi"
    )
    
    return len(available_drivers)

Caching Surge Multipliers:

# Cache surge multipliers per grid
def get_surge_multiplier(location):
    grid_id = get_grid_id(location)
    cache_key = f"surge:{grid_id}"
    
    # Check cache (5-minute TTL)
    cached = redis.get(cache_key)
    if cached:
        return float(cached)
    
    # Calculate and cache
    multiplier = calculate_surge_multiplier(location, time.now())
    redis.setex(cache_key, 300, multiplier)  # 5-minute TTL
    
    return multiplier

Payment Service

Problem

Process payments securely, handle failures, support multiple payment methods, process refunds.

Solution: Payment Processing Pipeline

Payment Flow:

1. Trip completes
   └─> Ride Service triggers payment

2. Payment Service
   ├─> Validates payment method
   ├─> Calculates final fare
   ├─> Creates payment record (status: PENDING)
   └─> Sends to Payment Gateway

3. Payment Gateway (Stripe, PayPal, etc.)
   ├─> Processes payment
   ├─> Returns transaction_id
   └─> Webhook callback

4. Payment Service (webhook handler)
   ├─> Updates payment status
   ├─> Updates ride payment_status
   ├─> Updates driver earnings
   └─> Sends receipt notification

Payment Processing:

class PaymentService:
    def process_payment(self, ride_id):
        """
        Process payment for completed ride
        """
        ride = self.get_ride(ride_id)
        
        # Create payment record
        payment = Payment(
            ride_id=ride_id,
            rider_id=ride.rider_id,
            amount=ride.actual_fare,
            payment_method_id=ride.payment_method_id,
            status='PENDING'
        )
        payment.save()
        
        # Process payment
        try:
            result = self.payment_gateway.charge(
                amount=ride.actual_fare,
                payment_method_id=ride.payment_method_id,
                description=f"Ride {ride_id}"
            )
            
            # Update payment
            payment.transaction_id = result.transaction_id
            payment.status = 'COMPLETED'
            payment.processed_at = time.now()
            payment.save()
            
            # Update ride
            ride.payment_status = 'COMPLETED'
            ride.save()
            
            # Update driver earnings
            self.update_driver_earnings(ride.driver_id, ride.actual_fare)
            
            # Send receipt
            self.send_receipt(ride.rider_id, payment)
            
        except PaymentException as e:
            # Handle payment failure
            payment.status = 'FAILED'
            payment.save()
            
            # Retry logic or notify user
            self.handle_payment_failure(ride, payment, e)

Idempotency:

def process_payment(ride_id, idempotency_key):
    """
    Process payment with idempotency
    """
    # Check if already processed
    existing_payment = Payment.objects.filter(
        ride_id=ride_id,
        idempotency_key=idempotency_key
    ).first()
    
    if existing_payment:
        return existing_payment  # Return existing result
    
    # Process payment with idempotency key
    payment = create_and_process_payment(
        ride_id=ride_id,
        idempotency_key=idempotency_key
    )
    
    return payment

Retry Logic:

def process_payment_with_retry(ride_id, max_retries=3):
    """
    Process payment with exponential backoff retry
    """
    for attempt in range(max_retries):
        try:
            return process_payment(ride_id)
        except PaymentException as e:
            if attempt == max_retries - 1:
                # Final attempt failed
                handle_payment_failure(ride_id, e)
                raise
            
            # Exponential backoff
            wait_time = 2 ** attempt  # 1s, 2s, 4s
            time.sleep(wait_time)

Notification Service

Problem

Send real-time notifications to riders and drivers for ride status updates, payment confirmations, etc.

Solution: Multi-Channel Notification System

Notification Types:

Push notifications (mobile app)
SMS notifications
Email notifications
In-app notifications

Notification Service:

class NotificationService:
    def send_ride_matched(self, rider_id, driver_info):
        """
        Notify rider that driver is matched
        """
        notification = {
            "type": "RIDE_MATCHED",
            "rider_id": rider_id,
            "message": f"Driver {driver_info.name} is on the way",
            "data": {
                "driver": driver_info,
                "eta": driver_info.eta
            }
        }
        
        # Send push notification
        self.push_service.send(
            user_id=rider_id,
            notification=notification
        )
        
        # Send SMS
        self.sms_service.send(
            phone=rider_info.phone,
            message=f"Your driver {driver_info.name} is on the way. ETA: {driver_info.eta} minutes"
        )
    
    def send_trip_completed(self, rider_id, ride_info):
        """
        Notify rider that trip is completed
        """
        notification = {
            "type": "TRIP_COMPLETED",
            "rider_id": rider_id,
            "message": "Your trip has been completed",
            "data": {
                "fare": ride_info.fare,
                "receipt_url": ride_info.receipt_url
            }
        }
        
        # Send push notification
        self.push_service.send(
            user_id=rider_id,
            notification=notification
        )
        
        # Send email receipt
        self.email_service.send_receipt(
            email=rider_info.email,
            ride=ride_info
        )

Message Queue Integration:

# Publish notification events to Kafka
kafka_producer.send(
    topic="notifications",
    value={
        "user_id": rider_id,
        "type": "RIDE_MATCHED",
        "message": "...",
        "channels": ["push", "sms"]
    }
)

# Notification Service consumes from Kafka
@kafka_consumer(topic="notifications")
def handle_notification(event):
    notification_service.send(event)

Trip Management Service

Problem

Manage trip lifecycle: request → match → pickup → in-progress → complete.

Solution: State Machine-Based Trip Management

Trip State Machine:

REQUESTED → MATCHED → DRIVER_ARRIVED → IN_PROGRESS → COMPLETED
     │         │            │              │
     └─────────┴────────────┴──────────────┴──→ CANCELLED

State Transitions:

class TripStateMachine:
    TRANSITIONS = {
        'REQUESTED': ['MATCHED', 'CANCELLED'],
        'MATCHED': ['DRIVER_ARRIVED', 'CANCELLED'],
        'DRIVER_ARRIVED': ['IN_PROGRESS', 'CANCELLED'],
        'IN_PROGRESS': ['COMPLETED', 'CANCELLED'],
        'COMPLETED': [],
        'CANCELLED': []
    }
    
    def transition(self, ride, new_status):
        """
        Transition ride to new status
        """
        current_status = ride.status
        
        if new_status not in self.TRANSITIONS.get(current_status, []):
            raise InvalidTransitionError(
                f"Cannot transition from {current_status} to {new_status}"
            )
        
        # Update status
        ride.status = new_status
        
        # Update timestamps
        if new_status == 'MATCHED':
            ride.matched_at = time.now()
        elif new_status == 'IN_PROGRESS':
            ride.started_at = time.now()
        elif new_status == 'COMPLETED':
            ride.completed_at = time.now()
        elif new_status == 'CANCELLED':
            ride.cancelled_at = time.now()
        
        ride.save()
        
        # Trigger side effects
        self.handle_status_change(ride, new_status)

Rating and Review Service

Problem

Allow riders and drivers to rate each other after trip completion.

Solution: Two-Way Rating System

Rating Flow:

class RatingService:
    def rate_driver(self, ride_id, rider_id, rating, review=None):
        """
        Rate driver after trip
        """
        ride = self.get_ride(ride_id)
        
        # Validate ride is completed
        if ride.status != 'COMPLETED':
            raise InvalidRideStatusError("Ride must be completed")
        
        # Validate rider
        if ride.rider_id != rider_id:
            raise UnauthorizedError("Not your ride")
        
        # Create rating
        rating_record = Rating(
            ride_id=ride_id,
            rated_user_id=ride.driver_id,
            rating_user_id=rider_id,
            rating=rating,
            review=review
        )
        rating_record.save()
        
        # Update driver average rating
        self.update_driver_rating(ride.driver_id)
        
        return rating_record
    
    def update_driver_rating(self, driver_id):
        """
        Recalculate driver average rating
        """
        ratings = Rating.objects.filter(rated_user_id=driver_id)
        avg_rating = ratings.aggregate(Avg('rating'))['rating__avg']
        
        driver = Driver.objects.get(driver_id=driver_id)
        driver.rating = avg_rating
        driver.save()

Batch Rating Update:

# Update ratings in batch (for performance)
@periodic_task(run_every=timedelta(minutes=5))
def update_driver_ratings():
    """
    Batch update driver ratings
    """
    drivers = Driver.objects.all()
    
    for driver in drivers:
        ratings = Rating.objects.filter(rated_user_id=driver.driver_id)
        avg_rating = ratings.aggregate(Avg('rating'))['rating__avg']
        
        driver.rating = avg_rating
        driver.save()

Scalability Considerations

Horizontal Scaling

Service Scaling:

Stateless services: Scale horizontally easily
Use load balancers to distribute traffic
Auto-scaling based on CPU/memory metrics

Database Scaling:

Read replicas for read-heavy workloads
Sharding for write-heavy workloads
Caching layer (Redis) to reduce database load

Location Service Scaling:

Redis Cluster for geospatial queries
Partition by geography (city/region)
Use Elasticsearch for complex location queries

Caching Strategy

Cache Layers:

Application Cache: Cache frequently accessed data (driver info, ride details)
Redis Cache: Cache location data, surge multipliers
CDN: Cache static assets (maps, images)

Cache Invalidation:

TTL-based expiration
Event-driven invalidation (on ride status change)
Cache-aside pattern

Database Optimization

Indexing:

Index on frequently queried fields (user_id, ride_id, status)
Geospatial indexes for location queries
Composite indexes for complex queries

Query Optimization:

Use read replicas for analytics queries
Batch operations where possible
Pagination for large result sets

Message Queue

Kafka Topics:

ride-requests: Ride request events
ride-updates: Ride status updates
location-updates: Location update events
payments: Payment events
notifications: Notification events

Benefits:

Decouple services
Handle traffic spikes
Enable event-driven architecture
Support replay for debugging

Security Considerations

Authentication and Authorization

JWT tokens for API authentication
OAuth 2.0 for third-party integrations
Role-based access control (RBAC)
API rate limiting

Payment Security

PCI DSS compliance
Tokenization for payment methods
Encryption for sensitive data
Secure payment gateway integration

Data Protection

Encrypt sensitive data at rest
Use HTTPS for all communications
PII (Personally Identifiable Information) protection
GDPR compliance

Driver Verification

Background checks
License verification
Vehicle inspection
Insurance verification

Monitoring & Observability

Metrics

Business Metrics: Rides per day, revenue, driver earnings
Technical Metrics: API latency, error rates, throughput
Location Metrics: Location update latency, geospatial query performance
Payment Metrics: Payment success rate, processing time

Logging

Structured logging (JSON format)
Centralized logging (ELK stack)
Log aggregation and analysis
Error tracking (Sentry)

Tracing

Distributed tracing (Jaeger, Zipkin)
Request tracing across services
Performance analysis
Debugging production issues

Alerting

Real-time alerts for critical issues
SLA monitoring
Anomaly detection
PagerDuty integration

Trade-offs and Optimizations

Consistency vs Availability

Strong Consistency: Payments, ride status
Eventual Consistency: Ratings, driver locations
Trade-off: Accept eventual consistency for location updates to improve availability

Latency vs Accuracy

Location Updates: 4-second updates (balance between accuracy and load)
Matching: Real-time matching (low latency) vs optimal matching (higher latency)
Trade-off: Prefer low latency for better user experience

Cost vs Performance

Caching: More cache = better performance but higher cost
Database Replicas: More replicas = better read performance but higher cost
Trade-off: Optimize for cost while meeting performance requirements

Complexity vs Features

Matching Algorithm: Simple distance-based vs complex ML-based
Pricing: Fixed pricing vs dynamic surge pricing
Trade-off: Start simple, add complexity as needed

What Interviewers Look For

Geospatial Systems Skills

Real-Time Matching
- Geospatial index usage (Redis Geo, Elasticsearch)
- Efficient driver-rider matching
- Distance calculations
- Red Flags: No geospatial index, inefficient matching, slow queries
Location Tracking
- Real-time location updates
- Time-series data storage
- Efficient location queries
- Red Flags: No real-time tracking, inefficient storage, slow queries
Dynamic Pricing
- Surge pricing algorithm
- Demand/supply calculation
- Grid-based pricing
- Red Flags: Fixed pricing, no surge, poor algorithm

Distributed Systems Skills

State Management
- Trip state machine
- State transitions
- Consistency guarantees
- Red Flags: No state machine, inconsistent states
Microservices Architecture
- Service decomposition
- Clear boundaries
- Service communication
- Red Flags: Monolithic, unclear boundaries, tight coupling
Scalability Design
- Geographic sharding
- Horizontal scaling
- Load balancing
- Red Flags: No sharding, vertical scaling, bottlenecks

Problem-Solving Approach

Scale Thinking
- Millions of concurrent rides
- Real-time location updates
- Global distribution
- Red Flags: Small-scale design, no real-time, single region
Trade-off Analysis
- Latency vs accuracy
- Consistency vs availability
- Red Flags: No trade-offs, dogmatic choices
Edge Cases
- No available drivers
- Driver cancellation
- Payment failures
- Red Flags: Ignoring edge cases, no handling

System Design Skills

Component Design
- Matching service
- Location service
- Pricing service
- Red Flags: Monolithic, unclear boundaries
Payment Processing
- Idempotency
- Retry logic
- Failure handling
- Red Flags: No idempotency, no retry, payment loss
Real-Time Updates
- WebSocket/SSE
- Pub/sub architecture
- Red Flags: Polling, high latency, no real-time

Communication Skills

Architecture Explanation
- Clear service design
- Data flow understanding
- Red Flags: Unclear design, no flow
Algorithm Explanation
- Can explain matching algorithm
- Understands pricing
- Red Flags: No understanding, vague

Meta-Specific Focus

Real-Time Systems Expertise
- Geospatial systems knowledge
- Low-latency design
- Key: Show real-time systems knowledge
Product System Design
- End-to-end thinking
- User experience consideration
- Key: Demonstrate product thinking

Summary

Key Takeaways

Real-Time Matching: Use geospatial indexes (Redis Geo, Elasticsearch) for efficient driver-rider matching
Location Tracking: Real-time location updates every 4 seconds, use time-series database for history
Dynamic Pricing: Surge pricing based on demand/supply ratio, cache multipliers for performance
Payment Processing: Secure payment processing with idempotency and retry logic
Microservices Architecture: Separate services for matching, location, pricing, payment, notifications
Scalability: Horizontal scaling, caching, read replicas, message queues
Geographic Sharding: Shard by city/region for locality
State Machine: Use state machine for trip lifecycle management

Architecture Highlights

API Gateway: Single entry point, load balancing, routing
Microservices: Ride, Matching, Location, Pricing, Payment, Notification services
Message Queue: Kafka for event-driven architecture
Data Stores: PostgreSQL (primary), Redis (cache + geo), TimescaleDB (location history)
Real-Time: WebSocket/SSE for location updates
External Services: Payment gateway, SMS gateway, push notification service

Scale Numbers

Users: 100M+ users
Rides: 15M rides/day, 1M concurrent rides (peak)
Location Updates: 375K updates/second
API Traffic: 26K QPS (peak)
Storage: 28.5TB/year (with replication: 85TB)

Common Interview Questions

How do you match riders with drivers?
- Geospatial queries, scoring algorithm, real-time matching
How do you handle surge pricing?
- Demand/supply ratio, grid-based calculation, caching
How do you track locations in real-time?
- Redis Geo index, time-series database, WebSocket updates
How do you ensure payment reliability?
- Idempotency, retry logic, webhook callbacks, failure handling
How do you scale to millions of users?
- Horizontal scaling, caching, database sharding, message queues

This design provides a solid foundation for a ride-sharing service like Uber, covering all major components and addressing scalability, reliability, and performance challenges.

Introduction

Table of Contents

Problem Statement

Requirements

Functional Requirements

Non-Functional Requirements

Capacity Estimation

Traffic Estimates

Storage Estimates

Bandwidth Estimates

Core Entities

User (Rider/Driver)

Driver

Ride

Location Update

Payment

Rating

API

Rider APIs

Driver APIs

Common APIs

Data Flow

Ride Request Flow

Database Design

Schema Design

Users Table

Drivers Table

Rides Table

Location Updates Table

Payments Table

Ratings Table

Database Sharding Strategy

High-Level Design

Deep Dive

Driver-Rider Matching Service

Problem

Solution: Geospatial Matching Algorithm

Location Tracking Service

Problem

Solution: Real-Time Location Tracking

Dynamic Pricing Service

Problem

Solution: Multi-Factor Pricing Model

Payment Service

Problem

Solution: Payment Processing Pipeline

Notification Service

Problem

Solution: Multi-Channel Notification System

Trip Management Service

Problem

Solution: State Machine-Based Trip Management

Rating and Review Service

Problem

Solution: Two-Way Rating System

Scalability Considerations

Horizontal Scaling

Caching Strategy

Database Optimization

Message Queue

Security Considerations

Authentication and Authorization

Payment Security

Data Protection

Driver Verification

Monitoring & Observability

Metrics

Logging

Tracing

Alerting

Trade-offs and Optimizations

Consistency vs Availability

Latency vs Accuracy

Cost vs Performance

Complexity vs Features

What Interviewers Look For

Geospatial Systems Skills

Distributed Systems Skills

Problem-Solving Approach

System Design Skills