System requirements

Functional Requirements

Core Functionalities:

1. User Accounts:
   • User registration, login, and profile management.
   • Address management for shipping.
   • Order history and wishlist management.
2. Product Discovery and Browsing:
   • Search functionality with filters (e.g., price, category, rating).
   • Product categories and personalized recommendations.
   • Detailed product pages with descriptions, reviews, and images.
3. Shopping Cart and Checkout:
   • Add/remove items to/from the cart.
   • Save items for later.
   • Checkout with multiple shipping and payment options.
4. Secure Payment Processing:
   • Integration with payment gateways (e.g., Stripe, PayPal).
   • Support for credit cards, debit cards, net banking, and wallets.
   • Fraud detection and secure handling of payment data.
5. Order Management:
   • Track orders with real-time updates.
   • View and cancel orders.
   • Notify users about order status changes (e.g., shipped, delivered).
6. Product Reviews and Ratings:
   • Allow users to submit reviews and ratings.
   • Display average ratings on product pages.
7. Recommendations:
   • Personalized product suggestions based on user behavior (e.g., past purchases, browsing history).
8. Admin Dashboard:
   • Manage products (add, update, delete).
   • View and process orders.
   • Generate sales and user activity reports.

Non-Functional Requirements

1. Scalability:
   • Handle millions of users and products with real-time updates.
   • Support thousands of concurrent transactions.
2. Availability:
   • Ensure 99.9% uptime for critical services like cart, checkout, and payment processing.
3. Performance:
   • Fast page load times (< 2 seconds) and low-latency APIs (< 200ms response time).
4. Security:
   • Protect user data using encryption (e.g., HTTPS, hashed passwords).
   • Implement secure payment protocols (e.g., PCI DSS compliance).
5. Reliability:
   • Ensure accurate order tracking and payment status updates.
   • Implement retry mechanisms for payment and order processing.
6. Data Consistency:
   • Maintain strong consistency for critical operations (e.g., inventory updates).
7. Extensibility:
   • Enable easy integration of new features like dynamic pricing or flash sales.
8. Monitoring and Logging:
   • Provide real-time monitoring and error tracking for system health.

Capacity estimation

Estimate the scale of the system you are going to design...

Assumptions:

1. Users:
   • Total registered users: 50 million.
   • Daily active users (DAU): 10% of total users (5 million).
   • Peak concurrent users: 1% of DAU (50,000 users).
2. Products:
   • Total products listed: 10 million.
   • Average size of product data: ~2 KB (metadata, description, images).
3. Search and Browsing:
   • Peak searches per second: 2,000.
   • Peak product views per second: 5,000.
4. Transactions:
   • Orders per day: 1 million.
   • Peak orders per second: 1,000.

Resource Estimation:

1. Storage:
   • Product data: 10M×2 KB=20 GB10M \times 2 \, \text{KB} = 20 \, \text{GB}10M×2KB=20GB.
   • User profiles: 50M×1 KB=50 GB50M \times 1 \, \text{KB} = 50 \, \text{GB}50M×1KB=50GB.
   • Order history: 1M/day×1 KB=365 GB/year1M/day \times 1 \, \text{KB} = 365 \, \text{GB/year}1M/day×1KB=365GB/year.
2. Bandwidth:
   • Product views: 5,000/sec×2 KB=10 MB/sec5,000/sec \times 2 \, \text{KB} = 10 \, \text{MB/sec}5,000/sec×2KB=10MB/sec.
   • Orders: 1,000/sec×1 KB=1 MB/sec1,000/sec \times 1 \, \text{KB} = 1 \, \text{MB/sec}1,000/sec×1KB=1MB/sec.
3. Database:
   • Read-heavy: Optimize for search, browsing, and recommendations.
   • Write-heavy: Handle high order and review submissions.

API design

Define what APIs are expected from the system...

1. User Management APIs

• POST /api/users/register: Register a new user.
• POST /api/users/login: Authenticate a user.
• GET /api/users/profile: Fetch user details.
• PUT /api/users/profile: Update user profile.
• GET /api/users/orders: Retrieve order history.

2. Product Management APIs

• GET /api/products: Fetch product listings with filters.
• GET /api/products/{id}: Fetch product details by ID.
• POST /api/products/add: Add a new product (admin only).
• PUT /api/products/{id}: Update product details (admin only).
• DELETE /api/products/{id}: Delete a product (admin only).

3. Shopping Cart and Checkout APIs

• POST /api/cart/add: Add an item to the cart.
• GET /api/cart: View cart items.
• DELETE /api/cart/remove/{id}: Remove an item from the cart.
• POST /api/checkout: Process checkout and payment.

4. Order Management APIs

• POST /api/orders/create: Place a new order.
• GET /api/orders/{id}: Get order details.
• PUT /api/orders/cancel/{id}: Cancel an order.

5. Recommendation and Search APIs

• GET /api/recommendations: Fetch personalized recommendations.
• GET /api/search: Search for products with filters.

6. Review and Rating APIs

• POST /api/reviews/add: Add a review for a product.
• GET /api/reviews/{product_id}: Fetch reviews for a product.

7. Admin APIs

• GET /api/admin/reports: Fetch sales and user activity reports.
• POST /api/admin/manage-discounts: Create or update discounts.

Database design

Defining the system data model early on will clarify how data will flow among different components of the system. Also you could draw an ER diagram using the diagramming tool to enhance your design...

1. User Database

• Schema Details:
  • Users Table:
    • user_id (Primary Key), email (Unique), password_hash, name, address, phone_number, created_at.
• Purpose:
  • Store user profiles, authentication details, and preferences.
• Tech Used:
  • Relational Database (e.g., PostgreSQL, MySQL).
• Tradeoff:
  • Pros: Strong consistency and support for complex queries.
  • Cons: Scaling requires sharding or replication.

2. Product Database

• Schema Details:
  • Products Table:
    • product_id (Primary Key), name, description, price, category, rating, inventory_count.
• Purpose:
  • Store product details, including descriptions, pricing, and inventory.
• Tech Used:
  • NoSQL Database (e.g., MongoDB, DynamoDB).
• Tradeoff:
  • Pros: Flexible schema for diverse product attributes.
  • Cons: Limited support for complex relational queries.

3. Order Database

• Schema Details:
  • Orders Table:
    • order_id (Primary Key), user_id (Foreign Key), product_ids (JSON array), status, total_price, created_at.
• Purpose:
  • Track orders and their statuses.
• Tech Used:
  • Relational Database (e.g., PostgreSQL, MySQL).
• Tradeoff:
  • Pros: Reliable ACID transactions for order consistency.
  • Cons: Requires careful scaling for write-heavy workloads.

4. Review Database

• Schema Details:
  • Reviews Table:
    • review_id (Primary Key), user_id (Foreign Key), product_id (Foreign Key), rating, comment, created_at.
• Purpose:
  • Store reviews and ratings for products.
• Tech Used:
  • NoSQL Database (e.g., MongoDB).
• Tradeoff:
  • Pros: Scales well with high read/write throughput.
  • Cons: Limited transactional guarantees.

5. Recommendation Database

• Schema Details:
  • UserRecommendations Table:
    • user_id (Primary Key), recommended_products (JSON array).
• Purpose:
  • Store personalized recommendations for users.
• Tech Used:
  • NoSQL Database (e.g., Redis, DynamoDB).
• Tradeoff:
  • Pros: Low-latency reads for real-time recommendations.
  • Cons: Requires periodic updates for freshness.

High-level design

You should identify enough components that are needed to solve the actual problem from end to end. Also remember to draw a block diagram using the diagramming tool to augment your design. If you are unfamiliar with the tool, you can simply describe your design to the chat bot and ask it to generate a starter diagram for you to modify...

1. User Management Service

Overview:

• Handles user registration, login, authentication, and profile management.
• Manages user addresses, preferences, and order history.

Responsibilities:

• Securely authenticate users.
• Maintain user profiles and preferences.
• Provide APIs for accessing and updating user data.

2. Product Catalog Service

Overview:

• Manages product details, categories, pricing, and inventory.
• Powers product discovery, search, and filtering functionalities.

Responsibilities:

• Store and update product metadata.
• Support advanced search and filtering.
• Synchronize inventory levels with the order system.

3. Shopping Cart Service

Overview:

• Manages user-specific shopping carts.
• Allows adding, updating, and removing items in the cart.

Responsibilities:

• Track cart items and quantities for each user.
• Handle cart persistence across sessions.

4. Order Management Service

Overview:

• Processes orders, updates their statuses, and integrates with payment systems.
• Tracks orders from placement to delivery.

Responsibilities:

• Create and update order records.
• Handle cancellations and refunds.
• Notify users about order status changes.

5. Payment Gateway Integration

Overview:

• Facilitates secure payment processing.
• Integrates with third-party payment providers (e.g., Stripe, PayPal).

Responsibilities:

• Process payments and handle payment retries.
• Ensure compliance with PCI DSS standards.
• Manage fraud detection mechanisms.

6. Recommendation Engine

Overview:

• Provides personalized product recommendations based on user behavior and preferences.

Responsibilities:

• Analyze user browsing, purchase history, and similar-user behavior.
• Generate real-time recommendations for products.

7. Notification Service

Overview:

• Sends notifications via email, SMS, or push messages about orders, offers, and account updates.

Responsibilities:

• Notify users about order status changes, promotions, and account events.
• Manage notification preferences for each user.

8. Search Service

Overview:

• Enables users to search for products using keywords and filters.
• Optimized for low-latency and high-relevance results.

Responsibilities:

• Index product data for fast retrieval.
• Support advanced search queries with filters and sorting.

9. Admin Dashboard

Overview:

• Provides tools for administrators to manage products, view orders, and analyze sales data.

Responsibilities:

• Manage product listings and pricing.
• View and process orders.
• Generate reports on system activity and sales performance.

Request flows

Explain how the request flows from end to end in your high level design. Also you could draw a sequence diagram using the diagramming tool to enhance your explanation...

1. User Login Request

Steps:

1. API Gateway:
   • User sends a login request (POST /api/users/login) with credentials.
   • The gateway forwards the request to the User Management Service.
2. User Management Service:
   • Validates the credentials and generates a JWT token if successful.
   • Updates the last login timestamp in the User Database.
3. Response:
   • Returns the JWT token and user profile to the client.

2. Product Search Request

Steps:

1. API Gateway:
   • User sends a search request (GET /api/search) with keywords and filters.
   • The gateway forwards the request to the Search Service.
2. Search Service:
   • Queries the Product Catalog Database or its search index (e.g., Elasticsearch).
   • Retrieves relevant product results and applies sorting and filtering.
3. Response:
   • Returns the list of matching products to the client.

3. Add to Cart Request

Steps:

1. API Gateway:
   • User sends a request (POST /api/cart/add) to add an item to their cart.
   • The gateway forwards the request to the Shopping Cart Service.
2. Shopping Cart Service:
   • Validates the product and user.
   • Updates the cart in the Shopping Cart Database.
3. Response:
   • Confirms the item was added to the cart.

4. Checkout Request

Steps:

1. API Gateway:
   • User sends a checkout request (POST /api/checkout) with payment and shipping details.
   • The gateway forwards the request to the Order Management Service.
2. Order Management Service:
   • Validates the cart and calculates the total price.
   • Reserves inventory in the Product Catalog Database.
   • Creates an order record in the Order Database.
3. Payment Gateway Integration:
   • The Payment Gateway Integration service processes the payment.
   • On success, updates the order status to "Confirmed."
4. Order Management Service:
   • Sends order confirmation to the Notification Service.
   • Updates inventory levels in the Product Catalog Database.
5. Response:
   • Returns the order ID and confirmation to the client.

5. Order Tracking Request

Steps:

1. API Gateway:
   • User sends a request (GET /api/orders/{id}) to track their order.
   • The gateway forwards the request to the Order Management Service.
2. Order Management Service:
   • Fetches the order status from the Order Database.
3. Response:
   • Returns the current order status and tracking information.

6. Product Recommendation Request

Steps:

1. API Gateway:
   • User sends a request (GET /api/recommendations) for personalized recommendations.
   • The gateway forwards the request to the Recommendation Engine.
2. Recommendation Engine:
   • Analyzes user behavior and retrieves recommendations from the Recommendation Database.
3. Response:
   • Returns a list of recommended products to the client.

7. Notification Request

Steps:

1. Order Management Service:
   • Triggers a notification after an order status update.
2. Notification Service:
   • Formats the message and sends it via the user's preferred channel.
3. Response:
   • Confirms notification delivery.

Detailed component design

Dig deeper into 2-3 components and explain in detail how they work. For example, how well does each component scale? Any relevant algorithm or data structure you like to use for a component? Also you could draw a diagram using the diagramming tool to enhance your design...

1. User Management Service

End-to-End Working:

The User Management Service manages user authentication, profile updates, and preferences. When a user logs in, the service validates credentials, generates a JWT token, and updates the last login timestamp. For profile updates, the service validates and applies changes to the User Database.

Data Structures/Algorithms:

• Hash Map for Caching User Data:
  • Caches user session information to minimize database queries.
• Password Hashing:
  • Uses bcrypt or Argon2 to securely hash passwords for authentication.
• Rate Limiting:
  • Implements token bucket algorithm to prevent brute force attacks on login.

Scaling for Peak Traffic:

• Horizontal Scaling:
  • Multiple instances behind a load balancer handle concurrent authentication requests.
• Caching:
  • Session caching in Redis reduces database read pressure.
• Read Replicas:
  • Scales user profile retrieval using database replicas.

Edge Cases:

• Forgotten Password:
  • Secure reset links sent to registered emails.
• Token Expiry:
  • Provides a token refresh endpoint.
• Concurrent Logins:
  • Ensures session consistency by invalidating old tokens on new login.

2. Product Catalog Service

End-to-End Working:

The Product Catalog Service handles product details, search, and inventory updates. When a product is viewed, the service fetches details from the database. Search queries use a full-text search engine for low-latency responses.

Data Structures/Algorithms:

• Inverted Index:
  • Stores mappings of keywords to product documents for fast search (e.g., Elasticsearch).
• Caching:
  • Popular product data cached in Redis for faster retrieval.
• Atomic Inventory Updates:
  • Ensures inventory consistency during order placement using transactional locks.

Scaling for Peak Traffic:

• Search Sharding:
  • Distributes search queries across Elasticsearch shards.
• Horizontal Scaling:
  • Scales product catalog nodes independently of other services.
• CDN:
  • Caches static product images to reduce server load.

Edge Cases:

• Inventory Mismatch:
  • Implements a reconciliation process to sync inventory counts periodically.
• Stale Search Results:
  • Uses index refresh strategies to ensure search engine consistency.

3. Order Management Service

End-to-End Working:

This service processes orders, updates their statuses, and integrates with payment gateways. Upon checkout, it validates the cart, reserves inventory, and records the order in the database.

Data Structures/Algorithms:

• Write-Ahead Log:
  • Logs order operations for recovery in case of failure.
• Retry Logic:
  • Implements exponential backoff for retries with external systems (e.g., payment gateway).
• Database Transactions:
  • Ensures atomic operations for order creation, inventory reservation, and payment updates.

Scaling for Peak Traffic:

• Partitioned Order Storage:
  • Shards orders by user region for balanced writes.
• Event-Driven Architecture:
  • Decouples services using message queues for order events.

Edge Cases:

• Partial Payment Failures:
  • Rolls back inventory reservations and order creation.
• Duplicate Orders:
  • Uses unique order tokens to prevent re-submissions.

4. Payment Gateway Integration

End-to-End Working:

Handles secure payment processing by interacting with third-party payment providers. It validates payment details, processes transactions, and updates order statuses.

Data Structures/Algorithms:

• Tokenization:
  • Replaces sensitive payment details with tokens to enhance security.
• Fraud Detection:
  • Uses machine learning models to flag suspicious transactions.

Scaling for Peak Traffic:

• Concurrent Connection Management:
  • Manages high volumes of payment requests using connection pools.
• Retry Queues:
  • Queues failed transactions for retries without user intervention.

Edge Cases:

• Payment Timeouts:
  • Handles user notifications and retries in case of third-party delays.
• Fraudulent Transactions:
  • Integrates fraud detection to block high-risk payments.

5. Recommendation Engine

End-to-End Working:

Analyzes user behavior to provide personalized product suggestions. It uses collaborative filtering and content-based recommendations.

Data Structures/Algorithms:

• Collaborative Filtering:
  • Suggests products based on similar users’ preferences.
• Matrix Factorization:
  • Reduces dimensionality for large user-item datasets.
• Real-Time Processing:
  • Uses streaming data pipelines (e.g., Kafka) to update recommendations.

Scaling for Peak Traffic:

• Batch and Real-Time Processing:
  • Combines precomputed recommendations with real-time updates.
• Distributed Compute:
  • Uses Spark for large-scale data processing.

Edge Cases:

• Cold Start Problem:
  • Uses popularity-based recommendations for new users.
• Overfitting:
  • Regularizes recommendation models to avoid bias.

Trade offs/Tech choices

Explain any trade offs you have made and why you made certain tech choices...

1. Relational vs. NoSQL Databases:
   • Trade-off: Relational for user and order data, NoSQL for products and recommendations.
   • Reason: Balances ACID compliance with scalability and flexibility.
2. Elasticsearch for Search:
   • Trade-off: Introduces operational overhead.
   • Reason: Provides low-latency and relevance-based search.
3. Event-Driven Architecture:
   • Trade-off: Adds complexity in managing message queues.
   • Reason: Decouples services for better scalability and fault tolerance.

Failure scenarios/bottlenecks

Try to discuss as many failure scenarios/bottlenecks as possible.

Database Bottleneck:

• High write loads can cause contention.
• Mitigation: Sharding and replication.

Payment Gateway Failures:

• Third-party downtime delays order confirmation.
• Mitigation: Implement retries with fallback options.

Inventory Mismatch:

• Concurrent updates can cause inconsistencies.
• Mitigation: Use atomic operations or distributed locks.

Recommendation Staleness:

• Delays in updating models affect personalization.
• Mitigation: Real-time data pipelines.

Future improvements

What are some future improvements you would make? How would you mitigate the failure scenario(s) you described above?

Microservices Transition:

• Migrate to microservices for independent scaling.
• Mitigation: Reduces contention among services.

Dynamic Pricing:

• Introduce real-time price adjustments based on demand.
• Mitigation: Prevents overloading during sales.

Predictive Scaling:

• Use ML models to predict traffic spikes and auto-scale resources.
• Mitigation: Ensures consistent performance during peaks.

Advanced Fraud Detection:

• Enhance fraud detection with real-time anomaly detection.
• Mitigation: Minimizes losses from fraudulent transactions.