System requirements

Functional Requirements

Core Functionalities:

Storefront Management:
- Allow businesses to create customizable storefronts with templates.
- Support SEO optimization and mobile responsiveness.
Product Management:
- Enable merchants to add, edit, and organize products with details (e.g., name, description, price, category).
- Support product variants (e.g., size, color).
Inventory Management:
- Track stock levels for each product.
- Alert merchants when stock is low or out.
Order Processing:
- Manage order lifecycle (e.g., pending, processing, shipped, delivered).
- Generate invoices and allow order cancellations or returns.
Payment Integration:
- Integrate with multiple payment gateways (e.g., Stripe, PayPal).
- Support different currencies and tax configurations.
Customer Management:
- Store customer details and order history.
- Enable customer segmentation for targeted marketing.
Analytics and Reports:
- Provide sales, traffic, and customer insights to merchants.
- Support exportable reports for further analysis.
Marketing and SEO Tools:
- Offer discount codes, email marketing integration, and social media tools.
- Provide tools for ad campaign tracking.
Multi-Tenant Architecture:
- Allow multiple businesses to host their stores on the platform with isolated data.
Scalability and Extensibility:
- Enable merchants to integrate third-party apps and plugins.

Non-Functional Requirements

Scalability:
- Handle thousands of stores and millions of customers concurrently.
- Support high transaction volumes during sales and holiday seasons.
Availability:
- Ensure 99.99% uptime for uninterrupted store access.
Performance:
- Ensure sub-second page load times for storefronts and backend.
- Process orders within milliseconds to avoid delays.
Security:
- Encrypt sensitive data (e.g., customer details, payment info).
- Implement robust access controls for merchants and customers.
Data Consistency:
- Ensure accurate inventory and order statuses across the platform.
Extensibility:
- Provide APIs for third-party integrations (e.g., shipping, payments, CRM).
Monitoring and Logging:
- Continuously monitor system health and log critical events for debugging.

Capacity estimation

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

Assumptions:

Stores:
- Total stores: 500,000.
- Active stores daily: 20% (100,000).
Products:
- Average products per store: 200.
- Total products: 500,000×200=100 million500,000 \times 200 = 100 \, \text{million}500,000×200=100million.
Customers:
- Total customers: 50 million.
- Active customers daily: 20% (10 million).
Orders:
- Average orders/day: 1 million.
- Peak orders/day: 5 million (during sales events).
Storage:
- Average product size (images + metadata): 1 MB.
- Total product data: 100 million×1 MB=100 TB100 \, \text{million} \times 1 \, \text{MB} = 100 \, \text{TB}100million×1MB=100TB.

API design

Define what APIs are expected from the system...

1. Storefront APIs

POST /api/storefront/create: Create a new storefront for a merchant.
PUT /api/storefront/update/{store_id}: Update store settings and templates.
GET /api/storefront/{store_id}: Fetch storefront details for rendering.

2. Product Management APIs

POST /api/products/add: Add a new product.
GET /api/products/{store_id}: Fetch products for a specific store.
PUT /api/products/update/{product_id}: Update product details.
DELETE /api/products/delete/{product_id}: Remove a product.

3. Inventory Management APIs

GET /api/inventory/{store_id}: Fetch inventory for a specific store.
PUT /api/inventory/update/{product_id}: Update stock levels.

4. Order Processing APIs

POST /api/orders/create: Create a new order.
GET /api/orders/{order_id}: Fetch order details.
PUT /api/orders/update/{order_id}: Update order status.
POST /api/orders/return: Process order returns.

5. Payment Integration APIs

POST /api/payments/charge: Process a payment.
GET /api/payments/status/{transaction_id}: Check payment status.

6. Customer Management APIs

POST /api/customers/add: Add a new customer.
GET /api/customers/{customer_id}: Fetch customer details and order history.

7. Analytics APIs

GET /api/analytics/{store_id}: Fetch sales and customer analytics.
POST /api/analytics/export: Export analytics data

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. Storefront Database

Schema Details:
- Table Name: Stores
  - store_id (Primary Key): Unique identifier for each store.
  - merchant_id: Owner of the store.
  - template: Selected store template.
  - created_at: Store creation date.
Purpose:
- Manage store settings, configurations, and templates.
Tech Used:
- Relational Database (e.g., PostgreSQL).
Tradeoff:
- Pros: Strong consistency for multi-tenant data.
- Cons: Sharding required for scalability.

2. Product Database

Schema Details:
- Table Name: Products
  - product_id (Primary Key): Unique identifier for each product.
  - store_id (Foreign Key): Associated store ID.
  - name: Product name.
  - price: Product price.
  - description: Product description.
  - category: Product category.
Purpose:
- Store product details for all stores.
Tech Used:
- Relational Database (e.g., MySQL).
Tradeoff:
- Pros: Supports complex queries for search and filtering.
- Cons: Indexing needed for optimal search performance.

3. Order Database

Schema Details:
- Table Name: Orders
  - order_id (Primary Key): Unique identifier for each order.
  - store_id (Foreign Key): Associated store ID.
  - customer_id (Foreign Key): Associated customer ID.
  - status: Order status (e.g., pending, shipped, delivered).
  - total_amount: Order total amount.
Purpose:
- Track and manage orders across the platform.
Tech Used:
- Relational Database with sharding (e.g., PostgreSQL).
Tradeoff:
- Pros: Ensures transactional consistency.
- Cons: Requires partitioning to handle large datasets.

4. Payment Database

Schema Details:
- Table Name: Payments
  - transaction_id (Primary Key): Unique identifier for each transaction.
  - order_id (Foreign Key): Associated order ID.
  - payment_method: Payment method used.
  - status: Payment status (e.g., success, failed).
  - amount: Payment amount.
Purpose:
- Store payment transaction details.
Tech Used:
- NoSQL Database (e.g., DynamoDB).
Tradeoff:
- Pros: High scalability for payment tracking.
- Cons: Limited support for complex relational queries.

5. Analytics Database

Schema Details:
- Table Name: Analytics
  - store_id (Foreign Key): Associated store ID.
  - date: Date of the data point.
  - sales: Total sales for the day.
  - visitors: Total visitors for the day.
  - conversion_rate: Sales-to-visitors ratio.
Purpose:
- Store aggregated data for merchant analytics.
Tech Used:
- Columnar Database (e.g., Amazon Redshift).
Tradeoff:
- Pros: Optimized for read-heavy analytical queries.
- Cons: Inefficient for frequent updates.

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. Storefront Management Service

Overview:

Handles the creation, customization, and management of online storefronts for merchants. It manages store templates, themes, and SEO configurations.

Responsibilities:

Provide tools for merchants to create and customize storefronts.
Manage template and theme settings for store design.
Handle SEO metadata and configurations for better search engine visibility.

2. Product Management Service

Overview:

Enables merchants to add, update, and organize their product catalogs. Supports product variations, pricing, and categories.

Responsibilities:

Manage product catalogs, including metadata and media uploads.
Support product variations (e.g., size, color).
Provide APIs for retrieving product details for storefronts.

3. Inventory Management Service

Overview:

Tracks and updates stock levels for all products across multiple stores. Sends alerts when inventory is low or out of stock.

Responsibilities:

Monitor and update inventory levels.
Provide APIs to fetch stock status for storefronts.
Trigger alerts or notifications for low-stock items.

4. Order Processing Service

Overview:

Handles the entire lifecycle of an order, from creation to fulfillment. Supports order tracking, returns, and cancellations.

Responsibilities:

Process orders and update their statuses.
Generate invoices and receipts.
Handle order modifications, cancellations, and returns.

5. Payment Gateway Integration

Overview:

Facilitates secure payment processing for orders. Integrates with multiple payment gateways (e.g., Stripe, PayPal).

Responsibilities:

Process payments and manage payment statuses.
Handle currency conversions and tax calculations.
Ensure PCI-DSS compliance for secure payment transactions.

6. Customer Management Service

Overview:

Manages customer information, including personal details, order history, and preferences. Supports segmentation for targeted marketing.

Responsibilities:

Store and update customer profiles.
Provide APIs to fetch customer details for order and marketing purposes.
Enable customer segmentation for analytics and campaigns.

7. Analytics Service

Overview:

Provides insights into store performance, customer engagement, and sales trends. Generates reports and visualizations for merchants.

Responsibilities:

Aggregate and analyze data from orders, inventory, and customer activity.
Provide dashboards for merchants with sales and performance insights.
Generate exportable reports for further analysis.

8. Search and Discovery Service

Overview:

Allows customers to search for products and browse categories. Implements personalization and filters for better discovery.

Responsibilities:

Index product metadata for fast retrieval.
Handle advanced search queries with filters and sorting.
Personalize recommendations based on customer preferences.

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. Merchant Creates a Storefront

Objective: Create a new storefront for a merchant.

Steps:

API Gateway:
- Receives a POST /api/storefront/create request with merchant details.
- Validates the request and forwards it to the Storefront Management Service.
Storefront Management Service:
- Creates a new storefront record in the Storefront Database.
- Assigns a default template and generates an initial URL.
Response:
- Confirms storefront creation and returns the store ID and URL.

2. Customer Searches for a Product

Objective: Retrieve products based on a search query.

Steps:

API Gateway:
- Receives a GET /api/search request with query parameters.
- Validates the input and forwards it to the Search and Discovery Service.
Search and Discovery Service:
- Queries the Product Database or search index for relevant results.
- Applies filters and ranking for personalization.
Response:
- Returns a list of matching products with metadata and pricing.

3. Customer Places an Order

Objective: Process an order for a customer.

Steps:

API Gateway:
- Receives a POST /api/orders/create request with order details.
- Authenticates the customer and forwards the request to the Order Processing Service.
Order Processing Service:
- Validates the order and reserves stock using the Inventory Management Service.
- Stores the order details in the Order Database.
Payment Gateway Integration:
- Processes the payment and updates the payment status in the Payment Database.
Response:
- Confirms the order and returns the order ID and payment receipt.

4. Merchant Views Analytics

Objective: Retrieve sales and performance data for a store.

Steps:

API Gateway:
- Receives a GET /api/analytics/{store_id} request.
- Authenticates the merchant and forwards the request to the Analytics Service.
Analytics Service:
- Aggregates data from the Order Database and Customer Database.
- Computes insights such as sales trends, popular products, and customer demographics.
Response:
- Returns the requested analytics data in a dashboard-friendly format.

5. Inventory Update for a Product

Objective: Update stock levels for a product.

Steps:

API Gateway:
- Receives a PUT /api/inventory/update/{product_id} request with stock details.
- Validates the request and forwards it to the Inventory Management Service.
Inventory Management Service:
- Updates the stock level in the Inventory Database.
- Sends a notification if the stock level is low.
Response:
- Confirms the inventory update.

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. Storefront Management Service

End-to-End Working:

The Storefront Management Service manages the creation and customization of online stores. When a merchant requests to create a store, the service validates the input, generates a unique store ID, assigns a default template, and saves the store configuration in the database. Merchants can later update themes, layouts, and SEO metadata through APIs, and these changes are immediately applied to the storefront.

Communication:

Protocols:
- HTTP/HTTPS: Handles external client requests for storefront creation and updates.
- REST APIs: Communicates with the Product Management Service to fetch product data for rendering.
- Message Queues (e.g., Kafka): Publishes events for analytics when store updates occur.
Inter-Service Communication:
- Fetches analytics data from the Analytics Service to display store performance metrics.
- Retrieves product catalogs via the Product Management Service.

Data Structures and Algorithms:

Trie for URL Management:
- Each store is assigned a unique subdomain (e.g., store123.shopify.com). A trie structure maps subdomains to store configurations for efficient lookup.
JSON Schema Validation:
- Validates storefront configuration updates using predefined schemas.
Cache Management:
- Frequently accessed store configurations are cached in Redis for quick retrieval.

Example Code:

python
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class StorefrontTrie:
    def __init__(self):
        self.trie = {}

    def add_store(self, subdomain, config):
        current = self.trie
        for char in subdomain:
            if char not in current:
                current[char] = {}
            current = current[char]
        current['config'] = config

    def get_config(self, subdomain):
        current = self.trie
        for char in subdomain:
            if char not in current:
                return None
            current = current[char]
        return current.get('config')

Scaling for Peak Traffic:

Horizontal Scaling:
- Stateless instances handle API requests, with a load balancer distributing traffic.
Edge Caching:
- Use CDNs to cache static assets (e.g., store templates, CSS) at edge locations to reduce latency.

Edge Cases:

Duplicate Store Names:
- Enforce uniqueness by appending random characters or suggesting alternative names.
Configuration Errors:
- Validate configurations before applying changes and maintain version control for rollback.

2. Product Management Service

End-to-End Working:

The Product Management Service enables merchants to add, update, and manage products. When a product is added, the service validates metadata, processes media uploads (e.g., images), and stores details in the database. It also supports variant management (e.g., size, color) and ensures product availability is synchronized with the Inventory Management Service.

Communication:

Protocols:
- HTTP/HTTPS: Handles external product creation and update requests.
- REST APIs: Fetches inventory details from the Inventory Management Service.
- gRPC: Low-latency communication with the Search and Discovery Service for product indexing.
Inter-Service Communication:
- Sends product details to the Search Service for indexing.
- Notifies the Inventory Service of new or updated products.

Data Structures and Algorithms:

Inverted Index for Search:
- Maps product attributes (e.g., name, tags, category) to product IDs for efficient search queries.
Hash-Based Deduplication:
- Identifies duplicate product images using SHA-256 hashes before uploading to storage.

Example Code for Product Indexing:

python
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class ProductIndex:
    def __init__(self):
        self.index = defaultdict(list)

    def add_to_index(self, product_id, attributes):
        for attribute in attributes:
            self.index[attribute].append(product_id)

    def search(self, query):
        return self.index.get(query, [])

Scaling for Peak Traffic:

Database Partitioning:
- Partition products by store ID to distribute load across shards.
Asynchronous Processing:
- Use message queues to handle background tasks like image processing and indexing.

Edge Cases:

Oversized Media Files:
- Limit file sizes during upload and provide compression.
Invalid Product Metadata:
- Validate metadata against a schema and return detailed error messages.

3. Order Processing Service

End-to-End Working:

The Order Processing Service handles the lifecycle of orders. When a customer places an order, the service validates the order details, calculates taxes and shipping, and reserves stock through the Inventory Management Service. It updates the order status (e.g., pending, shipped) and generates invoices or receipts.

Communication:

Protocols:
- REST APIs: Receives order requests and communicates with Payment Gateway and Inventory Services.
- Message Queues: Publishes events for Analytics and Notification Services.
Inter-Service Communication:
- Retrieves customer details from the Customer Management Service.
- Integrates with Payment Gateway APIs for payment processing.

Data Structures and Algorithms:

Queue for Order Lifecycle:
- Implements a state machine with states like pending, processing, shipped, and delivered.
Order Deduplication:
- Uses a unique hash (e.g., MD5) of the order details to prevent duplicate submissions.

Order Lifecycle Example:

python
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class OrderStateMachine:
    states = ['pending', 'processing', 'shipped', 'delivered']

    def __init__(self):
        self.state = 'pending'

    def advance_state(self):
        current_index = self.states.index(self.state)
        if current_index + 1 < len(self.states):
            self.state = self.states[current_index + 1]

    def current_state(self):
        return self.state

Scaling for Peak Traffic:

Horizontal Scaling:
- Use stateless order processing instances behind a load balancer.
Autoscaling:
- Automatically scale instances based on order volume spikes.

Edge Cases:

Payment Failures:
- Implement retry mechanisms and notify customers of failed transactions.
Inventory Mismatch:
- Use distributed locks to ensure consistency during stock reservation.

4. Payment Gateway Integration

End-to-End Working:

The Payment Gateway Integration service processes payments securely. When an order is placed, it interfaces with third-party payment providers (e.g., Stripe, PayPal) to validate and charge the customer. It updates the payment status and handles refunds or disputes.

Communication:

Protocols:
- HTTPS: Secure communication with external payment gateways.
- REST APIs: Receives payment requests from the Order Processing Service.
Inter-Service Communication:
- Sends payment statuses to the Order Processing and Analytics Services.

Data Structures and Algorithms:

Payment Tokenization:
- Replaces sensitive payment details with tokens for secure storage.
Retry Mechanism:
- Implements exponential backoff for retrying failed payment requests.

Tokenization Example:

python
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class PaymentToken:
    def tokenize(self, card_details):
        # Simulated token generation
        return hashlib.sha256(card_details.encode()).hexdigest()

    def validate_token(self, token):
        # Validation logic
        return token in self.valid_tokens

Scaling for Peak Traffic:

Multi-Region Setup:
- Deploy services in multiple regions to reduce latency for global customers.
Concurrent Processing:
- Handle high payment volumes using asynchronous processing and distributed queues.

Edge Cases:

Fraudulent Transactions:
- Integrate fraud detection systems with AI to flag suspicious activities.
Currency Mismatch:
- Provide real-time currency conversion and validation.

Trade offs/Tech choices

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

Microservices Architecture:

Trade-off: Increased operational complexity and inter-service communication overhead.
Reason: Enables independent scaling, fault isolation, and flexibility for adding new features.

Object Storage for Media Files:

Trade-off: Higher latency compared to local storage.
Reason: Scalable, cost-effective storage for large volumes of product images and media files.

Relational vs. NoSQL Databases:

Trade-off: Relational databases offer strong consistency but require sharding for scalability.
Reason: Strong consistency is critical for orders, payments, and inventory data.

Message Queues for Asynchronous Tasks:

Trade-off: Adds delay for non-critical operations (e.g., analytics, notifications).
Reason: Decouples services and improves overall system resilience.

Failure scenarios/bottlenecks

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

Inventory Mismatch:

Issue: Simultaneous orders may lead to incorrect stock levels.
Mitigation: Use distributed locks or optimistic concurrency control for inventory updates.

Payment Gateway Failures:

Issue: External payment gateways may experience downtime.
Mitigation: Implement retry logic with exponential backoff and allow customers to retry payments manually.

High Query Load on Search Service:

Issue: Large search traffic during peak sales could slow down response times.
Mitigation: Use Elasticsearch with horizontal scaling and caching for frequent queries.

Order Processing Delays:

Issue: High volume of orders during flash sales may overwhelm the system.
Mitigation: Use asynchronous processing and autoscaling for the Order Processing Service.

Data Loss in Analytics:

Issue: Overloaded message queues may drop analytics events.
Mitigation: Use redundant queues and implement dead-letter queues for failed events.

Future improvements

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

Predictive Autoscaling:

Improvement: Use AI/ML models to predict traffic spikes and scale resources proactively.
Mitigation: Prevent service outages during unexpected traffic surges.

AI-Powered Fraud Detection:

Improvement: Integrate AI-based fraud detection for payments.
Mitigation: Reduce chargebacks and fraudulent transactions.

Enhanced Search Personalization:

Improvement: Implement AI-based recommendations and personalized search results.
Mitigation: Improve customer experience and conversion rates.

Multi-Region Deployment:

Improvement: Deploy services in multiple geographic regions for better latency and fault tolerance.
Mitigation: Reduce downtime and improve performance for global users.

Blockchain for Order and Payment Records:

Improvement: Use blockchain to create tamper-proof logs for critical transactions.
Mitigation: Enhance transparency and trust in the system.

Solutions

john_chen - 03/31/2024

1

iridescent_luminous693 - 01/07/2025

0