The Challenge
You need a backend for an online store that handles product browsing, cart management, and checkout without managing servers. A serverless architecture keeps costs proportional to traffic, scales automatically during demand spikes like sales events, and removes the operational burden of patching and capacity planning.
What You'll Build
- → REST API with endpoints for products, cart, and orders
- → NoSQL database tables for e-commerce data
- → Asynchronous order processing pipeline
- → CDN-backed product image delivery
- → Transactional email for order confirmations
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Architecture Overview
This architecture uses a fully serverless approach to e-commerce backend infrastructure. API Gateway provides the HTTP interface, Lambda functions implement business logic, DynamoDB stores product and order data, and SQS decouples the checkout flow from order fulfillment. This separation means the customer-facing checkout call returns immediately after placing an order on the queue, while background processing handles payment validation, inventory updates, and email confirmation.
The serverless model is a deliberate trade-off. You give up the ability to run long-lived processes or maintain in-memory state across requests, but you gain automatic scaling, per-request billing, and zero server maintenance. For an e-commerce workload that is read-heavy (product browsing) with bursty writes (checkout surges), this trade-off strongly favors serverless.
Product images are stored in S3 and served through CloudFront, which caches them at edge locations globally. This offloads image delivery from your API entirely and ensures fast load times regardless of where customers are located.
API Layer
API Gateway exposes RESTful endpoints with request validation and rate limiting. Each endpoint maps to a Lambda function that handles the business logic for that domain: products, cart, or orders. Request validation at the gateway layer rejects malformed requests before they invoke Lambda, reducing cost and improving error reporting.
Data Model
DynamoDB tables store products, customer carts, and orders. Each table uses partition and sort keys designed around the access patterns of an e-commerce application: looking up products by category, retrieving a customer’s cart, and querying orders by customer or date range. Single-table design is possible but separate tables keep the data model easier to understand and evolve.
Async Order Processing
When a customer checks out, the API places the order on an SQS queue and returns a confirmation immediately. A separate Lambda function processes orders from the queue, handling payment finalization, inventory adjustment, and triggering confirmation emails through SES. If processing fails, the message returns to the queue for retry, ensuring no orders are lost.
Common Customizations
- Add payment gateway integration: Extend the prompt to include Stripe or other payment processor webhook endpoints and secret management through Secrets Manager or Parameter Store.
- Implement inventory reservations: Ask for a reservation pattern where adding items to the cart temporarily holds inventory, preventing overselling during high-demand events.
- Add search with OpenSearch: Request an OpenSearch domain for full-text product search with faceted filtering, connected to DynamoDB through a stream-triggered Lambda that keeps the search index in sync.
- Enable multi-currency pricing: Ask for pricing logic that supports multiple currencies with exchange rate management and region-based price display.
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