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Product Catalog Backend

Design a product catalog with categories, variants, and pricing.

1h 30m
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Requirements & Scope

Scale

  • 10M products, 50 variants/product
  • 10K categories
  • 100K reads/sec, 1K writes/sec

Features

  • Hierarchical categories (ltree)
  • JSONB for dynamic attributes
  • Dynamic pricing (regional, discount, bulk)

Key Points

  • Understanding Product Catalog Backend is essential for production systems
  • Always consider scalability and maintainability
  • Test thoroughly before deploying to production
  • Monitor performance and set up alerting

Common Patterns

  1. Validation: Always validate input at the boundary
  2. Error Handling: Use structured error responses
  3. Logging: Log key events for debugging
  4. Testing: Unit, integration, and load tests
  5. Documentation: Keep docs updated with code changes

Architecture & Data Model

PostgreSQL with ltree

CREATE TABLE categories (
  id BIGSERIAL PRIMARY KEY,
  name VARCHAR(200) NOT NULL,
  parent_id BIGINT REFERENCES categories(id),
  path LTREE
);

-- Hierarchy query
SELECT * FROM categories WHERE path @> 'clothing.shoes.running';

Dynamic Pricing

base_price -> regional -> discount -> bulk

Pagination

Cursor-based for large datasets (consistent performance)

Key Points

  • Understanding Product Catalog Backend is essential for production systems
  • Always consider scalability and maintainability
  • Test thoroughly before deploying to production
  • Monitor performance and set up alerting

Common Patterns

  1. Validation: Always validate input at the boundary
  2. Error Handling: Use structured error responses
  3. Logging: Log key events for debugging
  4. Testing: Unit, integration, and load tests
  5. Documentation: Keep docs updated with code changes

Database Design

Tables

  • categories(id, name, parent_id, path LTREE)
  • products(id, name, category_id, base_price, attributes JSONB)
  • product_variants(id, product_id, sku, attributes JSONB, price, stock)

Caching

  • Product cache: Redis 5min TTL
  • Category cache: Redis 1hr TTL
  • 95% reads, 5% writes - cache-aside pattern

Key Points

  • Understanding Product Catalog Backend is essential for production systems
  • Always consider scalability and maintainability
  • Test thoroughly before deploying to production
  • Monitor performance and set up alerting

Common Patterns

  1. Validation: Always validate input at the boundary
  2. Error Handling: Use structured error responses
  3. Logging: Log key events for debugging
  4. Testing: Unit, integration, and load tests
  5. Documentation: Keep docs updated with code changes

Practice Problems

0 / 3 solved
Implement Product Catalog Backend

Design and implement a solution for Product Catalog Backend in a backend system. Consider scalability, error handling, and production readiness.

Solution 
// Product Catalog Backend implementation
// Key aspects: validation, error handling, logging, testing

public class ProductCatalogBackend {
    // Production-ready implementation
}
Product Catalog Backend Edge Cases

Identify and handle edge cases for Product Catalog Backend. What happens under high load, with invalid input, or during failures?

Solution 
// Edge case handling:
// 1. Null/empty input -> validation
// 2. High load -> rate limiting, queuing
// 3. Failures -> retries, circuit breaker
// 4. Concurrent access -> locks, idempotency
Product Catalog Backend Testing Strategy

Write a testing strategy for Product Catalog Backend. Include unit tests, integration tests, and performance tests.

Solution 
// Test plan:
// - Unit: 80% coverage target
// - Integration: API contracts
// - Performance: latency, throughput
// - Chaos: failure injection

Quiz

1. Why use PostgreSQL ltree for categories?

Question 1 options

2. Which pagination is better for large datasets?

Question 2 options

3. What is a common mistake when implementing Product Catalog Backend?

Question 3 options

Flashcards

Question

Why ltree for categories?

Click to reveal answer

Answer

Efficient hierarchical queries without recursion

Question

Cursor vs offset pagination?

Click to reveal answer

Answer

Cursor: consistent. Offset: degrades on deep pages

Question

Product Catalog Backend best practices

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Answer

Follow SOLID principles, write clean code, test thoroughly, document decisions, and monitor in production.

Revision Notes

Key Takeaways

  • 1. ltree for category hierarchy
  • 2. JSONB for dynamic attributes
  • 3. Cache-aside for read-heavy catalog
  • 4. Cursor pagination for scale

Interview Tips

  • • Explain product variant model
  • • Discuss SQL vs NoSQL tradeoffs

Cheat Sheet

Product Catalog

  • Categories: PostgreSQL ltree
  • Attributes: JSONB (flexible schema)
  • Cache: Redis with 5min TTL
  • Pagination: Cursor-based for scale