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
- Validation: Always validate input at the boundary
- Error Handling: Use structured error responses
- Logging: Log key events for debugging
- Testing: Unit, integration, and load tests
- 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
- Validation: Always validate input at the boundary
- Error Handling: Use structured error responses
- Logging: Log key events for debugging
- Testing: Unit, integration, and load tests
- 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
- Validation: Always validate input at the boundary
- Error Handling: Use structured error responses
- Logging: Log key events for debugging
- Testing: Unit, integration, and load tests
- Documentation: Keep docs updated with code changes
Practice Problems
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
}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, idempotencyWrite 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 injectionQuiz
1. Why use PostgreSQL ltree for categories?
2. Which pagination is better for large datasets?
3. What is the primary purpose of Product Catalog Backend?
4. What is a common mistake when implementing Product Catalog Backend?
Flashcards
Question
Why ltree for categories?
Click to reveal answer
Answer
Efficient hierarchical queries without recursion
Question
Cursor vs offset pagination?
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Answer
Cursor: consistent. Offset: degrades on deep pages
Question
What is Product Catalog Backend?
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Answer
Product Catalog Backend is a key concept in backend development.
Question
When to use Product Catalog Backend?
Click to reveal answer
Answer
Use Product Catalog Backend when building production systems that require reliability, scalability, and maintainability.
Question
Product Catalog Backend best practices
Click to reveal answer
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