Filtering Patterns
Query Parameter Filtering
GET /products?category=electronics
GET /products?minPrice=100&maxPrice=500
GET /products?brand=Apple&inStock=true
Filter Types
| Type | Example | Use Case |
|---|---|---|
| Equality | ?status=active |
Exact match |
| Range | ?minPrice=100&maxPrice=500 |
Numeric ranges |
| Contains | ?name=laptop |
Text search |
| List | ?category=phone,tablet |
Multiple values |
| Date range | ?from=2025-01-01&to=2025-12-31 |
Time periods |
| Boolean | ?inStock=true |
True/false flags |
Advanced Filtering
# Nested filters
GET /products?category.name=electronics&category.level=2
# Multiple values (OR)
GET /products?status=active,pending
# Negation
GET /products?status=!cancelled
# Array contains
GET /products?tags=wireless,bluetooth
Filtering Implementation
@GetMapping("/products")
public ResponseEntity<List<Product>> getProducts(
@RequestParam(required = false) String category,
@RequestParam(required = false) BigDecimal minPrice,
@RequestParam(required = false) BigDecimal maxPrice,
@RequestParam(required = false) Boolean inStock,
Pageable pageable) {
return ResponseEntity.ok(
productService.filter(category, minPrice, maxPrice, inStock, pageable)
);
}
Best Practices
Key Principles
- Follow SOLID principles
- Write clean, readable code
- Test thoroughly
- Document decisions
- Monitor in production
Implementation
- Start simple, refactor as needed
- Use established patterns
- Consider trade-offs
- Review with peers
Continuous Improvement
- Learn from incidents
- Update documentation
- Share knowledge
- Mentor others
Key Points
- Understanding Filtering 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 Filtering in a backend system. Consider scalability, error handling, and production readiness.
Solution
// Filtering implementation
// Key aspects: validation, error handling, logging, testing
public class Filtering {
// Production-ready implementation
} Identify and handle edge cases for Filtering. 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 Write a testing strategy for Filtering. 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. How should multiple filter values for the same field be handled?
2. Which filtering pattern is most RESTful?
3. What is a common mistake when implementing Filtering?
Flashcards
Question
RESTful filtering pattern?
Click to reveal answer
Answer
GET /resource?field=value (query parameters)
Question
How to handle multiple filter values?
Click to reveal answer
Answer
Comma-separated: ?status=active,pending
Question
Filtering 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. Use query parameters for filtering: /products?category=electronics
- 2. Support equality, range, list, and boolean filters
- 3. Combine filtering with pagination and sorting
- 4. Make all filter parameters optional
Interview Tips
- • Design filtering for a given scenario
- • Know how to handle complex filter combinations
Cheat Sheet
Filtering
- Equality:
?status=active - Range:
?minPrice=100&maxPrice=500 - List:
?category=phone,tablet - Boolean:
?inStock=true - Rule: All filters optional, GET with query params