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Design search endpoints with query parameters and full-text search.

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Search Design

Search Endpoint Patterns

# Dedicated search endpoint
GET /products/search?q=wireless+mouse&category=electronics

# Query parameter on collection
GET /products?q=wireless+mouse

# POST for complex searches
POST /products/search
{
  "query": "wireless mouse",
  "filters": { "category": "electronics", "maxPrice": 50 },
  "sort": "relevance",
  "page": 1
}

Search Features

Feature Example Description
Full-text ?q=laptop Search across fields
Exact match ?category=electronics Exact field match
Fuzzy ?q=lapotp Handle typos
Autocomplete ?prefix=lapt Partial matching
Highlight ?highlight=true Show matched terms

Search + Filter + Sort

GET /products/search?q=wireless&category=electronics&minPrice=20&sort=relevance&page=1&limit=20

Search Implementation (Spring Data)

@Repository
public interface ProductRepository extends JpaRepository<Product, Long> {

    @Query("SELECT p FROM Product p WHERE " +
           "LOWER(p.name) LIKE LOWER(CONCAT('%', :query, '%')) OR " +
           "LOWER(p.description) LIKE LOWER(CONCAT('%', :query, '%'))")
    Page<Product> search(@Param("query") String query, Pageable pageable);

    // Full-text search with Elasticsearch
    @Query("{"multi_match": {"query": "?0", "fields": ["name", "description"]}}")
    List<Product> fullTextSearch(String query);
}

Architecture Patterns

Patterns

  • Layered: Traditional
  • Microservices: Distributed
  • Event-Driven: Async
  • Serverless: FaaS

Principles

  • Single Responsibility
  • loose coupling
  • High cohesion
  • Separation of concerns

Best Practices

  • Document decisions
  • Use ADRs
  • Consider trade-offs
  • Design for change

Key Points

  • Understanding Search Implementation 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

0/3solved
Implement Search Implementation

Design and implement a solution for Search Implementation in a backend system. Consider scalability, error handling, and production readiness.

Solution
// Search Implementation implementation
// Key aspects: validation, error handling, logging, testing

public class SearchImplementation {
    // Production-ready implementation
}
Search Implementation Edge Cases

Identify and handle edge cases for Search Implementation. 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
Search Implementation Testing Strategy

Write a testing strategy for Search Implementation. 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. When should you use POST for search instead of GET?

Question 1 options

2. What is fuzzy search?

Question 2 options

3. What is the primary purpose of Search Implementation?

Question 3 options

4. What is a common mistake when implementing Search Implementation?

Question 4 options

Flashcards

Question

GET vs POST for search?

Click to reveal answer

Answer

GET for simple queries, POST for complex/nested search requests

Question

What is fuzzy search?

Click to reveal answer

Answer

Search that tolerates typos using edit distance algorithms

Question

What is Search Implementation?

Click to reveal answer

Answer

Search Implementation is a key concept in backend development.

Question

When to use Search Implementation?

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Answer

Use Search Implementation when building production systems that require reliability, scalability, and maintainability.

Question

Search Implementation 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.Use GET for simple search, POST for complex search queries
  • 2.Combine search with filtering, sorting, and pagination
  • 3.Consider Elasticsearch for full-text search at scale
  • 4.Always validate and sanitize search queries

Interview Tips

  • •Design search endpoints for a given scenario
  • •Know when to use dedicated search services

Cheat Sheet

Search

  • Simple: GET /products?q=wireless
  • Complex: POST /products/search { filters, sort }
  • Features: Full-text, fuzzy, autocomplete, highlight
  • Scale: Consider Elasticsearch for full-text search