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Rate Limiting

Protect APIs from abuse with rate limiting and throttling.

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Rate Limiting

Rate Limiting Algorithms

Algorithm Description
Fixed Window X requests per minute
Sliding Window Smooth rate over time
Token Bucket Allow bursts, refill tokens
Leaky Bucket Process at fixed rate

Rate Limit Headers

X-RateLimit-Limit: 100
X-RateLimit-Remaining: 95
X-RateLimit-Reset: 1609459200
Retry-After: 60

Implementation

@RateLimiter(name = "apiLimiter", fallbackMethod = "rateLimitFallback")
@GetMapping("/api/products")
public ResponseEntity<List<Product>> getProducts() { ... }

public ResponseEntity<?> rateLimitFallback(Exception ex) {
    return ResponseEntity.status(429)
        .header("Retry-After", "60")
        .body("Rate limit exceeded");
}

Rate Limiting Strategies

Algorithms

  • Fixed Window: Simple, bursty
  • Sliding Window Log: Accurate, memory-heavy
  • Sliding Window Counter: Balanced
  • Token Bucket: Flexible, allows bursts

Response Headers

X-RateLimit-Limit: 100
X-RateLimit-Remaining: 95
X-RateLimit-Reset: 1640995200

Implementation

  • Use Redis for distributed limiting
  • Implement per-user limits
  • Return 429 status code
  • Include retry-after header

Key Points

  • Understanding Rate Limiting 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 Rate Limiting

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

Solution
// Rate Limiting implementation
// Key aspects: validation, error handling, logging, testing

public class RateLimiting {
    // Production-ready implementation
}
Rate Limiting Edge Cases

Identify and handle edge cases for Rate Limiting. 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
Rate Limiting Testing Strategy

Write a testing strategy for Rate Limiting. 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. HTTP status for rate limiting?

Question 1 options

2. Token bucket allows?

Question 2 options

3. What is the primary purpose of Rate Limiting?

Question 3 options

4. What is a common mistake when implementing Rate Limiting?

Question 4 options

Flashcards

Question

Rate limit status code?

Click to reveal answer

Answer

429 Too Many Requests

Question

Token bucket behavior?

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Answer

Allows bursts, tokens refill over time

Question

What is Rate Limiting?

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Answer

Rate Limiting is a key concept in backend development.

Question

When to use Rate Limiting?

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Answer

Use Rate Limiting when building production systems that require reliability, scalability, and maintainability.

Question

Rate Limiting 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.Rate limiting prevents abuse and ensures availability
  • 2.Return 429 with Retry-After header
  • 3.Choose algorithm based on needs
  • 4.Per-user and per-IP limiting

Interview Tips

  • •Implement rate limiting
  • •Know rate limiting algorithms

Cheat Sheet

Rate Limiting

  • Status: 429 Too Many Requests
  • Headers: X-RateLimit-Limit, Remaining, Reset
  • Algorithms: Fixed Window, Sliding, Token Bucket, Leaky
  • Retry-After: seconds until retry