Design Rate Limiter

What is Rate Limiter?

A Rate Limiter is a system that controls the rate of requests to prevent abuse and ensure fair resource usage. It's similar to systems used by API gateways, CDNs, or cloud services. The service provides request throttling, abuse prevention, and resource protection.

High-performance request throttling with distributed coordination is what makes systems like Rate Limiter unique. By understanding Rate Limiter, you can tackle interview questions for similar throttling systems, since the core design challenges—algorithm selection, distributed coordination, performance optimization, and accuracy—remain the same.

Functional Requirements

Core (Interview Focussed)

• Request Throttling: Limit requests per user, IP, or API key.
• Multiple Algorithms: Support different rate limiting algorithms.
• Distributed Coordination: Coordinate rate limits across multiple servers.
• Real-time Monitoring: Monitor rate limit usage and violations.

Out of Scope

• User authentication and authorization
• Rate limit configuration management
• Historical analytics and reporting
• Rate limit notifications
• Mobile app specific features

Non-Functional Requirements

Core (Interview Focussed)

• High performance: Sub-millisecond response time for rate limit checks.
• High availability: 99.9% uptime for rate limiting services.
• Scalability: Handle millions of requests per second.
• Accuracy: Ensure accurate rate limit enforcement.

Out of Scope

• Data retention policies
• Compliance and privacy regulations

💡 Interview Tip: Focus on high performance, high availability, and scalability. Interviewers care most about algorithm selection, distributed coordination, and performance optimization.

Core Entities

💡 Interview Tip: Focus on RateLimit, Request, and Usage as they drive rate limiting, request tracking, and usage monitoring.

Core APIs

Rate Limiting

• POST /rate-limit/check { user_id, endpoint, limit_type } – Check if request is allowed
• GET /rate-limit/status { user_id, limit_type } – Get current usage status
• POST /rate-limit/reset { user_id, limit_type } – Reset rate limit counter

Configuration

• POST /rate-limit/config { user_id, limit_type, limit_value, window_size } – Set rate limit
• GET /rate-limit/config { user_id, limit_type } – Get rate limit configuration
• DELETE /rate-limit/config { user_id, limit_type } – Remove rate limit

Monitoring

• GET /rate-limit/usage { user_id, time_range } – Get usage statistics
• GET /rate-limit/violations { user_id, time_range } – Get violation history
• GET /rate-limit/health – Get system health status

High-Level Design

Key Components

• Rate Limiter Service: Handle rate limit checks and enforcement
• Algorithm Engine: Implement different rate limiting algorithms
• Distributed Coordinator: Coordinate rate limits across servers
• Usage Tracker: Track and store usage data
• Configuration Manager: Manage rate limit configurations
• Database: Persistent storage for configurations and usage data

Mapping Core Functional Requirements to Components

Detailed Design

Rate Limiter Service

Purpose: Handle rate limit checks and enforce limits on incoming requests.

Key Design Decisions:

• Algorithm Selection: Choose appropriate algorithm based on use case
• Caching: Cache rate limit data for performance
• Async Processing: Process rate limit checks asynchronously
• Error Handling: Handle rate limit check failures gracefully

Algorithm: Rate limit check

1. Receive rate limit check request
2. Extract user_id and limit_type
3. Get rate limit configuration
4. Apply rate limiting algorithm:
   - Token Bucket: Check available tokens
   - Sliding Window: Check requests in time window
   - Fixed Window: Check requests in current window
5. If limit not exceeded:
   - Allow request
   - Update usage counter
   - Return success
6. If limit exceeded:
   - Reject request
   - Log violation
   - Return rate limit exceeded

Algorithm Engine

Purpose: Implement different rate limiting algorithms.

Key Design Decisions:

• Token Bucket: Allow burst traffic with token-based limiting
• Sliding Window: Provide smooth rate limiting with sliding time windows
• Fixed Window: Simple rate limiting with fixed time windows
• Algorithm Switching: Switch algorithms based on requirements

Algorithm: Token Bucket implementation

1. Initialize bucket with token capacity
2. For each request:
   - Check if tokens are available
   - If tokens available:
     - Consume one token
     - Allow request
   - If no tokens:
     - Reject request
3. Refill tokens at regular intervals
4. Handle burst traffic with token accumulation

Algorithm: Sliding Window implementation

1. Maintain sliding window of requests
2. For each request:
   - Add request to current window
   - Remove expired requests from window
   - Count requests in window
   - If count <= limit:
     - Allow request
   - If count > limit:
     - Reject request
3. Use efficient data structures for window management

Distributed Coordinator

Purpose: Coordinate rate limits across multiple servers.

Key Design Decisions:

• Consistent Hashing: Distribute rate limit data across servers
• Data Replication: Replicate rate limit data for availability
• Conflict Resolution: Handle conflicts in distributed rate limiting
• Synchronization: Synchronize rate limit state across servers

Algorithm: Distributed rate limiting

1. Hash user_id to determine server
2. Check rate limit on assigned server
3. If server unavailable:
   - Use fallback server
   - Apply rate limit with reduced accuracy
4. Synchronize rate limit state:
   - Broadcast updates to other servers
   - Handle network partitions
5. Maintain eventual consistency

Database Design

Rate Limits Table

Indexes:

• idx_user_limit_type on (user_id, limit_type) - User rate limits
• idx_limit_type on (limit_type) - Rate limit type queries

Usage Table

Indexes:

• idx_user_window on (user_id, window_start) - User usage history
• idx_window_start on (window_start) - Time-based cleanup

Violations Table

Indexes:

• idx_user_timestamp on (user_id, timestamp) - User violations
• idx_timestamp on (timestamp) - Time-based queries

Scalability Considerations

Horizontal Scaling

• Rate Limiter Service: Scale horizontally with load balancers
• Algorithm Engine: Use consistent hashing for data distribution
• Distributed Coordinator: Scale coordination with distributed systems
• Database: Shard usage data by user_id

Caching Strategy

• Redis: Cache rate limit data and usage counters
• Application Cache: Cache frequently accessed configurations
• Database Cache: Cache rate limit configurations

Performance Optimization

• Connection Pooling: Efficient database connections
• Batch Processing: Batch usage updates for efficiency
• Async Processing: Non-blocking rate limit checks
• Resource Monitoring: Monitor CPU, memory, and network usage

Monitoring and Observability

Key Metrics

• Check Latency: Average rate limit check time
• Throughput: Rate limit checks per second
• Violation Rate: Percentage of requests that exceed limits
• System Health: CPU, memory, and disk usage

Alerting

• High Latency: Alert when check time exceeds threshold
• High Violation Rate: Alert when violation rate increases
• System Errors: Alert on rate limit check failures
• Resource Exhaustion: Alert on high resource usage

Trade-offs and Considerations

Consistency vs. Availability

• Choice: Eventual consistency for distributed rate limiting
• Reasoning: Rate limiting can tolerate slight delays for better availability

Accuracy vs. Performance

• Choice: Use approximation algorithms for better performance
• Reasoning: Balance between rate limit accuracy and response time

Memory vs. Performance

• Choice: Use in-memory caching for better performance
• Reasoning: Balance between memory usage and response time

Common Interview Questions

Q: How would you handle distributed rate limiting?

A: Use consistent hashing, data replication, and eventual consistency to handle distributed rate limiting.

Q: How do you choose the right rate limiting algorithm?

A: Consider use case requirements: Token Bucket for burst traffic, Sliding Window for smooth limiting, Fixed Window for simplicity.

Q: How would you scale this system globally?

A: Deploy regional rate limiter servers, use geo-distributed databases, and implement data replication strategies.

Q: How do you handle rate limit bypassing?

A: Use multiple identification methods, IP-based limiting, and behavioral analysis to prevent bypassing.

Key Takeaways

1. Algorithm Selection: Different algorithms provide flexibility for different use cases
2. Distributed Coordination: Consistent hashing and data replication enable distributed rate limiting
3. Performance Optimization: Caching and async processing are essential for high-performance rate limiting
4. Scalability: Horizontal scaling and partitioning are crucial for handling large-scale traffic
5. Monitoring: Comprehensive monitoring ensures system reliability and performance