REL05-BP02: Throttle requests

Overview

Implement request throttling mechanisms to control the rate of incoming requests and prevent system overload. Throttling protects downstream services from being overwhelmed, maintains system stability during traffic spikes, and ensures fair resource allocation across different clients and request types.

Implementation Steps

1. Design Rate Limiting Strategies

Implement token bucket and leaky bucket algorithms
Configure rate limits based on client, API endpoint, and resource type
Design adaptive throttling based on system capacity and health
Establish different rate limits for different service tiers

2. Implement Client-Based Throttling

Apply rate limits per client ID or API key
Implement sliding window rate limiting
Design burst allowances for legitimate traffic spikes
Create whitelisting mechanisms for critical clients

3. Configure Resource-Based Throttling

Implement throttling based on CPU, memory, and database utilization
Design backpressure mechanisms for queue-based systems
Configure adaptive limits based on downstream service capacity
Implement priority-based throttling for different request types

4. Establish Graceful Throttling Responses

Return appropriate HTTP status codes (429 Too Many Requests)
Include retry-after headers with backoff recommendations
Provide informative error messages and guidance
Implement progressive throttling with warnings before limits

5. Monitor and Tune Throttling Parameters

Track throttling metrics and patterns
Implement automated tuning based on system performance
Monitor false positive throttling and adjust limits
Create dashboards for throttling visibility and analysis

6. Test Throttling Under Load

Conduct load testing to validate throttling effectiveness
Test throttling behavior during traffic spikes
Validate client retry behavior and backoff strategies
Ensure throttling doesn’t impact legitimate traffic

Implementation Examples

Example 1: Advanced Request Throttling System

AWS Services Used

Amazon API Gateway: Built-in request throttling with rate limiting and burst capacity
AWS Lambda: Serverless functions with reserved concurrency for throttling control
Amazon ElastiCache (Redis): Distributed throttling state management and rate limiting
Amazon CloudWatch: Monitoring throttling metrics and automated scaling triggers
AWS Application Load Balancer: Request rate limiting and connection throttling
Amazon DynamoDB: Throttling configuration storage and request tracking
AWS WAF: Web application firewall with rate-based rules and IP throttling
Amazon Kinesis: Stream throttling and backpressure management
AWS Step Functions: Workflow throttling and execution rate control
Amazon SQS: Message throttling and visibility timeout management
AWS Systems Manager: Parameter store for dynamic throttling configuration
Amazon CloudFront: CDN-level rate limiting and geographic throttling
AWS X-Ray: Distributed tracing for throttling pattern analysis
Amazon Route 53: DNS-based load balancing with health-based throttling
AWS Auto Scaling: Automatic scaling based on throttling metrics

Benefits

System Protection: Prevents system overload and maintains stability during traffic spikes
Fair Resource Allocation: Ensures equitable access to resources across different clients
Improved Performance: Maintains consistent response times by controlling request rates
Cost Control: Prevents excessive resource consumption and associated costs
Better User Experience: Provides predictable service availability and response times
DDoS Protection: Helps mitigate distributed denial-of-service attacks
Capacity Planning: Provides insights into system capacity and usage patterns
Service Level Management: Enables different service tiers with appropriate rate limits
Graceful Degradation: Allows systems to degrade gracefully under high load
Operational Stability: Reduces the risk of cascading failures due to overload