Throttling vs Rate Limiting

Posted Jul 16, 2024 Updated Aug 3, 2024

By kelvinBz

5 min read

Throttling vs Rate Limiting Definitions

Throttling is often applied within a single application or service to control resource use. It dynamically adjusts the rate of request processing based on current load and resource availability. Throttling is ideal for scenarios where maintaining application performance and stability is critical under fluctuating demand.

graph
  subgraph Throttling["Throttling"]
        throttlingDefinition["fa:fa-tachometer-alt Throttling"]:::throttling
        resourceConsumption["fa:fa-microchip Resource Consumption"]:::resource
        systemPerformance["💖 System Performance & Stability"]:::performance

        throttlingDefinition --" Limits  "--> resourceConsumption
        throttlingDefinition --" Protects "--> systemPerformance
    end
      classDef throttling fill:#89cff0,stroke:#1e90ff
        classDef resource fill:#ffd700,stroke:#daa520
    classDef performance fill:#ffb6c1,stroke:#ff69b4

Rate limiting, on the other hand, restricts the number of requests a user or service can make over a set period of time. It aims to protect APIs and services from abuse or excessive use, ensuring fair usage across all clients and maintaining overall system performance.

A service may throttle based on different metrics over time, such as the number of number of operations, the amount of data, the cost of the operation ( WCUs, RCUs, etc).

graph 
    subgraph RateLimiting["Rate Limiting"]
        rateLimitingDefinition["fa:fa-hourglass-half Rate Limiting"]:::rateLimiting
        requestCount["fa:fa-list-ol Request Count / Time Period"]:::request
        apiService["fa:fa-server API/Service Availability"]:::service
        fairUsage["fa:fa-balance-scale-right Fair Usage"]:::fairness
        security["fa:fa-user-shield Security"]:::security

        rateLimitingDefinition --" Limits  "--> requestCount
        rateLimitingDefinition --" Protects "--> apiService
        rateLimitingDefinition --" Protects "--> fairUsage
        rateLimitingDefinition --" Protects "--> security
    end
    
  
    classDef rateLimiting fill:#90ee90,stroke:#228b22
  
    classDef request fill:#add8e6,stroke:#00008b
    classDef service fill:#fffacd,stroke:#f0e68c
    classDef fairness fill:#d3d3d3,stroke:#696969
    classDef security fill:#f08080,stroke:#dc143c
    classDef control fill:#d8bfd8,stroke:#4b0082

Application Context

Throttling

Throttling is often applied within a single application or service to control resource use. It dynamically adjusts the rate of request processing based on current load and resource availability. Throttling is ideal for scenarios where maintaining application performance and stability is critical under fluctuating demand.

graph TD
  throttlingApplication["fa:fa-cogs Throttling Application"]:::throttling
  singleApplication["fa:fa-desktop Single Application"]:::application
  dynamicAdjustment["fa:fa-sliders-h Dynamic Adjustment"]:::control
  
  throttlingApplication --> |"Applied Within"|singleApplication
  throttlingApplication --> |"Adjusts Rate"|dynamicAdjustment
  
  classDef throttling fill:#89cff0,stroke:#1e90ff
  classDef application fill:#ffe4b5,stroke:#ffdead
  classDef control fill:#d8bfd8,stroke:#4b0082

Rate Limiting

Rate limiting is typically implemented at the API gateway or service level. It controls access to APIs by limiting the number of requests from a single client or IP address within a specified timeframe. Rate limiting is crucial for preventing abuse and ensuring fair usage, particularly in public APIs or multi-tenant environments.

graph TD
  rateLimitingApplication["fa:fa-server Rate Limiting Application"]:::rateLimiting
  apiGateway["fa:fa-network-wired API Gateway"]:::service
  multiTenant["fa:fa-building Multi-Tenant Environment"]:::tenant
  
  rateLimitingApplication --> |"Implemented At"|apiGateway
  rateLimitingApplication --> |"Ensures Fair Usage"|multiTenant
  
  classDef rateLimiting fill:#90ee90,stroke:#228b22
  classDef service fill:#fffacd,stroke:#f0e68c
  classDef tenant fill:#ffa07a,stroke:#ff4500

Strategies and Mechanisms

Throttling Strategies

Rejecting Requests: Temporarily rejecting requests when the system is under high load. This can be done by returning an error code like 503 (Service Unavailable).
Degrading Services: Reducing the quality of service for non-critical functionalities. For example, serving cached content instead of generating new data or reducing image quality.
Load Leveling: Distributing load evenly to prevent spikes in resource usage.

graph TD
  throttlingStrategies["fa:fa-list Throttling Strategies"]:::throttling
  rejectRequests["fa:fa-ban Reject Requests"]:::strategy
  degradeServices["fa:fa-adjust Degrade Services"]:::strategy
  loadLeveling["fa:fa-balance-scale Load Leveling"]:::strategy
  
  throttlingStrategies --> rejectRequests
  throttlingStrategies --> degradeServices
  throttlingStrategies --> loadLeveling
  
  classDef throttling fill:#89cff0,stroke:#1e90ff
  classDef strategy fill:#ffefd5,stroke:#ffdab9

Rate Limiting Mechanisms

Fixed Window: Limits requests based on a fixed time window. For example, allowing only 100 requests per minute. But this can lead to bursty traffic. If a user makes 100 requests at the end of one minute and another 100 at the start of the next minute, it results in 200 requests in a short period.
Sliding Window: Uses a moving time window to count requests, providing more flexible and smoother rate limitingg. If the limit is set to 100 requests per minute, the system continuously checks the number of requests made in the last 60 seconds, regardless of the current time.
Token Bucket: Allocates tokens that represent a number of allowed requests. Tokens are steadily added to the bucket at a predetermined rate (e.g., 10 tokens per second).Each time a request is made, a token is removed from the bucket. The bucket has a maximum number of tokens it can hold. If the bucket is full, any additional tokens are discarded.
Adaptive Rate Limiting: Adjusts the rate limit based on traffic patterns, sytemload, or other factors. For example, increasing the rate limit during off-peak hours and decreasing it during peak hours.

Rate Limiting can be applied at different layers of the system such as API Gateway, Service Layer, or Database Layer.

graph TD
  rateLimitingMechanisms["fa:fa-list Rate Limiting Mechanisms"]:::rateLimiting
  fixedWindow["fa:fa-clock Fixed Window"]:::mechanism
  slidingWindow["fa:fa-exchange-alt Sliding Window"]:::mechanism
  tokenBucket["fa:fa-bucket Token Bucket"]:::mechanism
  adaptiveRateLimiting["fa:fa-chart-line Adaptive Rate Limiting"]:::mechanism
  
  rateLimitingMechanisms --> fixedWindow
  rateLimitingMechanisms --> slidingWindow
  rateLimitingMechanisms --> tokenBucket
  rateLimitingMechanisms --> adaptiveRateLimiting
  
  classDef rateLimiting fill:#90ee90,stroke:#228b22
  classDef mechanism fill:#e6e6fa,stroke:#9370db

Use Cases

Throttling Use Cases

High Traffic Websites: Managing load during traffic spikes.
Real-Time Applications: Ensuring consistent performance in real-time systems.
Resource-Intensive Tasks: Preventing resource exhaustion during heavy processing.

graph TD
  throttlingUseCases["fa:fa-cogs Throttling Use Cases"]:::throttling
  highTraffic["fa:fa-users High Traffic Websites"]:::usecase
  realTime["fa:fa-clock Real-Time Applications"]:::usecase
  resourceIntensive["fa:fa-tasks Resource-Intensive Tasks"]:::usecase
  
  throttlingUseCases --> highTraffic
  throttlingUseCases --> realTime
  throttlingUseCases --> resourceIntensive

    classDef throttling fill:#89cff0,stroke:#1e90ff
  classDef usecase fill:#f5deb3,stroke:#deb887

Rate Limiting Use Cases

Public APIs: Preventing abuse and ensuring fair usage.
Subscription-Based Services: Managing access based on subscription levels.
Multi-Tenant Systems: Ensuring equitable resource distribution among tenants.

graph TD
  rateLimitingUseCases["fa:fa-server Rate Limiting Use Cases"]:::rateLimiting
  publicAPIs["fa:fa-globe Public APIs"]:::usecase
  subscriptionServices["fa:fa-credit-card Subscription-Based Services"]:::usecase
  multiTenantSystems["fa:fa-building Multi-Tenant Systems"]:::usecase
  
  rateLimitingUseCases --> publicAPIs
  rateLimitingUseCases --> subscriptionServices
  rateLimitingUseCases --> multiTenantSystems

  classDef rateLimiting fill:#90ee90,stroke:#228b22
  classDef usecase fill:#f5deb3,stroke:#deb887

Implementation Considerations

Throttling

Quick Response: Must quickly detect and respond to high load.
Error Codes: Use specific HTTP error codes like 429 (Too Many Requests) and 503 (Service Unavailable).
Temporary Measures: Can be used while autoscaling resources.

graph TD
  throttlingConsiderations["fa:fa-exclamation-circle Throttling Considerations"]:::throttling
  quickResponse["fa:fa-bolt Quick Response"]:::consideration
  errorCodes["fa:fa-code Error Codes"]:::consideration
  temporaryMeasures["fa:fa-clock Temporary Measures"]:::consideration
  
  throttlingConsiderations --> quickResponse
  throttlingConsiderations --> errorCodes
  throttlingConsiderations --> temporaryMeasures

    classDef throttling fill:#89cff0,stroke:#1e90ff
  classDef consideration fill:#ffefd5,stroke:#ffdab9

Rate Limiting

Configuration: Should be configurable to adapt to different usage patterns.
Client Notifications: Inform clients about rate limits and retry strategies using headers like Retry-After.
Fairness: Ensure fair distribution of resources among all clients. For premium users, consider higher rate limits.

graph TD
  rateLimitingConsiderations["fa:fa-exclamation-circle Rate Limiting Considerations"]:::rateLimiting
  configurable["fa:fa-cogs Configurable"]:::consideration
  clientNotifications["fa:fa-bell Client Notifications"]:::consideration
  fairness["fa:fa-balance-scale Fairness"]:::consideration
  
  rateLimitingConsiderations --> configurable
  rateLimitingConsiderations --> clientNotifications
  rateLimitingConsiderations --> fairness

    classDef rateLimiting fill:#90ee90,stroke:#228b22
  classDef consideration fill:#ffefd5,stroke:#ffdab9

Conclusion

Throttling and rate limiting are essential strategies for managing application performance and resource usage. Throttling controls real-time resource consumption to maintain system stability, while rate limiting restricts the number of requests over time to prevent abuse and ensure fair usage. Understanding and implementing these strategies effectively can help maintain optimal performance and reliability in high-demand environments.

References

Microsolt Learn: Throttling
GeeksforGeeks: Rate Limiting

Keywords To Remember


graph LR
  subgraph  
    throttling["fa:fa-tachometer-alt"]:::keyword
    systemPerformance["💖"]:::keyword
    quickResponse["fa:fa-bolt"]:::keyword
    degradeServices["fa:fa-adjust"]:::keyword
    500Error["fa:fa-ban"]:::keyword
  end
  
  subgraph  
  rateLimiting["fa:fa-hourglass-half"]:::keyword
  fairUsage["fa:fa-balance-scale-right"]:::keyword
  multiTenant["fa:fa-building"]:::keyword
  tokenBucket["fa:fa-bucket"]:::keyword
  slidingWindow["fa:fa-sliders"]:::keyword
  retryAfter["fa:fa-redo"]:::keyword
  end

system design

This post is licensed under CC BY 4.0 by the author.

Throttling vs Rate Limiting Definitions

Application Context

Throttling

Rate Limiting

Strategies and Mechanisms

Throttling Strategies

Rate Limiting Mechanisms

Use Cases

Throttling Use Cases

Rate Limiting Use Cases

Implementation Considerations

Throttling

Rate Limiting

Conclusion

References

Keywords To Remember

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