Throttling vs Rate Limiting
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