The Throttling design pattern is a technique used to control the rate at which requests are sent to a system. This pattern is used to prevent a system from becoming overwhelmed by too many requests and to ensure that the system remains available and responsive to all users.
In this pattern, the system implements a mechanism that limits the number of requests that can be sent to the system within a specified time period. This can be done by using a token bucket algorithm, which allows a fixed number of requests to be sent within a given time period, or by using a leaky bucket algorithm, which allows requests to be sent at a fixed rate. When the limit is reached, the system will either reject or delay additional requests until the next time period.
The Throttling design pattern has several advantages:
- Improved scalability: The pattern allows the system to handle variable loads by controlling the rate at which requests are sent to the system, which can help to improve scalability.
- Improved reliability: The pattern helps to improve the reliability of a system by preventing it from becoming overwhelmed by too many requests, which can help to prevent system failures caused by overload.
- Improved performance: The pattern can improve performance by allowing the system to process requests at a steady rate, which can help to improve the overall throughput of the system.
- Improved manageability: The pattern can improve manageability by allowing system administrators to monitor the rate at which requests are sent to the system, which can provide insight into the system’s load and performance.
- Improved security: The pattern can improve security by allowing incoming requests to be rate-limited, which can help to prevent denial of service attacks.
It’s important to consider the rate at which requests are sent to the system and the resources required to process them, in order to ensure that the system is able to handle the load. Additionally, it’s important to consider the type of requests that will be sent to the system, in order to ensure that the system remains available and responsive to all users.
Leaky bucket algorithm explained
In the leaky bucket algorithm, requests are placed into a bucket, and the bucket leaks at a fixed rate. The bucket has a maximum capacity and when it becomes full, additional requests are discarded until the bucket has room for more requests. The rate at which the bucket leaks is called the leak rate, and it controls the rate at which requests are processed.
Token bucket algorithm explained
In the token bucket algorithm, a fixed number of tokens are placed into a bucket at a fixed rate. Each time a request is sent to the system, a token is removed from the bucket. If the bucket is empty, the request is rejected or delayed until a token becomes available. The rate at which tokens are added to the bucket is called the fill rate, and it controls the rate at which requests are processed.
Sliding window algorithm explained
In the sliding window algorithm, a fixed time window is used to track the number of requests that have been sent to the system. The size of the window determines the time period over which the requests are tracked. The number of requests that are allowed to be sent within the window is also fixed. Each time a request is sent to the system, it is tracked within the window. If the number of requests within the window exceeds the allowed limit, the request is rejected or delayed until the next time period.
Drawbacks and considerations of throttling
- Complexity: The algorithms can be complex to implement, especially when tracking requests over a long time period or when tracking a large number of requests.
- Granularity: It can be difficult to achieve fine-grained control over the rate at which requests are sent to the system.
- State management: The algorithms requires maintaining the state of the requests that are tracked, which can be resource-intensive and can add complexity to the system.
- Limited accuracy: The algorithms only tracks the number of requests sent to the system within a fixed time period. It may not take into account the resources required to process those requests, which can lead to inaccurate results.
Not suitable for bursty traffic: The leaky bucket and token bucket algorithms can’t handle bursty traffic, which is traffic that arrives in short bursts and then subsides, if the rate of incoming requests exceeds the rate of request leakage, the bucket will eventually fill up and the requests will be rejected or delayed.
It’s important to consider the trade-offs between the benefits and drawbacks of the algorithms when deciding whether to use it in a particular system, and to choose the appropriate parameters (time period, number of requests, leak rate, bucket size) based on the specific requirements of the system.
For more information, refer to the Throttling pattern on the Microsoft website