How to use Golang to implement request current limiting

With the increasing use of modern network applications, many user requests begin to flood into the server, which leads to some problems. On the one hand, server performance is limited and there is no guarantee that all requests can be processed; on the other hand, a large number of requests arriving at the same time may make the service unstable. At this time, limiting the request rate has become an inevitable choice. The following will introduce how to use Golang to implement request current limiting.

What is current limiting

Current limiting refers to limiting the maximum number of requests or data traffic that an application, system or service can withstand within a certain period of time. Current limiting can help us mitigate network attacks and prevent bandwidth abuse and resource abuse. Usually we call this limit "flow control", which can prioritize requests of different types and sources and process requests of different types and sources at different proportions.

Implementing request current limiting

Window current limiting algorithm based on time window

The simplest and most direct algorithm is the current limiting algorithm based on time window. It checks whether the total number of requests sent in the most recent period exceeds a threshold. The length of the time window can be adjusted according to the characteristics of the application to achieve optimal performance and minimum false alarm rate.

Suppose we need to limit the maximum number of accesses per second to an API. We can use the time package in Golang to count traffic and use buffer channels to implement request queues. The code is as follows:

type ApiLimiter struct {
    rate       float64 // 时间窗口内最大请求数
    capacity   int // 请求队列最大长度，即最多能有多少请求同时被处理
    requestNum int // 时间窗口内已处理请求总数
    queue      chan int // 缓冲通道，用于实现请求队列
}

func NewApiLimiter(rate float64, capacity int) *ApiLimiter {
    return &ApiLimiter{
        rate:       rate,
        capacity:   capacity,
        requestNum: 0,
        queue:      make(chan int, capacity),
    }
}
func (al *ApiLimiter) Request() bool {
    now := time.Now().UnixNano()
    maxRequestNum := int(float64(now)/float64(time.Second)*al.rate) + 1 // 统计最近一秒内应该处理的请求数量
    if maxRequestNum <= al.requestNum { // 超过最大请求数，返回false
        return false
    }
    al.queue <- 1 // 将请求压入队列
    al.requestNum += 1
    return true
}

In this example, we use chan in Golang to implement the request queue, and use the time package to calculate the number of requests within the time window. After each request reaches the server, we will put the request into the queue, and the request volume will also be compared with the maximum number of requests. If the maximum number of requests is exceeded, false will be returned.

Leaky Bucket Algorithm

The leaky bucket algorithm is another famous current limiting algorithm. At any time, the leaky bucket retains a certain number of requests. When a new request arrives, first check whether the number of requests remaining in the leaky bucket reaches the maximum request amount. If so, reject the new request; otherwise, put the new request into the bucket and reduce the number of requests in the bucket by one. .

The leaky bucket algorithm can be implemented with the help of coroutines and timers in Golang. We can use a timer to represent our leaky bucket slowly flowing out requests over time. The code is as follows:

type LeakyBucket struct {
    rate       float64 // 漏桶每秒处理的请求量（R）
    capacity   int     // 漏桶的大小（B）
    water      int     // 漏桶中当前的水量（当前等待处理的请求个数）
    lastLeaky  int64   // 上一次请求漏出的时间，纳秒
    leakyTimer *time.Timer // 漏桶接下来漏水需要等待的时间
    reject     chan int // 被拒绝的请求通道
}

func NewLeakyBucket(rate float64, capacity int) *LeakyBucket {
    bucket := &LeakyBucket{
        rate:     rate,
        capacity: capacity,
        water:    0,
        reject:   make(chan int, 1000),
    }
    bucket.leakyTimer = time.NewTimer(time.Second / time.Duration(rate))
    return bucket
}

func (lb *LeakyBucket) Request() chan int {
    select {
    case <-lb.leakyTimer.C:
        if lb.water > 0 {
            lb.water -= 1
            lb.leakyTimer.Reset(time.Second / time.Duration(lb.rate))
               return nil // 请求被允许
        }
        lb.leakyTimer.Reset(time.Second / time.Duration(lb.rate))
        return lb.reject // 请求被拒绝
    default:
        if lb.water >= lb.capacity {
            return lb.reject // 请求被拒绝
        } else {
            lb.water += 1 // 请求被允许
            return nil
        }
    }
}

In this example, we use the timer in Golang to realize the outflow rate of the leaky bucket, and use chan to realize the request buffering. We first created a timer to regularly check the remaining number of requests (water) in the leaky bucket. Before the request passes, we will first check whether it has reached the maximum capacity to be processed. If so, we will return a rejection; if not, we will Please put it into a leaky bucket and add 1 to the amount of water.

Further Thoughts

In this article, we introduce two common request current limiting algorithms: window-based current limiting algorithm and leaky bucket algorithm. However, there are many other variations of these algorithms, such as flow control based on request importance or combined with queue data structures. Golang itself exhibits excellent concurrency and coroutine models, making it one of the best tools for implementing request throttling.

In the future, with the in-depth development of artificial intelligence, big data and other technologies, we will need better current limiting algorithms to support the operation of our applications. So, before we think any further, let’s explore and study this ever-changing and evolving field together.

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