ratelimit

Distributed rate-limit library based on Redis

Overview

The goal of this library is to be able to implement distributed rate limit functions simply and rudely. Similar to the usage of the ID generator, the client takes back the data from Redis - batch data (just a value here), as long as it Is not consumed.it doesn't exceed rate-limit.

• 中文 README

Advantage

• Less dependencies, only rely on Redis, no special services required
• use Redis own clock, The clients no need to have the same clock
• Thread (coroutine) security
• Low system overhead and little pressure on redis

Notice

Different types of limiters may have different redis-key data types in redis. So different types of limiters cannot use same name redis-key.

For example

127.0.0.1:6379> type key:leaky
string
127.0.0.1:6379> type key:token
hash
127.0.0.1:6379> hgetall key:token

"token_count"
"0"
"updateTime"
"1613805726567122"
127.0.0.1:6379> get key:leaky
"1613807035353864"

How to get

go get github.com/vearne/ratelimit

Usage

1. create redis.Client

with "github.com/go-redis/redis"
Supports both redis master-slave mode and cluster mode

	client := redis.NewClient(&redis.Options{
		Addr:     "localhost:6379",
		Password: "xxx", // no password set
		DB:       0,  // use default DB
	})

	client := redis.NewClusterClient(&redis.ClusterOptions{
		Addrs:    []string{"127.0.0.1:6379"},
		Password: "xxxx",
	})

2. create RateLimiter

limiter, err := ratelimit.NewTokenBucketRateLimiter(client,                
        "push", time.Second, 200, 20, 5)

Indicates that 200 operations per second are allowed

limiter, err := ratelimit.NewTokenBucketRateLimiter(client,                
        "push", time.Minute, 200, 20, 5)

Support multiple algorithms

2.1 Counter algorithm

func NewCounterRateLimiter(client redis.Cmdable, key string, duration time.Duration,
	throughput int,
	batchSize int) (Limiter, error)

2.2 Token bucket algorithm

func NewTokenBucketRateLimiter(client redis.Cmdable, key string, duration time.Duration,
	throughput int, maxCapacity int,
	batchSize int) (Limiter, error)

2.3 Leaky bucket algorithm

func NewLeakyBucketLimiter(client redis.Cmdable, key string, duration time.Duration,
	throughput int) (Limiter, error)

2.4 sliding time window

NewSlideTimeWindowLimiter(throught int, duration time.Duration, windowBuckets int) (Limiter, error)

Note: This limiter is based on memory and does not rely on Redis, so it may not be used in distributed frequency limiting scenarios.

example

more example

package main

import (
	"context"
	"fmt"
	"github.com/go-redis/redis/v8"
	"github.com/vearne/ratelimit"
	"math/rand"
	"sync"
	"time"
)

func consume(r ratelimit.Limiter, group *sync.WaitGroup,
	c *ratelimit.Counter, targetCount int) {
	defer group.Done()
	for {
		ok, err := r.Take(context.Background())
		if err != nil {
			ok = true
			fmt.Println("error", err)
		}
		if ok {
			value := c.Incr()
			if value >= targetCount {
				break
			}
		} else {
			time.Sleep(time.Duration(rand.Intn(10)+1) * time.Millisecond)
		}
	}
}

func main() {
	client := redis.NewClient(&redis.Options{
		Addr:     "localhost:6379",
		Password: "xxeQl*@nFE", // password set
		DB:       0,            // use default DB
	})

	limiter, err := ratelimit.NewTokenBucketRateLimiter(client, "key:token",
		time.Second,
		100,
		5,
		2)

	if err != nil {
		fmt.Println("error", err)
		return
	}

	var wg sync.WaitGroup
	total := 500
	counter := ratelimit.NewCounter()
	start := time.Now()
	for i := 0; i < 100; i++ {
		wg.Add(1)
		go consume(limiter, &wg, counter, total)
	}
	wg.Wait()
	cost := time.Since(start)
	fmt.Println("cost", time.Since(start), "rate", float64(total)/cost.Seconds())
}

Dependency

go-redis/redis

Thanks

The development of the module was inspired by the Reference 1.

Reference

1. Performance million/s: Tencent lightweight global flow control program

Thanks