A brief discussion on three implementation methods of Redis current limiting

This article will introduce to you three ways to implement current limiting in Redis. It has certain reference value. Friends in need can refer to it. I hope it will be helpful to everyone.

[Related recommendations: Redis video tutorial]

In the face of more and more high-concurrency scenarios, the current limit display Particularly important.

Of course, there are many ways to implement current limiting. Redis has very powerful functions. I have used Redis to practice three implementation methods, which can be implemented in a relatively simple way. Redis can not only do current limiting, but also can do data statistics, nearby people and other functions, which may be written later.

The first one: Redis-based setnx operation

When we use Redis's distributed lock, everyone knows that it relies on With the setnx instruction, during the CAS (Compare and swap) operation, the expiration practice (expire) is also set for the specified key. Our main purpose of limiting the current is to have and only N quantity within the unit time. request to be able to access my code program. So relying on setnx can easily achieve this function.

For example, if we need to limit 20 requests within 10 seconds, then we can set the expiration time to 10 during setnx. When the number of requested setnx reaches 20, the current limiting effect will be achieved. The code is relatively simple and will not be shown.

For the specific usage of setnx, please refer to my other blog A series of problems caused by Redis distributed lock under RedisTemplate

Of course, there are many disadvantages to this approach, such as when counting 1-10 seconds , it is impossible to count the M requests within 2-11 seconds. If we need to count M requests within N seconds, then we need to keep N keys in Redis and other issues

Second Type: Redis-based data structure zset

In fact, the most important thing involved in current limiting is the sliding window. It is also mentioned above how 1-10 becomes 2-11. In fact, the starting value and the end value are both 1.

And if we use the list data structure of Redis, we can easily implement this function

We can make the request into a zset array. When each request comes in, the value remains unique. Generated with UUID, and score can be represented by the current timestamp, because score can be used to calculate the number of requests within the current timestamp. The zset data structure also provides the range method so that we can easily get the number of requests within 2 timestamps

The code is as follows

public Response limitFlow(){
        Long currentTime = new Date().getTime();
        System.out.println(currentTime);
        if(redisTemplate.hasKey("limit")) {
            Integer count = redisTemplate.opsForZSet().rangeByScore("limit", currentTime -  intervalTime, currentTime).size();        // intervalTime是限流的时间 
            System.out.println(count);
            if (count != null && count > 5) {
                return Response.ok("每分钟最多只能访问5次");
            }
        }
        redisTemplate.opsForZSet().add("limit",UUID.randomUUID().toString(),currentTime);
        return Response.ok("访问成功");
    }

The above code can achieve the effect of sliding windows , and can guarantee at most M requests every N seconds. The disadvantage is that the data structure of zset will become larger and larger. The implementation method is relatively simple.

The third type: Redis-based token bucket algorithm

When it comes to current limiting, I have to mention it Token bucket algorithm. For details, please refer to Du Niang’s explanation Token Bucket Algorithm

The token bucket algorithm mentions input rate and output rate. When the output rate is greater than the input rate, the traffic limit is exceeded.

That is to say, every time we access a request, we can get a token from Redis. If we get the token, it means that the limit has not been exceeded. If we cannot get it, the result will be the opposite.

Relying on the above ideas, we can easily implement such code by combining the List data structure of Redis. It is just a simple implementation

Relying on the leftPop of the List to obtain the token

// 输出令牌
public Response limitFlow2(Long id){
        Object result = redisTemplate.opsForList().leftPop("limit_list");
        if(result == null){
            return Response.ok("当前令牌桶中无令牌");
        }
        return Response.ok(articleDescription2);
    }

Then rely on Java's scheduled tasks to rightPush the token into the List regularly. Of course, the token also needs to be unique, so I still use UUID to generate it.

// 10S的速率往令牌桶中添加UUID，只为保证唯一性
    @Scheduled(fixedDelay = 10_000,initialDelay = 0)
    public void setIntervalTimeTask(){
        redisTemplate.opsForList().rightPush("limit_list",UUID.randomUUID().toString());
    }

In summary, the code implementation is not started at all. It's difficult. For these current limiting methods, we can add the above code to AOP or filter to limit the current flow of the interface and ultimately protect your website.

Redis actually has many other uses. Its role is not only caching and distributed locking. Its data structures are not just String, Hash, List, Set, and Zset. Those who are interested can follow up on his GeoHash algorithm; BitMap, HLL and Bloom filter data (added after Redis 4.0, you can use Docker to install redislabs/rebloom directly) structure.

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