How does redis implement current limiting? Introduction to 3 implementation methods

The first one: Redis-based setnx operation

When we use Redis's distributed lock , as we all know, it relies on the setnx command. During the CAS (Compare and swap) operation, the expiration practice (expire) is set for the specified key. Our main purpose of limiting the current is to within the unit time. There are and only N number of requests that can 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.

Of course, there are many disadvantages to this approach. For example, when counting 1-10 seconds, it is impossible to count 2-11 seconds. If you need to count M requests within N seconds, then our Redis Problems such as the need to maintain N keys and so on

Second type: Redis-based data structure zset

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

If we use the list data structure of Redis, we can easily implement this function.

We can create 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.

Third type: Redis-based token bucket algorithm

When it comes to current limiting, we have to mention the token bucket algorithm. The token bucket algorithm is also called the bucket algorithm. For details, please refer to Du Niang’s explanation of the token bucket algorithm

The token bucket algorithm mentions the input rate and the output rate. When the output rate is greater than the input rate, then it is Traffic limit 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

Rely on the leftPop of 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);
 }

Rely on Java's scheduled task is 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 difficult to start with. 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.

For more redis knowledge, please pay attention to: redis introductory tutorial column.

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