Detailed explanation of Redis implementation of current limiting algorithm

In Internet applications, current limiting is a very important technical means. It can smoothly handle high concurrent traffic and ensure the stability and availability of services. As a high-performance, distributed NoSQL database, Redis has some features that can well support the implementation of current limiting algorithms. This article will introduce in detail the application of Redis in current limiting.

1. Token Bucket Algorithm

The token bucket algorithm is a relatively common current limiting algorithm, which is based on a bucket and a token generator. A certain number of tokens are stored in the bucket, each token represents a request, and the token generator generates tokens at a certain rate and adds them to the bucket. When a request comes, if there is a token in the bucket, the request is allowed to pass and a token is consumed from the bucket, otherwise the request is rejected.

The core idea of ​​the token bucket algorithm is to limit the number of concurrent requests through the number of tokens in the bucket, while the token generator can control the processing rate of requests. In Redis, the token bucket algorithm can be implemented by using ordered sets. For example, members in an ordered set can be represented as tokens, and their scores represent the token's expiration timestamp. When a request comes, you can use the ZREVRANGEBYSCORE command to obtain the number of unexpired tokens in the current bucket.

2. Leaky bucket algorithm

The leaky bucket algorithm is also a common current limiting algorithm. The difference between it and the token bucket algorithm is that the leaky bucket algorithm does not behave like the token bucket algorithm. Instead of generating tokens periodically like the card bucket algorithm, it maintains a constant outflow rate and distributes requests evenly over different time periods. This can effectively handle request traffic smoothly and prevent sudden requests from causing service instability.

In Redis, you can use a zset to simulate a leaky bucket, where each member represents a request, and its score represents the timestamp of the request's arrival. When a new request arrives, you can use the ZREVRANGE command to obtain the number of requests in the current leaky bucket to determine whether to allow the new request to pass. If allowed to pass, new requests are added to the zset and expired requests are removed from the zset using the ZREMRANGEBYSCORE command.

3. Counter algorithm

The counter algorithm is a simple and crude current limiting algorithm. It is based on a counter and a time window. When the number of requests within the time window reaches a certain When the threshold is reached, subsequent requests will be rejected. In Redis, you can use a counter and an expiration time to implement the counter algorithm. For example, you can use the INCR command to increment the counter. When the counter exceeds the specified threshold, it means that there are too many requests and need to be rejected.

4. Lua script implementation

In addition to the three common current limiting algorithms mentioned above, you can also use Lua scripts to implement custom current limiting algorithms. Lua scripts can access Redis data structures and commands and have strong flexibility and scalability. For example, a current limiter based on the time window and leaky bucket algorithm can be implemented in a Lua script. The code is as follows:

local limit_key = KEYS[1]
local limit = tonumber(ARGV[1])
local interval = tonumber(ARGV[2])
local current_time = tonumber(redis.call('TIME')[1])
local current_count = #redis.call('zrangebyscore', limit_key, '-inf', '+inf')
redis.call('zremrangebyscore', limit_key, '-inf', current_time - interval)
if current_count < limit then
redis.call('zadd', limit_key, current_time, current_time)
return 1
else
return 0
end

In the above code, limit_key represents the name of the leaky bucket, and limit represents the number of times the leaky bucket can accommodate. The maximum number of requests, interval represents the size of the time window (in seconds), and current_time represents the current timestamp. First, the script uses the zrangebyscore command to obtain the number of unexpired requests in the current leaky bucket. Then, use the zremrangebyscore command to delete expired requests. Next, determine whether the number of requests in the leaky bucket has reached the upper limit. If it has not reached the upper limit, use the zadd command to add new requests to the leaky bucket and return the flag that allows it to pass. Otherwise, a rejection flag is returned. Finally, during business processing, this script needs to be used in conjunction with the EVALSHA command to avoid the overhead of repeatedly compiling Lua code.

Summary

Current limiting is a very important technology in Internet applications. It can smoothly handle high concurrent traffic and ensure the stability and availability of services. In Redis, you can use common current limiting algorithms such as token bucket algorithm, leaky bucket algorithm, and counter algorithm, or you can use Lua scripts to customize the current limiter. These methods can effectively control request traffic and ensure the stability and availability of services.

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