How to use Redis and Java to implement distributed current limiting function

How to use Redis and Java to implement distributed current limiting function

Introduction:
With the rapid development of the Internet, the number of concurrent requests in the system is also increasing. Current limiting in high concurrency scenarios The questions are becoming more and more important. In distributed systems, how to implement effective current limiting strategies and protect the stability and performance of the system has become an urgent problem for developers to solve. This article will introduce how to use Redis and Java to implement distributed current limiting function, and give some code examples.

1. Introduction to Redis:
Redis is an open source memory-based data structure storage system with high performance, high availability and flexibility. Redis supports a variety of data structures, such as strings, hash tables, lists, sets, ordered sets, etc., and provides a rich set of instructions for operating on these data structures. It also provides advanced functions such as publish/subscribe, transactions, and persistence, allowing developers to respond to various scenarios more flexibly.

2. Current limiting algorithm:
The current limiting algorithm refers to limiting the number of concurrent requests accepted by the system within a certain period of time to prevent the system from being overwhelmed by too many requests and affecting the stability and performance of the system. . Common current limiting algorithms include counters, sliding windows, and token buckets. Below we will use code examples to implement the distributed current limiting function of the sliding window algorithm.

3. Code example:

1. First, we need to introduce the Java client library of Redis, such as Jedis.

   import redis.clients.jedis.Jedis;

2. Initialize Redis connection:

   Jedis jedis = new Jedis("localhost", 6379);

3. Define a method for current limiting, which requires passing in an identifier (such as IP address) and a time window size:

   public boolean limitAccess(String identifier, int windowSize) {
    long currentTime = System.currentTimeMillis();
    String key = identifier + ":" + currentTime / 1000;  // 按时间窗口划分key
   
    long count = jedis.incr(key);  // 原子操作，每次增加1
   
    if (count == 1) {
        jedis.expire(key, windowSize);  // 设置过期时间
    }
   
    if (count > 10) {  // 设置最大请求数
        return false;
    }
   
    return true;
   }

4. Call this method at the entrance of the system to determine the current limit:

   public void processRequest(String identifier) {
    int windowSize = 60;  // 设置时间窗口大小为60秒
   
    boolean isAllowed = limitAccess(identifier, windowSize);
   
    if (isAllowed) {
        // 处理请求
    } else {
        // 返回限流提示
    }
   }

4. Summary :
This article introduces how to use Redis and Java to implement distributed current limiting function, and gives a code example of the sliding window algorithm. By using Redis as a distributed caching and counting tool, we can easily implement a variety of current limiting algorithms and enhance the stability and performance of the system. Of course, in actual scenarios, it is necessary to choose an appropriate current limiting strategy based on specific needs and business characteristics to achieve the best results.

References:

1. Redis official website: https://redis.io/
2. Jedis GitHub page: https://github.com/redis/ jedis

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