How does Redis implement the LRU (Least Recently Used) elimination strategy?

How does Redis implement the LRU (Least Recently Used) elimination strategy?

In Redis, when the memory reaches the set maximum usage, some keys need to be selected for elimination to free up memory space. Redis provides a variety of elimination strategies, including LRU elimination strategy. The LRU elimination strategy means that the least recently used keys will be eliminated first. Redis implements the LRU elimination strategy by maintaining the access time information of a key.

The following is a sample code that uses Java to operate Redis to implement the LRU elimination strategy:

import redis.clients.jedis.Jedis;
import redis.clients.jedis.JedisPool;
import redis.clients.jedis.JedisPoolConfig;

public class RedisLRUEviction {
    
    

    private static final int MAX_MEMORY = 10000000; // 最大内存限制，单位字节
    private static final int MAX_KEYS = 1000; // 最大键数量

    private JedisPool jedisPool;

    public RedisLRUEviction() {
    
    
        JedisPoolConfig poolConfig = new JedisPoolConfig();
        poolConfig.setMaxTotal(10);
        poolConfig.setMaxIdle(5);
        jedisPool = new JedisPool(poolConfig, "localhost", 6379);
    }

    public void put(String key, String value) {
    
    
        try (Jedis jedis = jedisPool.getResource()) {
    
    
            jedis.set(key, value);
            // 更新键的访问时间
            jedis.lpush("lru", key);
            // 如果内存超过限制，进行淘汰
            if (jedis.dbSize() > MAX_KEYS || jedis.dbSize() > MAX_MEMORY) {
    
    
                evict(jedis);
            }
        }
    }

    private void evict(Jedis jedis) {
    
    
        // 获取最近最少使用的键
        String key = jedis.rpop("lru");
        // 删除键
        jedis.del(key);
    }
}

The above sample code demonstrates how to use Java to operate Redis to implement the LRU elimination strategy. In the code, we first define two constants sum MAX_MEMORY, MAX_KEYSwhich represent the maximum memory limit and the maximum number of keys respectively.

Then, we created a RedisLRUEvictionclass that contains a putmethod for inserting key-value pairs. In putthe method, we first use JedisPoolto get an Jedisinstance, and then use SETthe command to store the key-value pair into Redis.

Next, we use LPUSHthe command to store the name of the key into a list, which is used to record the access time of the key. If the Redis database size exceeds the maximum number of keys or the maximum memory limit, we call evictthe method for elimination.

In evictthe method, we use RPOPthe command to get the least recently used key and then use DELthe command to delete the key from Redis.

Through this sample code, we can better understand how Redis implements the LRU elimination strategy. Redis maintains a list to record the access time of keys. When a key needs to be eliminated, the least recently used key is selected for elimination. This ensures that the most frequently accessed keys are retained in memory, improving access efficiency.

To sum up, Redis implements the LRU elimination strategy by maintaining the access time information of a key. Using the LRU elimination strategy can ensure that the least recently used keys will be eliminated first, thereby freeing up memory space and improving system performance. In practical applications, the LRU elimination strategy can be used in scenarios such as cache systems and database systems to improve system access efficiency and response speed.