Redis数据淘汰策略

Redis数据淘汰策略

1. Redis数据淘汰策略

当Redis中的内存不够用时，此时向Redis添加新的key，那么Redis就会按照某一种规则将内存中的数据删除掉，这种数据的删除规则被称之为内存的淘汰策略

Redis支持8种不同策略来选择要删除的key:

# MAXMEMORY POLICY: how Redis will select what to remove when maxmemory
# is reached. You can select among five behaviors:
# 
# volatile-lru -> remove the key with an expire set using an LRU algorithm
# allkeys-lru -> remove any key according to the LRU algorithm
# volatile-random -> remove a random key with an expire set
# allkeys-random -> remove a random key, any key
# volatile-ttl -> remove the key with the nearest expire time (minor TTL)
# noeviction -> don't expire at all, just return an error on write operations
# 
# Note: with any of the above policies, Redis will return an error on write
#       operations, when there are no suitable keys for eviction.
#
#       At the date of writing these commands are: set setnx setex append
#       incr decr rpush lpush rpushx lpushx linsert lset rpoplpush sadd
#       sinter sinterstore sunion sunionstore sdiff sdiffstore zadd zincrby
#       zunionstore zinterstore hset hsetnx hmset hincrby incrby decrby
#       getset mset msetnx exec sort
#
# The default is:
#
# maxmemory-policy noeviction

2. LRU和LFU

• LRU（Least Recently Used）最近最少使用。用当前时间减去最后一次访问时间，这个值越大则淘汰优先级越高
• LFU（Least Frequently Used）最少频率使用。会统计每个key的访问频率，值越小淘汰优先级越高

2.1 使用LinkedHashMap实现LRU

public class LRUCache<K,V> extends LinkedHashMap<K, V> {

    private int capacity;

    public LRUCache(int capacity) {
        super(capacity, 0.75f, true);
        this.capacity = capacity;
    }

    @Override
    protected boolean removeEldestEntry(Map.Entry<K, V> eldest) {
        return super.size()>capacity;
    }

    public static void main(String[] args) {
        LRUCache<Integer, String> lruCache = new LRUCache<>(3);
        lruCache.put(1, "a");
        lruCache.put(2, "b");
        lruCache.put(3, "c");
        System.out.println(lruCache.keySet());

        lruCache.put(4, "d");
        System.out.println(lruCache.keySet());

        lruCache.put(3, "c");
        System.out.println(lruCache.keySet());

        lruCache.put(3, "c");
        System.out.println(lruCache.keySet());

        lruCache.put(5, "e");
        System.out.println(lruCache.keySet());
    }
}

输出结果：

[1, 2, 3]
[2, 3, 4]
[2, 4, 3]
[2, 4, 3]
[4, 3, 5]

3. 使用场景

1. 数据库中有100万数据，Redis只缓存20w数据，如何保证Redis中的数据都是热点数据？
   使用allkeys-lru淘汰策略，挑选最近最少使用的数据淘汰，留下来的都是经常访问的热点数据。
2. allkeys-random策略：业务中数据访问频率差别不大，没有明显冷热数据区分，建议使用这个。
3. volatile-lru策略：如果业务中有置顶的需求，同时置顶数据不设置过期时间，这些数据就一致不被删除，会淘汰其他设置过期时间的数据。
4. 如果业务中有高频访问的数据，可以使用allkeys-lfu或volatile-lfu策略。